SuccessfulWriting aThesis orDissertation.docx

Successful
Writing a
Thesis or
Dissertation
Fred C. Lunenburg ~ BBeevveerly J. Irbrly J. Irbyy
Successful
Writing a
Thesis or
DissertationDissertation
Tips and Strategies for
Students in the Social
and Behavioral Sciences
CORWIN PRESS
A SAGE Company
Thousand Oaks, CA 91320

Copyright © 2008 by Corwin Press, Inc.
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Library of Congress Cataloging-in-Publication Data
Lunenburg, Fred C.
Writing a successful thesis or dissertation : tips and strategies

for students in the social
and behavioral sciences / Fred C. Lunenburg, Beverly J. Irby.
p. cm.
Includes bibliographical references and index.
ISBN 978-1-4129-4224-9 (cloth)
ISBN 978-1-4129-4225-6 (pbk.)
1. Dissertations, Academic--Authorship. 2. Academic writing.
3. Social sciences—
Authorship. I. Irby, Beverly J. II. Title.
LB2369.L814 2008
808'.066378—dc22 2007031656
This book is printed on acid-free paper.
07 08 09 10 11 10 9 8 7 6 5 4 3 2 1
Acquisitions Editor: Elizabeth Brenkus
Managing Editor: Arnis Burvikovs
Editorial Assistants: Ena Rosen, Desirée Enayati
Production Editor: Jenn Reese
Typesetter: C&M Digitals (P) Ltd.
Proofreader: Paulette McGee
Indexer: Nara Wood
Cover Designer: Monique Hahn
Contents
Preface xi
Acknowledgments xv

About the Authors xvii
PART I. GETTING STARTED
1. Selecting a Suitable Topic 2
Sources of Topics 3
Textbooks 4
Professional Journals 4
Dissertations 5
Theories 6
Current Employment 8
Existing Database 8
Criteria for Topic Selection 10
Interest 10
Significance 11
Available Data 11
Knowledge and Skills 12
Manageability 13
Funding 13
Summary 15
2. Selecting a Chair and Committee 16
Criteria to Consider in Selecting a Chair 17
Expertise 17
Accessibility 17
Feedback 18
Success 18
Personality Styles 19
Attitudes Toward Methodology 19
Composition and Role of the Committee 20

Composition 20
Role 21
Research Prospective Committee Members 21
Your Own Experience 22
Other Students 22
Dissertations 22
Web Sites 23
The Desirable Student 23
Quality Product 23
Follow-Through 23
The Committee’s Time 23
Personal Integrity 24
Polished Drafts 24
Summary 25
PART II. WHAT YOU NEED TO KNOW
3. Quantitative Research Designs 28
Descriptive Research Design 30
Instrumentation 31
Data 32
Longitudinal Studies 32
Cross-Sectional Studies 33
Correlational Research Design 35
Bivariate Correlation 37
Regression and Prediction 38
Multiple Regression 39
Canonical Correlation 40

Discriminant Analysis 41
Factor Analysis 42
Path Analysis 42
Cross-Lagged Panel 44
Other Correlation Coefficients 45
Advantages and Disadvantages 45
Causal-Comparative Research Design 45
Weakness Controlled 46
Quasi-Experimental Research Design 49
Nonequivalent Control Group Design 49
Time Series Design 52
Experimental Research Design 54
Factorial Experiment 54
Theory Development 59
Summary 61
4. Basic Statistics 62
Descriptive Statistics 63
Measures of Central Tendency 63
Measures of Variability 64
The Normal Curve 65
Measures of Relative Position 65
Measures of Relationship 66
Inferential Statistics 67
The Null Hypothesis 68
Tests of Statistical Significance 69
Effect Size 69
Statistical Analysis 70

The t Test 70
One-Way Analysis of Variance 72
Post Hoc Procedures 73
Factorial Analysis of Variance 74
Analysis of Covariance 75
Multivariate Analysis of Variance 76
Nonparametric Tests 76
Chi Square 77
Other Nonparametric Tests 78
Multivariate Correlational Statistics 80
Multiple Regression 80
Discriminant Analysis 83
Canonical Correlation 83
Path Analysis 84
Factor Analysis 85
Structural Equation Modeling 86
Summary 87
5. Qualitative Research Designs 88
Phenomenological Research Design 89
Techniques/Approaches/Methods 90
Case Study Research Design 96
Ethnographic Research Design 99
Grounded Theory Research Design 102
Mixed Methods Research Design 106
Summary 109
PART III. THE DISSERTATION CHAPTERS
6. Writing the Introduction Chapter 112
Background of the Study 113
Statement of the Problem 114
Purpose of the Study 116
Significance of the Study 117

Definition of Terms 118
Theoretical Framework 122
Models 123
Research Questions (or Hypotheses) 126
Types of Research Questions 126
Specifying Research Hypotheses 127
Limitations 133
Delimitations 134
Assumptions 135
Organization of the Study 135
Summary 136
7. Writing the Literature Review Chapter 137
Searching the Literature 138
Handbooks and Encyclopedias 138
Annual Reviews 138
Review Articles 139
Abstracting and Indexing Services 139
Government Documents 140
Major Public Search Engines 141
Writing the Literature Review 142
Organize Your Material in a Funnel 143
Be Selective 144
Make an Outline 146
Write the Introduction 147
Use Headings 150
Use Transitions 150
Write a Summary 152
Be Careful Not to Plagiarize 153

Synthesizing the Literature 154
Historical Context 154
Gaps in the Literature 155
Different Approach 156
Trends and Themes 157
Reporting Practical Significance 158
Reconciling Conflicting Theories 159
Using Tables 160
Inconsistent Findings 162
Continuing Line of Inquiry 163
Summary 164
8. Writing the Methodology Chapter 165
Introduction 166
Selection of Participants 167
Who Participated in the Study? 167
Sampling Procedures 169
Selecting a Nonrandom Sample 174
Sampling in Qualitative Research 176
How Many Participated in the Study? 178
Instrumentation 180
Validity 181
Reliability 182
Locating Instruments 191
Qualitative Instrumentation 192
Data Collection 194
Data Analysis 200
Summary 204
Conclusion 204

9. Writing the Results Chapter 206
Introduction 209
Descriptive Statistics 210
Testing the Research Questions (or Hypotheses) 213
Assumptions of Statistical Tests 216
Clarity and Consistency 217
Tables 218
Figures 220
Presenting the Results of Qualitative Research 221
Additional Analyses 222
Summary 223
Conclusion 224
0. Writing the Discussion Chapter 225
Introduction 226
Summary of the Study 226
Discussion of the Findings 228
Guidelines to Structure Your Discussion Section 229
Sampling 230
Instrumentation 230
Research Design 230
Implications for Practice 236
Recommendations for Further Research 238
Conclusions 240
Summary 242
ART IV. THE DEFENSE AND AFTERWARD
1. The Proposal and Final Oral Defense 244
Prepare a Well-Written Document 245
Know the Format 245

Prepare Your Presentation 246
Practice Your Presentation 248
Anticipate Questions 248
Final Oral Defense 249
Tips for Avoiding Common Mistakes 251
Summary 253
2. Presenting and Publishing Your Dissertation 254
Presentations 254
Paper Presentation 255
Poster Session 260
Job Interview 261
1
P
1
1
Academic Journals 262
Select a Suitable Journal 263
Prepare the Manuscript 265
Submit the Manuscript 265
Books 266
Chapters in Books 269
Popular Press 269
Internet Publishing 269
Desktop Publishing 270
Planning the Writing Process 270

Summary 272
Appendix A: Initial Letter Soliciting Participation 273
Appendix B: First Follow-Up Letter 274
Appendix C: Second Follow-Up Letter 275
Appendix D: Dissertation Proposal Outline (Correlational) 276
Appendix E: Dissertation Proposal Outline (Analysis of
Variance and Regression) 280
Appendix F: Dissertation Proposal Outline (Multivariate
Analysis of Variance) 284
Appendix G: Dissertation Proposal Outline (Qualitative) 288
Appendix H: Qualitative Research Critique 293
Appendix I: Agreement: Guidelines for Chairing a Dissertation
295
Appendix J: Checklist for Dissertation Quality 299
References 305
Index 321
xi
Preface
PURPOSE OF THIS BOOK

We have written this book to help graduate students write the
dissertation
from beginning to end successfully. Each of us has taught
courses focused
on writing the dissertation. We have a combined total of more
than 40
years of experience supervising doctoral dissertations. Together
we have
chaired more than 100 dissertations. In addition, we have been
external
examiners of doctoral dissertations for several universities in
Australia,
Canada, New Zealand, the Netherlands, and the United States.
Thus, we
have written this book to compile the best of our wisdom on
how to make
the process of writing the dissertation a less mysterious and
more reward-
ing experience. Our approach is applicable also to writing
master’s theses,
which we view as limited-scope dissertations.
From our combined experience, we have found that if the key
elements
of each dissertation/thesis chapter are clearly identified with
correspond-
ing examples of those elements (or sections), it takes the
mystery out of
writing the dissertation. Thus, we have designed this book to
explicitly
describe and define the elements (sections) of each dissertation
chapter
and provide examples of completed dissertations that illustrate
typical
ways to write the sections of each chapter. We have extracted

examples
from more than 100 completed dissertations from well-known
universi-
ties. We present multiple viewpoints that include quantitative,
qualitative,
and mixed methods approaches. Our writing style throughout is
inten-
tionally conversational, as if we were talking directly to the
student.
HOW TO USE THIS BOOK
Our goal is to provide advice to those learning how to write the
major ele-
ments (sections) of a dissertation. Thus, in each chapter of the
book, we pro-
vide specific information about sections commonly found in
dissertations,
such as how to write research questions or hypotheses, how to
select a sam-
ple for the study, how to write descriptions of instruments, how
to write
results of data analyses, how to interpret the results, and so
forth. Our goal
is to define and explain the rationale for the common elements
(sections) of
each chapter of the dissertation. Then we support our advice
with numer-
ous examples to illustrate how previous doctoral students have
written
those sections. The model we use is the traditional five-chapter
dissertation.
We realize that there are many variations to this model.

Students and faculty
who are chairing dissertations should feel free to modify the
approach to
reflect advising style, unique subject area, and institutional
requirements.
WHO SHOULD READ THIS BOOK
This book should be of special interest to students in the social
and behav-
ioral sciences, including education, psychology, social work,
and business.
Its contents should be applicable also to those studying nursing
and other
health sciences with a behavioral base, certain aspects of
anthropology,
sociology, and criminal justice. Other students will find the
book useful as
a dissertation guide. As mentioned previously, our approach is
applicable
also to writing master’s theses.
The book can be used as the principal text in courses focused on
writing the dissertation or master’s thesis. It may be used also
as a supple-
mentary text in seminars that introduce students to graduate
education or
in research methods courses, particularly those in the social and
behavioral
sciences.
ORGANIZATION OF THE BOOK
This book contains 12 chapters in four major parts and 10
appendixes. In
Chapter 1, we discuss how to go about selecting a suitable topic

for a doc-
toral dissertation or master’s thesis. We suggest that students
begin their
search for a suitable topic at the outset of their graduate
programs. Good
sources of possible topics include: textbooks, professional
journals, disser-
tations, theories, current employment, and existing databases.
The charac-
teristics of a good research topic include: whether it is of
personal interest,
significant, feasible in terms of available data, appropriate to
knowledge
and skill level, manageable, and attractive for funding.
Chapter 2 includes valuable tips about selecting a chairperson
and
other committee members. Issues to consider in the selection
process
include: the reputation of the faculty members, their interest
and expertise
in the topic, their accessibility, the feedback they provide the
student, and
the goodness of fit between the student and the dissertation
chair and the
other committee members. The chapter also deals with ways to
identify
prospective chairpersons and committee members, including the
student’s
own experience with the faculty member, other students’
opinions, and an
examination of completed dissertations and faculty Web sites.
xii Writing a Successful Thesis or Dissertation

Chapter 3 contains quantitative research designs, including
descriptive
research, correlational research, causal-comparative research,
quasi-experimental,
and experimental research. In Chapter 5, we discuss qualitative
and mixed meth-
ods designs, including case study, ethnography, ethology,
ethnomethodol-
ogy, grounded-theory, phenomenology, symbolic interaction,
and
historical research. The overall purpose of these two chapters is
to provide
an overview of the two basic approaches to conducting research:
quantita-
tive and qualitative. Sandwiched between Chapters 3 and 5 is
Chapter 4,
which deals with basic statistical procedures.
The overview of basic statistical procedures in Chapter 4
includes
descriptive statistics and inferential statistics. Parametric and
nonpara-
metric tests are discussed, as are the statistical procedures
commonly used
in social and behavioral science research. The focus is on the
application of
the common statistical procedures used in the social and
behavioral
sciences for given research designs.
Chapter 6 contains the structure and writing of the introduction
chapter of the dissertation or master’s thesis, including the
elements (or
sections) that comprise that chapter. These sections include:
background of

the study, statement of the problem, purpose of the study,
significance of
the study, definition of terms, theoretical framework, research
questions or
hypotheses, limitations, delimitations, and assumptions. The
section orga-
nization of the study, concludes the introduction chapter of the
disserta-
tion. We follow specific guidance on what to include in each
section of the
chapter with examples from completed dissertations.
Chapter 7 is divided into three parts. In the first part of the
chapter, we
provide an introduction on how to systematically search and
review the
literature. We discuss six sources: handbooks and
encyclopedias, annual
reviews, review articles, abstracting and indexing services,
government
documents, and public search engines. In the second part of the
chapter,
we discuss eight techniques to help the student write a clear and
effective
review of the literature chapter. They include: organizing
material in a fun-
nel, being specific, making an outline, writing the introduction,
using
headings, using transitions, writing a summary, and being
careful not to
plagiarize. The chapter concludes with several additional
strategies to help
the student critically synthesize the body of literature. We
provide
examples from dissertations and published articles throughout
the

chapter.
Chapter 8 includes the structure and writing of the methodology
chapter, including the elements (sections) that comprise that
chapter.
These sections include: selection of participants,
instrumentation, data col-
lection, and data analysis. We provide pertinent information
related to
each of the sections of the methodology chapter, as well as
numerous
examples of each of these sections from completed
dissertations.
Chapter 9 contains information on how to write the results
chapter.
Each element (or section) of the chapter is described, followed
by
xiiiPreface
examples from completed dissertations. We discuss different
methods of
organizing results for both quantitative and qualitative studies:
by
research questions or hypotheses, variables, or themes.
Chapter 10 consists of information on how to structure and
write the dis-
cussion chapter of a dissertation or master’s thesis. We discuss
each element
(or section) that should be included in the discussion chapter,
and provide
examples of each section of the chapter from completed

dissertations.
Chapter 11 contains advice on the steps to be taken to ensure a
suc-
cessful proposal defense and final oral defense of the
dissertation. These
steps include: preparing a well-written document, knowing the
format of
the defense, preparing the presentation, practicing the
presentation, and
anticipating questions. We provide an explanation of how
decisions are
made by dissertation committees at the defense, and what
students should
do after a decision has been reached.
Chapter 12 includes suggestions on seeking a wider audience
for the
completed dissertation. Issues explored include preparing a
paper for a
professional conference or job interview, finding a publisher for
the man-
uscript, or converting the dissertation into a journal article,
monograph, or
book.
Appendixes A, B, and C are sample letters used in survey
research.
Appendix A is the initial letter sent to a prospective participant
requesting
participation in a study. Appendixes B and C are follow -up
letters
designed to increase response rate of the study sample.
Appendixes D, E, F, and G contain dissertation proposal
outlines for

quantitative and qualitative studies. Each dissertation proposal
outline
uses a different method of analysis. The dissertation proposal
outline is the
first step in writing the dissertation proposal.
Appendix H contains guidelines used to critique a qualitative
research
study. Much can be learned from critiquing a qualitative study
using the
guidelines provided.
Appendix I is a typical agreement between a doctoral student
and a
dissertation chair, which has been used by the authors of this
book. Among
other things, it describes guidelines the authors use when
agreeing to chair
a dissertation. It has been field tested for the past 10 years.
Appendix J is a detailed checklist used by the student for
monitoring
the quality of a dissertation. The checklist is structured for a
traditional
five-chapter dissertation and conforms to the elements (or
sections) con-
tained within each dissertation chapter.
xiv Writing a Successful Thesis or Dissertation
xv
Acknowledgments

appr
This book has been a cooperative effort between scholars of the
field
and experienced editors and publishers. We wish to express our
eciation to the reviewers and others whose suggestions led to
improvements in this book.
We also wish to thank the people at Corwin Press whose
contributions
made this a much better book.
We are grateful to our dean, Genevieve Brown, for creating an
envi-
ronment conducive to research and contributing the necessary
resources to
complete this book. Special thanks are also extended to Alicia
Raley and
Dacey Ellington, who typed portions of this book.
Fred C. Lunenburg
Beverly J. Irby
Corwin Press gratefully acknowledges the contributions of the
following
people:
Mary Betsy Brenner, Professor
Gevirtz School of Education
University of California—Santa Barbara
Santa Barbara, CA
Sharon Toomey Clark, Educational Consultant
Clark & Associates
Claremont, CA

Randy L. Joyner, Adjunct Professor
Appalachian State University
Corwin Press Author
Boone, NC
Maria Piantanida, Adjunct Associate Professor
Carlow University and University of Pittsburgh
Pittsburgh, PA
Carol Roberts, Professor
University of La Verne
Corwin Press Author
La Verne, CA
Mark H. Rossman, Professor Emeritus
Capella University
Minneapolis, MN
William A. Rouse, Jr., Assistant Professor
Department of Educational Leadership
East Carolina University
Greenville, NC
xvi Writing a Successful Thesis or Dissertation
xvii
About the Authors
Fred C. Lunenburg is the Jimmy N. Merchant Professor of
Education and

Senior Research Fellow in the Center for Research and Doctoral
Studies in
Educational Leadership at Sam Houston State University. Prior
to moving
to the university, he served as a teacher, principal, and
superintendent of
schools. He has authored or coauthored more than 100 articles
and 20
books, including Educational Administration: Concepts and
Practices
(Thomson/Wadsworth, 1991, 1996, 2000, 2004, 2008), The
Principalship:
Vision to Action (Thomson/Wadsworth, 2006), Shaping the
Future (Rowman
& Littlefield, 2003), The Changing World of School
Administration (with
George Perreault) (Rowman & Littlefield, 2002), and High
Expectations: An
Action Plan for Implementing Goals 2000 (Corwin Press, 2000).
Beverly J. Irby is Professor and Chair of the Department of
Educational
Leadership and Counseling at Sam Houston State University.
Previously
Director of Field Experiences, she has been a school
psychologist, educa-
tional diagnostician, director of special education, elementary
school prin-
cipal, and superintendent of schools. She has authored or
coauthored 12
books and more than 150 articles in education. In addition, she
has
secured grants totaling more than $25 million and often serves
as a con-
sultant to school districts. She is a member of the International
Who’s Who

of Women and has received the Texas Council of Women
School
Educator’s Outstanding Educator Award, the Renaissance Group
Research Fellow Award, and the AERA Willystine Goodsell
Award.
PART I
Getting Started
1
Selecting a Suitable
Topic
have known what they wanted to study fr
The selection of a suitable topic is the first major step in
writing a
thesis or dissertation. For some students this is an easy task.
They
om the time they entered grad-
uate school and perhaps even before, but for many others,
selecting a the-
sis or dissertation topic is one of the most difficult parts of their
graduate
programs. The thought of developing a “completely original”
idea for

such a large-scale project may seem overwhelming to them.
The notion that a dissertation must be completely original is a
mis-
conception, for no research is completely original. All research
is based
on the work of others to some extent. In fact, the most useful
kind of
research simply builds on research that has already been done.
Some of
the most successful theses and dissertations simply extend the
knowl-
edge base one step further in an area by examining a new
variable within
a well-established line of inquiry, collecting data on a different
sample,
testing a new methodology, or introducing a new statistical
technique.
Thus, as you begin to focus progressively on a broad search for
a topic,
you gain a more thorough understanding of what has been done
in an
area and what needs to be done. Afterwards, originality may
cease to be
an issue. The Council of Graduate Schools (2002) clarified the
point. The
term original “implies some novel twist, fresh perspective, new
hypothe-
sis, or innovative method that makes the dissertation project a
distinctive
contribution” (p. 10).
2

Students often ask when the search for a topic should begin. In
some
universities students do not begin to search for a thesis or
dissertation
topic until after they have passed the comprehensive
examination. We rec-
ommend that a serious search for a dissertation topic start as
soon as doc-
toral study begins. By selecting a dissertation topic early in the
graduate
experience, you can then use assigned course research papers as
a means
of doing preliminary work on your projected research.
As soon as you select a broad area of study, you need to
immerse your-
self in the literature in that area, with an eye toward the
dissertation
(thesis) proposal. Also, you should read and review your
university’s doc-
toral handbook, to see if there are guidelines concerning what
goes into the
proposal and how long it is expected to be.
Most universities have very definite requirements for the
dissertation
proposal. These requirements can range from a 10- to 20-page
description
of your proposed study to the completion of the first three
chapters of the
dissertation. In some universities, the dissertation chair (or
advisor) is the
only one who has to approve the proposal. Other universities
may require
a formal oral defense of the proposal before the entire
dissertation commit-

tee. The purpose of the proposal is to get agreement on the
merits of the
proposed study before the student begins to collect data and
makes formal
application to the university’s Human Subjects Committee.
Even schools
or colleges within the same institution may have different
requirements.
The proposal requirements for master’s theses may be less
rigorous than
they are for doctoral dissertations.
Our institution decided that most dissertation proposals should
con-
sist of Chapter One, Two, and Three of the dissertation.
Typically Chapter
One is titled “Introduction,” Chapter Two, “Review of the
Literature,” and
Chapter Three, “Methodology.” However, for some qualitative
disserta-
tions, the proposal may appear differently. In most cases, after
you collect
and analyze your data, your Chapters One, Two, and Three will
require
only minor revisions to be ready for the final dissertation. Even
if you
must edit some later, you should be writing the proposal and the
disserta-
tion simultaneously. For those of you who are doing a
qualitative disserta-
tion, you may have to do some major rewriting of the first three
chapters
as your data emerge.
SOURCES OF TOPICS

As mentioned previously, you should begin your topic selection
by identi-
fying two or three broad areas in which many different types of
research
may be pursued. Examples of what is meant by broad topic
areas are: teach-
ing methods, leadership styles, bilingual education, school
improvement,
and so forth. Sources of ideas for broad topic areas include: (a)
textbooks,
3Selecting a Suitable Topic
(b) professional journals, (c) dissertations, (d) theories, (e)
current employ-
ment, and (f) existing databases.
Textbooks
Textbooks that you are currently using in your courses or that
you
have used in previous courses can be a source of ideas for broad
topic
areas. Often, the authors of textbooks point out areas of
controversy or
gaps in the research on specific topics. For example, in the first
chapter
of their textbook Educational Administration: Concepts and
Practices,
Lunenburg and Ornstein (2008) identified some current issues in
assess-
ment, which include testing minority students and
confidentiality of test
data. In a subsequent chapter, they discussed current and

emerging issues
in the measurement of disabled children. In another chapter,
they shared
problems with the validation of some leadership theories and
the use of
majority samples in the development of some of these theories.
In each of
these chapters, the authors pointed out several broad areas in
need of fur-
ther research.
Professional Journals
Although reading textbooks will give you a broad overview of
topic
ideas for your thesis or dissertation, you need to steep yourself
in the lit-
erature in your field and related fields. (If you are an education
student,
related fields are psychology, sociology, anthropology,
economics, and
business management.) This will enable you to examine the
specifics of
how other scholars have conducted research. These specifics
can be found
in reports of original, empirical research (which include both
quantitative
and qualitative studies) published in professional journals. Such
reports
can be accessed electronically. If you are unfamiliar with
conducting such
searches, consult the research librarian at your university. Some
electronic
databases provide the full text of research articles; others
provide abstracts
only. If an abstract interests you, obtain copies of the full

article and care-
fully read it through.
As you read, pay particular attention to the purposes, research
ques-
tions, or hypotheses that are stated in the articles. Consider the
methods
used to examine the research questions or test the hypotheses,
including
participants, instrumentation, data collection, and data analysis
the
researcher(s) used, and, of course, the findings. Consider
reliability and
validity issues of the studies you review. You should also pay
particular
attention to the discussion sections. In their discussions,
researchers often
describe implications of their research for theory and practice,
discuss lim-
itations of their studies, and suggest possibilities for further
research. Such
an examination of the specifics of empirical research will assist
you in
developing and refining your own thesis or dissertation
proposal.
4 Getting Started
Reading professional journals related to your field will help you
keep
abreast of research trends in your discipline and enable you to
explore
broad topic areas at the same time. Pay particular attention to
periodicals

that publish review articles, such as the Review of Educational
Research,
Harvard Educational Review, Sociological Review, Annual
Review of Psychology,
and Review of Research in Education. Review articles are
helpful to you
because they organize a great deal of literature efficiently. A
topical review
represents the analytical thinking of some scholar who has
examined
existing literature, interpreted it, and pointed out the theoretical
issues it
raises. These reviewers are invited to write reviews, because
they are con-
sidered to be among the best scholars in their fields. A review
article also
provides you with an extensive reference list that can form the
basis for a
complete review of the literature once you select a topic for
your thesis or
dissertation. The review article is also a good model for the
dissertation
proposal and the abstract of your dissertation. It is relatively
short and
usually includes the purpose, research questions or hypothesis,
methods,
results, implications, and limitations.
In some fields, books are published annually that are devoted to
the
review of significant recent theoretical and practical research
develop-
ments. Four such annual publications include the Annual
Review of
Anthropology, Annual Review of Psychology, Annual Review
of Sociology, and

Yearbook of the National Society for the Study of Education
(NSSE). Each
yearly volume of the annual reviews contains highly
comprehensive and
integrated reviews of numerous research areas in anthropology,
psychol-
ogy, sociology, and education, respectively. Some topics
contained in
these volumes are reviewed annually, while others are reviewed
every
five years. The researcher should look over the six or seven
most recent
volumes to get an idea of topical coverage. Other useful sources
of
reviews of research include the Handbook of Research on
Teaching,
Handbook of Qualitative Research, Encyclopedia of Educational
Research,
Handbook of Research on Educational Administration, and
Encyclopedia of
Educational Leadership and Administration. The articles
contained in these
documents are written by distinguis hed scholars in specific
content
areas. The topics are selected for their timeliness at the time of
writing
and their theoretical or practical value to researchers. Because
these
volumes are not published annually, some of the contents may
not be as
current as the aforementioned annual reviews, but may be
appropriate
for the selection of broad topic areas.
Dissertations

Other completed dissertations can serve as another good source
of
topic selection. Be sure to secure exemplary projects to serve as
models.
Try to find: (a) award-winning dissertations in your field or
related fields,
(b) recent dissertations in the selected field at various
universities, (c) good
recent dissertations suggested by faculty in your department,
and (d) the
best dissertations suggested by your dissertation chair.
Examine the titles of these dissertations, as well as titles
published in
Dissertation Abstracts International. If a title interests you, read
the abstract
of the study. If you are still interested, get a copy of the
document and read
specifically the review of the literature and the suggestions for
further
research. Dissertations are now online through most university
libraries.
There are certain advantages of searching for a topic from
completed
dissertations that go far beyond topic selection. For example, a
recently
completed dissertation includes a comprehensive review of the
literature
up to the point of completion. Updating the most recent studies
will be an

easy task. Your study might include a different population,
other vari-
able(s), or another methodology. However, other dissertations
can help
you in identifying acceptable approaches to: (a) writing
research questions
or hypothesis, (b) choosing an appropriate sample size, (c)
examining how
data were collected and analyzed, and (d) observing what kinds
of conclu-
sions can be drawn from the results, and/or (e) formulating a
theory.
These specifics of conducting research can be valuable
information learned
from examining completed doctoral dissertations.
Theories
Theories are developed to explain phenomena in a field or to
provide
structure or framework to the knowledge base in a field. A new
theory
may be developed, or an existing theory may be modified or
extended. For
example, Paul Hersey (1976) did a theory dissertation in which
he devel-
oped with his dissertation chair, Ken Blanchard, a new
leadership theory
known as the situational leadership theory (see Hersey &
Blanchard, 2007 for
an explanation of the situational leadership theory). One of our
doctoral
students, Salvatore Pascarella (1985), did a field test of Hersey
and
Blanchard’s situational leadership theory in a school setting
using a sample

of elementary school principals (see also, Pascarella &
Lunenburg, 1988).
Doris Delaney (2005), another one of our doctoral students,
completed a
dissertation that further field-tested Hersey and Blanchard’s
situational
leadership theory using a sample of prekindergarten principals.
The development and testing of theory is important work and
can
make an important contribution to the field. Many theories have
received
only limited empirical testing. In addition, many theories have
been tested
using only majority populations. For example, many of the
management
theories developed in industrial settings between 1900 and 1960
used only
men as participants.
One of the authors of this book has developed a new gender -
inclusive
theory of leadership called the synergistic leadership theory
(SLT) (Irby,
Brown, Duffy, & Trautman, 2002). The theory can be applied to
any orga-
nization. Developed through a qualitative approach, the SLT has
been
6 Getting Started
validated quantitatively, qualitatively, and with mixed methods
designs
nationwide and is currently being validated international ly by

our doc-
toral students, using samples of principals, superintendents, and
higher
education leaders. These validation studies have included
nonmajority pop-
ulations (Bamberg, 2004; Hernandez, 2004; Holtkamp, 2001;
Schlosberg,
2004; Trautman, 2000; Truslow, 2004). All of the
aforementioned disserta-
tions have used quantitative, qualitative, or mixed methods
designs.
Wanda Bamberg and Teresa Schlosberg validated the theory
through qual-
itative methodology. Rose Hernandez and Leslie Holtkamp used
a quanti-
tative methodology. Diane Trautman and Kimberly Truslow
used a mixed
methods design. More details on the methodology used in these
disserta-
tions are provided in subsequent chapters.
You should not limit yourself to theories within your own
discipline.
Many professionals study the same problems educators do,
including
anthropologists, psychologists, sociologists, business
executives, and
others. Thus, theories from other disciplines provide new ways
of examin-
ing topics in your discipline. For example, a social
psychological theory
such as Bandura’s (1997) self-efficacy theory has been tested in
applied
areas of education and has greatly expanded the existing
knowledge base.
Woolfolk and Hoy (1990) tested Bandura’s self-efficacy theory

in relation
to prospective teachers’ control beliefs. Later, Hoy and
Woolfolk (1993)
tested self-efficacy theory in relation to the organizational
health of
schools. Tschannen-Moran and others (1998) modified
Bandura’s teacher
efficacy scale. Building on the work of Bandura, Hoy,
Tschannen, and
Woolfolk, Lauren Black (2003), one of our doctoral students,
developed
and validated a new instrument, the principal efficacy scale,
which was
the subject of her dissertation. Lauren Black used a mixed
methods design
in her dissertation.
Some dissertations are guided by theory. Others are not. Look
for
reported research (survey textbooks, research journals,
dissertations) that
is grounded in a solid theoretical framework. Such works make
a substan-
tial contribution to the knowledge base. An excellent way to
justify pro-
posed research to your thesis or doctoral committee is to show
that your
research will either test certain aspects of an important theory
or has its
foundation in such a theory. Thus, we suggest that you consider
whether
your research could be guided by a particular theory and how
you can
create empirical ways of testing the theory or theories.
Many quantitative studies are designed to test a theory that has

been
developed to explain educational phenomena. Such theory-
driven studies
use hypotheses as the starting point for designing the research
methodol-
ogy. Qualitative research studies can be designed so that data
are collected
first, and then a theory is derived from those data (Denzin &
Lincoln, 2005;
Gall, Gall, & Borg, 2007; Gay, Mills, & Airasian, 2006). Such
qualitative stud-
ies might be informed by a theoretical or epistemological
framework,
but formal hypotheses are not typically used to frame the study
(Gall et al., 2007). For example, in grounded theory
methodology, hypothe-
ses are the outcome of the study rather than the initiators of it
(Glaser, 1978).
Current Employment
Another source of dissertation topics can be derived from your
current
employment. For example, one of our doctoral students, Vickie
Basham
(1988), was able to relate her dissertation topic to her
employment (see
also, Basham & Lunenburg, 1989). Vickie worked for the
Kentucky
Department of Education as the Director of Assessment. She
wanted to

determine the relationships between strategic planning, student
achieve-
ment, and school district financial and demographic factors. At
that time,
the state of Kentucky had just implemented a statewide strategic
planning
model. In addition, the state administere d annually a
standardized
achievement test to all students in grades 3, 5, 7, and 10 in three
content
areas: reading, language arts, and mathematics.
The achievement data, as well as the financial and demographic
factors
she was studying, were easily accessible to her through the
Kentucky
Department of Education’s database. (However, there was no
instrument
to measure the use of strategic planning in the state.) The State
Superintendent of Schools was very interested in the study and
sent a
letter to all superintendents in the state requesting them to
participate in
Vickie’s study. The letter from the top administrator in the state
opened
doors for data collection, resulting in a nearly 80% return rate
from the
total population of superintendents in the state (her study
sample); com-
plete access to the state’s database; and free assistance from
state depart-
ment measurement experts in the statistical analysis of her data,
as well as
survey-item construction of the strategic planning instrument.
These mea-
surement experts also assisted her with the pilot study needed to

validate
the strategic planning scale she developed with her dissertation
chair.
Thus, after a careful review of the literature, Vickie developed
an accept-
able dissertation topic, conceptualized a problem, and
completed a litera-
ture review, methodology, and supporting evidence that were
firmly
grounded in theory.
Existing Database
Drawing on your current employment and having access to an
existing
database can be a real advantage in selecting a dissertation
topic. Keith
Clark (2002), one of our doctoral students, was chief financial
officer (CFO)
for a large, urban school district. Drawing on his work
experience in
finance, he examined student achievement, financial and
demographic
factors, and property wealth as related to the educational
resources in
public school districts. Another one of our doctoral students,
Danielle Lutz
(2006), was Director of Grants in another large, urban school
district.
8 Getting Started
Drawing on her work experience in grants she examined, using
a mixed

methods design, the relationship between bilingual federal grant
pro-
grams and student achievement in public schools. Because the
school dis-
tricts were interested in these students’ studies, they received
free clerical
assistance and access to computer services. Moreover, each
student could
retrieve a portion of his or her data through the state’s existing
database.
Sally Craycraft (1999), another one of our doctoral students,
became
intrigued by the availability of a large database at Hermann
International
that contained elements from a certified administration of the
Herman
Brain Dominance: Thinking Styles Assessment Instrument to
366 school
superintendents and 599 chief executive officers (CEOs). The
sample
included individuals from across the United States and included
males
and females; different ethnic-racial groups; and urban,
suburban, and
rural organizations, equivalent to the national population. Using
this exist-
ing database, she examined the relationship between brain
dominance and
leadership styles of school superintendents and business CEOs
by gender,
race, and organization size.
Likewise, one of our international doctoral students, Ying-
Chiao Tsai
(2006), used an existing database of Progress in International

Reading
Literacy Study (PIRLS) reading achievement for English-
language
students in the United States and Singapore. She compared
reading
achievement levels for majority and minority language-students
by gen-
der for both countries. The federal government provides
national data-
bases related to early childhood, special education, longitudinal
household surveys, higher education, the National Assessment
of
Educational Progress (NAEP), and the like. Most of these are
provided
through the National Clearinghouse of Educational Statistics
(NCES).
Three other of our doctoral students, Melinda Wooderson-
Perzan
(2000), Darlene Blair (2001), and Heather Thielemann (2004),
used an exist-
ing database to examine some, but not all, of their study
variables.
Melinda and Darlene used an existing database for their student
achieve-
ment and school district financial and demographic factors. For
the other
variables in their studies (leadership styles and instructional
leadership/
management tasks profiles) of superintendents and principals,
respec-
tively, they used Bass and Avolio’s (1994) Multifactor
Leadership
Questionnaire (MLQ) and Lunenburg’s (1982, 2000)
Instructional
Leadership/Management Tasks Profile.

Heather collected data on the effectiveness of enrollment
management
programs in four-year universities and community colleges in
Texas using
an adaptation of Noel-Levitz enrollment management survey.
She com-
pared the data collected in Texas colleges and universities with
the Noel
Levitz national database of community colleges and four-year
universities.
After a careful review of the literature, Keith, Danielle, Sally,
Ying-Chaio,
Melinda, Darlene, and Heather developed an acceptable
dissertation and
completed their studies.
We have found quite often that research topics (and their
accompany-
ing research questions), including the aforementioned studies,
need to be
reworked by the student and dissertation chair (advisor) before
they are
approved. In some cases, a research topic may be completely
rejected by
the dissertation or master’s thesis chair (advisor) as
inappropriate for a
dissertation or master’s thesis.
CRITERIA FOR TOPIC SELECTION
In selecting an appropriate topic for a thesis or dissertation, the

following
criteria should be considered as important: (a) interest, (b)
significance,
(c) available data, (d) knowledge and skills, (e) manageability,
and (f) fund-
ing. Let’s examine these criteria to see why they are considered
important.
Interest
It is the fortunate dissertation student who investigates a topic
that is
both professionally and personally compelling. Writing a
dissertation is
hard work. Try to find a topic for which you have substantial
curiosity and
perhaps even passion to sustain you though the process. Here is
an
example from one of the authors of this book.
When I began my career as a high school teacher and coach, I
was
selected to be a member of the school district’s teachers’
collective bargain-
ing team. While serving as a member of that team, I came in
contact with
many teachers. In talking with them, it became apparent to me
that, in
some schools in the district, teacher morale was high and in
other schools
it was low. I was curious why this was so.
At the same time, I was a master’s student in educational
administration
at the local university. I began reading about organizational
climate. In my

graduate classes at the university, I heard about a new
instrument devel-
oped a few years earlier for measuring climate called the
Organizational
Climate Description Questionnaire (OCDQ) (Halpin & Croft,
1963).
Furthermore, the concept of organizational climate and the
OCDQ was
getting a great deal of attention in the professional literature at
that time.
There was also some controversy about the eight dimensions of
organiza-
tional climate and the six climate categories conceptualized
through factor
analysis by Halpin and Croft, who originally developed the
instrument in
their study of 71 elementary schools chosen from six different
regions of
the United States.
The university I attended required a thesis as a requirement for
the MA
degree in educational administration. I chose as my MA thesis
topic: “A
Validation of the OCDQ in Elementary Schools” (Lunenburg,
1969). I drew
my sample from 35 elementary schools in Newark, New Jersey,
a large
urban school district. The school was the unit of analysis. I
conducted a
10 Getting Started
factor analysis of the OCDQ with my sample of elementary

schools and
found seven discrete climate categories instead of six, and eight
dimensions
of climate. However, only three dimensions were particularly
strong pre-
dictors of open and closed organizational climates for my
sample of 35 ele-
mentary schools: esprit, thrust, and disengagement. My analysis
was based
on a description of these schools given by 1,050 teacher
respondents and 35
principal respondents. In addition to conducting a factor
analysis of the
OCDQ, I examined climate in relation to school size,
socioeconomic status
of the community, and differences in perceptions of principals
and teachers.
Over the next few years, my interest in organizational climate
led to my
doctoral dissertation topic: “Organizational Climate,
Dogmatism, and
Pupil Control Ideology” (Lunenburg, 1972; see also, Lunenburg
& O’Reilly,
1974). I personally administered three instruments (one of
which was the
OCDQ) during regularly scheduled faculty meetings in 55
elementary
schools located in six urban, suburban, and rural school districts
in
Ontario, Canada. The analysis was based on a description of
these vari-
ables by nearly 1,200 respondents. I have continued to work
with doctoral
students in the area of school climate using a variety of climate
constructs,

other variables, and a variety of research methodologies,
including quan-
titative, qualitative, and mixed methods designs.
Significance
Most university catalogs and dissertation handbooks suggest
that the
dissertation is a test of your preparation to conduct independent
research
and make a significant contribution to the field. This is not
quite as true for
master’s theses, however. We view master’s theses as limited
scope disser-
tations. Basically, a significant topic has the potential to do at
least one of
the following: (a) contribute to the development of a new
theory, (b) test
an existing theory, (c) uncover new facts or principles, (d)
challenge exist-
ing truths or assumptions, (e) suggest relationships between
phenomena,
(f) provide new insights into phenomena, (g) suggest new
interpretations
of known facts, (h) alter other people’s perceptions about
phenomena, and
(i) extend a research methodology or statistical procedure.
Available Data
Earlier in this chapter, we discussed how some of our doctoral
students
were able to take advantage of existing databases. However, not
all
students will have the opportunity to use existing databases to
collect their

data. The ability to collect data needed for your study is a major
consider-
ation. Access to data is so important that you might consider
identifying
your research participants first and then seek to select a topic.
Sternberg (1981) provided an excellent example of a problem
with
access to data. The key question Sternberg posed is whether a
researcher
can gain admission to topic-loaded samples or groups. He uses
as an
example a topic in social psychology to make his point.
According to
Sternberg, an apparently worthwhile and relevant dissertation
topic
would be “The National Security Council as a Small Group: A
Test of
Bales’s Interaction Process Matrix.” “Bales and his associates,
watching,
recording, and videotaping Harvard undergraduates for decades
through
one-way mirrors have constructed a set of ‘laws’ about how
members will
behave, which they assert are generalizable to all small ‘task-
oriented’
groups” (p. 48). However, it is not likely that a doctoral student
would get
a chance to set up shop in a room adjacent to the National
Security Council
conference room. Therefore, such a topic would have to be

eliminated.
Furthermore, you need to be certain the data for your
dissertation will
be available and accessible when you come to the collection
phase of your
dissertation. The timing of the data collection is just as
important as the
topic itself in the calculation of its researchability. If your
topic-loaded
sample or group will disappear in six months or a year; or if
personnel
who granted you access change, resulting in new personnel not
willing to
honor their predecessors’ offer of entry, you must drop the
topic. These
reversals do happen. We recommend that you get your
commitments to
data access in writing.
Knowledge and Skills
Selecting a topic consistent with your knowledge and skills is
also
important. First, you will save time, because you will be dealing
with a
familiar topic. Second, you can talk knowledgeably about your
topic. Third,
you are more likely to get support from your dissertation chair
and com-
mittee for a topic about which you have some knowledge.
Recall that the
doctoral dissertation of one of the authors of this book was a
spin off of his
master’s thesis. Even though master’s theses are being phased
out of many

master’s programs, you may have already done research papers
and taken
courses in a particular subject area. Thus, you may find that you
have a con-
siderable amount of work already completed regarding the
literature
search. Another example of a useful knowledge base, also akin
to an expe-
riential base, is observed in a recent dissertation proposal by Yu
Fen Lin Li.
She is an ordained Presbyterian minister and has a master’s
degree in the-
ology. Her PhD dissertation in counseling is qualitative and is
based on her
knowledge of the Bible and feminism, and her experience as a
female pas-
tor in a patriarchal culture. Her topic is the development of a
feminist ther-
apy for Taiwanese female pastors in the Presbyterian Church.
In addition, selecting a topic that matches your skills is also
important.
For example, if you undertake a historical study, you must have
some
familiarity with documentary research techniques. Courses in
historiogra-
phy are helpful. Similarly, qualitative dissertations will demand
knowl-
edge of specialized qualitative research techniques, such as
interviewing,
12 Getting Started
observing, document analysis, textual analysis, focus groups,

visual meth-
ods, autoethnography, data management methods, computer-
assisted
analysis, and applied ethnography (see Denzin & Lincoln,
2005). Choosing
a quantitative dissertation may not be a deterrent, for many
individuals
are available to help with statistical analysis. Many areas will
require a
combination of skills; all demand literary proficiency.
Manageability
Most doctoral students, and master’s students as well, begin
with a
topic that is too broad. Examples that illustrate what is meant
by broad
topic areas include the following: teaching methods, leadership
styles,
school improvement, bilingual education, or counseling
theories. Your dis-
sertation chair cannot provide useful advice until you get it
clear in your
own mind exactly what it is that you want to study. By
narrowing your
topic, you make the purpose of your research clear to yourself
and others.
Sufficiently narrowing your topic helps you organize your
literature
review and produce a specific problem statement with an
accompanying
theoretical framework. Part of this narrowing process is writing
meaning-
ful research questions. A further discussion of writing research
questions
can be found in Chapter 6.

In addition, we recommend that you try to assess the amount of
time
you will need to complete the thesis or dissertation early in the
selection
process. From our experience, although you may have been
working on
your topic since you began the doctoral program, the average
amount of
time required for the final completion of dissertations is from
one year to 18
months. The time may be longer if you work full time. The
average master’s
student requires from four to nine months to complete the
master’s thesis.
We suggest that you select a dissertation topic that you can
complete within
a year or so. An example of a topic that does not meet the
completion time
criterion we suggested is a dissertation topic that proposes to
study the
development of high school students from freshman to senior
years. This is
a longitudinal study, which would have to extend over four or
more years
and, therefore, would not meet the one-year completion time
criterion.
Funding
Most thesis and dissertation research is not funded, so you incur
all of
the expenses associated with the project. These financial
constraints
greatly limit the scope of the research you can undertake. You
may check

with your department, college, or university for internal funding
opportu-
nities. Many federal agencies and some private foundations
offer grants
for graduate students to pursue degrees and write dissertations.
Grants
are usually not available for writing master’s theses. Topics that
are cur-
rent and have some unique approach are usually more likely to
receive
14 Getting Started
Table 1.1 Grant Opportunities
Dissertation Fellowship Program
Agency: Spencer Foundation
Deadlines: October 7
Amount: $20,000
Guidelines:
http://www.spencer.org/programs/fellows/dissertation.htm
Dissertation Fellowships
Agency: American Association of University Women (AAUW)
Amount: $20,000 (51 awards)
Guidelines: http://www.aauw.org/3000/fdnfelgra/american.html
AERA/OERI Dissertation Grants Program
Agency: American Educational Research Association and the
U.S. Department
of Education Office of Educational Research and Improvement

Deadlines: October 15, April 15
Amount: $15,000 for 1-year projects: $25,000 for 2-year
projects (four awards
per year)
Guidelines: http://www.aera.net/anews/announce/af01-002.htm
Dissertation Grants Program
Agency: American Educational Research Association
Deadlines: September 5, January 10, and March 20
Amount: $15,000
Guidelines: http://www.aera.net/programs/
Dissertation Fellowship Program
Agency: Association for Institutional Research (AIR)
Deadlines: January 15
Amount: $15,000
Guidelines: http://www.airweb.org/page.asp/page=40
Grants for Improving Doctoral Dissertation Research—Human
Cognition and
Perception
Agency: National Science Foundation
Deadline: Continuous
Amount: $8,000 North America; $12,000 other areas
Guidelines: http://www.nsf.gov/cgi-bin/getpub/nsf0111
funding. We provide a sampling of grant opportunities related
to educa-
tion and psychology (see Table 1.1). These grants are extremely
competi-
tive. Therefore, when considering specific grant applications,
you should
seek assistance from grant specialists in your university or
elsewhere to
help you prepare the application.

SUMMARY
In this chapter, we presented guidelines for selecting a suitable
topic for a
thesis or dissertation. We recommend that you begin looking for
a topic as
soon as your graduate program commences.
Begin by identifying two or three broad topic areas that interest
you.
By searching the literature, narrow these broad areas down to
one or two
specific topics that you might wish to research. Good sources of
possible
topics include textbooks, professional journals, dissertations,
theories, cur-
rent employment, and existing databases.
The topic you finally select should be interesting to you,
significant,
feasible in terms of available data, appropriate to your
knowledge and
skills, manageable, and attractive for funding. In the next
chapter, we
describe how you select a dissertation/thesis chair and
committee.
16

2
Selecting a Chair
and Committee
S electing your committee is a very important step in the
process of
preparing your dissertation or master’s thesis. The chairperson
of the
committee usually has broad power and influence throughout
the process
of completing the dissertation or master’s thesis. Therefore, the
selection of
a chairperson for your project is a very important decision. In
collaboration
with your chair and committee, you will delimit your topic,
develop your
proposal, conduct your research, and write your dissertation or
master’s thesis. Ultimately, your committee will judge the
quality of your
project. In this chapter, we present some suggestions that might
help you in
selecting your dissertation or thesis chair and other committee
members.
Before choosing a faculty member as your chairperson, consider
the
chair’s role. As mentioned previously, your chair will have
broad power and
influence over the dissertation or thesis process. While the
specifics of this
role vary from institution to institution, from department to
department,
and from chairperson to chairperson, some general functions of

the chair are
relatively universal. First, the chairperson will approve your
dissertation or
thesis topic. Second, the chairperson will approve, in
consultation with you,
the other committee members. Third, the chairperson will
approve every
line, section, and chapter of the dissertation. Fourth, the
chairperson will
determine how committee members will be involved in the
dissertation or
thesis process. Fifth, the chairperson will decide when you are
ready to
defend your dissertation or master’s thesis. And, ultimately, the
chairperson
will determine whether you will be granted the degree.
Most departments have rules concerning who may and who may
not
serve as dissertation or thesis chairpersons. Some universities
allow only
those individuals who are on the graduate faculty to serve as
dissertation
chairs; that is, faculty who have adequate, recent publication
records and
who teach graduate classes. These rules are based on the
rationale that fac-
ulty who do not have active programs of research will lack the
necessary
skills to guide a doctoral research project. Rules regarding who
may chair
master’s theses may not be as stringent as those concerning
doctoral dis-
sertations. Because practice varies on who may and who may

not serve as
dissertation chairs, we recommend that you learn your
institution’s rules
as soon as possible. Knowing your institution’s local ground
rules will
help you avoid considering a potential chairperson who is not
eligible to
chair a dissertation or thesis.
CRITERIA TO CONSIDER IN SELECTING A CHAIR
You must consider the following factors in choosing a chair: (a)
expertise,
(b) accessibility, (c) feedback, (d) success, (e) personality style,
and (f) attitudes
toward methodology. The importance of each one will be
discussed in turn.
Expertise
Ideally, it is in your best interest to find a chair with expertise
in your
topic area. You may want to read some of your potential chair’s
publica-
tions. In our opinion, following this advice generally will
produce a better
product. Obviously, the closer your chair’s area of expertise is
to your
topic, the more competent he or she will be to (a) identify
difficulties you
may encounter as your proceed with your study, (b) direct you
toward lit-
erature sources pertinent to your topic, and (c) guide your
choice of meth-
ods for collecting and analyzing data. Furthermore, a chair who
has an

interest and competence in your topic area is likely to be more
invested in
your project; that is, think through the project more fully and
keep a vigi-
lant eye on your progress than one who is not knowledgeable
about your
topic area, and, therefore, may lack interest in it as well.
Accessibility
Another important factor to consider in selecting a chair is
accessibil-
ity. Several things can interfere with a chair being consistently
accessible to
you during the life of your project. When considering someone
as a possi-
ble chair, you should think about these things. Nationally
known scholars
may be too busy with their own research activity to give you the
time you
need. Other faculty may have active clinical practices or be
away from
campus frequently due to consulting commitments. Faculty
members who
17Selecting a Chair and Committee
have nine-month contracts with the university may not be
available dur-
ing the summer. Faculty who are planning a sabbatical leave
may poten-
tially interrupt your progress. Another faculty member may be
planning to
take a position in another university and, therefore, may not be

available
during the progress of your project. One of the authors of this
book had
her chair go on sabbatical leave during the final semester of her
disserta-
tion work; therefore, a new chair had to be appointed. Popular
chairs may
have an excessive number of dissertations or theses to monitor,
because
they are in high demand.
Then there is the issue of tenure. Whereas nontenured faculty
contracts
may not be renewed, tenured faculty members are likely to be
more stable.
You will need to consider the relative accessibility and stability
of potential
chairs, along with your own time constraints and projections for
completion.
Feedback
Typically, the chair provides the first line of quality control for
the dis-
sertation or thesis. And usually the chair will approve the
proposal and
final version of the project before you will be permitted to
forward chap-
ters of the dissertation or thesis to other committee members.
Therefore,
look for a chair with a reputation for reading, critiquing, and
returning
written drafts promptly.
What is a good turnaround time? A good rule of thumb is to
allow two

weeks for a response. After that, a tactful inquiry may be
appropriate.
Obviously, students should recognize that it might take longer
during very
busy periods (e.g., end of grading periods, holidays, and before
gradua-
tion deadlines when all students want to finish their projects).
You should balance timeliness of response with the
thoroughness with
which the potential chairperson reads submitted material. Some
chairs
provide vague feedback (e.g., rewrite this section), while others
may pro-
vide detailed comments (e.g., “You need to identify the three
main factors
and then evaluate them in light of the theories you have
discussed.”).
Waiting longer for a chapter to be returned by a chair may have
some pos-
itive consequences. First, if you satisfy a chair who provides a
thorough
critique of your work, you are less likely to encounter serious
problems
with other committee members. Second, you will be better
prepared for
your proposal defense and final oral defense of your dissertation
or thesis.
Third, once you have satisfied your chair’s standards, he or she
is more
likely to support you if one of your other committee members
becomes
overly or unreasonably critical of your work.
Success

Success at bringing students to graduation is an important factor
to con-
sider when selecting a chair. Because you are concerned with
completing
your degree, count how many successful students your potential
chair has;
18 Getting Started
that is, what percentage of the chair’s students finish their
degrees. Consider
this criterion cautiously, because some faculty members may
not have had
the opportunity to chair doctoral dissertations or master’s
theses.
Personality Styles
Personality styles matter to some people. Writing a dissertation
or the-
sis is a collaborative process between you and your chairperson.
Obviously, you want a chair with whom you can work
reasonably well.
You will need to assess the match between what you expect
from your
chair and your chair’s notion of the best way to perform his or
her role.
Chairpersons vary greatly in how they work with students on
disser-
tations and theses. Those at one end of the continuum closely
monitor each
phase of the students’ work, in some cases stipulating exactly
what is to be

done at every step, and then require the student to submit each
section of
material for critique. Chairs at the other end of the continuum
tell students
to progress on their own and to finish a complete draft of the
project before
submitting it for evaluation. Most chairs will probably fall
somewhere
between these two extremes.
Chairpersons also differ in the way they provide criticism.
Some are
blunt and even derisive. Others are direct and kindly in
critiquing
students’ work. Still others are so cautious of students’ feelings
when
pointing out weaknesses that they fail to guide their students in
correcting
deficiencies. In the latter case, someone else on the committee
will have to
step up and perform that duty; for the role of the chair and
committee is
to ensure that the candidate has met the university, college, and
depart-
ment standards.
Students also have personal preferences with whom they want to
work,
in general. For example, some students prefer to work with
female faculty
members, while others prefer to work with male faculty. Some
students
prefer to work with older people, while others prefer younger
faculty.
Attitudes Toward Methodology

Faculty members often differ concerning their preferences for a
particu-
lar research method. A research method comprises the strategy
followed in
collecting and analyzing data. The major distinction in
classifying research
by method is the distinction between quantitative and
qualitative research
(Gay, Mills, & Airasian, 2006). Quantitative and qualitative
research can be
broken down further into several distinct types, each designed
to answer
a different kind of research question. Quantitative research
involves the
collection and analysis of numerical data, which are usually
rendered in
the form of statistics. Advocates of quantitative studies tend to
prefer
such research types as descriptive (or survey), correlational,
causal-
comparative, and experimental research. Proponents of such
studies claim
that their work is done from within a value-free framework
(Denzin &
Lincoln, 2005).
Qualitative research involves mostly nonnumerical data, such as
extensive notes taken at a research site, interview data,
videotape and
audiotape recordings, and other nonnumerical artifacts.

Qualitative
researchers stress the socially constructed nature of reality, the
intimate
relationship between the researcher and the participant, and the
situa-
tional constraints that shape inquiry. Qualitative researchers
emphasize
the value-laden nature of inquiry (Denzin & Lincoln, 2005).
Proponents of
qualitative studies tend to favor such research approaches as
case study,
ethnography, ethology, ethnomethodology, grounded theory,
phenome-
nology, symbolic interaction, and historical research.
You need to examine the match between your preference and
your
potential chair’s preference for a research method. Many faculty
members
accept both quantitative and qualitative research methods,
including the
authors of this text. We believe that the issue is not which
method is better,
but rather which method (quantitative, qualitative, or mixed
methods)
will best answer the particular research question or direction of
inquiry.
COMPOSITION AND ROLE OF THE COMMITTEE
Dissertation and thesis committees vary in size, composition,
and role
depending on the institution’s rules and the degree awarded.
The com-
mittee usually consists of the chair and an additional number of
faculty

members. Typically, the thesis committee has three members,
and the dis-
sertation committee has between three and five members.
Sometimes there is a requirement that at least one of the
committee
members be from a department other than the student’s major
depart-
ment. Some universities require that the majority of the
committee be
members of the graduate faculty. Institutions usually require
that the com-
mittee chair be a member of the graduate faculty. The
committee usually
serves through the proposal stage to the satisfactory completion
of the
dissertation or master’s thesis.
Composition
The composition of committee members can be very important
to the
quality of the completed thesis or dissertation. We recommend
that you
start at the top. First, select your committee chair. Then,
working in col-
laboration with your chair, you can complete the selection of
the other
committee members. Your chair will be in the best position to
help you.
You may offer some suggestions of your own, but you should
allow your
chair to guide you in the decision. Your chair likely will know
what exper-
tise respective committee members can contribute to your
project. And

your chair may work better with certain colleagues than with
others.
20 Getting Started
Ideally, committee members should supplement your chair’s
expertise.
If your study requires complex statistical procedures and your
chair is not a
statistician, you may want to add to your committee a faculty
member who
teaches your department’s statistics courses. We call this person
a resident
statistician. At our institution, we include the resident
statistician on all com-
mittees that involve complex statistical procedures. If your
study is qualita-
tive, and your chair is not strong in qualitative research
techniques, adding
a qualitative researcher to your committee may be useful. Aim
for balance
on your committee. A well-balanced committee might include a
faculty
member who is strong in theory, someone who is knowledgeable
about the
literature in your field, and another person who is a careful
editor.
Role
Although specific duties of committee members vary depending
on
the institution, general roles include the following: (a) the
committee pro-

vides consultation to the student throughout the process of the
research;
(b) committee members establish the direction of study by
approving the
proposal and assisting the chairperson in providing direction for
the
study; (c) they offer additional assurances to the university,
college, and
department that the dissertation or thesis standards have been
met;
(d) they examine the student and approve the final oral defense;
and
(e) they make judgments about the student’s written work,
including sub-
stance, style, and usage.
In a specific sense, however, the role of committee members can
vary
greatly by institution or department. In some departments,
committee
members read the dissertation or thesis proposal and final
written docu-
ment only after the chair has approved these documents.
Approval fol-
lows extensive work between the student and the chair
concerning the
research questions/hypotheses, design of the study, and other
details of
the project. Committee members offer suggestions and
criticisms, and
participate in the proposal and final oral defense meetings. In
other
departments, each committee member will actively contribute
expertise
at each stage of the dissertation (thesis) process. Chapters will
be sub-

mitted simultaneously to all committee members, after the
chair’s
approval. In most cases, the specific model used will probably
fall some-
where between the two extremes.
RESEARCH PROSPECTIVE
COMMITTEE MEMBERS
Researching the pool of prospective chairpersons and committee
members
is essential. There are four main sources of information to
explore: (a) your
own experience, (b) other students, (c) dissertations, and (d)
Web sites.
Your Own Experience
The first source of information to explore is your own
experience with
the potential chairperson or committee member. You may have
taken a
class from a particular faculty member. Some questions you
might ask
yourself include: What kind of relationship did I have with
Professor X in
class? Do I have confidence that Professor X can guide me
through the
research process? Am I ready to accept direction and criticism
from
Professor X? Be open-minded in your exploration. Sometimes

faculty
members are different in one-to-one relationships than they are
in classes.
The relationship between you and your chair and other
committee
members is collaborative. Ideally, you want to create an equal
relationship,
but in reality this is rarely the case (Brause, 2004). The student
usually con-
forms to the styles of the professors. Some negotiation of
procedures for
progressing through the research process may be possible;
however, pro-
fessors usually set the rules. And each professor has
idiosyncrasies, to
which doctoral (master’s) students eventually learn to adapt, or
they
choose to work with other faculty members. That is to say that
faculty
members have a history of collaborating in different ways. It is
important
that you understand the rules for working collaboratively with
faculty. It
may require you to utilize a different set of interaction skills
than you used
when taking classes.
Other Students
A second source of information comes from students who have
worked with the prospective chair or committee member.
Interview sev-
eral students to get an adequate sample. Listen carefully to
students. Ask
for opinions. Ask such questions as: Who selected the topic—

you or the
chair? Did the chair help you improve the proposal? How many
substan-
tive changes were made to the proposal once submitted? Was
the chair
available on a regular basis? How many drafts of each section
or chapter
did you write? (We think it not unusual for a student to write
several
drafts of each section or chapter.) What kind of feedback did
the faculty
member provide—vague, detailed, useful, or not useful? How
did the
chair respond to issues the committee raised? What issues did
the com-
mittee raise? Did individual committee members cause any
problems dur-
ing the research process? What were they? Questions such as
these ask
about specific faculty behavior and not merely subjective
opinions.
Dissertations
A third source of information comes from reviewing recent
disserta-
tions completed in your department. Data you will gather from
reading
dissertations will answer questions such as: What method of
research
(quantitative, qualitative, or mixed methods) do Professor X’s
students
do? Are all dissertations related to a single topic area? Are the
dissertations
22 Getting Started

similar in format? What kind of populations and samples were
used in
the studies? How many pages do the dissertations contain? Who
were the
committee members? Who were the students? In most
universities, the
student can download dissertations from their own institution by
author,
title, or chair. Dissertations can be obtained from University
Microfilms
International, and through interlibrary loan.
Web Sites
A final source of information comes from department Web sites.
Many
departments now have Web sites on which faculty research
interests are
provided. Spending some time researching Web sites may help
you locate
potential chairpersons and committee members both inside and
outside of
the department. At this point, you may have a potential list of
chairper-
sons and committee members.
THE DESIRABLE STUDENT
Thus far, we have described desirable qualities of the chair and
committee
members. What are some desirable qualities of a doctoral or
master’s
student from the standpoint of the chairperson and committee

members?
In general, they want: (a) a student who will produce a quality
dissertation
or master’s thesis in a reasonable amount of time, (b) a student
who
accepts guidance and follows through on suggestions and
criticisms from
the committee, (c) a student who uses the committee’s time
effectively,
(d) a student who has personal integrity, and (e) a student who
resists the
impulse to give the chair or committee members rough copy or
first drafts.
Quality Product
Completed dissertations are archived in Dissertation Abstracts
International. The names of both the chair and the student, as
well as the
names of all committee members appear on the signature page
of the dis-
sertation. Faculty members with national reputations will be
careful about
the quality of work with which their name is associated.
Chairing or serv-
ing on a dissertation committee is something most faculty take
seriously.
Faculty members will be much more willing to chair or serve on
a com-
mittee if the student demonstrates that a quality dissertation
will result.
Follow-Through
You are likely to write multiple drafts of your dissertation or
thesis,

with each draft reflecting a more refined understanding of the
important
issues of your research. As you are writing these drafts, you are
learning
the genre of scholarly writing acceptable at your institution and
as it
conforms to the style of writing acceptable in professional
journals in your
field. Moreover, you need to learn to accept the suggestions and
criticisms
of your chair and committee members graciously and follow
through on
them. Nothing is more frustrating for a chair or committee
member than
to see an error in a final document that was corrected by a chair
or com-
mittee member previously; that is, the student failed to follow
through on
committee suggestions. In our combined experience of chairing
more than
100 dissertations, we have seen this happen quite frequently.
The Committee’s Time
You need to look at the dissertation or thesis process from the
chair-
person’s and committee member’s viewpoints. A good chair will
provide
expertise in your topic area, timely feedback on your drafts, and
general
support. Assuming you made a good choice, your committee

members
will be interested in your dissertation (thesis) and will provide
competence
that supplements your chair’s expertise. However, dissertation
chairs and
committee members also teach classes; do their own research;
write jour-
nal articles and books; provide service to the department,
college, and uni-
versity; and attend professional conferences. You are one small
part of
their professional lives. The student who is organized and who
exempli-
fies the stated purpose of a doctorate, one who has the ability to
do inde-
pendent research, will more likely demonstrate that the time
demands on
committee members will be reasonable.
Personal Integrity
The student with personal integrity will take the writing of the
disser-
tation (thesis) seriously. The focus will be toward producing a
high-
quality product. The student who lacks personal integrity
approaches the
task of writing the dissertation (or thesis) as simply “getting the
thing
done,” without investing the time required to produce a good
dissertation.
A student who accepts guidance and follows through on
suggestions
and criticism, is organized and uses the committee’s time
effectively, and
produces a quality product has personal integrity.

Polished Drafts
Some students give their chair and committee members rough
copy or
first drafts. This will give the impression that you are not really
serious
about your work. Always submit pages on which you have
expended seri-
ous time and effort. Use the following rule of thumb: Be 100
percent satis-
fied that your pages, which you are prepared to submit, are the
very best
work that you can do. Any dissertation or master’s thesis may
need to go
through several drafts. If possible, have someone—preferably
an expert in
24 Getting Started
your field—review the chapter after you have written what you
consider
to be your final draft.
SUMMARY
The composition of your dissertation or thesis committee can be
very
important to the quality of the finished product. The chairperson
of the
committee has broad power and influence throughout the
process of
preparing the dissertation or thesis. Therefore, we recommend
that you

start at the top. Select the chair of your committee first. Then,
working
with your chair, select the other members of your committee.
Ideally, com-
mittee members should supplement your chair’s expertise.
Collecting
reliable information about potential chairpersons is essential.
You want a
chairperson with whom you can work reasonably well. You will
need to
assess the match between what you expect from your chair and
your
chair’s notion of the best way to perform his or her role. You
will want
committee members who can work well together and with your
chair.
The next chapter begins Part II, “What You Need to Know.”
Chapter 3
contains valuable information on quantitative research designs.
This
chapter is followed by a chapter on basic statistics. Part II is
concluded
with a chapter on qualitative research designs.
PART II
What You Need to Know

3
Quantitative
Research Designs
In this chapter, we share with you an important component of
the dis-
sertation process—determining the type of inquiry and research
design
you will use for your study. This actually will not be a specific
chapter in
your dissertation, but is all important in developing your
proposal and car-
rying out your research. The inquiry techniques and/or methods
presented
in this chapter all have their beginnings in basic human
observation and
curiosity. We are describing science in the broadest sense of the
word—a
way of reflecting on our world. Just as children experience
science via atti-
tudes, processes, and products, we do also as adult researchers.
Your attitude as a researcher is critical. First, you must think of
your-
self as a researcher and writer, and not just as a graduate or
doctoral
student. Your attitude will carry you far as a budding scientist.
It will
encourage you further in your own curiosity of your topic and
of others’
topics; it will provide you with perseverance for the task of
conducting the

research; it will pick you up when you fail and help you learn
from your
mistakes; and it will aid your open-mindedness and assist you in
cooper-
ation with others. Furthermore, a positive attitude toward the
research will
provide you with a desire to seek reliable and valid sources of
information;
a desire to provide and to tolerate alternative viewpoints; an
avoidance of
overgeneralizations; a restraint to make a judgment until all
evidence is
examined or evaluated, or to make claims without having proof
or
descriptors; and an open mind toward questions related to your
own
research.
28
Processes of research will aid you in working through your
study in a
critical and creative way. In the simplest terms, processes may
include
observing, classifying, contrasting, communicating, measuring,
estimat-
ing, predicting, and inferring. You also will use the processes of
identify-
ing and controlling variables, operationalizing definitions,
hypothesizing,
questioning, experimenting, investigating, interpreting data, or
forming
theories or models.

The product of your research, your dissertation, provides your
chosen
field with a greater knowledge base; therefore, because
knowledge is con-
sidered power, you also carry with you during your research
much
responsibility as you plan your study, choose your method of
inquiry, and
conduct your research. You have an ethical obligation to do the
very best
research that can be produced. You ask: even at the dissertation
stage? The
answer is yes, at the dissertation stage of your research career.
Though you
may be questioned on your study, you also know that much of
science
changes over time and that knowledge is challenged as it is
produced.
Remember this about your research, as Slavin (1992) said, “the
best
research design is one that will add to knowledge, no matter
what the
results are” (p. 3). Your research product, whether you find a
small or large
effect size along with significance, may be interpreted or used
differently
by different audiences, depending on their circumstances and
experiences.
Your dissertation, as a product, may take the form of a
hierarchy, such
as basic factual or uniconceptual research, principles of
research that relate
to multiple concepts, or theories, the highest level of research.
This hierar-
chy can be observed in Figure 3.1. The closer the products of

the research
are to the top point of the pyramid, the more complex the study.
In the
development of theory, we note that the complexity may involve
dual
methodologies, both quantitative and qualitative.
A couple of important questions should be considered as you
deter-
mine your method of inquiry. First, you must ask yourself: what
is my
intent or purpose of the research? Second: what are my research
questions?
These initial components will drive the method you select. You
may deter-
mine that a quantitative analysis will suffice in answering your
research
question or will respond to your purpose. On the other hand,
you may
conclude that it is qualitative, the deeper understanding of the
topic, that
responds to your purpose, or ultimately, you may decide that a
mixed
methods approach, using both quantitative and qualitative
analyses, is the
best method of inquiry for your dissertation.
In this chapter, we provide you with an overview of each of the
data
analysis techniques or inquiry methods you could use if your
questions or
hypotheses necessitated a quantitative approach. Certainly, this
chapter is not
all inclusive, and we know that you will want to “dig deeper” on
your own
once you settle on a specific method of inquiry. For example,

you will need to
consult statistics and research methods textbooks and review
your proposed
method with your dissertation advisor. You must be thoroughly
familiar with
your method of inquiry and the assumptions for the statistical
procedures.
29Quantitative Research Designs
30 What You Need to Know
Theories
(Quantitative)
Principles (Correlational,
Causal-Comparative,
Quasi-Experimental, Experimental)
Factual or Uniconceptual
(Descriptive-Quantitative)
Figure 3.1 Hierarchy of Products of Research
Your research design or method of inquiry for quantitative
research
will fall into one of four categories of research (a) descriptive
research,
(b) correlational research, (c) causal-comparative research, and
(d) quasi-

experimental and experimental research. In addition to
providing you
with a brief overview of each category, we also provide
examples of
research purposes or questions that would justify a specific type
of quan-
titative research design or method of inquiry.
DESCRIPTIVE RESEARCH DESIGN
Descriptive research is one of the most basic forms of research.
It lies at the
bottom of the hierarchy as depicted in Figure 3.1. Even though
descriptive
research is at the base of the pyramid, it does not mean it is
unimportant,
unscientific, or unworthy. This type of research involves the
description of
phenomena in our world. In this type of inquiry, the phenomena
described
are basic information, actions, behaviors, and changes of
phenomena, but
always the description is about what the phenomena look like
from the
perspective of the researcher or the participants in the research;
it is not
about how the phenomena function. Prior to beginning the
research, you
will need to have established from theory or from prior research
what it is
about the phenomenon you will study; from this perspective,
descriptive
research is theory or research driven.

Descriptive research studies are important to the public and to
educa-
tors. For example, many reports produced by the federal
government are
good examples of necessary descriptive research (the U.S.
Census Report
or the many reports from the National Center for Education
Statistics).
Such reports provide the impetus for many other research
studies.
Descriptive research tends to answer basic informational
questions as
indicated in the following examples. What leadership behaviors
do super-
intendents who serve rural, suburban, and urban school distr icts
exhibit?
What pedagogies can be observed in teachers who teach in
bilingual edu-
cation classrooms? What types of technology are implemented
by teachers
who have had technology classes at the university level? Which
leadership
and/or organizational textbooks used in educational or business
adminis-
tration courses include gender-inclusive leadership theory?
Such ques-
tions in descriptive research will generally ask the question of
who, what,
when, where, how, or which.
When conclusions are drawn from descriptive research, there is
a
reporting of the facts, but there should also be researcher -based
conclu-
sions connecting the data to theory or prior research.

. . . illustrating the importance of how a phenomenon is
conceptual-
ized in descriptive research, Thurstone, Guilford, and others
promoted
a different conception of intelligence that emphasized instead
the spe-
cific nature of human abilities. Thurstone, using a somewhat
different
mathematical model of factor analysis than had the previous
researchers emphasizing general intelligence (Gould, 1981),
found,
instead of general intelligence, several distinct dimensions of
intelli-
gence that he termed “primary mental abilities.” Subsequently,
Guilford developed and described a complex “structure of
intellect”
model that first posited 120 separate factors, theoretically
derived
(Guilford,1967), and later 150 (Guilford, 1977). Notice that the
strategy
was theory driven: Guilford’s complex conceptual model of
intelli-
gence, for example, largely based on logically derived theory,
provided
a basis for designating the specific tasks he then used to
measure each
specific dimension of intelligence as described in his model.
(Anastas,
1999, pp. 128–129)
Guilford used prior research and theory to build his str ucture of
the intel-
lect model that has been used many times in developing
curriculum and
in research related to gifted children.

Instrumentation
Instrumentation is critical to descriptive research. If you are
using
archived data, you will likely use an instrument that measures
achieve-
ment among students or classrooms or schools. Perhaps you are
asking the
question: What is the achievement among various groups of
students
based in the state academic achievement test? You define the
various
groups of students as ethnicities, gender, and special -needs
students. You
would, of course, need to describe the instrument in detail,
along with its
norm reports on validity and reliability, particularly as it relates
to the var-
ious groups of students. Because the research question is based
in a stan-
dardized achievement test, the results of the study will only be
as good as
the instrument. Other standardized instruments may include
personality
inventories, intelligence measures, or attitudinal scales.
In most descriptive research studies, instruments must be
developed
by the researcher due to the fact that the study is related to a
specific phe-

nomenon. You will need to pay special attention to your
instrument devel-
opment and describe all the specifics of (a) how you developed
your
instrument, (b) where you obtained information to include in
your instru-
ment, (c) how you ensured validity and reliability, and (d) if
others assisted
you in your development. Surveys are some of the most common
instru-
ments you will use in descriptive research studies. In fact,
sometimes
descriptive research is even called survey research.
Data
Descriptive research reports data as measures of central
tendency,
which include mean, median, and mode, and as measures of
dispersion,
which include deviance from the mean, variation, range, and
quartile. It is
usually conducted as indicated previously through surveys, or it
may be
conducted via observation, interviews, portfolios, or cases.
Polls, surveys,
or questionnaires are typical examples of descriptive research
studies.
However, antiquities that are archived, film, video, Internet, and
e-mail,
may also be used for gathering data for descriptive research
studies.
Longitudinal Studies
In descriptive studies, there may be more than one variable, but

usu-
ally descriptive research includes only one variable. This
variable may be
measured at one point in time, but it also may be measured
across time.
The latter is considered as a longitudinal descriptive study, or it
could be
noted as a descriptive trend study. An example of such
descriptive studies
would be the reports on the National Assessment of Educational
Progress
(NAEP) or the Longitudinal Early Childhood Studies (for U.S.
Department
of Education reports see http://www.ed.gov). In longitudinal
descriptive
studies, data are taken from the same group or sample over
varying points
in time. In a trend descriptive study, data are gathered from a
different
group at various points in time, but within a population that
changes as
well. The NAEP study is an example of this type because
students are
assessed every year on reading and mathematics in specific
grade levels.
In other words, the population for all fourth grade students
changes from
year to year. Trends are established on a measure over time with
changing
populations.

Another type of descriptive study is a descriptive cluster study.
In the
cluster study, data are taken on measures from samples within
the same
population at various points in time. For example, all children
beginning
kindergarten in 2006 in a large urban school district or in a state
would be
considered the population. Those children would be followed
throughout
their school career; however, each year or at each point when
data are
taken, a cluster would be sampled within the same population of
students
who began their school careers in 2006.
Cross-Sectional Studies
A simulated longitudinal descriptive study can be completed by
col-
lecting data obtained at a point in time from samples of
individuals who
represent a cross section of the population. This is typically
called a cross-
sectional descriptive study. You may survey parents of students
at grades
K through 5 in a large urban district on their perceptions of
teacher atti-
tudes toward their children or on some variable of interest. You
may
administer a test to a sample of students from grades K through
12 at a
point in time to gather data on a particular phenomenon.
Mobility of
students and attrition are not accounted for in this type of
research and are

considered limitations.
Several dissertation abstract examples follow that demonstrate
effective
use of descriptive research design. (See Examples 3.1, 3.2, and
3.3.) Following
is an example of a dissertation dealing with a training program
for school
psychologists completed by Bridgewater (2006). (See Example
3.1.)
Example 3.1
Abstract
Despite the need for early identification and intensive
intervention for
social emotional concerns in young children, there exists a
shortage of
personnel trained to provide mental health services with early
childhood
populations (Klien & Gilkerson, 2000). The purpose of this
study was to
examine, through descriptive research, the current status of
preservice
school psychology training in relation to the assessment of
social-emotional
development in preschool children. Specifically, this study
sought to
explore the training school psychology programs offer, in
preparing pre-
service school psychologists to provide mental health services
for
preschool populations, and evaluate the perceptions of trainers
and
students regarding the coursework, competencies, and

instruments nec-
essary for preparing school psychologists to provide social -
emotional
assessment and mental health services in early childhood
settings.
Participants in this study included 108 program directors
(trainers) of
school psychology training programs within the United States,
as well as
4 advanced level students from each program, randomly
selected from
the master’s/specialists (n = 151) and doctoral level (n = 74) of
study in
each program; a total of 225 students. Two corresponding forms
of the
“Preschool Social-Emotional Assessment Training”
questionnaire were devel-
oped for this study. Each questionnaire was worded
appropriately for the
different groups of respondents. The questionnaire items were
developed
based upon two previous studies conducted by Boyer (1996) and
Gettinger, Stoiber, Goetz, and Caspe (1999), and the current
literature con-
cerning the preparation of school psychologists and related
service per-
sonnel in the delivery of early childhood metal health
assessment and
intervention services. (Bridgewater, 2006, p. ii)
The second abstract example is a dissertation that reports

parental reg-
ulations on television viewing (Salvato, 2006). (See Example
3.2.)
Example 3.2
Abstract
Childhood obesity is the most prevalent chronic disease among
North
American children and has reached epidemic proportions.
Increased televi-
sion viewing has been shown to increase the chances of a child
becoming
obese. The purpose of this descriptive study was to determine
the amount
of media children are using and the parental practices being
used to regu-
late television viewing among elementary school students. The
sampling
frame consisted of parents of children attending an elementary
school in
Connecticut. A questionnaire was adapted to determine the
parental regu-
lations being used to monitor children’s television viewing.
Parents self-
reported their child’s height and weight so that a correlation
could be made
between regulations and childhood obesity. The results of this
study have
increased the data on parental regulations regarding the use of
television,
and revealed that many parents are not aware of the effects that
excessive
television use can have on their child’s BMI. (Salvato, 2006, p.
iii)

The third dissertation example relates to a higher education
study on
writing centers and the questions that are asked by the tutors.
This disser-
tation was completed by Cook (2006). (See Example 3.3.)
Example 3.3
Abstract
This dissertation examines two research questions: What
questions do
tutors ask of writers during tutorials, and what apparent effect
do such
questions have on the tutorial? Writing center theory has largely
bulked
question types together, causing many to hail questions as
harmful while
others to view them as essential.
In this study, 20 videotaped tutorials were observed wherein a
total of
10 university writing tutors and 20 university student writers
collabo-
rated for nearly 427 minutes. During viewing, every question a
tutor
asked of a writer was logged and each context was analyzed. All
sessions
were viewed at least three times.

Tutors in this study asked 473 questions, allowing for 16 types
of tutor
questions to be identified. The types are labeled according to
their effect
and are further categorized into two categories. The first
category is
Interpersonal Tutor Questions, and it contains the following
types:
process, consent, rapport, gauging, filler, distracting,
refocusing, and ori-
enting. Effects include the ability to manage tutorials, gain
permission,
establish rapport, check writer understanding or mood,
participate in
chat, distract writers, refocus writers, and inform tutors. The
second cate-
gory is Making Meaning Questions, and it contains the
following types:
clarifying, verifying, transferring, suggesting, prompting,
modeling,
drawing, and exploring. The effects are the ability to clarify
tutor under-
standing, verify that tutor understanding is correct, transfer
expertise,
suggest changes, lead writers through discovery, model thought
processes, draw out information from writers, and challenge and
stimu-
late writers’ ideas and views.
This study is a descriptive study, which examines tutor
questions and
their effects; it does not examine the mental processes behind
questioning.
The study’s conclusions are: tutors employ 16 tutor question
types for
various purposes; effective questioning requires the

understanding of dif-
ferent question types; without an awareness of question types,
tutors may
find it difficult to know how to ask probing or challenging
questions; the
writing center field’s understanding of tutor questions can
benefit from
question research in other fields, but direct parallels made
without con-
sideration of the unique culture of writing centers may lead to
tutor con-
fusion. This study provides an important step toward tutor
questioning
training. (Cook, 2006, pp. iv–v)
CORRELATIONAL RESEARCH DESIGN
Correlational research is based in “relationship” just as its name
implies. It
is grounded in interactions of one variable to another; for
example, as
scores on one variable go up the related scores on another
variable go
down. You might find an inverse correlation between positive
praise and
acting out behavior—as the amount of praise increases, the
amount of act-
ing out behavior decreases. In correlational research, the degree
to which
the variables are related is important as well as the direction of
the rela-
tionship. This type of research is not specifically causal -
comparative
research nor is it ex post facto research. Where correlational
research actu-
ally relates scores from two or more variables from the same

sample, ex
post facto research compares scores from two or more groups on
the same
variable. Generally, correlational research does not signify
causality, but
some correlational research designs, such as path analysis and
cross-
lagged panel designs, allow for causal conclusions.
In correlational research, if a relationship is found between two
or
more variables, then the variables are correlated. This
correlation must be
interpreted based on the strength and direction via the
correlational coef-
ficient that ranges between −1 and +1. The direction of the
correlation or
the association between the two variables can indicate a positive
associa-
tion (0 to +1). This means that as one variable increases, the
other variable
also increases, and vice versa. Correlation coefficients between
0 and −1
represent a negative association. In this sense, as one variable
increases,
the other variable decreases. The magnitude or strength of the
correlation
or association between the two or more variables is signified as
stronger by
correlation coefficients closer to 1 (either positive or negative).
A correla-

tion coefficient of +1 indicates a perfect positive relationship,
while a coef-
ficient of 0 indicates no relationship, and a coefficient of −1
indicates a
perfect negative relationship.
The correlational coefficient is represented by the letter r. In
education,
generally a correlation of 0.30 may be considered significant,
and any cor-
relation above 0.70 is almost always significant. A Pearson’s
product
moment correlation, represented by r, is the most widely used
statistic of
correlation. It is used when variables you want to correlate are
normally
distributed and are measured on an interval or a rati o scale. As
you
learned in statistics, the r is not a percentage score, but when
you square
the r (r2), then you have an index called the coefficient of
determination that
indicates how much of the variance of X has in common with
the variance
of Y. For example if r is .50, then the r2 would be 25%. This
means that the
two variables have 25% of their variance in common with each
other. It can
also be interpreted that 25% of the variance in Y can be
accounted for by X.
The coefficient of determination should be reported as it lends
meaning to
your findings by giving you the size or degree of the
relationship. Cohen
(1988) indicated that correlations can yield effect size in the r2
and deter-

mined that a small correlation effect size would be r2 = .01, a
medium effect
size would be represented by r2 = .09, and a large effect size
would be
r2 = .25. Effect size is recommended to be reported in the
findings as it
quantifies the distance from the null hypothesis (in correlational
study the
null hypothesizes that no relationship exists between the two
variables)
(Thompson, 2000).
Correlational research is used in a wide array of studies that
intend, of
course, to determine relationships, but also that aim to assess
consistency,
as well as predictions. As you may have learned from the
chapter on
statistics or your course in statistics, when the p-calculated is
less than
p-critical (in education it is usually established before a study is
conducted
and set as .05), such a relationship is significant. In
correlational research,
sample size is an important consideration. As you already know
the
smaller the sample size, the larger the size of the correlation has
to be to be
statistically significant; on the other hand, the larger the sample
size, the
more likely a significant correlation will be found even if it is

with small
magnitude. Therefore, in research with large sample size,
reporting effect
size is meaningful and critical.
Following are some types of correlations that may be used in
correla-
tion research when data are specific to ratio or interval scales.
Bivariate Correlation
For bivariate correlation, you would be investigating the
relationship
between two variables. For example, you may be interested in
determining
the relationship between the amount of time sixth grade students
spend
reading in structured partnership reading in school and their
reading scores
at the end of a six-week period. An example of a dissertation
that used
bivariate correlation was conducted by Siemers (2006). (See
Example 3.4.)
Example 3.4
Abstract
Aggression negatively impacts children’s psychological and
academic
well being. The playground environment is more susceptible to
aggressive
behavior than more structured academic settings like the
classroom. In this
study, the relation between staff and student reports of student
play-

ground aggression was examined. Then, the relation between
environ-
mental playground factors (i.e., playground activities,
playground
supervisor ratios, active supervision, and playground rules) and
student
reports of aggression was examined. Finally, the relation of
playground
aggression to student self-reports of playground worries was
evaluated.
School Climate Theory provided the conceptual framework for
evaluating
playground characteristics, student aggression, and worry.
Participants
included 767 third, fourth, and fifth grade students and 57
playground
supervisors from 10 Midwest elementary schools. Participants
completed
reports of aggression (students and staff), playground worries
(students
only), and playground environmental factors (staff only).
Bivariate corre-
lations were used to examine the relation between staff and
student
reports of aggression. Hierarchical Linear Modeling analyses
were con-
ducted to examine the relation among playground factors,
aggression, and
playground worries. This study used a novel approach to
measuring the
predictive relation of aggression on children’s worries through
student
self-report measures. Results of this study showed that staff and
students’
reports of playground aggression were inconsistently correlated.
Although

overt physical and verbal playground aggression scores were
significantly
correlated, relational playground aggression and playground
conflict were
not associated. Additionally, four playground characteristics
were linked
to playground aggression: cooperative games, supervisor ratios,
active
monitoring, and playground rules. Finally, when students
reported more
playground aggression, their reports of playground worry were
higher.
(Siemers, 2006, pp. ii–iii)
Regression and Prediction
For regression and prediction, you would be assessing if a
correlation
exists between two variables. You might know the score on one
and wish
to predict the score on the second variable, and regression is
related to how
well you can make that prediction. The closer the correlation
coefficients
are to −1 or +1, the better your predictions become. A strong
prediction is
evidenced on packs of cigarettes—the more a person smokes,
the higher
the prediction of the person developing lung cancer or other
cancers.
An example of a dissertation related to compensation strategy

on non-
profit organizations is provided by Hoke (2006). (See Example
3.5.)
Example 3.5
Abstract
This study examined the effects of a total compensation strategy
on the
culture of a nonprofit. This study also examined employee
perceptions of
the new total compensation strategy. A paired-samples t test
was used to
determine whether a statistically significant difference existed
between
the means of the variables designated as productivity,
innovation, and
culture before and after the implementation of a new total
compensation
strategy. A Pearson r was used to determine the degree to which
produc-
tivity, innovation, and culture variables were associated. A
simple linear
regression was used to examine how effectively the total
compensation
strategy variable allowed prediction of the value of the
variables desig-
nated as productivity, innovation, and culture. The study was
initiated in
order to increase understanding of those elements that influence
organi-
zational productivity, innovation, and cultural change. The
setting was a
donative nonprofit in St. Louis, Missouri. The data analysis in
this study

revealed that the independent variable; i.e., the new total
compensation
strategy, had a statistically significant effect on the culture of
the nonprofit
organization. The findings presented in this study also
illuminated ways
in which employees viewed the new total compensation strategy
as most
helpful; i.e., providing a sense of ownership and motivation,
and con-
versely, as least helpful; i.e., confusion about how the strategy
works. An
integrated analysis of the quantitative and qualitative data also
suggested
ways in which the effect of the new total compensation strategy
could be
increased. For example, the analysis suggested that more
support for
being creative and doing new things would increase
organizational pro-
ductivity and innovation. As a second example, the analysis
suggested
that more freedom for employees to exercise discretion in
decision mak-
ing would increase organizational productivity and innovation.
Future
research that addresses the application of organizational
interventions
developed in the private sector to the nonprofit sector and
considers the
impact of personal value systems will further refine the body of
evidenced-based interventions. These interventions will assist
nonprofit
organizations to enhance sustainability, contribute to the
effectiveness of

organizational functioning, and finally the satisfaction and well -
being of
those employed by the organization. (Hoke, 2006, pp. i–ii)
Multiple Regression
For multiple regression, you would add more variables and more
power in terms of making more accurate predictions. The
variable that you
would be predicting in multiple regression is called the
criterion, outcome,
or dependent variable, while the variables you use to make the
prediction are
called the predictor or independent variables. If you are trying
to predict
English reading scores for English language learners in a
structured inter-
vention, you might have as predictor variables the amount of
time taught
in English, the level of English of the parents, and the amount
of time
spent in academic oral language engagement with the child. You
would
have one criterion variable measured by some predetermined
reading
assessment and you would have three predictor variables. Do
not forget to
calculate your effect size for multiple regression and other
correlational
designs. An example of the effect size for multiple regression
follows.
Cohen’s f 2 is the appropriate effect size measure to use in the

context of
multiple regression or multiple correlations. The f 2 effect size
measure for
multiple regression is as follows:
f 2 R2
=
1 − R2
(R2 is the squared multiple correlation derived from the
regression model).
By convention, f 2 effect sizes of 0.02, 0.15, and 0.35 are
considered small,
medium, and large, respectively (Cohen, 1988). Other multiple
regressions,
such as hierarchical multiple regression, will have a slightly
different for-
mula for the effect size. You will need to consult a statistics
manual.
The following is an example of a dissertation in which multiple
regression
was used in a correlational research design (Bean, 2007). (See
Example 3.6.)
Example 3.6
Abstract
Rates of obesity in children are rising at an alarming rate,
particularly
among girls and ethnic minorities. Engaging in regular physical
activity
can help reduce this risk. Little is known about factors

associated with
physical activity (PA) in preadolescent populations, an age
when inter-
vention is ideal. Guided by Social Cognitive Theory, this study
used a
repeated-measures design to examine PA and its correlates,
including PA
self-efficacy, outcome expectations, and social influences (from
parents
and peers), among participants (N = 57) in Girls on the Run, an
innovative
PA intervention for elementary school girls. Participants (M age
= 9.4)
predominately include girls from ethnic groups at highest risk
for obesity,
with 74% African American and 18% Hispanic. Multiple
regressions indi-
cated that, at baseline, girls with higher self-efficacy were
significantly
more likely to report greater intentions to be physically active
(β = .40,
p < .05). Further, although no mean changes in study outcomes
were
found, an examination of factors associated with the variance in
PA behav-
iors and intentions at posttest can further understanding of PA
in this age
and ethnic group. Processes of change regressions suggested
that, after
adjusting for baseline levels, increases in both self-efficacy and
social influ-

ences were significantly associated with higher physical activity
behaviors
and intentions at posttest (p < .05). Outcome expectations, or
belief in the
benefits of physical activity, was not a significant variable in
the models
(p > .05). Overall, findings suggest the importance of targeting
physical
activity self-efficacy and fostering high levels of peer and
parental support
for physical activity to help girls meet recommended guidelines.
Implications for future interventions are discussed. (Bean, 2007,
p. vii)
Canonical Correlation
Canonical correlation uses multiple correlations and has more
than
one criterion variable to a prediction equation. It allows you to
see which
of the variables are most important to relationships between the
sets of
independent and dependent variables. An example of a
dissertation that
included canonical correlation as its primary technique was
done by
McCoy (2005). (See Example 3.7.)
Example 3.7
Abstract
The purpose of this study was to examine hypothesized
relationships
between participants’ characteristics (i.e., academic interest
area, prior

exposure to persons with disabilities, the positive personal
characteristics
of humor, hope, and gratitude) and their self-report of
cognitive, affective
and behavioral responses to persons with disabilities, whi le
controlling
for social desirability. Undergraduate student participants at
three Mid-
Atlantic colleges were recruited from introductory courses in
human ser-
vices to complete survey packets.
The instruments used to gather data for this study consisted of a
demographic survey designed by the author, the Marlowe-
Crowne Social
Desirability Scale (Crowne & Marlowe, 1960), the Humor
Styles
Questionnaire (Martin, Puhlik-Doris, Larsen, Gray, & Weir,
2003), the
Adult Dispositional Hope Scale (Snyder et al., 1991), the
Gratitude
Questionnaire-6 (McCullough, Emmons, & Tsang, 2002), the
Attitudes
Toward Disabled Persons Scale—Form O (Yuker, Block, &
Campbell,
1960), the Relationships with Disabled Persons Scale (Satcher
& Gamble,
2002), the Interaction with Disabled Persons Scale (Gething &
Wheeler,
1992), and the Situational Response Questionnaire (Berry &
Jones, 1991).

A canonical correlation analysis was used to identify a
combination of
personal characteristics that are correlated with a combination
of reac-
tions to persons with disabilities. The primary multivariate
finding of this
study was that the combination of some, but not all, of the
personal char-
acteristics explored were correlated with the combination of
some, but not
all, of the measures of reaction toward persons with disabilities.
A con-
struct composed of the personal characteristics of affiliative
humor and
majoring in a human services field and, to a lesser degree, self-
defeating
humor, hope, and prior exposure to persons with disabilities was
corre-
lated with a construct composed of cognitive attitudes toward
persons
with disabilities, and to a lesser degree, affective responses
toward
persons with disabilities. Though the findings of this study were
signifi-
cant, the amount of variability in criterion variables that was
explained by
the predictor variables was relatively small. This suggests that
other fac-
tors account for the rather large amount of variability
unexplained by the
predictors included in this study. (McCoy, 2005, p. iii)
Discriminant Analysis
Discriminant analysis is used to compare two or more groups on

a set
of variables. The criterion variable in this correlational analysis
is the
group membership. Predictor variables are input and a formula
or model
results. An example of discriminant analysis employed in a
dissertation
from Thanasui (2005) follows. (See Example 3.8.)
Example 3.8
Abstract
This study investigated the influence of past witnessed or
experienced
abuse on heterosexual cohabiting couples’ Premarital Personal
and
Relationship Evaluation for Cohabiting Couples (PREPARE-
CC) couple
relationship types. The researcher utilized preexisting data from
5,000
cohabiting couples who had previously participated in the
PREPARE mar-
riage preparation program and had completed the PREPARE-CC
inven-
tory including a demographic section that elicited information
about past
abuse. Discriminant analysis was conducted in SPSS to answer
the ques-
tion of whether the presence of past witnessed or experienced
abuse could
successfully predict relationship type among cohabiting
couples.
Results of the discriminant analysis yielded no significant
ability to

classify cohabiting couples by individuals’ experience of past
abuse, how-
ever, isolating females and males with the highest frequencies
of past
abuse indicated that males abused “very often” had a higher
frequency of
higher-satisfaction relationship types than the general sample
consisting
mostly of individuals with little or no history of abuse. Females
reporting
abuse “very often” did not follow this same pattern.
Recommendations
were made for future longitudinal studies and for strength-based
research
on healthy heterosexual cohabiting couples in an effort to
understand
what contributes to these couples’ success. (Thanasui, 2005, p.
iii)
Factor Analysis
Factor analysis is another statistical procedure that uses
correlations to
identify basic patterns of variables. If a large number of
variables having
intercorrelations are present, then a common factor could be
identified for
that set of variables. Several factors could be identified among a
large set
of variables if you are studying leadership and organizations.
For

example, you might find two factors such as leadership
behaviors and
organizational structures. The two factors might be helpful in
describing
the success of superintendents. An example of factor analysis
used in a dis-
sertation by Evans (2006) follows. (See Example 3.9.)
Example 3.9
Abstract
The purpose of this study was to examine the validity and
reliability of
scores from a survey instrument, the Technology Integration
Survey (TIS),
designed to measure technology integration skills in preservice
teachers.
All of the items used in the survey were based on the ISTE
NETS for
Teachers (NETS-T) standards. Participants in the study were
undergradu-
ate students enrolled in the preservice teacher education
program at a
large university in southeastern North Carolina. Validity and
reliability
studies were conducted on the TIS including: (a) a study of
representa-
tiveness and alignment of survey items to the NETS-T
standards,
(b) analysis of the internal structure using exploratory factor
analysis,
(c) pre/posttest testing after technology intervention, (d)
correlations
between the TIS and an instrument with known validity and
reliability,

(e) correlations between TIS and field experiences or online
portfolios,
and (f) analysis of internal consistency. Four factors were
extracted using
a principal components analysis. The factors (a) Planning and
Designing
Learning Environments and Experiences, (b) Teaching and
Professional
Practice, (c) Social, Ethical, and Human Issues, and (d)
Assessment and
Evaluation explained 63% of the variance. Reliability
coefficients on the
four subscales ranged from .83 to .89. Evidence from the study
supports
the use of the TIS as a viable tool to measure technology
integration skills
in preservice teachers. (Evans, 2006, p. iii)
Path Analysis
Path analysis may be used to assess which of a number of
pathways
connects one variable to another. For example, in the
relationship of
smoking and lung cancer, there might be a small to medium path
that runs
through healthy activity, but a major path that goes through
smoking
behavior. In this, you could surmise that even with running,
walking, and
eating properly, if a person continues to smoke heavily, he or

she has a
stronger likelihood to develop lung cancer.
A dissertation example that used path analysis was completed
by Choi
(2005). (See Example 3.10.)
Example 3.10
Abstract
The purpose of this study was to identify the physical activity
behavior
(leisure-time, household, job-related, and transportation-related)
and to
examine the relationships between physical activity behavior
and the cor-
relates of physical activity including acculturation as well as
environ-
mental resource, social support, and cognition (self-efficacy,
decisional
balance) in 200 adult Korean immigrant women aged 20–64.
Although the
health benefits of physical activity are well established, the
current phys-
ical activity level of the U.S. public, particularly for ethnic
minority and
immigrant women, does not reach the Healthy People 2010
goals. There
are few studies of physical activity and its correlates for Asian-
American
women and none include Koreans. A descriptive cross-sectional
survey
design was used. Korean ethnic church-based recruitment was
the pri-
mary means of accessing this population. Physical activity was

measured
with the long form of the International Physical Activity
Questionnaire.
Acculturation was measured with proxy measures (language,
years of
residence in the U.S., age at immigration) and the Vancouver
Index of
Acculturation. McAuley’s Barriers Self-Efficacy measure and
Marcus’
Decisional Balance measure provided measures of cognition.
The mean
age of the participants was 41 years, 84.5% were married, and
71% had a
college degree or higher. In general, the women were not
physically active
in their leisure time, but most of them were physically active
when other
domains of physical activity were examined together. A path
analysis
showed that years of residence in the U.S., self-efficacy, and
decisional
balance had direct positive effects, and number of children
under five
years had a direct negative effect on leisure-time physical
activity. Age
and American acculturation had indirect positive effects through
self-
efficacy while depression had an indirect negative effect
through deci-
sional balance on leisure-time physical activity. A total of 14
percent of the
variance in leisure-time physical activity was explained. The
findings
showed that acculturation is an important correlate of leisure-
time physi-
cal activity among Korean immigrant women. In addition, self-

efficacy
and decisional balance were essential cognitive correlates of
leisure-time
physical activity and they played mediating roles. Findings
provide direc-
tion for developing targeted physical activity interventions for
Korean
immigrant women based on their self-efficacy, decisional
balance, and
acculturation. (Choi, 2005, n.p.)
Cross-Lagged Panel
Cross-lagged panel (longitudinal) correlational designs measure
two
sets of variables simultaneously at two points in time. For
example, the
correlation between early exposure to oral English literacy in
prekinder-
garten and reading achievement in fifth grade is compared with
the corre-
lation between later reading achievement in fifth grade and later
exposure
to oral English literacy in first grade. Some of the data points in
this design
are treated as temporality delayed or lagged values of the
outcome vari-
able. According to Mackinnon, Nohre, Cheong, Stacey, & Pentz
(2001):
Cross-lagged correlation approaches were criticized for several
rea-

sons, including problems due to measurement error (Kessler &
Greenberg, 1981). Measurement error was later incorporated in
the
cross-lagged model by the estimation of latent variable models
for the
observed measures. These methods enjoyed several years of
wide and
convincing applicability (Bentler & Speckart, 1979, 1981;
Kessler &
Greenberg, 1981) until their limitations were clearly described
(Rogosa,
1980). According to Rogosa and Willett (1985), these models do
not
explicitly specify individual change, do not easily generalize to
more
than two waves of data, and the data analysis only includes the
covari-
ance matrix and not the means. Stoolmiller and Bank (1995)
added that
autoregressive models fail when there is high-rank order
stability over
time and require the questionable assertion that the prior score
on a
variable causes the subsequent score on that variable. (p. 221)
An example of a cross-lagged panel correlational design was
used in a
dissertation by Coon-Carty (1998). (See Example 3.11.)
Example 3.11
Abstract
Vygotsky argued that children’s development is most likely to
occur
when, in the course of collaboration, assistance is provided

within their
zone of proximal development—the distance between what a
child can
achieve independently and what he or she can do with the
assistance of a
more competent peer. This dissertation discusses the results
from a longi-
tudinal investigation exploring the impact of learning in either a
multiage
or traditional classroom on students’ subject-specific academic
self-concepts.
Results from a cross-lagged panel correlation analysis, testing
the predomi-
nant causal flow from subject-specific self-concepts to
academic achieve-
ment are also described. Participants in the study consisted of
189 first
(106) and fourth (93) grade children who were drawn from a
public
elementary school in Salt Lake City, serving predominantly
European-
American middle- and lower middle-class students. Eighty-one
of the
participants were in a multiage classroom with the remaining
108 in a
traditional classroom. Multiple self-concept measures and a
standardized
achievement test were given to the participants toward the
beginning and
at the conclusion of the academic year. Results indicated no
significant

differences for math and reading self-concepts between the
multiage and
traditional participants and the end of the academic year.
Learning in a
multiage classroom did not increase the participants’ math or
reading
self-concepts over the course of the academic year. Significant
differences
were found as a function of setting for male participants. Fourth
grade
males in the multiage classroom reported significantly lower
control over
performance scores at the end of the academic year compared to
the
fourth grade males in the traditional classrooms. Results from
the cross-
lagged correlation design demonstrated a causal flow from
reading self-
concept to subsequent reading achievement, suggesting that
perceptions
of reading abilities cause future reading achievement. The study
did not
show the same significant influence between math self-concept
and math
achievement. The pattern of associations between math self-
concept and
math achievement is more suggestive of a reciprocal
relationship rather
than a causal one. (Coon-Carty, 1998, pp. x–xi)
Other Correlation Coefficients
For data that are represented by rank scores, an ordinate
coefficient of
correlation, Spearman’s rho (ρ), is used. It is interpreted in the
same way as

the Pearson r and ranges from −1 to +1. A phi coefficient (Φ) is
used when
both variables are dichotomous scores (1 or 0). This is often
used when sex
of individuals is a variable. For example, you might want to
determine on
a particular college campus if drinking is more related to men
than to
women. The phi coefficient reveals both strength and direction
of relation-
ships. Although we have presented only three types of
correlation coeffi-
cients, there are others that are available to you for use with
nominal and
ordinal data and that can be used with more than two variables.
Advantages and Disadvantages
There are advantages and disadvantages to correlational
designs. First,
the advantage is that almost any variable that you wish to study
can be
investigated. Additionally, it can be used in a predictive manner
with one
variable or more predicting another. Disadvantages that have
been dis-
cussed are that correlational research is subject to extraneous
variables and
that causation cannot be inferred.
CAUSAL-COMPARATIVE RESEARCH DESIGN
Causal-comparative research, or ex post facto (after the fact)
research, is
the most basic design for determining cause-and-effect
relationships

between variables. Causal-comparative research is different
from
experimental research in that you do not manipulate the
independent
variable since it has already occurred, and as already indicated,
you can-
not control it. Additionally, causal-comparative research will
not meet
the experimental research requirement for random assignment of
par-
ticipants from a single population or pool of participants. At
least two
comparison groups are needed in causal-comparative research to
be
compared on a dependent variable. One group may be students
who
have been identified by their teachers as being potentially
gifted, and
the other group may be those students not identified by their
teachers
as being potentially gifted. These two groups could be measured
on
standardized IQ tests to determine if the group differences
identified by
the teacher perception are accurate as compared by the
standardized
measures.
When you discuss the groups, you will want to make certain you
describe the groups and their characteristics in detail and the
independent

variable that distinguishes the groups. Why is this important? It
is impor-
tant because the group constitution and definitions can affect
the general-
izability of your findings. Give ample detail to understand all
about each
of the groups in terms of their comparability and differences.
You may
obtain your groups from two independent established
populations, such
as those students who have had public school prekindergarten
and those
who did not. You may want to compare these students on
English reading
achievement at the end of kindergarten or first grade. To obtain
your sam-
ple you may conduct random sampling (not random assignment
from one
single population as in experimental research). You will want
your groups
to be as similar as possible on some variables. For example, in
the prekinder-
garten versus no prekindergarten groups, you may want to go
through the
random samples and match the samples (to have better control)
on age,
ethnicity, socioeconomic status, and gender. You want the
groups to be as
similar as possible so that the difference or lack of differences
you see in
the end of your research is more likely to be attributed to your
indepen-
dent variable. Because causal-comparative research does not
rest on ran-
domization or manipulation or control of variables, you w ill
know up

front that is a weakness if you so choose this type of design.
Weakness Controlled
One way to control for that weakness has already been
mentioned—
match the samples. Another way to control for confounding
variables is to
compare homogenous groups. In our previous example, we
could take all
students who had been to prekindergarten and who had not who
were all
English language learners and who were non-English fluent
upon entry to
kindergarten. Another way to further control the study would be
to take
that same group of students and separate them into subgroups
that would
represent varying degrees of English language fluency. Perhaps
you might
group the students into non-English fluent and limited-English
fluent.
This would permit you to determine the influence of English
fluency when
checking the independent variable’s influence on the dependent
variable.
If you like this approach, you may wish to test control variables
by using
factorial analysis of variance (known as ANOVA). This
approach will
allow you to determine the effect of the independent variable, as

well as
the control variable (separately and combined), on the
dependent variable.
A factorial analysis provides information about the interaction
of the inde-
pendent variable on the control variable at the different
subgroup levels.
Students who are limited-English fluent upon entry to
kindergarten may
indeed show significantly higher beginning English reading
(literacy)
scores at the end of the kindergarten year.
Another statistical technique that may be used as well is
analysis of
covariance (ANCOVA). ANCOVA adjusts for initial differences
in your
groups. For example, if students who have better English skills
at the out-
set of the study and demonstrate in a pretest score significantly
higher in
English literacy/reading, then the ANCOVA will adjust for that.
It adjusts
both groups to equal standing. As instruction is then given to
both groups
using the same curriculum, the final outcome can be better
assessed in
terms of the actual kindergarten year intervention.
All methods discussed here are univariate analyses. When you
have more
than one dependent variable you may want to resort to
multivariate analysis.
Data analysis techniques used in causal-comparative research
designs

usually consist of inferential statistical techniques such as t test
(compari-
son between two groups), analysis of variance (comparison
among more
than two groups), and chi square (when outcome variable is
dichoto-
mous). We have a couple of words of caution as you begin your
analysis
and conduct your statistical tests. First, be careful on your
interpretation of
causality. Look at your data carefully to make certain in which
direction
the causal relationship flows. Additionally, remember to
conduct a test for
effect size for each of the typical statistical tests for this
research design.
Effect size takes various forms, such as r2 (see previous
sections on corre-
lational research). We present here a basic effect size type for t
test for
means; however, you will want to consult your committee
statistician on
this topic for calculating or obtaining these via your statistical
package you
use for your dissertation. Typically for a t test for testing
means, you
would use Cohen’s d. Here is the formula:
mean mean
d
−
=
1 2
√

(
SD2
1 + SD2
2
)
/2
You will also want to report confidence intervals as well. The
bottom line
is that, whether it is chi square or t test, all must be followed by
an effect
size calculation for your research findings to be rendered
worthwhile, as
this calculation provides you with the strength of the
association to the
larger population as it relates to your study.
Following are two examples of causal-comparative research
designs
used in recent dissertations. (See Examples 3.12 and 3.13.)
Example 3.12
Abstract
The purpose of this study was to determine differences in

teacher prepa-
ration and retention for two groups of Texas A&M University–
Corpus
Christi graduates certified in 2002 and 2003. Factors that
influenced
teacher attrition, such as teaching assignments, working
conditions,
salary, benefits, scheduling, organizational skills, rapport with
adminis-
trators, colleagues, parents and students were examined to
determine
whether graduates who completed a formal mentoring program
stay in
the teaching profession at higher rates than the graduates who
did not
participate in the Strategies of Success (SOS) formal mentoring
program.
Participants included 95 certified TAMU-CC graduates who met
the
inclusion criterion of no prior teaching experience.
A causal-comparative research design was used to compare two
preex-
isting groups, non-SOS and SOS TAMU-CC certified graduates
applying for
certification in 2002 and 2003. Outcome measures examined
were retention,
reasons for leaving the field of education and satisfaction with
the teacher
education program at this regional university. The TAMU-CC
Teacher
Education Survey (Arnold & Ramirez, 2003) was used to collect
data for this
study. Results of the analyses showed no statistically significant
difference

between the non-SOS and SOS groups. (Arnold, 2006, pp. iii–
iv)
Example 3.13
Abstract
This study analyzed test scores of economically disadvantaged
students
who attended two elementary schools implementing different
types of
Title I models from 1999–2001. Test scores from the Texas
Assessment of
Academic Skills (TAAS), the Iowa Test of Basic Skills (ITBS),
and the
Stanford Achievement Test (SAT-9) were analyzed. One school
imple-
mented the targeted assistance model (less than 50% poverty),
which
focused resources on students identified as failing or at risk of
failing. The
other a schoolwide model (95% poverty), which used resources
to help all
students in a school regardless of whether they ware failing, at
risk of fail-
ing, or economically disadvantaged.
The quantitative approach was used with a causal-comparative
design. A cohort of continuously enrolled students was
identified for
the TAAS (n = 155) and the ITBS/SAT-9 (n = 135). Descriptive
statistics

such as the frequency, mean, and standard deviation, were used
to
measure differences on the Texas Learning Index (TLI) for the
TAAS,
and Normal Curve Equivalent (NCE) on the ITBS/SAT-9.
Analysis of
Covariance (ANCOVA) was used to partially adjust for
preexisting dif-
ferences among the groups and because randomization was not
possi-
ble. The independent variable was type of Title I model,
targeted
assistance or schoolwide. The dependent variable was the
achieve-
ment measure, and the covariate was the initial achievement
scores in
third grade (pretest).
The ANCOVA reports and descriptive statistics showed that
eco-
nomically disadvantaged students performed better in reading
and
math on TAAS and ITBS/SAT-9 at the targeted assistance
school in
1999 and 2001, with mixed results in 2000. The academic
performance
of economically disadvantaged students at the targeted model
was
consistent all three school years. They scored slightly lower
than
the non-economically disadvantaged students, but higher than
their
peers at the schoolwide model. The students’ third grade pretest
score
was the most significant predictor of future performance.

(Hinojosa,
2005, n.p.)
QUASI-EXPERIMENTAL RESEARCH DESIGN
Even though the best causal research is reflected in true
experimental
designs, most research in education that requires causal
inferences can-
not be conducted under true experimentation due to the inability
to
randomly assign participants to experimental and control groups
or the
inability to secure a control or comparative group. Additionally,
much
of true experimentation is expensive in that it often requires
training in
the intervention and always monitoring for fidelity. In this case,
quasi-
experimental designs are available to give you adequate control
over
threats to validity. There are several types of quasi -
experimental
designs, which we believe would be advantageous to the
development
of your dissertation research.
Nonequivalent Control Group Design
This design is used most frequently in educational research.
This
design includes at least an experimental group and a control
group with
random assignment of participants to groups; however, there is
random
assignment of intact groups to treatments. Cook and Campbell

(1979) out-
lined 11 nonequivalent control group research designs. Table
3.1 shares
several of those 11 possible designs.
Examples of nonequivalent control group research designs from
dis-
sertations follow. (See Examples 3.14 and 3.15.)
Example 3.14
Abstract
Purpose. The purpose of this study was to describe Advanced
Placement
(AP) calculus teachers’ and administrators’ perceptions of AP
calculus
program characteristics that have increased student enrollment
in
selected San Bernardino County public comprehensive high
schools.
Methodology. Descriptive research methodology and a posttest
only,
one-group design was used to identify the current
characteristics of AP
Table 3.1 Nonequivalent Control Group Research Designs
Research Design Explanation

One-Group
Posttest-Only
Design
Posttest-Only
Design with
Nonequivalent
Comparison
Groups Design
One-Group
Pretest-Posttest
Design
Two-Group
Pretest-Posttest
Design Using an
Untreated
Control Group
Because there is no accurate information on pretest data
from the group, the results of this type of design cannot
be considered completely valid because changes in the
dependent variable may be due to treatment, or they
may be due to any number of threats to validity, such as
history or researcher expectation. A caution is noted to
gather and report as much information as possible
related to the pretest conditions. However, this design is
fairly inexpensive. It allows you to conduct research
when there is no comparison group readily available.
Often this design is used in evaluation research.
This design consists of the administration of a posttest to
two groups—usually a treatment group and a
comparison group. Because the two groups may not be

the same prior to the beginning of the instruction, it is
difficult to draw valid conclusions about treatment effect
based solely on posttest information on two
nonequivalent groups because effects may be due to
treatment or to nonequivalencies between the groups.
This is a common design (0 × 0) comparing
performances of a single group of participants, but this
design has weaknesses. It is subject to such threats to
internal validity, such as history, maturation, regression
toward the mean, testing, selection, and mortality. It is
difficult to determine if the results are from the
intervention or from confounding variables between the
pretest and posttest.
In this design, threats to validity are reduced because there
is a comparison group; however, the groups are not
equivalent. Therefore, there remains the possibility of the
threats of selection. Performance trends in both the
treatment group and the control group, can be established
by using multiple pretests. There should be a change in the
trend line for the treatment group but not the control group.
calculus programs in the county of San Bernardino. The study
examined the
responses of twenty-five AP calculus teachers and twenty-seven
site admin-
istrators in the county of San Bernardino. Teachers and
administrators were
surveyed with a questionnaire to obtain information on student
prepared-
ness for AP calculus classes, student learning, access, and
placement. The
study examined the educational/training of AP calculus

teachers, as well as
the types of instructional, administrative, district, and parental
support AP
calculus programs received. Free response questions were
elicited to deter-
mine the teachers’ and administrators’ perceptions of AP
calculus programs.
Findings. Administrators and teachers both perceived that there
were
gaps to students’ preparedness for AP calculus classes. Teachers
were more
pessimistic that AP calculus classes were supported from their
school dis-
trict. Teachers and administrators perceived that they received a
lot of sup-
port from their site principal, but teachers were more
pessimistic on
parental support. The findings confirm that teachers and
administrators
perceived that students receive AP practice exams for support,
after-school
sessions, and individual tutoring, but students are not perceived
to receive
Saturday support sessions or guidance counseling for AP
calculus classes.
Conclusions and Recommendations. AP calculus teachers and
site admin-
istrators must generate strategies to increase student enrollment
in AP calcu-
lus classes in selected San Bernardino County public
comprehensive high
schools. The study recommends that AP calculus teachers and
site adminis-
trators collaborate to develop and implement strategies on

student prepared-
ness for AP calculus classes, student learning, access,
placement, and the
implications of those strategies for the design and integration of
curriculum,
instruction, assessment, and professional development. The
study presents a
set of recommended actions that could significantly improve
existing pro-
grams for approaches to AP calculus courses and serve to
promote programs
for advanced study in the mathematics discipline. (Rodriguez,
2005, pp. iii–iv)
Example 3.15
Abstract
This study assessed the effects of general and specific
supervisory feed-
back on counselors’-in-training ratings of counseling self-
efficacy (CSE).
Fifty-four students in counseling-related graduate programs
from two
universities in the Southeast and one in the Midwest
volunteered as par-
ticipants. Thirty-seven participants were female, 14 were male,
and three
did not indicate their sex. Forty out of 54 participants indicated
they were
Caucasian-American, five were African American, one was
Hispanic-
American, one was Asian-American, and three indicated
“other.” The
median number of months of previous clinical supervision for
the partic-

ipants was one month. This study made use of a two-group
pretest-
posttest design. The independent variable was performance
feedback,
with two levels (specific and general). The dependent variables
included
posttest scores on the Counselor Activity Self-Efficacy Scales
(CASES),
(Lent, Hill, & Hoffman, 2003). State anxiety was also assessed
with the state
scale of the State-Trait Anxiety Inventory (STAI-S),
(Spielberger, 1982). After
completing pretest measures, participants performed a ten-
minute mock
counseling session with a confederate. After the mock
counseling session,
the confederate provided either specific feedback or general
feedback
statements to the participants. Participants then filled out
posttest
measures. After the measures were collected, participants
received an
extensive debriefing. The three hypotheses for counseling self-
efficacy
were: (a) participants receiving specific feedback would obtain
higher
counseling self-efficacy scores; (b) posttest counseling self-
efficacy scores
would be significantly higher than pretest scores; and (c) while
both
groups were expected to score similarly in the pretest measure

of coun-
seling self-efficacy, it was predicted that the participants
receiving specific
feedback would report higher counseling self-efficacy scores in
the post-
test measures. There was no difference between counseling self-
efficacy
scores of participants who received general or specific
supervisory feed-
back. Postmeasure counseling self-efficacy scores were
significantly
higher than the premeasure scores. There was no group × time
interac-
tion. There was no difference between anxiety scores of
participants who
received general or specific supervisory feedback. Postmeasure
anxiety
scores were significantly higher than the premeasure scores.
There was no
group × time interaction. Implications and issues to be
considered in
future research were discussed. (Clark, 2005, pp. vi–vii)
Time Series Design
The time series design is a variation of the one-group pretest–
posttest
design. Such a design is diagrammed in Figure 3.2. The one- or
single-group
time series design is where one group of participants is tested
repeatedly
both before and after treatment. The repeated testing establishes
stability
prior to the treatment, and the repeated testing after the
treatment estab-
lishes confidence in the effectiveness of the treatment. You

would essen-
tially investigate the pattern of test scores pre- and post-
treatment. There
are threats to validity related to history and instrumentation if
the instru-
ment is changed during the research. A multiple time series
design, adding
a control group, can eliminate the threats.
One-Group Interrupted Time Series Design
Treatment
Group
X
Pre-treatment
OOOO
Post-treatment
OOOO
Figure 3.2 One-Group Interrupted Time Series Design
Following are examples from dissertations using time series
designs.
(See Examples 3.16 and 3.17.)
Example 3.16

Abstract
This study assessed the impact of Pennsylvania’s zero-tolerance
juvenile
drunk driving law. Enacted in Pennsylvania on August 01, 1996,
the law
lowered the Blood Alcohol Concentration (from .10 mg/ml to
.02 mg/ml)
necessary to charge a juvenile (under age 21) with the offense
of drunk
driving. The intent of the law, which was enacted nationally,
was to lower
motor vehicle fatality accident rates involving juveniles who
consumed
alcohol and then drove.
Using interrupted time series designs (ITS) two outcome
measures
(PA juvenile drunk driving arrest rates and PA juvenile alcohol -
related
motor vehicle fatality accident rates) were used to assess the
law’s impact.
Additional ITS designs, as well as informal, semi-structured
interviews
with law enforcement supervisors and an archival data review
(various
newspapers published within Pennsylvania) also were employed
to con-
trol for validity threats to the research findings.
The data indicated that the law was ineffective in reducing
juvenile
alcohol-related motor vehicle fatality accident rates. In both
Pennsylvania
and the control state of Ohio, the data revealed that over the

seven year span
after the law was initiated juvenile alcohol-related motor
vehicle fatality
accident rates increased slightly (statistically insignificant),
mirroring the
effect of juvenile non-alcohol-related motor vehicle fatality
accident rates.
The data further revealed that monthly juvenile drunk driving
arrest
rates in both Pennsylvania and Ohio significantly increased (p <
.000) after
the law was enacted, but the perceived increase in certainty and
severity
of punishment failed to deter juvenile drivers from drinking and
driving.
A brief policy analysis is offered as to why deterrence theory
failed to
reduce incidents of juvenile drunk driving, along with policy
recommen-
dations about how to reduce juvenile alcohol-related incidents
and acci-
dents by taking an educational approach to juvenile alcohol use
instead of
a punitive approach. (Lewis, 2006)
Example 3.17
Abstract
The purpose of this study was to examine the relationship
between expo-
sure to opportunities inherent in jobs and self-perceived
changes in lead-
ership behaviors and characteristics. Qualitative research in the
early

1980s pointed out the importance of work-related experiences to
devel-
opment as a manager and leader. Most of the follow -up
quantitative
research has been on the relationship of on-the-job
opportunities and
learning or managerial growth. However, there does not appear
to be any
quantitative research identifying which on-the-job
developmental oppor-
tunities are related to changes in leadership and characteristics.
This study examined the relationship between exposure to on-
the-job
challenges and self-reported changes in leadership behavior and
charac-
teristics. This quantitative study employed a quasi-
experimental, multiple
time series design to assess whether exposure to on-the-job
developmen-
tal opportunities were associated with changes in leader
behavior and
characteristics. Additionally, the study investigated which on-
the-job
developmental opportunities were associated with changes in
leader
behavior and characteristics. On-the-job developmental
opportunities
were assessed using McCauley, Ohlott, and Ruderman’s Job
Challenge
Profile (JCP). Leadership behavior and characteristics were

assessed
using Sashkin’s The Leadership Profile (TLP).
Results revealed significant, positive relationships between
level of
job challenge and self-reported changes in leadership behavior
and char-
acteristics. Additionally, findings confirmed that job challenges
arising
from “Managing at High Levels of Responsibility,” “Managing
Boundaries,” and “Creating Change” were associated with
significant,
positive changes in one or more of the leadership components
measured
by the TLP for one or more of the cohort groups involved in this
longitu-
dinal study. (Thompson, 2003, pp. i–ii)
EXPERIMENTAL RESEARCH DESIGN
Experimental research designs are considered to be the Rolls
Royce of
quantitative research. Few dissertations in education are
completed using
a true experimental design; therefore, if you have the
opportunity to con-
duct such a study, it would be advantageous to your academic
career.
Experimental design allows you to determine whether a program
or
treatment intervention is the cause of the outcome, and in such a
case, you
must have strong internal validity. It requires the manipulation
of at least
one independent variable and an attempt to hold all other

variables except
for the dependent variable constant. The essence of a truly
experimental
design is its random selection from population of interest and
random
assignment to treatment and control groups.
Specifically, Cook and Campbell (1979) mentioned 10 types of
experi-
mental design that use or approximate randomization of subjects
into
treatment and control groups. The control group may receive no
treat-
ment, or it may receive a standard or typical practice treatment,
such as
students receiving enhanced English as second language (ESL)
classes ver-
sus those receiving typical practice ESL instruction.
Factorial Experiment
The factorial experiment is one in which you would determine
the
effect of two or more independent treatment variables, both
independently
and in interaction with each other, on a dependent variable. The
main
effect is reported as the effect each independent variable has on
the depen-
dent variable. The interaction effect is the effect of two or more
indepen-

dent variables on the dependent variable. Some two-factor
experiments
involve a 2×2 design (four cells)—meaning two variations of
one factor
and two variations of another manipulated simultaneously.
There would
be four treatment groups in this design with each group
receiving a dif-
ferent combination of the two factors and with participants
having been
randomly assigned to each group. Statistics typically used for
this design
are first descriptive statistics, ANOVA, ANCOVA, or multiple
regression.
There may be three-, four-, or five-factorial experiments as
well. There are
also single subject experimental designs.
Two major classic experimental designs are:
1. Between subjects design. There are different participants for
each level
of the independent variable(s). A participant is observed in one
and
only one treatment combination. In this design, you will rely on
ran-
domization of subjects among the treatment groups to control
for
unmeasured variables, though sometimes stratification of
subjects is
employed to guarantee proportions on certain key variables such
as
ethnicity or socioeconomic levels.
2. Within subjects (repeated measures) design. The same
subjects are used

for each level of the independent variable. With this design,
subjects
are observed prior to any treatment, then they receive a
treatment,
and finally they are observed at posttest. It is possible to extend
this
design to include a maintenance testing at a designated later
time.
This type of design may also be called repeated-measures
designs or lon-
gitudinal experimental designs. In real-life situations, some
measures are
time-relevant or dependent; for example, language proficiency,
height, leadership skills, and maturity levels. Therefore, a study
con-
ducted over a longer period is expected to provide more reliable
and
valid results. Advanced statistical approaches for this type of
research
design include structural equation modeling (SEM), and
hierarchical
linear modeling (HLM). The purpose of using these techniques
is to
detect the change of outcome on the same unit over time.
SEM analyses subsume confirmatory factor analysis (CFA) and
explanatory factory analysis (EFA). The advantage of using
SEM finds its
consideration of the potential measureme nt errors that cannot be
con-
trolled during the study. It can be employed to identify casual
relationship
among latent factors that are represented by various observed
variables
and is particularly useful in trajectory identification upon a
factor (known

as latent curve modeling) (Bollen & Curran, 2006). You may be
interested
in evaluating the long-term effect of a running program from
fourth grade
to eighth grade in terms of students’ height growth. With
repeated mea-
sures (for instance, at the end of each school year over 4 years),
you can
describe the growth trend of height on individual students, and
the sam-
ple population as well. You may also compare across multiple
samples
with different intervention.
HLM is a more recent method of analyzing longitudinal
repeated-measure
data. It is developed on the concept that the society has complex
levels of
nesting, such as the family, the school district, and county, or
any other com-
munity. One may want to explore the effectiveness of a
different reading pro-
gram to see under what circumstances individual participants
may benefit
from a certain type of program. Because participants are nested
within class-
rooms, and classrooms within school, the influence of such
external factors
(teachers’ instruction and school contexts) may also be studied
if there is any.

The application of SEM and HLM usually requires larger
sample size
(n > 100) and normal distribution of observed variables. SEM
and HLM are
also common in quasi-experimental design.
Note that poor design can never be compensated by
sophisticated sta-
tistical tools, and scientific research design becomes more valid
and gen-
eralizable through advanced methods of analyses.
There are other types of experimental designs. For example, one
is called
matched-pairs designs. With this design, the repeated
measurement is not of
the same subjects but of very similar subjects matched to have
like key char-
acteristics. Equivalent time series designs are used when
treatment cannot be
delivered simultaneously to all, also legitimating giving
treatment to some
participants but, temporarily, not to others, as when all teachers
are to
receive professional development, but in rotations, such that
different types
of development can be delivered to different groups of teachers.
Examples of experimental designs used in dissertations follow.
(See
Examples 3.18, 3.19, and 3.20.)
Example 3.18
Abstract

Working memory is a system that consists of multiple
components. The
visuospatial sketchpad is the main entrance for visual and
spatial informa-
tion, whereas acoustic and verbal information is processed in
the phono-
logical loop. The central executive works as a coordinator of
information
from these two subsystems. Numerous studies have shown that
working
memory has a very limited capacity. Based on these
characteristics of work-
ing memory, theories such as cognitive load theory and the
cognitive theory
of multimedia learning provide multimedia design principles.
One of these principles is that when verbal information
accompany-
ing pictures is presented in audio mode instead of visually,
learning
can be more effective than if both text and pictures are
presented visually.
This is called the modality effect. However, some studies have
found
that the modality effect does not occur in some situations. In
most exper-
iments examining the modality effect, the multimedia is
presented as
system-paced. If learners are able to repeat listening as many
times as
they need, the superiority of spoken text over visual text seems

lessened.
One aim of this study was to examine the modality effect in a
learner-
controlled condition. This study also used the one-word-at-a-
time tech-
nique to investigate whether the modality effect would still
occur if both
reading and listening rates were equal.
There were 182 college students recruited for this study.
Participants
were randomly assigned to seven groups: a self-paced listening
group, a
self-paced reading group, a self text-block reading group, a
general-paced
listening group, a general-paced reading group, a fast-paced
listening
group, and a fast-paced reading group. The experimental
material was a
cardiovascular multimedia module. A three-by-two between-
subjects
design was used to test the main effect. Results showed that
modality
effect was still present but not between the self-paced listening
group and
the self text-block reading group. A post-study survey showed
partici-
pants’ different responses to the two modalities and their
preferences as
well. Results and research limitations are discussed and
applications and
future directions are also addressed. (Chung, 2006, pp. vi –vii)
Example 3.19

Abstract
This quantitative study derived from an ongoing federal
experimental
research project targeting Spanish-speaking English language
learners
(ELLs) receiving services in four program models:
control/experimental
transitional bilingual education (TBE) and control/experimental
struc-
tured English immersion (SEI). The purpose of my study was
(a) to capture
the growth trajectory and rate of oral English acquisition, (b) to
investigate
the role of oral English development in acquiring English
reading skills,
and (c) to compare program models in order to identify
practices that pro-
mote ELLs’ English oral and reading competency at the early
elementary
level. Structural equation modeling was utilized. Participants
consisted of
534 Spanish-speaking ELLs who started at kindergarten and
continued
through first grade in their respective models.
Striking similarities were found among the four instructional
models
that English oral proficiency improved significantly (p < .05) in
a linear
fashion over two years. However, the magnitude differed in that
the exper-
imental TBE demonstrated a steeper growth (p < .025) than that
of the con-
trol group that started at the same level. Even though
experimental SEI

group started at a much lower level in oral English, they
progressed at a
rate significantly higher (p < .05) than that of the control group.
In relation to English reading comprehension, for experimental
SEI
groups, the initial level of English oral proficiency is of great
concern in
reading achievement (p < .05). For both TBE groups, effective
intervention
is desired because the growth of English oral proficiency
strongly impacts
reading achievement (p < .05), and, in addition, initial level
strongly pre-
dicts reading comprehension.
The intervention was successfully implemented so that students
advanced to a substantial amount in academic English oracy. It
is also evi-
dent that first language (L1) instruction did not impede the
learning of a
second language. On the contrary, for those students receiving a
larger
proportion of L1 instruction, alterations in program models are
needed to
nurture English oracy at a faster rate of growth, which in turn
facilitates
English literacy acquisition. Findings also indicate that without
effective
English intervention, students placed in control TBE classrooms
remain
below all the students in oral English proficiency. (Tong, 2006,

pp. iii–iv)
Example 3.20
Abstract
In the past two decades, North American, European, and Pacific
countries
have experienced record levels of immigration, yet researchers
have con-
ducted very few comparative studies to provide policymakers
with a
clear understanding of the important influences of immigrant
achieve-
ment. However, research has shown that immigrant student
achievement
on international tests lags behind that of native students in a
substantial
number of countries, even when controlling for other
background charac-
teristics. The study seeks to provide a more comprehensive
understand-
ing of factors related to immigrant success among seventeen
immigrant
receiving countries by estimating how school context and
instructional
variables are related to differences in foreign-born student
achievement,
as well as the relative importance of individual and school
variables. This
goal is achieved by using data from the Programme for
International
Student Assessment (PISA) conducted by the Organization for
Economic
Cooperation and Development (OECD).

This study employs quantitative analytic techniques, including
regression and hierarchical linear modeling to examine key
policy-related
questions: (1) Does immigrant status have a separate effect on
student
achievement after accounting for socioeconomic status, and how
do these
two factors appear to relate across countries? (2) Do students in
different
immigrant status groups get tracked into schools with different
contexts
and quality and quantity of instruction, and what are the
implications for
immigrant student achievement? These two research questions
serve as
the guide for the development of the quantitative results into
taxonomy
of immigrant student achievement in the seventeen case
countries.
Finally, I employ additional data from Germany and the United
States for
a more in depth exploration of immigrant student achievement
in these
two countries. First, I investigate the role of country of origin
and race for
immigrant student achievement. I find that there are significant
differ-
ences in reading and math achievement among different
immigrant
groups. I also use additional school data to control for tracking
between
schools (in Germany) and within schools (in the United States).
I also find
that these controls are important for understanding the
relationships
between immigrant status, socioeconomic status and student

achieve-
ment. (Christensen, 2004, p. iv)
THEORY DEVELOPMENT
Developing and testing theory takes research to a level of
synthesis, analy-
sis, and evaluation. Because theory is the essence of what drives
our actions
and because theory describes, explains, and even predicts
human phenom-
ena, it is important for theory developers to “get it right,” or at
least “get it
on the right track.” Kerlinger (1986) stated of theory, “The
ultimate aim of
science is the generation and verification of theory” (p. 7).
According to
Dorin, Demmin, and Gabel (1990), theory (a) provides a general
explana-
tion for observations made over time, (b) explains and predicts
behavior,
(c) can never be established beyond all doubt, (d) may be
modified, and
(e) seldom have to be thrown out completely if thoroughly
tested, but
sometimes may be widely accepted for a long time and later
disproved.
Theory can be developed and tested quantitatively. Qualitative
theory
development is discussed in Chapter 5. More often than not, in
terms of

quantitative analysis, theories are tested quantitatively as
opposed to
being developed quantitatively. Quantitative analysis generally
leads to
support of the theory or reconsideration of the theory. As Crown
(1998)
indicated of policy, and which would be indicative of other
concepts in
education:
. . . theory is . . . tested by using data to estimate a statistical
model that
provides numerical estimates of the relationships between a
policy
variable (or variables) of interest and a set of explanatory
variables.
The estimated model may fully support, partially support, or
contra-
dict the theory. Of course, from the researcher’s standpoint, it is
nice to
have the estimated model support the theory. This happens when
the
key variables have the expected signs and are statistically
significant.
All too frequently, though, key variables to one’s theory turn
out to be
statistically insignificant or to have the “incorrect” sign. When
this
happens the theory must be reconsidered, the model respecified,
or
new data collected with which to test the original theory. (p. 2)
A dissertation example from the counseling field by van Eden-
Moorefield (2006) is provided that utilized a quantitative design
in theory
development and testing. (See Example 3.21.)

Example 3.21
Abstract
Given recent calls for theory development and extension
relevant to gay
relationships, this study examined the relationships of gay men
by testing
a mid-range model derived from identity theory (Stryker, 1968)
and the
extant literature. The model asserted that the extent to which
one holds a
gay status affects his level of identity commitment which, in
turn, affects
couple verification. Further, couple verification affects couple
identity
directly and indirectly through distress and relationship
satisfaction.
Theoretical extensions of the construct of identity commitment
included
both personal and symbolic dimensions.
Data were collected via an Internet-based survey. The sample
consisted of 188 gay men in current relationships of at least 3
months dura-
tion and representing 26 states and the District of Columbia; the
participa-
tion rate was 83%. Overall, the sample was White, well-
educated, and
middle class.

As a preliminary step, a series of exploratory factor analyses
were
conducted to assess the factor structure of the measures used.
Composite
scores were created, and data were fit to the model using path
analysis.
Results demonstrated that the data did not fit the original
model. A mod-
ified model was then tested and fit the data. The modified
model added
direct links from identity commitment to both relationship
satisfaction
and couple identity. Each path in the modified model was
significant
except for the path between couple verification and distress, and
54% of
the variance in couple identity was explained. Suggestions for
theory and
future research are discussed. (van Eden-Moorefield, 2006, n.p.)
Another example is provided for you related to the testing of a
theory.
This example is from Truslow’s dissertation (2004). (See
Example 3.22.)
Example 3.22
Abstract
Purpose. The purpose of this study was to identify the
differences in con-
flict management modes of male and female public school
superinten-
dents and to examine a relationship between superintendents’
conflict

management modes and the Synergistic Leadership Theory
(SLT).
Methods. The target population of this study included all 13,728
public
school superintendents in the United States. The stratified
random sample
included 500 female and 500 male superintendents. Quantitative
and qual-
itative data were collected through the Thomas-Kilmann
Inventory (TKI),
an open-ended interview protocol, and the Organizational
Leadership
Effectiveness Inventory to answer the following research
questions: (1) Is
there a significant difference in gender distribution among
conflict man-
agement modes of superintendents? (2) Is there a relationship
between
conflict management modes of female superintendents and the
Synergistic
Leadership Theory? (3) Is there a relationship between conflict
manage-
ment modes of male superintendents and the Synergistic
Leadership
Theory? (4) Is there a relationship among gender, conflict
management
modes, and the four factors of the Synergistic Leadership
Theory?
Findings and Conclusions. The data obtained in the current
study
resulted in the conclusion that male and female superintendents
have sig-
nificantly different conflict management modes as illustrated by
gender

distribution. A relationship was identified between the four
factors of the
Synergistic Leadership Theory and each conflict management
mode.
There was no difference between gender and the SLT within
each conflict
management mode. Both male and female superintendents
identified the
same factors as sources of incongrue nce within each conflict
mode. This
suggests that gender is related to each conflict management
mode; how-
ever, gender is not related to perceived incongruence among
factors of the
SLT and conflict within each conflict management mode.
Recommendations for Further Research. The following
recommendations
are suggested for further study and development: (1) Further
research
should be conducted regarding the length of tenure of
superintendents in
relation to the alignment of factors of the SLT. (2) Additional
research
should be pursued in regard to the relationship of the
superintendent’s
conflict management mode to the board president’s conflict
management
mode. (3) Continued research should be conducted to include
the intro-
duction of student achievement data and district ratings in

relationship to
the superintendent’s conflict management mode. (Truslow,
2004, pp. 4–5)
SUMMARY
In this chapter, we have reviewed four categories of quantitative
research:
(a) descriptive research, (b) correlational research, ( c) causal-
comparative
research, and (d) quasi-experimental and experimental research.
In addi-
tion to providing you with a brief overview of each category,
we also pro-
vided examples of research purposes or questions that would
justify a
specific type of quantitative research design or method of
inquiry. The next
chapter contains specifics on basic statistics.
4
Basic Statistics
S tatistics are mathematical procedures for describing,
synthesizing,
and analyzing numerical data. The statistical procedures you
choose
will be determined by your research questions or hypotheses
and the type
of data you will collect. Thus, different research questions or

hypotheses
and different types of data require different statistical analyses.
The statis-
tical procedures you use should be described in detail in your
Methodology Chapter under the heading “Data Analysis” and
again in
your Results Chapter. Data analysis is a very important
component of
any research study. No matter how well you conduct your study,
inappro-
priate statistical analyses will likely result in inappropriate
research
conclusions.
Many statistical procedures are available to you. In this chapter,
we
describe those commonly used in the social and behavioral
sciences. The
focus is on how to apply these statistics to your dissertation or
master’s
thesis. Contrary to popular opinion, you do not have to be a
mathemati-
cian to use statistics. All you have to know is when to use an
appropriate
statistic to accomplish your research purposes, and a computer
program
will do the rest.
Generally, we recommend using a computer for data analysis,
particu-
larly if you have a large amount of data or if multiple analyses
are to be
performed. The most commonly used software for statistical
analysis is the
Statistical Package for the Social Sciences (SPSS). Another
widely used sta-

tistical program is Statistical Analysis System (SAS). Both
SPSS and SAS
programs can perform all of the statistical procedures described
in this
chapter.
62
DESCRIPTIVE STATISTICS
Descriptive statistics are mathematical procedures for
organizing and
summarizing numerical data. The major types of descriptive
statistics are
(a) measures of central tendency, (b) measures of variability, (c)
measures
of relative position, and (d) measures of relationship. These are
the types
of statistics you will present at the beginning of your Results
Chapter
under the heading “Descriptive Statistics” in a quantitative
dissertation or
master’s thesis (see Table 9.1, Chapter 9). Descriptive statistics,
and even
inferential statistics, are sometimes used in some types of
qualitative stud-
ies as well (see Slater, 2001, referenced in Chapter 9).
Measures of Central Tendency
Measures of central tendency are indices that represent the
typical or
average score among a distribution of scores. Measures of
central tendency

can describe a set of numerical data with a single number. The
three most
frequently used measures of central tendency are the mean,
median, and
mode. The choice usually depends on two factors: the type of
measure-
ment scale used and the purpose of the research. There are four
measure-
ment scales: nominal, ordinal, interval, and ratio.
Nominal data classify persons or objects into two or more
categories: sex
(male or female), type of school (public or private), IQ (high,
average, low),
political party (Democrat or Republican), personality type
(dominant or
passive), race/ethnicity (African American, Asian, Hispanic,
White).
Ordinal data not only classify persons or objects but also rank
them in terms
of degree to which they possess a characteristic of interest. In
other words,
ordinal data puts participants in order from highest to lowest.
For example,
15 doctoral cohort members might be ranked from 1 to 15 with
respect to
height. Percentile ranks are ordinal data. Most standardized
tests, like the
GRE, provide a raw score, as well as a percentile rank from 100
to 0. Interval
data have all of the characteristics of nominal and ordinal data,
but in addi-
tion, they are based on predetermined equal intervals. Most tests
used in
social science research, such as achievement tests, aptitude
tests, and intel-

ligence tests, represent interval data. Ratio data are derived
from scales that
have an absolute zero and so enable relative comparisons to be
made, such
as length of school day, class size, age, speed, and dollars.
Mean, Median, and Mode
The mean is the arithmetic average of the scores. It is calculated
by
adding up all of the scores and dividing that total by the number
of scores.
The mean is the appropriate measure of central tendency when
the
data represent either an interval or ratio scale. Because most
quantitative
63Basic Statistics
measurement in the social sciences uses an interval scale, the
mean is the
most frequently used measure of central tendency.
The median is the midpoint of a group of scores. For example,
for the
scores 10, 20, 30, 40, 50, the median is 30. If the number of
scores is even,
the median is the point halfway between the two middle scores.
For
example, for the scores 10, 20, 30, 40, 50, 60, the median is 35.
The median
is the appropriate measure of central tendency when the data
represent an
ordinal scale.

The mode is the score that occurs most frequently in a
distribution of
scores. The mode is seldom used because of the problems
associated with
it. For example, a set of scores may have two or more modes.
Such a distri-
bution of scores is referred to as bimodal. Another problem with
the mode is
that equal-sized samples randomly selected from the same target
popula-
tion can have different modes. Nevertheless, the mode is the
appropriate
measure of central tendency when the data represent a nominal
scale.
Measures of Variability
Measures of variability show how spread out the distribution of
scores
are from the mean of the distribution, or how much, on the
average, scores
differ from the mean. The three most frequently used measures
of vari-
ability are (a) standard deviation, (b) quartile deviation, and (c)
range.
Standard Deviation
The standard deviation is the most frequently used measure of
variabil-
ity. The standard deviation is the appropriate measure of
variability when
the data represent either an interval or ratio scale. Like the
mean, the stan-
dard deviation is the most stable measure of variability. It

includes every
score in the calculation of scores in a distribution of scores. By
knowing the
mean and standard deviation of a set of scores, you will have a
good idea
of what your distribution of scores look like. This will become
much
clearer in the impending discussion of the normal curve.
Quartile Deviation
The quartile deviation is one half of the difference between the
upper
quartile and the lower quartile in a distribution of scores. Thus,
the upper
quartile of any distribution of scores is the 75th percentile, that
point below
which are 75% of the scores. The lower quartile is the 25th
percentile, that
point below which are 25% of the scores. Calculation of the
quartile devi-
ation is done by subtracting the lower quartile from the upper
quartile and
then dividing the result by 2. If the quartile deviation is small,
the scores
are close together. If it is large, the scores are more spread out.
The quar-
tile deviation is the appropriate measure of variabili ty when the
data rep-
resent an ordinal scale.
Range

The range is the difference between the highest and the lowest
score
and is the result of subtraction. For example, for the scores 5, 5,
6, 7, 8, 8,
the range is 3. If the range is small, the scores are close
together. If the
range is large, the scores are more spread out. The range is the
appropriate
measure of variability when the data represent nominal data.
The range,
like the mode, is not a very stable measure of variabi lity.
However, it does
give a quick, rough estimate of variability.
The Normal Curve
The normal curve (or bell-shaped curve) is a theoretical
distribution in
which the height of the curve indicates the percentage of cases
under por-
tions of the normal curve. As shown in the example in Figure
4.1, the test
scores of the majority of individuals tend to cluster close to the
mean. Fewer
cases occur as we move farther from the mean. Specifically,
about 68% of
the sample will have scores within the range of plus or minus
one standard
deviation from the mean. Approximately 95% of the sample will
have
scores within the range of plus or minus two standard deviations
from the
mean. And more than 99% of the sample will have scores within
the range
of three standard deviations from the mean. Many variables

(e.g., height,
weight, IQ scores, achievement test scores) yield a curve similar
to the one
shown in Figure 4.1, providing a large enough random sample is
used.
Measures of Relative Position
Measures of relative position indicate how a person has
performed in
comparison to all other persons in the sample who have been
measured on
the same variable. This is called norm referencing. The two
most frequently
used measures of relative position are percentile ranks and
standard scores.
65Basic Statistics
Percent of cases
under portions of
the normal curve
Standard
Deviations
Cumulative
Percentages
Rounded
−4σ −3σ

0.1%
0.13% 2.14% 2.14% 0.13%
2.3%
2% 16%
13.59% 34.13% 34.13% 13.59%
50% 84% 98%
15.9% 50.0% 84.1% 97.7% 99.9%
−2σ −1σ +1σ +2σ +3σ +4σ0
Figure 4.1 Normal Curve
Percentile Ranks
A percentile rank indicates the percentage of scores that are at
or below a
given score. Percentile ranks are appropriate measures of
relative position
when the data represent an ordinal scale. Percentile ranks are
not used much
in research studies. They are often used, however, in the public
schools to
report student’s standardized test scores. They are also used in
making deci-
sions regarding acceptance to colleges and graduate schools.
The SAT and
ACT used as one criterion for admission to undergraduate

colleges and uni-
versities include both a score and the percentile rank associated
with that
score. This is true for the GRE and MAT used to admit students
to graduate
school. The percentile rank provides the decision maker with a
measure of
how well the applicant did in relation to all other test takers.
Standard Scores
A standard score indicates how far a given raw score is from the
mean,
in terms of standard deviation units. A standard score is the
appropriate
measure of relative position when the data represent an interval
or ratio
scale. The most commonly used standard scores are z scores and
stanines.
z Scores. The z score is the most basic standard score, wi th a
mean of 0 and
a standard deviation of 1. Thus, a z score of +1 is at the 84th
percentile for
a normal distribution, −1 is at the 16th percentile, and −2 is at
the 2nd per-
centile. Other standard scores are linear transformations from
the z score,
with arbitrarily selected means and standard deviations. That is,
it is pos-
sible to choose any mean and any standard deviation. Most IQ
tests, for
example, use 100 as the mean and 15 or 16 as the standard
deviation. The
resultant IQ score is a standard score (the ratio IQ, mental age
divided by

chronological age times 100, is rarely used today).
Stanines. Stanines are standard scores that divide a distribution
of scores
into nine parts. Stanines, like percentile ranks, are often
reported in norms
tables for standardized tests. Stanines are often used in the
public schools
as a basis for ability grouping, and they are used also as a
criterion for
selecting students for special programs. For example, on the one
hand, a
remediation program may select students who scored in the first
and sec-
ond stanine on a standardized reading test. On the other hand, a
gifted
program may select students who scored in the eighth and ninth
stanine.
Like percentile ranks, the use of stanines is a reporting device
that is
understandable to most lay people.
Measures of Relationship
The descriptive statistics presented thus far involve the
description of
a single variable. Measures of relationship indicate the degree
to which
two variables are related. Correlation is often used for this
purpose. The
two most frequently used correlational statistics are the

product-moment
correlation coefficient (r) and rank-difference correlation (rho).
For other
bivariate correlational techniques (i.e., relationship between two
variables),
such as the phi coefficient, biserial correlation, correlation ratio
(eta), partial
correlation, and others, see standard statistics textbooks.
Product-Moment Correlation Coefficient
The product-moment correlation coefficient (r) (sometimes
called
Pearson r) is the most appropriate measure of relationship when
the data
represent an interval or ratio scale. In the social sciences, most
of the mea-
sures represent interval scales. For this reason, the Pearson r is
the statistic
most often used for determining relationship. For example, if
we adminis-
ter an intelligence test and an achievement test to the same
group of
students, we will have two sets of scores. Pearson r would be
the appro-
priate correlational statistic to use for determining the
relationship
between the students’ scores on the two measures. The product-
moment
correlation can also be used to compute a correlation matrix in
which sub-
jects’ scores on a large number of variables are correlated with
each other
(e.g., see Basham & Lunenburg, 1989; this study is discussed in
greater
detail in Chapter 6).

Rank-Difference Correlation (Rho)
Spearman’s rho, as it is sometimes called, is used when the data
are
ranks rather than raw scores. For example, assume the principal
and assis-
tant principal have independently ranked the 15 teachers in their
school
from first, most effective, to 15th, least effective, and you want
to assess
how much their ranks agree. You would calculate the
Spearman’s rho by
putting the paired ranks into the Pearson r formula or by using a
formula
developed specifically for rho.
Spearman’s rho is interpreted the same as is Pearson r. Like the
Pearson
product-moment coefficient of correlation, it ranges from −1.00
to +1.00.
When each individual has the same rank on both variables, the
rho corre-
lation will be +1.00, and when their ranks on one variable are
exactly the
opposite of their ranks on the other variable, rho will be −1.00.
If there is no
relationship at all between the rankings, the rank correlation
coefficient will
be 0. If you have a computer or calculator program for Pearson
r, you can
calculate Spearman’s rho by putting the ranks into that program.
INFERENTIAL STATISTICS
Inferential statistics deal with inferences about populations

based on the
results of samples (Gay, Mills, & Airasian, 2006). Most social
science
67Basic Statistics
research deals with samples drawn from larger populations.
Thus, inferen-
tial statistics are data analysis procedures for determining the
likelihood
that results obtained from a sample are the same results that
would have
been obtained for the entire population.
Example 4.1
To illustrate: You have randomly selected a sample of ninth
grade
students. Based on a pretest in mathematics and other criteria,
you have
created two closely matched samples of students. Group A will
be taught
mathematics using a computer, and Group B will be taught
mathematics
using a traditional method. You want to know if the mean scores
for
Group A are significantly different from the mean scores of
Group B.
Results of the posttest in mathematics reveals that Group A has
a mean
score of 93, and the mean score for Group B is 85.
You have to make a decision as to whether the difference
between the

two means represents a true, significant difference in the
treatment (in this
case, method of instruction) or simply sampling error. A true
difference is
one caused by the treatment (the independent variable) and not
by chance
(Gall, Gall, & Borg, 2007; Gay et al., 2006). Thus, an observed
difference is
either caused by the treatment, as stated in the research question
(or
research hypothesis), or is the result of chance, sampling error.
The Null Hypothesis
The research hypothesis typically states a difference or
relationship in
the expected direction. A null hypothesis states that no
difference or rela-
tionship exists. The null hypothesis is preferred when applying
statistical
tests. You can never prove your hypothesis, only disprove it.
Hypothesis
testing is a process of disproving or rejecting, and the null
hypothesis is
best suited for this purpose (see Gall et al., 2007; Gay et al.,
2006).
The initial step in hypothesis testing then is to establish a null
hypothe-
sis. For instance, the null hypothesis for our example can be
stated as follows:
No significant difference exists between the mean mathematics
scores of
ninth grade students who receive computer mathematics
instruction and

ninth grade students who receive traditional mathematics
instruction.
After formulating the null hypothesis, the researcher carries out
a test
of statistical significance to determine whether the null
hypothesis can be
rejected (i.e., whether there is a true or real difference between
the groups).
This test enables us to make statements of the type:
If the null hypothesis is correct, we would find this large a
difference
between sample means only once in a hundred experiments (p <
.01).
Because we have found this large a difference, the null
hypothesis
quite probably is false. Therefore, we will reject the null
hypothesis and
conclude that the difference between sample means reflects a
true dif-
ference between population means. (Gall et al., p. 138)
Tests of Statistical Significance
Concepts underlying tests of statistical significance follow:
1. The purpose of using a test of statistical significance is to
determine
if the difference in two scores (or the characteristics of the
sample is

different from the population) is significant or the result of
chance
fluctuation/or sampling error.
2. To determine statistical significance, the data are analyzed
via a for-
mula and a score is obtained—a t score, chi square score, or F
score.
3. You then determine the degrees of freedom (number of
subjects
minus one).
4. Then compare the calculated significance score with the table
score
in the appropriate probability table using the calculated degrees
of
freedom. Select the .05 level of confidence. (This would be the
min-
imum level you should accept.)
5. If the calculated significance score is greater than the one in
the table
(at the .05 level), then this indicates that the difference in
student
scores is statistically significant at the .05 level of confidence
and not
the result of chance fluctuation. This means that the difference
in
student scores is so great that it could only be obtained by
chance
only 5% of the time. And 95% of the time, the difference
between the
student scores is the result of the impact of the independent
variable
on the dependent variable. Hence, the null hypothesis is

rejected.
6. If the calculated significance score is less than the one in the
proba-
bility table (at the .05 level), then this indicates that the
difference in
student scores is statistically insignificant at the .05 level and is
the
result of chance fluctuation. This means that the difference in
the
student scores is so small that the independent variable had no
impact on the dependent variable. Therefore, 95% of the time,
the
difference in student scores is the result of chance fluctuation.
And
only 5% of the time could the independent variable have
impacted
the dependent variable. Hence, the null hypothesis is accepted.
Effect Size
One of the reasons that statistically significant p values can be
mislead-
ing is that the value that is calculated is directly related to
sample size.
Thus, it is possible to have a very large sample, a very small
difference or
relationship, and still report it as “significant.” For example, a
correlation of
.44 will be statistically significant at p < .05 with a sample as
small as 20, and
69Basic Statistics

a sample of 5,000 will allow a statistically significant .05
finding with a cor-
relation of only .028, which is, practically speaking, no
relationship at all.
The American Psychological Association (2001) and several
research
journals now strongly recommend or require that investigators
report
appropriate indicators that illustrate the strength or magnitude
of a differ-
ence or relationship along with measures of statistical
significance. These
effect magnitude measures, as they are called, are either
measures of
strength of association or effect size. Measures of association
are used to
estimate proportions of variance held in common, similar to the
coefficient
of determination. Effect size is more commonly used. It is
typically
reported in a generalized form as the ratio of the difference
between the
group means divided by the estimated standard deviation of the
popula-
tion. According to Cohen (1988), the effect size index then
provides an indi-
cation of the practical or meaningful difference. Effect size
indexes of
about .20 are regarded as small effects, of about .50 as medium
or moder-
ate effects, and .80 and above large effects.
Statistical Analysis
Different statistical analyses are appropriate for different types

of data.
It is essential that you select the appropriate statistical test,
because an
incorrect test can result in incorrect research conclusions.
Basically there
are two types of statistical tests: parametric and nonparametric.
Your first
decision when selecting a statistical test is to determine whether
a para-
metric or nonparametric test is appropriate. The use of a
parametric test
requires that three assumptions be met: the variable measured is
normally
distributed in the population, the data represent an interval or
ordinal
scale, and the selection of participants is independent. Most
variables
examined in social science research are normally distributed.
Most mea-
sures used in social science research represent an interval scale.
And the
use of random sampling will fulfill the assumption of
independent selec-
tion of participants. A nonparametric test is an appropriate
statistical test to
use when the three parametric assumptions are not met and
when the data
represent an ordinal or nominal scale.
The following parametric statistical tests are described below:
(a) t test;
(b) analysis of variance, including post hoc procedures; (c)
factorial analy-
sis of variance; (d) analysis of covariance; and (e) multivariate
analysis of
variance. In the section following, several important

nonparametric tests
are discussed.
The t Test
In many research situations, a mean from one group is compared
with
a mean from another group to determine the probability that the
corre-
sponding population means are different. The most common
statistical
procedure for determining the level of significance when two
means are
compared is the t test. The t test is a formula that generates a
number, and
this number is used to determine the probability level (p level)
of rejecting
the null hypothesis.
Two different forms of the equation are used in the t test, one
for inde-
pendent samples and one for samples that are paired, or
dependent.
Independent samples are groups of participants that have no
relationship
to each other; the two samples have different participants in
each group,
and the participants are usually either assigned randomly from a
common
population or drawn from two different populations.

Example 4.2
If you are testing the difference between an experimental group
and a con-
trol group mean in a posttest-only design, the independent
samples t test
would be the appropriate statistic. Comparing leadership styles
of two
groups of superintendents would also utilize an independent
samples t test.
The second form of the t test can be referred to by several
different
names, including paired, dependent samples, correlated, or
matched t test.
This t test is used in situations in which the participants from
the two
groups are paired or matched in some way.
Example 4.3
A common example of this case is the same group of
participants tested
twice, as in a pretest–posttest study (e.g., see Pascarella &
Lunenburg,
1988). Whether the same or different subjects are in each group,
as long as
a systematic relationship exists between the groups it is
necessary to use
the dependent samples t test to calculate the probability of
rejecting the
null hypothesis.
In the Pascarella and Lunenburg (1988) study, elementary
school prin-
cipals from two school districts received leadership training

using Hersey
and Blanchard’s situational leadership framework. Pretests and
posttests
were administered to the principals and a sample of their
teachers before
and after training to determine the effects of training on
principals’ lead-
ership effectiveness and style range. The study provided partial
support
only for Hersey and Blanchard’s situational leadership theory.
Using
dependent samples t tests, principals were perceived as more
effective
three years after training than before training: t (principals) =
6.46 (15) < .01
and t (teachers) = 3.73 (59) < .01. However, no significant
differences were
found in principals’ effectiveness immediately following
training, nor in
principals’ leadership style range before and after training.
Although the formulas and degrees of freedom are different for
each
form of t test, the interpretation and reporting of the results are
the same,
the df for the dependent t test is the number of pairs minus one.
Thus, you
need not worry about whether the correct formula has been
used.
71Basic Statistics
Example 4.4

The t test can be used for purposes other than comparing the
means of
two samples. For example, the t test is used when a researcher
wants to
show that a correlation coefficient is significantly different
from 0 (no cor-
relation). The mean of a group can be compared with a number
rather
than another mean, and it is possible to compare variances
rather than
means. Because there are so many uses for the t test, it is
frequently
encountered in reading social science research.
Example 4.5
A more concrete explanation of using the t test is the following
example.
Suppose a researcher is interested in finding out whether there
is a signif-
icant difference between high school boys and girls with respect
to math-
ematics achievement. The research question would be: Is there a
difference in mathematics achievement (the dependent variable)
of boys
compared with girls (the independent variable)? The null
hypothesis
would be: There is no difference between boys and girls in
mathematics
achievement. To test this hypothesis, the researcher would
randomly
select a sample of boys and girls from the population of all high
school
students. Let us say that the sample mean for boys achievement
is 540, and
the sample mean for girls is 520. Because we assume the null

hypothesis—
that the population means are equal—we use the t test to show
how often
the difference of scores in the samples would occur if the
population
means are equal. If our degrees of freedom (total sample size
minus 1) is
60 and the calculated t value 1.29, we can see by referring to
the t test table
that the probability of attaining this difference in the sample
means, for a
two-tailed test, is .20, or 20 times out of 100. We accept the
null hypothe-
sis and say that there is no statistically significant difference
between the
mathematics achievement of high school boys and girls.
One-Way Analysis of Variance
If a study is conducted in which two or more sample means are
com-
pared on one independent variable, then to test the null
hypothesis the
researcher would employ a procedure called one-way analysis of
variance
(ANOVA). ANOVA is simply an extension of the t test. Rather
than using
multiple t tests to compare all possible pairs of means in a study
of two or
more groups, ANOVA allows you to test the differences
between all
groups and make more accurate probability statements than
when using a
series of separate t tests. It is called analysis of variance
because the statis-
tical formula uses the variances of the groups and not the means

to calcu-
late a value that reflects the degree of differences in the means.
Instead of
a t statistic, ANOVA calculates an F statistic (or F ratio). The F
is analogous
to the t. It is a three- or four-digit number that is used in a
distribution of
F table with the degrees of freedom to find the level of
significance that
you use to reject or not reject the null. There are two degrees of
freedom.
The first is the number of groups in the study minus one, and
the second
is the total number of subjects minus the number of groups.
These
numbers follow the F in reporting the results of ANOVA. For
example, in
reporting F(4, 80) = 4.25, the degrees of freedom mean that five
group
means are being compared and 85 subjects are in the analysis.
ANOVA addresses the question: Is there a significant difference
between the population means? If the F value that is calculated
is large
enough, then the null hypothesis (meaning there is no difference
among
the groups) can be rejected with confidence that the researcher
is correct in
concluding that at least two means are different.
Example 4.6

Let us assume, for example, that a researcher is comparing the
quality of
school life of three groups of students in urban, suburban, and
rural
school districts (e.g., see Lunenburg & Schmidt, 1989). The
researchers in
the cited study selected a random sample from each group,
administered
a quality of school life (QSL) instrument, and calculated the
means and
variances of each group. The sample group means for QSL
were: urban = 18,
rural = 20, and suburban = 25. The null hypothesis that is
tested, then, is
that the population means of 18, 20, and 25 are equal, or, more
correctly,
that these are different only by sampling and measurement
error. The cal-
culated F in the study was 5.12 and p < .01. Lunenburg and
Schmidt (1989)
concluded that at least two of the means were different, and that
this con-
clusion will be right 99 times out of 100. Many other variables
were exam-
ined in the study referenced above. Results of the ANOVA
indicated that
the means were different only, but not where the differences
occurred.
Post hoc procedures are necessary to determine exactly where
the differ-
ences in mean scores occurred.
Post Hoc Procedures
When a researcher uses ANOVA to test the null hypothesis that

three
means are the same, the resulting statistically significant F ratio
tells the
researcher only that two or more of the means are different.
Usually the
researcher needs to employ further statistical tests that will
indicate
those means that are different from each other. These tests are
called post
hoc comparisons.
There are five common multiple comparison tests: Fisher’s
LSD,
Duncan’s new multiple range test, the Newman-Keuls multiple
range test,
Tukey’s HSD, and the Scheffé test. Each test is used in the
same way, but
they differ in the ease with which a significant difference is
obtained; for
some tests, that is, the means need to be farther apart than for
other tests
for the difference to be statistically significant. Tests that
require a greater
difference between the means are said to be conservative, while
those that
73Basic Statistics
permit less difference are said to be liberal. The listing of the
tests above is
sequential, with Fisher’s test considered most liberal and
Scheffé’s test
most conservative. The two most common tests are Tukey and
Scheffé, but

different conclusions can be reached in a study depending on
the multiple
comparison technique employed.
Factorial Analysis of Variance
One-way ANOVA has been introduced as a procedure that is
used
with one independent variable and two or more levels identified
by this
variable. It is common, however, to have more than one
independent vari-
able in a study. In fact, it is often desirable to have several
independent
variables because the analysis will provide more information.
For
example, if a group of researchers investigates the relative
effectiveness of
three reading curricula, they would probably use a 1 × 3
ANOVA to test the
null hypothesis that no difference in achievement exists
between any of
the three groups. If the researchers were also interested in
whether males
or females achieved differently, gender would become a second
indepen-
dent variable. Now there are six groups, because for each
reading group
males and females are analyzed separately.
Example 4.7
In this hypothetical situation, two independent variables are
analyzed
simultaneously and one dependent variable (achievement). The
statistical

procedure that would be used to analyze the results would be a
two-way
ANOVA (two-way because of two independent variables).
Because factor is
another word for independent variable, factorial means more
than one inde-
pendent variable. Factorial ANOVA, then, is a generic term that
means that
two or more independent variables are analyzed together. The
more spe-
cific term, such as two-way or three-way ANOVA, tells the
exact number of
independent variables. Researchers can be even more precise in
indicating
what the analysis is by including the levels of each independent
variable.
As pointed out earlier, levels refers to the subgroups or
categories of each
independent variable. In the example mentioned above, reading
curriculum
has three levels and gender two levels. The levels can be shown
by numbers
that precede the ANOVA. In our reading example, it is a 2×3
ANOVA.
An interaction is the effect of the independent variables
together; that
is, the impact of one factor on the dependent measure varies
with the level
of a second factor. Stated differently, it is the joint effect of the
indepen-
dent variables on the dependent variable. An interaction is
evident if the
differences between levels of one independent variable are
inconsistent
from one level to another of the other independent variable. In

other
words, an interaction exists if the effect of one variable differs
across dif-
ferent levels of the second variable. Now it is clear how a
factorial
ANOVA can provide more information than one-way ANOVAs.
Analysis of Covariance
Analysis of covariance (ANCOVA) is a statistical procedure
used in
cases similar to ones in which a one-way or factorial ANOVA is
used.
ANCOVA has two major purposes: to adjust initial group
differences sta-
tistically on one or more variables that are related to the
dependent vari-
able but uncontrolled, and to increase the likelihood of finding
a
significant difference between group means.
Example 4.8
For the first purpose, consider the following example. A
researcher uses two
classes to investigate whether computer-assisted instruction or
noncomputer
(traditional) instruction is more effective. On the basis of a
pretest, the
researcher knows that one class has greater knowledge of the
dependent
variable (achievement in mathematics) than the other group (for

example, the
computer group pretest mean is 12 and the noncomputer group
pretest mean
is 10). If a posttest is given and it is found that the computer
group mean is
24 and the noncomputer group mean 20, the researcher might be
tempted to
conclude that the computer group achieved more than the
noncomputer
group. This would be likely to happen if the pretest scores were
ignored. An
alternative approach would be to look at pretest–posttest gain
scores and use
a t test to determine whether the gain scores are significantly
different. This
approach would result in comparing 12 (24 − 12) to 10 (20 −
10).
While this approach is theoretically better than not using the
pretest
scores, for reasons beyond the scope of this chapter, there are
technical
problems with comparing gain scores. The best method of
analyzing the
data in this circumstance is by using ANCOVA. ANCOVA
would statisti-
cally adjust the posttest scores by the differences that existed
between the
groups on the pretest. In this example, the posttest score of the
computer
group would be lowered by one point, because this group’s
mean was
higher by one point than the mean of both groups on the pretest.
Similarly, because the noncomputer group pretest mean is one
point
lower than the mean of the two pretests, the posttest score of 20

would be
raised by one point to 21. Instead of comparing 20 to 24,
ANCOVA would
thus compare 21 to 23.
The variable that is used in ANCOVA to adjust the scores (in
the above
example the pretest) is called the covariate or concomitant
variable.
Covariates are often pretest scores or results from achievement,
attitude, or
aptitude tests that would be related to the dependent variable.
IQ scores
and scores on prior standardized achievement tests, for instance,
are com-
monly used as covariates.
The second purpose of covariance analysis is to increase what is
called
the power of the statistical test to find differences between
groups. A full
explanation of the concept of power is beyond the scope of this
chapter.
Briefly, power is the probability of detecting a significant
difference. It is
75Basic Statistics
used to increase power when the sample size is low or when the
researcher
has reason to believe that the differences between the groups
will be small.
ANCOVA can be used in several situations: with two groups

and one
independent variable in place of a t test; with one independent
variable
that has more than two groups in place of one-way ANOVA; and
with fac-
torial analysis of variance. Studies can also use more than one
covariate in
a single ANCOVA procedure. The reporting of ANCOVA is
very similar to
the reporting of ANOVA.
Because ANCOVA is used frequently with intact groups,
without ran-
dom assignment, it should be noted that the interpretation of
results
should weigh the possibility that other uncontrolled and
unmeasured
variables are also related to the dependent variable and hence
may affect
the dependent variable. In other words, while statistical
adjustment of the
effect of the covariate can be achieved, the researcher cannot
conclude that
the groups are equal in the sense of random assignment.
Multivariate Analysis of Variance
Multivariate analysis of variance (MANOVA) differs from
ANOVA in
only one respect: it incorporates two or more dependent
variables in the
same analysis, thus permitting a more powerful test of
differences among
means. It is justified only when the researcher has reason to
believe corre-
lations exist among dependent variables. Similarly, multivariate

analysis
of covariance (MANCOVA) extends ANCOVA to include two
or more
dependent variables in the same analysis. The specific value
that is calculated
is Wilk’s lamda, a number analogous to F in analysis of
variance. Table 4.1
summarizes some multivariate tests that correspond to
procedures used
with one dependent variable.
Nonparametric Tests
In our discussion of statistical tests up to this point, we have
been con-
cerned with procedures that use sample statistics to estimate
characteristics
of the population. These characteristics of the population are
called parame-
ters, and the statistical procedures are referred to as parametric
procedures. In
addition, parametric statistics are used when the researcher can
assume
Table 4.1 Corresponding Univariate and Multivariate Tests
Univariate Test Multivariate Test
t test Hotellings T2
ANOVA MANOVA (multivariate analysis of variance)
ANCOVA MANCOVA

that the population is normally distributed, has homogeneity of
variance
within different groups, and has data that are interval or ratio in
scale.
As long as the assumptions on which parametric statistics are,
for the
most part, met, the researcher uses a t test, ANOVA, ANCOVA,
MANOVA,
or some other parametric procedure. If these assumptions are
not met—
that is, if the data are not interval or ratio or are not distributed
normally—
the researcher should consider using a nonparametric analog to
the
parametric test. For most parametric procedures, a
corresponding non-
parametric test can be used. The interpretation of the results is
similar with
both kinds of tests. What differs is the computational equation
and tables
for determining the significance level of the results. Both
procedures test a
hypothesis and report a level of significance for rejecting the
null. In con-
trast to parametric tests, however, nonparametric tests do not
test
hypotheses about characteristics of a population. Rather,
nonparametric
procedures test hypotheses about relationships between
categorical vari-
ables, shapes of distributions, and normality of distribution.
While para-
metric procedures use means, nonparametric techniques are

concerned
with frequencies, percentages, and proportions. The parametric
tests are
generally more powerful in detecting significant differences and
are used
frequently even when all assumptions cannot be met.
Table 4.2 gives the names of nonparametric tests that are
analogous to
parametric tests we have already discussed. Each one is
discussed briefly
below.
Chi Square
Chi square is a nonparametric statistical test appropriate when
the data
are in the form of frequency counts or percentages and
proportions that
can be converted into frequencies (Gay et al., 2006). These
frequency
counts can be placed into two or more categories. Thus, chi
square is
77Basic Statistics
Table 4.2 Commonly Used Inferential Techniques
Parametric Nonparametric
Quantitative t test for independent means Mann-Whitney U test
t test for correlated means Kruskal-Wallis one-way
analysis of variance
Analysis of variance (ANOVA) Wilcoxon signed rank test
Analysis of covariance Friedman two-way analysis

(ANCOVA) of variance
Multivariate analysis of
variance (MANOVA)
t test for r
Categorical t test for difference in Chi square
proportions
appropriate when the data are a nominal scale (e.g., male or
female,
Democrat or Republican). A chi square test compares the
proportions actu-
ally observed in a study to the proportions expected, to see if
they are sig-
nificantly different. Expected proportions are usually the
frequencies that
would be expected if the groups were equal, although
occasionally they
also may be based on past data. The chi square value increases
as the dif-
ference between observed and expected frequencies increases.
To deter-
mine whether chi square is significant, you must consult a chi
square table.
One-dimensional chi square. The chi square can be used to
compare
frequencies occurring in different categories or groups.
Example 4.9
For example, you might wish to investigate whether doctoral

students pre-
fer to study alone or with others. Tabulation, based on a random
sample of
100 doctoral students, might reveal that 45 prefer to study
alone, and 55
prefer to study with others. The null hypothesis of no preference
would
suggest a 50–50 split. In order to determine whether the groups
were sig-
nificantly different, you would compare the observed
frequencies (45, 55)
with the expected frequencies (50, 50) using a chi square test of
significance.
Two-dimensional chi square. The chi square may be used also
when fre-
quencies are categorized along more than one dimension, sort of
a factor-
ial chi square.
Example 4.10
In the study sequence example above, you might select a
stratified sample,
comprising 50 males and 50 females. Responses could then be
classified
by study preference and by gender, a two-way classification that
would
allow us to see whether study preference is related to gender.
Although 2×2 applications are quite common, contingency
tables may
be based on any number of categories, for example, 2×3, 3×3,
2×4, and so
forth. When a two-way classification is used, calculation of
expected fre-

quencies is a little more complex, but is not difficult.
Numerous examples of the aforementioned parametric and
nonpara-
metric tests have been provided from sample dissertations in
Chapter 3.
Many more of the most commonly used parametric and
nonparametric
statistical tests are presented in Chapter 9.
Other Nonparametric Tests
Of the nonparametric tests of significance, chi square probably
is the
most frequently used by social science researchers. Other
nonparametric
tests sometimes are used, particularly when the research data
are in the
form of rank-order scores or interval scores that grossly violate
the para-
metric test assumptions of normal distribution and homogeneity
of
variance. They include (a) the Mann-Whitney U test, (b)
Kruskal-Wallis
one-way analysis of variance, (c) Wilcoxon signed rank test,
and
(d) Friedman two-way analysis of variance.
Mann-Whitney U Test
The Mann-Whitney U test is a nonparametric alternative to the t

test
used when a researcher wishes to analyze ranked data. The
researcher
intermingles the scores of the two groups and then ranks them
as if they
were all from just one group. The test produces a value (U),
whose proba-
bility of occurrence is then checked by the researcher in the
appropriate
statistical table. The logic of the test is as follows: If the two
groups are
essentially similar, then the sum of the pooled rankings for each
group
should be about the same. If the summed ranks are markedly
different, on
the other hand, then this difference is likely to be statistically
significant.
Kruskal-Wallis One-Way Analysis of Variance
The Kruskal-Wallis one-way analysis of variance is used when
researchers
have more than two independent groups to compare. The
procedure is
quite similar to the Mann-Whitney U test. The scores of the
individuals in
the several groups are pooled and then ranked as though they all
came
from one group. The sums of the ranks added together for each
of the sep-
arate groups are then compared. This analysis produces a value
(H), whose
probability of occurrence is checked by the researcher in the
appropriate
statistical table.

Wilcoxon Signed Rank Test
The Wilcoxon signed rank test is used when a researcher wants
to ana-
lyze two related (as opposed to independent) samples. Related
samples
are connected in some way. For example, a researcher will try
to equalize
groups on IQ, gender, age, or some other variable. The groups
are matched
on these variables. Another example of a related sample is when
the same
group is both pretested and posttested. Each individual is tested
on two
different occasions (as with the t test for correlated means).
This test is very easy to use. The researcher simply lines up the
pairs of
related subjects and then determines how many times the paired
subjects in
one group scored higher than those in the other group. If the
groups do not
differ significantly, the totals for the two groups should be
about equal. If there
is a marked difference in scoring (such as many more in one
group scoring
higher), the difference may be statistically significant. The
probability of this
occurrence can be determined by consulting the appropriate
statistical table.
79Basic Statistics
Friedman Two-Way Analysis of Variance

If more than two related groups are involved, then the Friedman
two-
way analysis of variance test can be used. For example, if a
researcher
employs four matched groups, this test would be appropriate.
Multivariate Correlational Statistics
Earlier we discussed two commonly used bivariate correlational
statis-
tics: product-moment correlation coefficient (Pearson r) and
Spearman’s
rho coefficient of correlation (Spearman’s rho). These
correlational tech-
niques can be used to analyze the degree of relationship
between two vari-
ables. Because two variables are involved, these techniques are
called
bivariate correlational statistics. However, many research
problems in the
social sciences involve interrelationships between three or more
variables.
There are a number of commonly used multivariate correlational
statistics:
(a) multiple regression, (b) discriminant analysis, (c) canonical
correlation,
(d) path analysis, (e) factor analysis, and (f) structural equation
modeling
(Gall et al., 2007). Many of the dissertation examples discussed
in the
Chapter 3 used many of these statistical procedures.
Multiple Regression
Multiple regression is a prediction equation that determines the

correlation
between a combination of two or more predictor variables and a
criterion
variable. For example, we might use undergraduate GPA,
graduate GPA, and
GRE scores (verbal, quantitative, and analytical) to predict
success in gradu-
ate school. Multiple regression is one of the most widely used
statistical tech-
niques in the social sciences, because of its versatility and
precision. It can be
used to analyze data from any of the major quantitative research
designs:
experimental, causal-comparative, and correlational. It can
handle nominal,
ordinal, interval, and ratio data. And it provides not only the
relationship
between variables but also the magnitude of the relationships.
Example 4.11
The use of multiple regression is illustrated in a relationship
study con-
ducted by Lunenburg and Columba (1992). The purpose of their
study was
to identify personality factors and education level that
distinguish high
performing urban principals from average performing urban
principals
(n = 179). Four independent criteria were used to measure
principal per-
formance (dependent variable): supervisor’s ratings, paired
comparison
ratings, peer nomination ratings, and teacher ratings, resulting
in an over-
all performance score. Sixteen personality factors (measured by

the 16PF)
and education level (master’s or doctorate) were the predictors
(indepen-
dent variables). Stepwise multiple regression analysis revealed
that factors
E (dominant), M (imaginative), Q2 (self-sufficient), A (warm),
and doctoral
education level were consistent predictors of superior
performance of
urban principals (see Table 4.3). Because multiple regression is
one of the
most widely used statistical techniques in the social sciences,
we will
explain the procedure in some detail using the example as a
model.
81Basic Statistics
The first step in multiple regression usually is to compute the
correla-
tion between the best single predictor variable and the criterion
variable.
This procedure yields a multiple correlation coefficient (R),
which is
shown in Table 4.3. Because Factor E: dominant is the best
predictor, it is
the first predictor entered into the multiple regression. Unless
you specify
otherwise, the computer program will start the multiple
regression analy-
sis with the most powerful predictor of the criterion variable.

Suppose you have not specified the order in which the predictor
vari-
ables are to be entered into the multiple regression analysis. In
this case,
after selecting the best predictor, the computer program will
search for the
next best predictor of the criterion variable. This second
predictor is not
chosen on the basis of its product-moment correlation (r) with
the crite-
rion. Rather, the second predictor is chosen on the basis of how
well it
improves on the prediction achieved by the first variable.
What qualities should a variable have to be a good second
predictor?
First, it should correlate as little as possible with the first
predictor vari-
able. The second quality of a good second predictor is obvious:
It should
correlate as highly as possible with the criterion variable. In
short, a good
second predictor is one that correlates as little as possible with
the first pre-
dictor and as highly as possible with the criterion.
The third predictor entered in the multiple regression analysis is
deter-
mined by whether it improves on the prediction made by the
first two pre-
dictors. The computer program will keep adding predictor
variables until
none are left. Each new predictor variable will contribute less to
R than the
preceding predictor, however, in which case there are rapidly

diminishing
returns for adding new predictors.
Table 4.3 Multiple Regression Analysis of Personality Factors
and
Demographic Variables with Principal Performance
Criterion
Variable Constant R R2 F
Prinicpal
Performance X1 X2 X3 X4 X5
Rating
beta .48 .28 .12 .09 .13 21.4 .64 .41 4.68*
R .49 .60 .62 .62 .64
X1 = Factor E, X2 = Factor M, X3 = Factor Q2, X4 = Factor A,
X5 = Educational Level
* p < .01
At this point we can consider further the meaning of R. The
multiple cor-
relation coefficient (R) is a measure of the magnitude of the
relationship
between a criterion variable and some combination of predictor
variables.
The value of R will increase with each variable that enters the
multiple regres-
sion analysis. Thus, we see in Table 4.3 that the value of R to

predict superior
performance gradually increases from .49 to .64 as each
predictor variable is
added. The value of .64 represents the best prediction one can
make of supe-
rior principal performance from the predictor variables listed in
Table 4.3.
The value of R can range from 0.00 to 1.00; negative values are
not possible.
The larger the R, the better the prediction of the criterion
variable.
If R is squared, it will yield a statistic known as the coefficient
of deter-
mination (R
2
). The 10th column of Table 4.3 shows the R
2
coefficient corre-
sponding to the R in the ninth column. For example, the R
2
coefficient is
.41, which is the square of the corresponding R coefficient
(.64). R
2
expresses the amount of variance in the criterion variable that is
explained
by a predictor variable or combination of predictor variables.
Each b value in the multiple regression equation is a regression

weight,
which can vary from −1.00 to 1.00. A regression weight
(sometimes called
a b weight) is a multiplier term added to each predictor variable
in a
regression equation in order to maximize the predictive value of
the vari-
ables. The b weights were converted to beta (B) weights. Beta
weights are
the regression weights in a multiple regression equation in
which all the
variables in the equation are in standard score form. Some
researchers pre-
fer beta weights because they form an absolute scale. For
example, a beta
weight of +.40 is of greater magnitude than a beta weight of
+.30, irre-
spective of the predictor variable with which it is associated. In
contrast,
the magnitude of a b weight is dependent on the scale form of
the predic-
tor measure with which it is associated.
There are several variations of multiple regression analysis:
stepup
(also called forward), stepdown (also called backward), and
stepwise. Each
variation uses a different procedure for selecting a subset of
predictor vari-
ables that yields the best prediction of a criterion variable. In
stepup multi-
ple regression, the predictor that leads to the largest increase in
R is added
to the current set until the addition no longer leads to a
statistically signif-
icant increase. In stepdown multiple regression, all possible

predictor vari-
ables are entered into the analysis first, and then, step by step,
the variable
that results in the smallest decrease in R is deleted until a
statistically sig-
nificant decrease occurs. In stepwise multiple regression, both
the stepup and
the stepdown procedures are combined. Lunenburg and Columba
(1992)
used a stepwise multiple regression procedure.
Multiple regression analysis is sometimes misused by
researchers. One
common problem is to confuse prediction with explanation. The
proce-
dures are relatively straightforward if the purpose is to optimize
predic-
tion of a criterion variable. You should be careful, however, if
you have a
theory that attributes causal significance to the predictor
variables. In this
situation, you should not confuse the causal significance of a
predictor
variable with its regression weight or R
2
increment value in a multiple
regression equation. If you wish to test a causal theory by using
multi-
variate correlational data, consider path analysis or structural
equation

modeling rather than multiple regression. Both procedures are
discussed
later in the chapter.
You should maintain a reasonable balance between sample size
and
the number of predictor variables used. A rule of thumb is to
increase sam-
ple size by at least 15 subjects for each variable that will be
included in the
multiple regression analysis. Using this rule, you would select a
sample of
at least 45 subjects for a multiple regression analysis involving
three pre-
dictor variables.
Discriminant Analysis
Discriminant analysis is a statistical procedure related to
multiple corre-
lation. It uses a number of predictor variables to classify
subjects into two
or more distinct groups such as dropouts versus persisters,
successful ver-
sus unsuccessful students, delinquents versus nondelinquents,
and so on.
The criterion in discriminant analysis is a person’s group
membership. The
procedure results in an equation, or discriminant function,
where the scores
on the predictors are multiplied by weights to predict the
classification of
subjects into groups. When there are just two groups, the
discriminant
function is essentially a multiple correlation equation with the
group

membership criterion coded 0 or 1. But with three or more
groups as the
criterion, discriminant analysis goes beyond multiple
correlation.
Example 4.12
Discriminant analysis might be used to identify predictors of
success in a
school of education doctoral program. You could identify the
variables
that discriminated membership into one of two groups: those
who suc-
cessfully completed doctoral study and those who did not. A
number of
different predictors might be used: Miller Analogies Test
(MAT) scores,
Graduate Record Examination (GRE) scores, undergraduate
GPA, gradu-
ate GPA, time lapse between the master’s degree and entrance
into the
doctoral program, doctoral major, age at entrance, gender,
race/ethnicity,
and marital status. Complex correlational analysis would
produce an
equation showing the variables that were significant in
predicting the cri-
terion, success or lack of success in a doctoral program.
Canonical Correlation
Canonical correlation is a generalization of multiple regression
that adds
more than one dependent variable to the prediction equation.
Recall that a
multiple correlation coefficient shows the correlation between

the “best”
combination of independent variables and a single dependent
variable.
83Basic Statistics
Canonical correlation extends the analysis to more than one
dependent
variable. In other words, canonical correlation is an analysis
with several
independent variables and several dependent variables. It takes
into
account the X and Y scores, the relations between the X
variables, between
the Y variables, and between the X and Y sets of variables. The
result is a
canonical correlation coefficient that represents the maximum
correlation
possible between sets of X scores and sets of Y scores. It als o
indicates the
relative contributions of the separate independent and dependent
vari-
ables to the canonical correlation, so you can see which
variables are most
important to the relationships between the sets. For more
information on
canonical correlation, see Thompson’s Canonical Correlation
Analysis (1984).
Path Analysis
Path analysis is a set of statistical procedures designed to test a
hypoth-
esized causal model about the relationships among three or

more vari-
ables. Using theory and existing knowledge, the researcher
proposes a
causal model and then applies path analysis to determine if the
causal
model is consistent with the empirical data. Models inconsistent
with the
data are rejected, whereas those not rejected are viewed as
plausible causal
patterns to be subjected to further investigation.
Example 4.13
Lunenburg (1984, 1991a, 1991b), Lunenburg and Cadavid
(1992),
Lunenburg and O’Reilly (1974), and Lunenburg and Mankowsky
(2000)
tested a path model of the influences affecting teachers’ job
satisfaction.
The model used in the four studies hypothesized that job
satisfaction is a
function of a teachers’ belief systems, locus of control,
perceptions of
organizational climate, dimensions of school bureaucratization,
pupil
control orientation and behavior, and several demographic
variables
including, gender, age, father’s education, mother’s education,
teacher’s
academic achievement, teaching experience, teacher’s
commitment to the
teaching profession, and race and ethnicity in the later studies
(Lunenburg & Cadavid, 1992; Lunenburg & Mankowsky, 2000).
There
were significant relationships between teachers’ job satisfaction
and per-

ceptions of teachers’ belief system, locus of control, dimensions
of school
bureaucratization, organizational climate, and pupil-control
orientation
and behavior. The researchers concluded that the prototypic
profile of the
dissatisfied teacher is one who has a closed-minded belief
system, an
external locus of control, perceives a closed organizational
climate, a high
level of school bureaucratization, and has both a custodial
pupil-control
orientation and behavior.
The analysis was done separately for samples of African
American
teachers and White teachers. In general, the path coefficients
were similar for
African American teachers and White teachers. One exception
was found
when satisfaction was related to academic achievement. Lower -
achieving
White teachers tended to be more satisfied in their jobs than did
their
higher-achieving counterparts; for African Americans, no such
difference
was found. Another observed difference occurred when
satisfaction was
related to gender. African American men tended to be more
satisfied with
their jobs than African American women, but White women

tended to be
more satisfied than White men. This finding is consistent with a
study by
Culver, Wolfe, and Cross (1990). For both groups of White and
African
American teachers, demographic variables such as age, sex,
father’s edu-
cation, mother’s education, and years of teaching experience
were found
to be of little importance to job satisfaction. For further
discussion of path
analysis, see Loehlin (2004).
Factor Analysis
Another widely used procedure based on correlation is factor
analy-
sis. This procedure analyzes the intercorrelations among a large
set of
measures to identify a smaller number of common factors.
Factors are
hypothetical constructs assumed to underlie different types of
psycho-
logical measures, such as intelligence, aptitude, achievement,
person-
ality, and attitude measures. Factor analysis indicates the extent
to
which tests or other instruments are measuring the same thing,
enabling researchers to deal with a smaller number of
constructs.
Some factor analysis studies of intelligence tests, for example,
have
identified underlying verbal, numerical, spatial, memory, and
reason-
ing factors.

The first steps in factor analysis involve selecting variables to
be
included in the analysis and developing a correlation matrix that
shows
the correlation of each measure with every other measure. There
may be
a very large number of correlations in the matrix. The matrix is
then sub-
jected to computations with a factor analysis computer program
that
produces clusters of variables that intercorrelate highly w ithin
the clus-
ter but have low correlations with other clusters. These clusters
are the
factors, and the object is to identify a smaller number of
separate under-
lying factors that can account for the covariation among the
larger
number of variables.
Example 4.14
Consider the hypothetical correlation matrix in Table 4.4. An
“inspec-
tion” of the factor analysis shows three clusters. The correlation
between
vocabulary and analogies is high (.50), but neither correlates
highly with
the other variables. Arithmetic and numerical reasoning
correlate highly
with each other, but not with the other variables. A similar
pattern
can be seen with picture completion and block design. Three
factors
appear to underlie performance on these six measures—verbal,
numeri-

cal, and spatial.
85Basic Statistics
Table 4.4 Correlation Matrix of Six Factors Related to
Intelligence Tests
1 2 3 4 5 6
1. Vocabulary — .50 .15 .12 .12 .15
2. Analogies — .12 .15 .10 .18
3. Arithmetic — — .55 .15 .12
4. Numerical Reasoning — — — .20 .22
5. Picture Completion — — . .52
6. Block Design —
Several variations of factor analysis are available. For example,
a factor
analysis can be done to yield an orthogonal solution, meaning
that the result-
ing factors are uncorrelated with each other. An orthogonal
solution is desir-
able if we seek a pure set of factors, with each measuring a
construct that
does not overlap with constructs measured by other factors. In
certain situ-
ations, however, it is desirable to do a factor analysis with an

oblique solution,
meaning that factors can be derived that do correlate with each
other.
Example 4.15
Lunenburg and Schmidt (1987) administered a 64-item
instrument, which
they developed, to 227 public high school educators in Illinois,
Iowa, and
Wisconsin. A principal components factor analysis using an
orthogonal
rotation with varimax solution, revealed three interpretable
factors. Items
that exhibited factor loadings whose absolute value was .40 or
greater
were used to define a factor. Application of the Scree Test
(Cattell, 1966)
clearly identified three interpretable factors. The three factors
accounted
for 45% of the variance. The 35 items that did not conform
psychometri-
cally or theoretically to any factor were eliminated. Alpha
coefficients
(Cronbach, 1951) of .93 for the total scale and for respective
factors (.91,
.79, and .73) supported their moderately high reliability. Results
of the fac-
tor analysis are presented in Chapter 8. For readable discussions
of factor
analysis, see Kim and Mueller (1978) and Thompson (2004).
Structural Equation Modeling
Structural equation modeling is a multivariate technique that
can be used

to test theories of causal relationships between variables. The
method is
similar to but more powerful than path analysis, because it
provides a
more powerful test of causal relationships specified by a theory.
LISREL
(Analysis of Linear Structural Relationships) is the computer
program that
was developed to run it. For further discussion of structural
equation
modeling, see Byrne (2006) and Loehlin (2004). A good, recent
example of
structural equation modeling is provided by Hoy, Tarter, and
Hoy (2006).
Reference to this study is provided in Chapter 7.
SUMMARY
Statistics are mathematical procedures for analyzing numerical
data. The
statistical procedures you choose will be determined by your
research ques-
tions or hypotheses and the type of data you will collect. Thus,
different
research questions (hypotheses) and different research desi gns
require dif-
ferent statistical analyses. No matter how well you conduct your
study,
inappropriate statistical analyses likely will result in
inappropriate research
conclusions. Many statistical procedures are available to you. In
this
chapter, we described those commonly used in the social and

behavioral
sciences. In the following chapter, we describe qualitative
research designs.
87Basic Statistics
5
Qualitative
Research Designs
In this chapter, we continue the discussion of methods of
inquiry from
Chapter 3; however, in this chapter we guide you through
qualitative
and mixed methods research designs. Just as quantitative
research begins
with basic human observation and curiosity, qualitative research
does also.
The science of qualitative research seeks to have the researcher
look deeply
into the world of individuals and phenomena.
Your dissertation, as a product of qualitative research, will
likely be
based on small nonrandom samples, or it may relate to multiple
concepts,
phenomena, or theories, or it may yield theory. Basic factual
information
or uniconceptual research in qualitative designs report
information on one
concept or phenomenon and does not attempt to make broader

commen-
taries on the data. It is confined to one dimension and the small
sample. It
may be applied to similar groups in similar situations.
Qualitative research
can become more complex as the researcher uses methods of
inquiry that
promote cross-analyses or comparisons of data. Theory
development,
grounded theory, is often the most difficult, but most rewarding
outcomes
of the methodological techniques.
Just as in quantitative research, also in qualitative research, you
must
ask yourself: what is my intent or purpose of the research?
Second, you
must ask: what are my research questions? Again, these initial
components
will drive the method you select. If you conclude that you need
to go obtain
a deeper understanding of the topic, you will select qualitative
methodol-
ogy. Ultimately, you may decide that a mixed methods
approach, using
88
both quantitative and qualitative analyses, is the best method of
inquiry for
your dissertation. In this chapter, we provide an overview of
each of the
data analysis techniques or inquiry methods you could use if
your ques-

tions or purpose necessitate a qualitative approach. Certainly,
this chapter
is not all inclusive, and we know that you will want to
investigate more
fully other options on your own once you settle on a specific
method of
inquiry. You will need to consult qualitative research methods
textbooks
and review your proposed methodology with your dissertation
advisor.
You must be thoroughly familiar with your method of
qualitative inquiry.
Your research design or method of inquiry for qualitative will
likely fall
into one of four major categories of research: (a)
phenomenological research,
(b) case study research, (c) ethnographic research, and (d)
grounded theory
research. In addition to providing you with a brief overview of
each category,
we also provide examples of research purposes or questions that
would justify
a specific type of qualitative research design or method of
inquiry. We also give
several other types of research in qualitative inquiry. In a final
section of this
chapter, we discuss mixed methods research and provide some
examples of it.
According to Creswell (2007),
A qualitative study is defined as an inquiry process of
understanding
based on distinct methodological traditions of inquiry that
explore a

social or human problem. The researcher builds a complex,
holistic pic-
ture, analyzes words, reports detailed views of informants, and
con-
ducts the study in a natural setting. (p. 15)
Therefore, qualitative research emphasizes understanding by
closely
examining people’s words, actions, and records, as opposed to a
quantitative
research approach that investigates such words, actions, and
records at a
mathematically significant level, thus quantifying the results of
observations.
Qualitative research examines the patterns of meaning that
emerge from data
gathered; such patterns are often presented in the participants’
own words.
Many students believe that qualitative research is easier than
quantitative
research; however, it takes not only a logical mind, but one that
can take and
make sense of ambiguity by searching for patterns and
connections. Your
task in qualitative research is to find patterns within words and
observed
actions, and to present those patterns for others to inspect while
at the same
time staying as close to the construction of the world of the
participants as
they originally experienced it. Again, your job as a researcher is
not to make
broad generalizations; rather, your job is to contextualize the
findings.
PHENOMENOLOGICAL RESEARCH DESIGN

Phenomenological research is one of the most basic forms of
research. This
type of research involves the description of phenomena in our
world. In
this type of inquiry, the phenomena described are basic
information,
89Qualitative Research Designs
actions, behaviors, and changes of phenomena, but always the
description
is about what the phenomena “look like” from the perspective of
the
researcher and the participants in the research; it is not about
how the phe-
nomena function. Prior to beginning the research, you will need
to have
established from theory or from prior research what it is about
the phe-
nomenon you will study; from this perspective, descriptive
research is
theory or research driven.
Husserl, the twentieth-century philosopher and the father of
phenom-
enology, was concerned with the study of “experience” from the
perspec-
tive of the individual, and believed that the researcher could
approximate
those experiences through intuiting and rigorous examination of
the
subjects, objects, or people’s lived experiences, behaviors, or
actions. He

believed that researchers could gain subjective experience,
essential reali-
ties, and insights into a person’s or persons’ motivations and
actions; thus,
researchers could minimize presuppositions and traditionally
held beliefs.
The researchers’ interpretations of the phenomena allows it to,
as in action
research, inform, support, or challenge policy, procedures, and
actions in
society or organizations.
In a phenomenological research design, the researcher is
concerned
with clarifying the specific and recognizing phenomena through
the eyes
of the participants. Deep and rich descriptions of the
phenomenon or phe-
nomena are usually gathered through inductive, qualitative
methods such
as interviews, focus group discussions, and participant
observation.
Although phenomenological research has been related to other
essentially
qualitative approaches including ethnography (discussed later in
this
chapter), hermeneutics (analysis of the written word), and
symbolic inter-
actionism (making meaning of individual’s interactions), it
usually is akin
to descriptive research in that it is about describing rather than
explaining.
Much of this type research has the researcher come to it without
any pre-
conceived notions or hypotheses; however, it is extremely
unlikely that

you, as the researcher, will not have a preconditioned paradigm,
purpose,
or hypothesis. Therefore, you, as the researcher, never can be
removed
from your own presuppositions about the phenomena; therefore,
you
should admit your own perspective under a heading in your
dissertation
methodology as “Researcher’s Perspective” or “My Personal
Perspective.”
Techniques/Approaches/Methods
Phenomenological research can be based in either single-case
designs or
purposefully selected samples. A variety of qualitative
techniques,
approaches, or methods can be used in phenomenological
research, includ-
ing interviews, focus groups, participant or direct observation,
and docu-
ment or personal entry analyses. Of course in any of these
approaches,
the establishment of trust is important via good rapport and
empathetic
listening. The techniques are explained in more detail in Table
5.1.
(Text continues on page 95)

Table 5.1 Phenomenological Technique, Method, Approach
Phenomenological Technique, Method,
Approach Defined Explanation
Interview—The interview, both
factual and meaningful, seeks to
describe the meanings of central
themes in the life world of the
subjects. The main task in
interviewing is to understand the
meaning of what the interviewees
say (Kvale, 1996).
The interview can be structured,
semistructured, or unstructured. In
a structured or semi-structured
interview, you will need an interview
protocol that has been face validated.
The interview is usually recorded and
then transcribed with notes taken
during the interview added later.
Prior to the interview, you must:
(a) secure a location with the least
distraction, (b) explain the purpose of
the interview, (c) address
confidentiality and provide a consent
form for signature, (d) explain the
format of the interview, (e) indicate
the length of the interview, (f) give
your contact information, (g) allow
interviewee to ask any questions
about the interview, and (h) determine
how you will record the interview
data (if the tape recorder is used,
verify that it is working periodically

throughout the interview and if you
take notes, be thoughtful of how and
when notes are taken, not to make the
interviewee feel uncomfortable—
notes may be used for observations
during the interview only).
During the interview, you will need
to attend to: (a) careful listening,
(b) nonverbal cues, (c) the progress
of the conversation, (d) probing when
needed, (e) taking notes, and (f) not
responding during the interview.
Questions in the interview should be
sequenced as follows: (a) ask factual,
basic questions first, simply getting
the interviewee involved, then place
factual questions throughout the
interview, (b) present-based
questions should be asked prior to
(Continued)
Table 5.1 (Continued)
past-based or future-based questions,
and (c) allow the interviewee to add
any additional information at the end

of the interview.
Note: the interview is time-
consuming and resource extensive.
Focus Groups—This technique is a You, or someone you train,
can lead
form of interview, but with a group. or moderate the group,
which
It focuses on data generated via usually consists of four to
twelve
observation and communication people. The less experienced
the
between and among participants. interviewer, the more people
in the
Denzin and Lincoln (1994, p. 365) group may be likely to be
frustrating
stated that Merton et al., coined the or unwieldy; however, the
less
term focus group in 1956 to apply to a experienced the
interviewer, the
situation in which the interviewer fewer people may likely not
be
asks group members very specific encouraged to generate the
questions about a topic after information needed that a few
more
considerable research has already in a group could generate. In
this
been completed, while Kreuger technique, you would have an
(1988) defined a focus group as a interview protocol established
and
“carefully planned discussion validated. Additionally, it should
be
designed to obtain perceptions in a noted that you should not be
defined area of interest in a alarmed if one participant’s
permissive, nonthreatening discussion is influenced by another.

environment.” (p. 18) This is normal in focus group
It is less time consuming and
resource intensive than the
research. We often call it
“piggybacking.”
individual interview. The protocol should go from more
general questions to more specific
and should consist of about 10
questions.
Participant or Direct Observation— You will, in participant
observation
The participant observation method, intentionally put yourself
in
technique is an observational method the context or location of
the
in qualitative research that uses the phenomena being studied
over an
five senses to describe the (a) setting, extended period. As a
participant
(b) people, (c) occurrences, and observer, you will become a
part of
(d) meaning of what was observed. the group and become fully
engaged
In other words, it examines the in experiencing what the
participants
intricacies of the interactions and are experiencing. You may
combine

relationships of individuals. It some unstructured interviews
during
investigates the phenomena of this. the time you are also
observing. The
This technique is usually done onsite; extent to which you
participate and
however, with the virtual world, it the length of participation is
up to
may be conducted using you as the researcher. It truly
photographs, videotaping or depends on your research purpose.
streaming, and audiotaping or
streaming.
There may be limitations in terms of
gender, time, language, age,
In direct observation, you will socioeconomics, or culture that
you,
conduct systematic observation and as the researcher, will need
to
documentation of phenomena in its consider prior to entering as
a
usual setting or location. participant. You may consider having
other researchers make observations
at the same time you do. You may
wish to observe at other times—vary
your times of observation to make
your finding more reliable.
In direct observation you will be a
quiet observer and will not interact
with the participants; you will not

intervene in situations in an attempt
to influence the outcome of the study.
As participant observers, you become
a part of the group and are fully
engaged in experiencing what those
in the study group are experiencing.
You may, however, conduct focus
groups or individual interviews at
another time during the study. But,
unlike participant observation, the
interviews will be considerably more
structured and will likely be based
on things you have observed so that
you can obtain more in-depth
understanding about what it is that
you observed.
In both participant and direct
observation, you will want to make
notes on: (a) the physical
environment, (b) sociological aspects
(Continued)
of community, (c) hierarchical
structures, (d) language,
(e) communication venues,

(f) unspoken communications or
norms, (g) activities, and (h) your own
thoughts and responses. Take down as
much of the direct conversations as
possible for reproduction of thoughts
of the individuals and group.
Document Analysis—This particular
technique in qualitative research is
related to the critique or analysis of
documents for significance, meaning,
and relevance within a particular
context and phenomenon.
Documents can consist of historical
papers, personal entries, clinical
records (if available), video, photos,
electronic media, collections (stamps,
clothes, other materials), books,
newsletters, or newspapers (not an
all inclusive list).
Using this technique, you will
(a) define your purpose(s), (b) collect
your documents, and (c) review and
analyze your documents. When you
review your documents, you will
want to label your documents and
perhaps use differing colors of Post-it
tabs for varying themes you find.
You will want to use the constant
comparison method as defined by
Patton (1990) as “to group
answers. . . to common questions
[and] analyze different perspectives
on central issues.” (p. 376)

Discourse Analysis—Discourse
analysis is actually not a specific
research method, but can inform the
phenomenon being studied. It
provides a deconstructive reading
and interpretation of a problem or
text. Discourse analysis can be stated
as a set of methods and theories for
investigating language in use and
language in social contexts (Yates,
Taylor, & Wetherell, 2001). It can be
used as well in ethnography to
understand and examine a person’s
social world. Approaches to
discourse analysis may include
discursive psychology; conversation
analysis; critical discourse analysis
and critical linguistics; and
sociolinguistics.
The defining of discourse analysis is
part of its explanation. Stubbs (1983)
defined it as being (a) concerned with
language use beyond the boundaries
of a sentence/utterance,
(b) concerned with the
interrelationships between language
and society, and (c) concerned with
the interactive or dialogic properties
of everyday communication. The
validity and reliability of discourse
analysis may be found in four
approaches: (a) deviant case analysis,
(b) participant understanding,
(c) coherence, and (d) reader’s
evaluation. (Potter, 1996)

Following are examples of phenomenological studies by
Meyertons
(2006) and Gitonga (2006). (See Examples 5.1 and 5.2.)
Example 5.1
Abstract
This phenomenological study investigated the experiences of a
set of fac-
ulty who taught classes in hybrid format at a small liberal arts
university
in Salem, Oregon. For this study, a “hybrid format” course was
defined
as a course that includes elements of both traditional face-to-
face and
technology-enhanced (often Internet) course components. The
study con-
sisted of a set of heuristic interviews with faculty members
identified
through an empirical survey I conducted in Fall 2002 as part of
my duties
as Director of Instructional Design and Development for the
university’s
technical services department.
Higher education leaders have consistently identified
technology
integration as an important priority for their faculty. Since i n
many cases
faculty have proven reluctant to do so, it is clear that there has
been some
dissonance between leadership expectations and faculty

experiences. An
extensive review of relevant literature indicates that little
research has
been conducted specifically on the faculty experience with
educational
technology, although much evidence has been gathered on the
student
experience and on learning outcomes. The goal of this study
was to dis-
cover if there were any common elements that faculty
experience in work-
ing with hybrid formats, and to try to distill these elements into
a set of
recommendations to higher education leaders for improving
faculty expe-
riences with educational technology. The broader goal was to
help
develop practices that might improve ways faculty use
educational tech-
nology to enhance teaching and learning. (Meyertons, 2006, pp.
1–2)
Example 5.2
Abstract
The purpose of this study was to explore beginning counselors’
experi-
ence of their first counseling position in their first year. Four
counselors,
who shared a similar counselor education background but who
also dif-
fered by their degree majors, described their experiences. Using
phenom-
enological methodology to collect and analyze data, two rounds
of

interviews were conducted in order to allow the four
participants to indi-
vidually describe their experiences. A focus group composed of
all the
participants verified the accuracy of the research findings. The
study
resulted in a fusion of common themes. Within the theme of
hope and
expectations, the essence of the beginning counselors’
experience
revolved around support, clientele, salary, time, and continuity.
Other
common themes that emerged from the study were: (1) the
process of
transitioning from students to counselors, (2) exciting as well as
chal-
lenging experiences as beginning counselors, and their
perceptions of
their level of preparedness for the first counseling position. In
these
themes, there were commonalities as well as “uniquenessalities”
of the
essence of their experience. (Gitonga, 2006, p. x)
CASE STUDY RESEARCH DESIGN
Case studies are specific explorations of individuals, but also
such investiga-
tions can be on groups, cohorts, cultures, organizations,
communities, or pro-
grams. If a case study is based in biography, it may be called

life history, which
focuses on major circumstances, situations, events, problems,
celebrations,
and/or decisions of a person, group, or organization. If a case
study is
focused on one individual or group, it is called single-case
design. If the case
targets multiple individuals and the same phenomena or it
targets various
communities related to a similar phenomenon, then this type of
design is
called multiple-case study design. It may even be called a
phenomenological case
study, combining as we indicated the case study and
phenomenology.
More than likely, you will be using purposeful sampling in case
study
design. You must describe your sampling procedure and case
selections in
detail. Share all the characteristics of the selection criteria and
distinctive
and uncommon features. Also indicate the duration of the study,
as that
may have a bearing on the outcome.
Cases may also be classified as one of the following: deviant or
extreme
case, critical case, convenience case, typical case, or politically
important
case (Patton, 2002). An extreme case study would be if you
were to study
one or more persons at some extreme. For example, you might
study
superintendents who have served in urban school districts only
as super-

intendents. A critical case would be one that makes a point. It is
like this: if
it happens in this case, then it will happen in any case, or vice
versa. An
example of a critical case is related to language. For example, if
a letter is
sent home in English to predominately Vietnamese-speaking
homes and
the parents understand the letter, then all parents of other
languages and
of English would be expected to understand the letter. A
convenience case is
one in which cases are selected based on availability. It is not
purposeful
or strategic in nature and is the weakest type of case. A typical
case is one
in which you would select to typify the norm when describing a
group or
program. A politically important case would be one in which
cases are
selected based on political sensitivity. For example, a case
study of dual
language programs in the state would be politically sensitive
and infor-
mative in a year in which the legislature was considering
funding or not
funding dual language programs or other types of bilingual
programs.
Your case data may include, but not be limited to: (a) basic
demo-
graphic information about the individual that is written in
narrative for-
mat, (b) family history (or if the case is about a program or
organization,
it would relate the program’s or organization’s history), (c)

document
analyses relating to the individuals or programs, (d) interview
data,
and/or (e) observational records. Once you have sufficient data,
you will
compile the case data that tells the story of the individual,
program, or
organization. You likely will have themes, which may be by
type of phe-
nomena revealed or from a chronological perspective. When you
write,
you will need to provide sufficient explanation with deep
description, so
nothing is left for the reader to surmise. If you have definitions
specific to
your study, be certain to explain those as well. Just as in a
traditional dis-
sertation, it is helpful to have a separate listing of definitions in
qualitative
dissertations as well in the Introduction Chapter. You must
bring your
reader right into the case itself. Perhaps if you visited the
individual on
location, you might take photos to place into your dissertation
(see, for
example, Christensen’s case study dissertation, 2003).
If you have multiple case studies, you will want to analyze each
sepa-
rately, then compare and contrast all of the cases for a final
cross-case

analysis (e.g., Bamberg’s case study dissertation, 2004). Of
course, as in all
qualitative research, you will need to report themes, categories,
subcate-
gories, or subthemes. Any anomalies found must be reported in
detail as
well. Data reduction, as it spirals from the more general to the
specific,
may include, as Miles and Huberman (1994) suggested, data
matrices,
tables, and figures to draw comparisons across cases. When you
are writ-
ing your interpretation, you must establish the significance of
the findings
or themes, linking them to your theoretical framework.
Implications for
practice are also important in conveying the significance of
your findings,
but generalization to broader populations is typically not
appropriate for
this type of research. Even in writing your findings, you should
be cau-
tious in making what we call, “great leaps,” from the data to
your impli-
cations. Your data represent exactly what they represent and
nothing
more, so make sure you have given the reader an appropriate
amount of
information in any claims that you make. Your findings must be
credible
as well as convincing and, again, based in a theoretical frame
and previous
literature and studies that are also credible. If you want to build
theory
itself from your case study, you will need to make certain you
have a sat-

isfactory number of case studies that will provide sufficient
information to
justify the development of a theory.
Examples of case studies conducted for dissertations follow.
The
first example is of a single case study conducted by Ozawa
(2006). (See
Example 5.3.)
Example 5.3
Abstract
The purpose of this single case study was to explore authentic
communi-
cation for Japanese language learning in second-year Japanese
classes at a
small, private university in the Midwest. Types of authentic
Japanese
communication and materials in and outside of Japanese class
were stud-
ied from four learners’ and one professor’s perspectives.
Data were collected throughout one academic year, the first
semester
of 2004 through the second semester of 2005. Multiple methods
of data
collection were used in this study including personal interviews,
casual
conversations, participant observations of classes and related

events, and
studying relevant documents, including the textbook, students’
study
sheets, videos, oral exam transcripts, e-mail copies, reflection
sheets, and
web log copies.
Qualitative research procedures were used to study second-year
Japanese language learners’ authentic communication. Data
were analyzed
by categorizing into codes, then themes and subthe mes. Five
themes
emerged in this study: (1) the e-mail writing process, (2) the e-
mail read-
ing process, (3) the learning process, (4) learning through e-
mail, and
(5) authentic Japanese. Lastly, implications and
recommendations based on
the data were concluded. (Ozawa, 2006, n.p.)
The second example is a multiple case dissertation by Menconi
(2006).
(See Example 5.4.)
Example 5.4
Abstract
Looping is an instructional practice that allows the teacher to
keep the
same students over a two-year period. Multiage grouping is a
more com-
plex form of the multiyear configuration where the same teacher
instructs
students from two or three grade levels until they have
completed the

highest grade represented in that classroom. Both configurations
present
a logical approach toward developing long-term relationships
which ben-
efit students, teachers, parents, and administrators. Looping and
multiage
instruction beginnings go back to the one-room schoolhouse
long before
the Common School Reform Movement which introduced the
graded
school system of the 1860s. Even though the graded school
system is over
150 years old, it still remains entrenched today. Consequently,
looping
and multiage instruction represent practices that require a
paradigm shift
from the graded school system.
While there are philosophical differences between looping and
multiage
instruction, there are also many similarities. Perhaps the most
significant sim-
ilarity is the long-term relationship that is developed between
the child,
teacher, and parent when the teacher has the same child for two
years or two
grade levels. This distinction develops many benefits such as
curriculum
coherence, safety, continuity, and student confidence. Two
years with the same
child also provides administrators and principals with many
benefits. There is
a tremendous savings of time because the students do not have
to learn new
routines and classroom management is easier. And there is a
tremendous

amount of professional development benefits for the teachers,
since the
teachers become better acquainted with the child and the
curriculum.
Principals have indicated several concerns regarding the
implemen-
tation of looping and multiage instruction, since it is a change
from the
standard graded school system. These concerns include the
issues of more
developmentally appropriate means for educating children,
blending the
curricula for two grade levels, and identifying staff members
willing to
undertake this paradigm shift.
The purpose of this study is to identify principal perspectives of
the
strengths and weaknesses of looping and multiage instruction
from prin-
cipals and lead teachers who have implemented them
successfully. A
multiple-case study design is used to investigate looping and
multiage
practices and provide a comparison of sites or cases so a range
of gener-
ality or conditions is established. These sites include a wealthy,
suburban
elementary school using multiage practices, an integrated,
middle-class,
urban elementary school using looping, and a low-income,

urban middle
school using looping. Different information gathered from
multiple sites
and principal perspectives establishes a range of aspects or a
conceptual
framework that can assist principals in understanding these
multiyear
practices better. It is anticipated that this conceptual framework
will be of
value to those principals and administrators interested in
implementing
looping and multiage practices in their own schools or school
districts.
(Menconi, 2006, p. x)
ETHNOGRAPHIC RESEARCH DESIGN
Ethnographic research requires that you conduct fieldwork to
become
involved with the individuals or group in a personal manner,
using par-
ticipant observation as a technique for gathering data for telling
the
group’s or individual’s story via rich narrative description. You
will typ-
ically gather the data via interviews during the participant
observation,
videography, photography, and document analysis. These
techniques of
data gathering will yield thick and rich descriptions necessary
for your
ethnographic dissertation in the form of quotations (low
inference
descriptors), descriptions of the group and the contexts, and
parts of doc-
uments. You will, as in participant observation, investigate the

behaviors
of the people, their language, their actions, and their artifacts.
You will
look for norms, mores, and customs. Prior to going in to
conduct such
research, you must be clear about your own biases, about
colonization,
about who the “other” is, about your impact on the group to be
studied,
and about basic respect. To conduct fieldwork for the
ethnography,
you will need to gain the trust of the individuals in charge;
these are the
gatekeepers.
In ethnographic studies, depending on your purpose, you may
use
convenience sampling (as explained previously under case study
method).
You may use stratified sampling that seeks out groups from
various levels
such as socioeconomic levels. Snowball sampling may also be
used where
referrals from your initial contacts are made to add to the group.
Ethnography does not come without serious consideration. Some
criti-
cal features to consider prior to the selection of an ethnographic
design are:
(a) your own understanding of culture and cultural
anthropology, the foun-
dation of ethnography; (b) your ability to write in a narrative

style so that
others may understand the cultural occurrences and norms of the
group;
(c) your ability to be a part of the group, yet remain apart from
the group as
the researcher, thus creating a fine line and balance between the
researcher
and the researched; (d) the ethical implications for studying the
group or
individuals; and (e) an extensive time commitment for the
fieldwork.
Although conversation analysis was mentioned under the
approach of
discourse analysis within the phenomenological research
method previ-
ously, we could consider conversation analysis as a separate
approach and
list it under ethnography and classify it as an ethnographic
approach. In
this type of analysis, one studies natural conversations in a
variety of con-
ventional settings (interviews, courts, schools, telephone
conversation,
restaurant, family conversations) to determine how the
participants inter-
act, construct conversation across time, identify problems,
and/or exhibit
gestures and eye contact.
We are placing narrative analysis within the larger context of
the
ethnographic research method, because according to Connelly
and
Clandinin (1990) narrative analysis is “how humans make
meaning of

experience by endlessly telling and retelling stories about
themselves”
(p. 14). Mishler (1986) frames the narrative through four
categories: (a) ori-
entation that describes the setting and character, (b) abstract
that summa-
rizes the events or incidents of the story, (c) complicating
action that offers
an evaluative commentary on events, conflicts and themes, and
(d) reso-
lution that describes the outcomes of the story or conflict (pp.
236–237).
Examples of ethnographic studies follow. The first ethnographic
study
comes from a master’s thesis by Quon (2006). (See Example
5.5.)
Example 5.5
Abstract
This study examines gendered patterns of participation in teens
of class-
room floor access and control with elementary school children.
Primary par-
ticipants in the study include approximately 97 students from
four intact
classrooms and 4 teachers. Eight students were also selected for
observation.
Field notes from participant observation, informal interviews,
and
video/audio taped classroom interactions provided the data for
this class-
room ethnography. A portion of the data was analyzed in detail

for time
spent in effort and ratified speaking tone. The remainder of the
data was
analyzed through constant cross comparison for patterns of
student talk
participation in whole class interactions.
Findings revealed that success in talk is contingent on teacher
organiza-
tion of talk. Tightly organized floors rendered more equitable
distribution of
talk between boys and girls. Loosely organized floors rendered
more differ-
ences in talk distribution in favor of boys. In all four
classrooms, however,
the girls are just as successful as boys in securing floor spaces,
although they
had to invest much more effort in hand raises. Data analysis of
vocal
children revealed that language proficiency can provide a
context in which
children gain self-confidence. Self-confidence allows the
children to learn how
to use language proficiency in order to gain status with other
children.
The general improvement of women’s social status in the
workforce
and other contexts may help explain why gendered gap in
participation is
diminishing in the classrooms. (Quon, 2006, pp. 1–2)

The second example is from a doctoral dissertation by
Thompson
(2006), who employed an ethnographic approach. (See Example
5.6.)
Example 5.6
Abstract
The purpose of this study was to describe and interpret the
dimensions of
performance creativity within a Montessori culture.
Performance creativity
is a reconceptualization of how creative activity is recognized
and encom-
passes four elements: self, self-expression, meaning, and
cultural signifi-
cance. The conceptual framework for this study articulates
creativity as a
dynamic self-construction influenced by the meaning one draws
from a cul-
tural learning environment. The reconceptualization of
creativity is built
upon a frame of neuroscience research, Montessori philosophy,
and
selected Eastern/Asian, African, and Native American views of
creativity.
This qualitative study was conducted in a Montessori primary
and
elementary school in the western United States, and it employed
both the
traditional ethnographic techniques of extended observations
and artifact
analysis. The study also used the performance ethnographic

method of
the collaborative dialogic interview.
The goal of performance ethnography is to produce a research-
based
performance narrative which views research participants as
collaborators
in the composition of the performance script. One of the facets
of a per-
formative study is that it initiates a dialogue within a larger
community.
The goal for this study is that it will invite educators to begin to
reexam-
ine how and why children express creativity and how creativity
can be
envisioned through a multicultural lens.
The findings of this work present emerging connections among
neu-
rological, Montessori, and multicultural perspectives of
creativity that
were observed in a Montessori learning environment.
Educational impli-
cations related to this study include the potential for the fields
of neuro-
science and education to be drawn closer together, by using a
multicultural avenue through which to describe creativity. The
goal of
designing curriculum and teacher education that is supported by
neuro-
logical evidence is one that can be supported by further research
in this
area. (Thompson, 2006, n.p.)

McFadden (2006) used narrative analysis to conduct her
dissertation
study. (See Example 5.7.)
Example 5.7
Abstract
Abundant research exists on the relationship that religion or
spirituality
has played in the lives and recovery of individuals diagnosed
with phys-
ical ailments, while little research is found regarding the role
that religion
or spirituality has played in the lives of individuals di agnosed
with a
chronic or severe mental illness. Therefore, the researcher
undertook to
explore, investigate, understand, and describe the lived
religious or spir-
itual experiences of these individuals, using a qualitative
research inquiry
and narrative analysis.
Data were collected during unstructured, informal interviews
with
seven adult volunteer participants. Data analysis of the
interviews pro-
vided three major relationship themes common to all
participants: with
self, others, and God.
All participants acknowledge some form of religious or spiritual
upbringing, and all report that their religious or spiritual

practices
improved once they were diagnosed. The participants identify a
holistic
approach to their recovery that incorporates their physical,
mental, emo-
tional, and spiritual well-being, and they further suggest a need
for health
care professionals to include a spiritual aspect in their
treatment. Finally,
all participants report a renewed sense of hope, meaning, and
purpose for
their lives, which they attribute to their religion and spirituality.
(McFadden, 2006, p. iii)
GROUNDED THEORY RESEARCH DESIGN
Grounded theory, first described by Glaser and Strauss (1967),
is intended
to generate or discover a theory inductively from data gathered
about a
specific phenomenon. Three elements of grounded theory are
concepts,
categories, and propositions. Concepts are the basic units of
analysis.
Corbin and Strauss (1990) stated:
Theories can’t be built with actual incidents or activities as
observed or
reported; that is, from “raw data.” The incidents, events,
happenings
are taken as, or analysed as, potential indicators of phenomena,
which
are thereby given conceptual labels. If a respondent says to the
researcher, “Each day I spread my activities over the morning,
resting
between shaving and bathing,” then the researcher might label

this
phenomenon as “pacing.” As the researcher encounters other
inci-
dents, and when after comparison to the first, they appear to
resemble
the same phenomena, then these, too, can be labelled as
“pacing.” Only
by comparing incidents and naming like phenomena with the
same
term can the theorist accumulate the basic units for theory. (p.
7)
Catagories is the second element of grounded theory and are
defined
by Corbin and Strauss (1990) as:
. . . higher in level and more abstract than the concepts they
represent.
They are generated through the same analytic process of making
com-
parisons to highlight similarities and differences that is used to
pro-
duce lower level concepts. Categories are the “cornerstones” of
developing theory. They provide the means by which the theory
can be
integrated. We can show how the grouping of concepts forms
cate-
gories by continuing with the example presented above. In
addition to
the concept of “pacing,” the analyst might generate the concepts
of

“self-medicating,” “resting,” and “watching one’s diet.” While
coding,
the analyst may note that, although these concepts are different
in
form, they seem to represent activities directed toward a similar
process: keeping an illness under control. They could be
grouped
under a more abstract heading, the category: “Self Strategies for
Controlling Illness.” (p. 7)
Propositions, the third category of grounded theory, indicate
generalized
relationships between a category and its concepts, as well as
between dis-
crete categories. As an iterative process, grounded theory is not
generated
a priori and then subsequently tested. Rather, it is,
. . . inductively derived from the study of the phenomenon it
repre-
sents. That is, discovered, developed, and provisionally verified
through systematic data collection and analysis of data
pertaining to
that phenomenon. Therefore, data collection, analysis, and
theory
should stand in reciprocal relationship with each other. One
does not
begin with a theory, then prove it. Rather, one begins with an
area of
study and what is relevant to that area is allowed to emerge.
(Corbin &
Strauss, 1990, p. 3)
Four research criteria are important in grounded theory for
validation
of the study and for establishing reliability of the findings of

the study.
Actually, these are recommended for review for all qualitative
studies.
They are: (a) construct validity, (b) internal validity, (c)
external validity,
and (d) reliability. Construct validity is accomplished by
establishing clearly
specified operational procedures. Internal validity can be
reached by estab-
lishing causal relationships in which certain conditions are
shown to lead
to other conditions, as distinguished from false relationships,
and it
addresses the findings’ credibility or “truth value.” External
validity is
the extent to which the study’s findings can be generalized and
to the
extent that you, as the researcher, establish the context in which
it can be
generalized. Generalization is usually to some broader theory or
other
valid studies (quantitative and qualitative) and not the
population. Finally,
reliability requires demonstrating that the operations of a
study—such as
data collection procedures—can be repeated with the same
results.
Triangulation is another method for ensuring that the study is
robust,
valid, and reliable. Triangulation may appear as four basic

types: (a) data tri-
angulation, involving time, space, and persons, (b) investigator
triangulation,
which consists of the use of multiple, rather than single
researcher/observers,
(c) theory triangulation, which consists of using more than one
theoretical
frame in the interpretation of the phenomenon, and (d)
methodological trian-
gulation, which involves using multiple methods. Multiple
triangulation may
be used when you combine in one dissertation, multiple
observers, theoreti-
cal perspectives, sources of data, and methodologies.
You may also use reflexivity in the study to establish better
credibility
and trustworthiness.
Reflexivity requires an awareness of the researcher’s
contribution to
the construction of meanings throughout the research process,
and an
acknowledgment of the impossibility of remaining “outside of”
one’s
subject matter while conducting research. Reflexivity then urges
us to
explore the ways in which a researcher’s involvement with a
particu-
lar study influences, acts upon and informs such research.
(Nightingale & Cromby, 1999, p. 228)
Willig (2001) indicated:
There are two types of reflexivity: personal reflexivity and
epistemolog-

ical reflexivity. “Personal reflexivity” involves reflecting upon
the ways
in which our own values, experiences, interests, beliefs,
political com-
mitments, wider aims in life, and social identities have shaped
the
research. It also involves thinking about how the research may
have
affected and possibly changed us, as people and as researchers.
“Epistemological reflexivity” requires us to engage with
questions such
as: How has the research question defined and limited what can
be
“found?” How has the design of the study and the method of
analysis
“constructed” the data and the findings? How could the research
ques-
tion have been investigated differently? To what extent would
this have
given rise to a different understanding of the phenomenon under
inves-
tigation? Thus, epistemological reflexivity encourages us to
reflect upon
the assumptions (about the world, about knowledge) that we
have made
in the course of the research, and it helps us to think about the
implica-
tions of such assumptions for the research and its findings. (p.
10)
Two examples of grounded theory methodology used in
dissertation
research follow. The first example is from Jodry (2001), and the
second
example is from Ericksen (2006). (See Examples 5.8 and 5.9.)

Example 5.8
Abstract
Purpose. The overarching purpose of this study was to develop a
grounded theory that can guide action in advancing the
academic
achievement for Hispanic students. Specifically, the study
focused on the
discovery of relationships and factors within the contexts of the
home,
school, and community that positively influenced the academic
achieve-
ment of six academically able Hispanic students in an urban
area, non-
border, high school advanced diploma program.
Methods. Following an extensive review of pertinent literature,
I devel-
oped a conceptual framework of factors of support, motivation,
and edu-
cation within the contexts of the home, school, and community
found to
have an impact on Hispanic student achievement. The research
questions
focused on identifying the factors that were present in
relationships within
the home, school, and community that positively influenced the
achieve-
ment of academically able Hispanic students in an urban,
nonborder, high
school advanced diploma program. Using ethnographic case

study
methodology, findings were applied to the development of a
theory and an
accompanying model titled, The Hispanic Academic
Advancement Theory.
Results. From a research-based conceptual framework and data
gath-
ered in an ethnographic study, a grounded theory that can guide
action in
advancing the academic achievement of Hispanic students was
devel-
oped. The Hispanic Academic Advancement Theory includes
three
factors, 18 subfactors, and 12 relationships. The three factors
are: (a) sup-
portive factors, (b) motivational factors, and (c) educational
factors. The
18 subfactors are (a) positive communication, (b) positive adult
relation-
ships, (c) a climate of caring, (d) collaboration, (e) value for
academic
goals, (f) students viewed as assets, (g) advocacy orientation,
(h) positive
adult role models, (i) service learning, (j) safe environment, (k)
high
expectations, (l) programs, (m) development of self-advocacy,
(n) respect
for language and heritage, (o) culturally and linguistically
responsive
pedagogy, (p) shared culture and language, (q) parents as assets,
and
(r) culturally and linguistically responsive leadership. The 12
relation-
ships are: (a) mother, (b) father, (c) older siblings, (d)
grandparent,

(e) aunts/uncles, (f) elementary bilingual educator, (g)
secondary special
area teacher, (h) secondary counselor, (i) principal, (j) peers in
the
advanced program, (k) college/university relationships, and (l)
church
relationships. (Jodry, 2001, pp. iv–v)
Example 5.9
Abstract
This dissertation utilized primarily qualitative methods of data
collection
and analysis to examine how 11 early elementary special
education teachers
in 10 schools in a large school district in the Mid-Atlantic
region of the
United States approached the task of developing and providing
literacy instruction to students with significant disabilities who
received
most of their education in a self-contained setting. An
environmental obser-
vation, semistructured interviews, and classroom observations
were used
to (1) describe the teachers’ existing practices related to early
emergent
reading, writing, and communication, (2) explore the extent to
which
teachers followed recommended best practices, and (3) to
develop a begin-

ning theory of how early elementary special education teachers
approach
early literacy learning given the many challenges of their
students.
Six critical features were identified from the literature related
to liter-
acy instruction for students with significant disabilities. Those
features
were: (a) a responsive and supportive literacy rich environment,
(b) inte-
gration of computers, assistive technology (AT) and alternative
and aug-
mentative communication (AAC) strategies, (c) direct
instruction, (d) social
engagement and meaning-making, (e) individualization based on
local
understanding, and (f) high expectations. Most of the teachers
were
actively engaged in teaching literacy skills to their students. In
general
they provided a responsive and supportive literacy environment
and
direct instruction. Computers were widely used as were AT and
AAC;
however, AT and AAC strategies were not systematically
integrated
across activities. Teachers had high hopes for their students but
did not
expect many of them would achieve conventional literacy.
Emphases on
meaning-making and social engagement were not observed, nor
did
teachers share these perspectives during interviews.
Using grounded theory methodology, a theory was constructed

of five
related concepts: (a) instructional outlook, (b) institutional
expectation,
(c) instructional set, (d) instructional fit, and (e) vigilance -
adaptation. This
theory was used to explain how teachers approach literacy
instruction. It
was also found useful for predicting poor instructional fit,
which was
defined as the match among the teacher, her students, and the
instructional
demands and supports in an instructional setting. (Ericksen,
2006, p. x)
MIXED METHODS RESEARCH DESIGN
Mixed methods research can refer to those studies that have
engaged both
quantitative and qualitative research questions and/or that have
used
both probability and purposeful sampling. It is a field of
research that is
still emerging.
According to Johnson and Onwuegbuzie (2004), mixed methods
research is “the class of research where the researcher mixes or
combines
quantitative and qualitative research techniques, methods,
approaches,
concepts, or language into a single study” (p. 17). They further
indicated:
Mixed methods research offers great promise for practicing
researchers
who would like to see methodologists describe and develop
techniques

that are closer to what researchers actually use in practice.
Mixed
methods research as the third research paradigm can also help
bridge the schism between quantitative and qualitative research
(Onwuegbuzie & Leech, 2004a). Methodological work on the
mixed
methods research paradigm can be seen in several recent books
(Brewer & Hunter, 1989; Creswell, 2007; Greene, Caracelli, &
Graham,
1989; Johnson & Christensen, 2004; Newman & Benz, 1998;
Reichardt
& Rallis, 1994; Tashakkori & Teddlie, 1998, 2003). (p. 15)
Johnson and Onwuegbuzie further iterated eight distinct steps in
mixed methods design: (a) determine the research question, (b)
determine
whether a mixed design is appropriate, (c) select the mixed
methods or
mixed-model research design, (d) collect the data, (e) analyze
the data,
(f) interpret the data, (g) legitimate the data, and (h) draw
conclusions (if
warranted) and write the final report.
Examples of mixed methods research in dissertation studies
follow.
The first example is from Christopher (2006) and the second is
from Wilbur
(2005). (See Examples 5.10 and 5.11.)
Example 5.10

Abstract
Secondary English language learners (ELLs) spend most of their
school
day in classes with regular education teachers who have little or
no train-
ing in methods for teaching English for Speakers of Other
Languages
(ESOL). This mixed methods (qualitative and quantitative)
study investi-
gated teacher use of accommodations and strategies for ELLs
and
assessed how the format of the resources made available to them
affected
their teaching. The participants were mainstream secondary
teachers at
the district-designated ESOL high school in an urban
Midwestern city.
The participating teachers were randomly divided into 2 groups
and pre-
sented with a lesson plan addendum designed to help them
increase their
use of accommodations and strategies for ELLs. The first group
received
a CD version of the addendum which included Internet-linked
resources
explaining the strategies and accommodations. Participants in
the second
group received a paper version of the lesson plan addendum,
and printed
versions of the Internet-linked resources were available in the
school
media center. The researcher observed the teachers’ classes
before and
after receiving the lesson plan addendum. She recorded the

strategies and
accommodations demonstrated in their teaching, (before and
after), in
order to determine if the teachers in both groups changed their
strategy
use. Additionally, she compared the results from each group to
ascertain
if there was a difference in the frequency of strategy use
between the two
groups. Finally, the teachers were interviewed after the
quantitative data
were collected to determine if their self-perceptions about using
strategies
for ELLS aligned with the researcher’s observation of their
teaching.
The results of this study showed that providing mainstream
teachers
with a lesson plan addendum focusing on English language
learners
does, in fact, lead to an increase in strategies and
accommodations used.
The results on the preferred way to provide the lesson plan
addendum are
inconclusive, although there is evidence that the resources
linked via the
Internet are more likely to be accessed than those that are made
available
in the media center at the school. Finally, based on interviews
with the
participants, the researcher concluded that they had a good

understand-
ing of how they changed their teaching as a result of using the
lesson plan
addendum. (Christopher, 2006, p. ii–iv)
Example 5.11
Abstract
This dissertation focused on the application of a known
organizational
development strategy to facilitate the conversion of a vast
administrative
software system at an institution of higher education. Castle and
Sir
(2001) reported on how to use what they called the five I’s of
instructing,
informing, involving, intervening, and incenting to bring
employees
directly involved in the change through the adaptive work they
needed to
do during the process. In applying this 5 I strategy, I used as a
platform
the theoretical framework of Bennis, Benne, and Chin (1965),
using people
technologies during change in thing technologies to assist
workers in
adapting to the change. Using the participatory action research
method-
ology of planning, acting, observing, and evaluating results, this
study
employed a mixed method approach to data collection, using
both quali-
tative and quantitative methods, as defined by Johnson and
Onwuegbuzie (2004). The purpose of the study was to discover
the fears,

needs and concerns of the staff involved in the conversion
through the
collection of field notes, journal entries, interviews, and
surveys. I then
applied the appropriate five I strategy to the change process to
mitigate
feelings of fear and loss during an imposed organizational
change
process. The impact of the action research project was the result
of inten-
tional interventions to mitigate feelings of fear and loss during
an
imposed organizational change process. This study also sought
to engage
the researcher in the practice of reflective leadership. A mixed
methods
approach to the data collection for this part of the study
included taking
field notes, journaling, and the administration of the Leadership
Practices
Inventory (LPI) of Kouzes and Posner (2003). The study showed
a first-
order change at Orchard University as a result of the intentional
insertion
of organizational development strategies into the learning
process. The
study also showed how reflective leadership practice changed a
leader’s
self-perception and set the stage for individual leadership
growth.
(Wilbur, 2005, n.p.)
Your choice of methodology, whether it is qualitative or mixed
meth-
ods, will certainly depend on your purpose and research
questions. A

mixed methods study offers a way to lend credibility to your
study and tri-
angulate your data while providing rigor to your study. Be sure
to discuss
this option with your dissertation chair or advisor.
SUMMARY
In this chapter, we provided an overview of several data
analysis tech-
niques or inquiry methods you could use if your questions or
purpose
necessitate a qualitative approach. We offered you four major
categories
of qualitative research: (a) phenomenological research, (b) case
study
research, (c) ethnographic research, and (d) grounded theory
research. In
addition to providing you with a brief overview of each
category, we pro-
vided examples of research purposes or questions that justified
a specific
type of qualitative research design or method of inquiry. We
also offered
several other types of research in qualitative inquiry and briefly
discussed
mixed methods research, and provided some examples of it. In
the next
section, we share the typical approach to writing the
dissertation chapters.

PART III
The Dissertation
Chapters
6
Writing the
Introduction Chapter
One of the things a doctoral or master’s student wants to know
is
“What does a dissertation or master’s thesis look like?” While
there
are variations among universities, among departments, and even
among
dissertation committees, faculty members and doctoral
(master’s)
students are recognizing the need for some skeletal structure
around
which to assemble the researchers’ ideas, the data, and their
conclusions.
Chapter One, the introduction of your dissertation or master’s
thesis,
provides an orientation to your study. In the first chapter, you

begin with
a background of study, describe the problem, state the purpose
of the
study, and tell why the study is important. This is followed by a
definition
of key terms used in your study. A synopsis of the theoretical
framework
undergirding your study constitutes a major component of the
introduc-
tory chapter. This is followed immediately by the research
questions or
research hypotheses.
Some qualitative studies are designed so that data are collected
first,
and then a theory is derived from those data (Glaser, 1978).
Moreover,
some qualitative research studies might be informed by a
theoretical or
epistemological framework, but formal hypotheses are not
typically used
to frame the study. An exception is a type of qualitative
research that is
framed within grounded theory methodology. In this type of
qualitative
research, hypotheses are the outcome of the study rather than
initiators of
it (Gall, Gall, & Borg, 2007).
112
Next, limitations, delimitations, and assumptions of the study
are
addressed. Chapter One concludes with a section called

“Organization of
the Study,” which provides an overview of the contents of each
chapter of
your dissertation or master’s thesis.
In this chapter, we discuss the components that are usually
found in
Chapter One of a dissertation or master’s thesis; however, as
mentioned
previously, not all dissertations will follow this protocol. Not
all students
will include each of the elements we recommend. Nevertheless,
the con-
tents typically found in Chapter One of a dissertation or
master’s thesis are
shown in Table 6.1.
113Writing the Introduction Chapter
Table 6.1 Contents for Chapter One of a Dissertation
CHAPTER I
INTRODUCTION
Background of the Study
Statement of the Problem
Purpose of the Study
Significance of the Study
Definition of Terms
Theoretical Framework
Research Questions (or Hypotheses)
Limitations
Delimitations
Assumptions
Organization of the Study

The contents of the chapter will be presented in the same order
as
depicted in Table 6.1. We provide a more detailed description
of each of
these elements (sections), as well as examples from completed
dissertations.
BACKGROUND OF THE STUDY
Use this section of your dissertation or master’s thesis to
acquaint the
reader with your study. You can do a number of things to make
this section
useful. First, identify the context within which your study will
be con-
ducted and give any background information needed to clarify
the context.
Second, tell the reader why the study is important and timely.
Third, build
a case for the statement of the problem to follow. Finally,
highlight the key
theoretical constructs you will describe in greater depth later in
this
chapter, and in more detail in Chapter Two, “Review of the
Literature.”
An example background of the study from Ying-Chiao Tsai’s
(2006)
dissertation follows. (See Example 6.1.)
114 The Dissertation Chapters
Example 6.1

Educational researchers have long been intrigued by the effects
schooling can have on overcoming the impact of student and
family char-
acteristics. For the past 20 years, research has shown a positive
correlation
between effective schools and high student achievement
(Hallinger &
Heck, 1997). From the history of effective schools and school
effects
research, the general findings showed that the quality of
schooling shares
a noticeable portion of the educational outcomes and cannot be
ignored.
Cuttance (2001) indicated that national and international
research
revealed approximately 10% to 18% of the variance in student
academic
achievement was due to between-school differences, while 50%
was due
to between-classroom differences. Therefore, both school
factors and
teachers’ classrooms practices assume a substantial role in
students’ suc-
cess. This study intended to verify the schooling effects into an
interna-
tional context. Two language groups of student populations
were examined:
English-language majority students and English-language
minority
students in the United States and Singapore. The Progress in
International
Reading Literacy Study (PIRLS), conducted by the International
Association

for the Evaluation of Educational Achievement (IEA) in 2001,
was the
data source for this study. (Tsai, 2006, p. 1)
Example 6.1 contains only the first paragraph of Tsai’s
dissertation.
Her “Background of the Study” section continued for an
additional six
pages in which she incorporated two additional subheadings
“Challenges
in the United States” and “Rationale for Studying PIRLS Data
and
Singapore.” After reading the background of the study, a reader
should be
able to determine fairly accurately what the research problem is.
The back-
ground of the study leads logically to the statement of the
problem, the
next section in Chapter One of a dissertation or master’s thesis.
STATEMENT OF THE PROBLEM
The statement of the problem is a definition of what you
investigated in
your study. It is actually a formal and succinct version of the
process you
went through in selecting and delimiting your topic (see Chapter
1).
Accordingly, it clarifies, outlines, limits, and brings into
existence an
expression of the problem you investigated. It answers the
question of
what is being done in the study.
In conducting your study, the statement of the problem performs
two

major functions: (a) to give direction to the study, and (b) to
unify all of the
efforts undertaken during the conduct of the study (Castetter &
Heisler,
1984). The directional function of the statement of the problem
may be
compared to a school district policy. A school district policy
statement
clarifies intent. It is a promise to follow a specific course of
action. It
follows that the statement of the problem acts as a control
mechanism. It
identifies the dimensions of the problem, defines the research
design,
determines literature sources and data collection, influences
data analysis
techniques needed to explain the data, focuses the findings on
the purpose
of the study, and serves as a basis for discussing conclusions
and recom-
mendations in subsequent chapters of the dissertation or
master’s thesis.
The unifying task of the statement of the problem means that
the steps
taken to solve the problem are determined by the intent
specified in the
statement of the problem. The presentation and analysis of data,
and the
findings, conclusions, and implications found in subsequent
chapters of
your dissertation or thesis are all focused on the statement of
the problem.

Researchers spend time doing research because there is a
problem that
needs an answer. Such problems arise because of a lack of clear
or com-
plete information in a discipline. More specifically, the problem
usually
evolves out of four circumstances (Martin, 1980):
1. Little or no research on a particular topic exists.
2. There is some research, but it has not been applied to enough
sam-
ples or in enough situations to be considered a reliable
phenomenon.
3. Research abounds, but the findings are contradictory.
4. Two theories explain the same phenomena but recommend or
pre-
dict different outcomes.
An example statement of the problem from Lauren Black’s
(2003) dis-
sertation follows. (See Example 6.2.)
Example 6.2
Considering the demands of the principalship, what influence
does
the principal’s belief in his/her own abilities and skills impact
his/her
success as a principal? Extensive research has been conducted
on self-efficacy

(Bandura, 1977; Bandura, 1997; Cervone, 2000; Choi, Fuqua, &
Griffin, 2001;
Hoy, 1998; Pajares, 1996) and teacher efficacy (Benz, Bradley,
Alderman, &
Flowers, 1992; Coladarci, 1992; Dembo & Gibson, 1985;
Gibson & Dembo,
1984b; Guskey & Passaro, 1994; Herbert, Lee,& Willamson,
1998; Hoy &
Woolfolk, 1993; Parkay, Greenwood, Olejnik, & Proller, 1988;
Petrie,
Hartranft, & Lutz, 1995; Tschannen-Moran, Hoy, & Hoy, 1998).
While
these studies have described and developed the construct of
teacher effi-
cacy, there is little empirical research on principal efficacy
(Hoy, 1998).
Exploring principal efficacy may provide a source of valuable
information
for educational leaders. Unless principals “believe they can
make a dif-
ference through their actions, there is little incentive to act”
(Hoy, 1998,
p. 4). A core task of administrators is decision making, which
requires the
synthesis of multiple cognitive processes to incorporate
knowledge into
creative solutions. Principals pull from personal knowledge and
experi-
ence to make decisions on a daily basis.
Bandura (1997) suggested that a strong sense of self-efficacy is

neces-
sary to access skills and knowledge while at the same time
remaining
focused on the task in a complex environment. Hoy (1998)
proposed that
self-efficacy is equally important for administrators in the
processes of
communication, leading, and motivating others. Additionally,
Hoy (1998)
added, “The concept of administrator efficacy, unlike the
concept of
teacher efficacy, has been neglected in the educational arena”
(p. 157).
This study sought to identify the components of principal
efficacy and
develop an instrument to measure principal efficacy. (Black,
2003, pp. 1–2)
Note how skillfully and succinctly Black provided her readers
with the
intent of her study. Notice how she supported her intent with
appropriate
literature related to it. Her statement of the problem provided
direction to
all phases of the study resulting in the development of an
instrument to
measure principal efficacy.
PURPOSE OF THE STUDY
A section titled “Purpose of the Study” follows the statement of
the prob-
lem. The purpose of any study is to help solve the stated
problem. This
section of your dissertation or master’s thesis is designed to
give the read-

ers a brief overview of how you plan to solve the problem
defined previ-
ously. This is a section that committee members may go back to
several
times to make certain they understand exactly what your
purpose is in
conducting the study. Developing this section often helps you
become
much clearer about the purpose of your study as well.
An example purpose of the study from Darlene Blair’s (2001)
disserta-
tion follows. (See Example 6.3.)
Example 6.3
The purpose of this study was to explore the relationship of
princi-
pals’ leadership styles and their school ratings of Exemplary,
Recognized,
Acceptable, or Low-Performing, assigned by the Texas
Education Agency.
A second purpose was to explore the relationship between
principals’
leadership styles and the time they spend on instructional
leadership and
management tasks. Thirdly, this study determined how time
spent by
principals on instructional leadership and management tasks is
related to
their school ratings. Finally, data collected for the study
provided evi-
dence as to whether or not principals devote different amounts
of time to

instructional leadership and management tasks. (Blair, 2001, pp.
4–5)
Note how Blair, in one brief paragraph, stipulated the four main
pur-
poses of her study. She described her purpose succinctly. As
mentioned
previously, this is a section of the dissertation that readers will
go back to
several times to make sure they understand exactly what the
researcher’s
purpose is in conducting the study.
SIGNIFICANCE OF THE STUDY
The significance of the study is your argument that the study
makes a sig-
nificant contribution to the field. Do not assume that just
because there is
little research on your topic that the subject is worthy of
investigation. You
must convince your committee members that your topic has not
been
examined in the way you examined it. Perhaps you may be using
variables
that have never been examined before; or the instrument that
you are
developing and validating is better for your purpose than others
that are
on the market to measure the same variable; or that your study
is the next
logical step in a continuous line of inquiry that will eventually

solve an
important problem in your discipline.
In preparing the significance section in your dissertation or
master’s
thesis, contemplate the following questions: (a) Why is my
study impor-
tant or valuable? (b) Will it revise, extend, or create new
knowledge?
(c) Does it have theoretical and/or practical application? You
must build a
case for the study’s contribution to the field. Arguments for the
research
might include such things as the recommendations of prior
research, con-
flicting findings in other studies, new and improved
methodologies, or
political, social, or psychological trends. Your committee
members will be
in a better position to understand your study and its relationship
to the
field by learning the significance of finding answers to the
problem.
An example significance of the study from Scott Barrett’s
(2000) dis-
Example 6.4
The significance of this study is paramount as it contributes
valuable
insight and theory into the successful implementation of
technology as a

decision-making tool, especially in light of the amount of
resources being
dedicated to the implementation of information systems.
Visscher and
Spuck (1991) observed “there is great need for systematic
research, not
only because of its scientific value but also because of i ts
ability to
improve the practice of school administrative computing and
conse-
quently school effectiveness” (p. 166). The results of this study
may be
utilized to develop improved training models, environments,
and imple-
mentation strategies to promote the effective use of student
management
information systems. Igbaria et al., (1997) stated that “while
system usage
may not ensure improvement user task performance, it remains a
critical
variable for organizations to derive any benefit from new
technology” (p.
88).
It is increasingly important for school principals to be able to
utilize
information technology for immediate insight and decision
making in
support of the accountability factors associated with job
performance and
campus accountability. By identifying utilization variables, a

conceptual
starting point can be established for the successful
implementation of
training. Providing appropriate training in the proper contextual
envi-
ronment can enhance the use of a management information
system to
support informed decision making.
Gustafson (1985) summarized the importance of this research on
the
campus-wide impact of computerized information systems.
Teachers will have access to the same information to which only
administrators were privy, thus eliminating the need for a
school hier-
archy. Site administrators will have the information available
which
would make curriculum specialists, superintendents, school
boards,
and even those in higher positions on the county and state
levels,
obsolete. The computer, if fully implemented, will force new
organi-
zations and policy-making structures upon the school. (p. 153)
It is evident that the potential impact of a MIS cannot be
overlooked.
The results of this study may contribute valuable insight and
theory into
the successful implementation of technology as an
accountability tool,
especially in light of the amount of resources being dedicated to
the
implementation of information systems. (Barrett, 2000, pp. 15–
16)

Note how Barrett provided a convincing argument for the
significance
of his study of the value of implementing a MIS system in the
public
schools. He supported his argument with appropriate literature.
For
example, quotes from Visscher and Spuck, “there is great need
for system-
atic research,” and Igbaria et al., “it remains a critical variable
for organi-
zations to derive any benefit from new technology,” are
powerful
statements used to support his position.
DEFINITION OF TERMS
You should define all key terms central to your study and used
through-
out your dissertation or master’s thesis. The key terms selected
to be
defined should be chosen based on the following scientific
principle:
Distinctions are made between a constitutive definition and an
operational
definition. A constitutive definition involves using other words
to define a
term. An operational definition ascribes meaning to a term
according to
specific operations used to measure it (Kerlinger, 1986).
Typically
researchers define all variables contained in a research question
or hypoth-
esis. You should define attributes of a population, also called
control

variables. And you should define theories and models on which
your
research is based. Constitutive definitions should be specific
(e.g., behav-
ior, knowledge, imitation). Operational definitions (e.g.,
intelligence, attri-
bution, reinforcement) should be specific enough so that another
researcher can replicate your study.
Your definition of terms can be obtained from a dictionary or a
profes-
sional reference source, such as a handbook or encyclopedia in
a specific
discipline. Scholars you reference in your dissertation (or
thesis) will likely
have defined the term in their studies. Also, you can review the
various
definitions that have surfaced in your review of the literature.
List your
terms in alphabetical order, with appropriate citations.
An example of definition of terms from Diane Trautman’s
(2000) dis-
Example 6.5
Definition of Terms
To avoid confusion, this study provided definitions and
delineations
to distinguish between an approach, model, and theory.

Additionally, a
distinction was made between a leadership, management, and
organiza-
tional theory. And, finally feminist theory was defined to
differentiate a
feminine style of leadership and feminist theory. Many people
tend to
confuse and mix these terms in their studies; therefore, it was
necessary to
separate them and show them as individual terms.
Approach
An approach is a “theoretical orientation” or “theoretical
perspec-
tive,” not an actual theory (Bogdan & Biklen, 1998). It is
similar to a par-
adigm or a loose collection of logically related assumptions,
concepts, or
propositions that orient thinking and research (Bogdan &
Biklen. 1998). It
is a way of examining the world, the assumptions people have
about
what is important, and what makes the world work.
Model
A model is a construct or diagram that explains the
underpinnings of
a theory base; however, the model itself is not the theory and
therefore will
not be tested or validated (Bogdan & Biklen, 1998). Daresh and
Playko
(1995) describe a model as interrelationships of variables or
factors in a the-
oretical statement depicted graphically. These graphic

depictions or theo-
retical models constitute part of the basic theory-building cycle
(Daresh &
Playko, 1995). A model is a description used to show complex
relationships
in easy-to-understand terms (Stoner, Freeman, & Gilbert, 1995).
Theory
“Theory is an attempt to describe phenomena and
interrelationships
found in the real world in terms that reflect the true nature of
the world”
(Daresh & Playko, 1995, p. 72). One approach to theory
development is to
start by formulating a theory and then submitting it to a test by
collecting
empirical data. This process includes three steps: (1)
formulation of a
hypothesis, (2) deduction of observable consequences of the
hypothesis,
and (3) testing of the hypothesis by making observations and
collecting
research data (Gall, Borg, & Gall, 1996). Theory often serves as
the basis
for further empirical research, in which “theory-testing and
theory-
verification” is a stated goal and leads to the generation of new
research
(Daresh & Playko, 1995, p. 73).

Macro Organizational Behavior
This type of organizational behavior is described as structures
and
processes within major subsystems, organizations, and their
environ-
ments, and the linkages between them (Miles, 1980). It focuses
on the
broader definition of organizational behavior (Robbins, 1990).
Micro Organizational Behavior
Micro organizational behavior looks at structures and processes
(cog-
nitive, emotional, and physical) within individuals, small
groups, and
their leaders, and the linkages between them (Miles, 1980). The
focus here
is on relatively isolated or immediate social settings (Robbins,
1990).
Organizational Theory
Organizational theory can be defined as the discipline that
studies the
structure and design of organizations, referring to the
descriptive and
prescriptive aspects of the discipline (Robbins, 1990). This type
of theory
describes how organizations are actually structured and offers
sugges-
tions on how they can improve their effectiveness (Robbins,
1990). It can
also be thought of as a group of related concepts, principles,
and hypothe-
ses that is used to explain the characteristics of organization

and how they
behave (Hodge & Anthony, 1991). Organizational theory takes a
macro
perspective, because it is concerned with the overall
organization’s ability
to adapt and achieve its goals (Robbins, 1990).
Organizational Behavior
Organizational behavior presents a micro view by emphasizing
indi-
viduals and small groups; it focuses on behavior in
organizations and a
narrow set of employee behaviors such as employee
productivity, absen-
teeism, turnover, and job satisfaction (Robbins, 1990).
Individual behavior
topics in organizational behavior include perception, values,
learning,
motivation, and personality (Robbins, 1990). Others define
organizational
behavior by four processes: individual processes, group and
interpersonal
processes, organizational processes, and organizational issues
(Hellriegel,
Slocum, & Woodman, 1998).
Feminist Organizational Theory
Feminist organizational theory examines and critiques current
organi-
zational bureaucracy and hierarchy and contests the ideal of
impersonal,

role based, and instrumental social relations characteristic of
bureaucracy
(Morgen, 1994). This theory asserts that organizations of any
kind should
pay attention to the personal needs of members, not just the
instrumental
needs of the organization (Morgen, 1994). This type of
organizational
theory would take a micro view because of its concern with the
individual
needs and interpersonal processes (Miles, 1980).
Management Theory
Management theory is an explanation of how managers behave
and
management behavior consists of the things a manager does in
carrying
out his or her responsibilities (Hodge & Anthony, 1991). The
basic activi-
ties of a manager are planning, organizing, leading and
controlling
(Stoner et al., 1995). These activities can be grouped into two
basic func-
tions: decision making and influence (Hodge & Anthony, 1991).
All orga-
nizations require managerial functions; therefore, management
theory is
the study of how managers consciously and continually shape
organiza-
tions (Stoner et al.).
Leadership Theory

Leadership theory is the study of leader behavior and its effects
on
the organization (Lunenburg & Ornstein, 2000). Where
managers are con-
cerned with shaping the structures and processes of the
organization to
produce desired results, leaders have a commitment or vision
and inspire
others to follow it (Lunenburg & Ornstein, 2000). Stanford et
al. (1995)
contend that there are three classifications of leadership
theories: personal
behavior theories, trait theories, and contingency theories.
Personal
behavior theories focus on what the leader does to car ry out the
manage-
rial job; trait theories describe certain inherent characteristics
that will
make individuals effective leaders; and contingency theories
hypothesize
that the situation is the main determinant of what constitutes an
effective
leader (Stanford et al.).
Feminist Theory
According to Rosemarie Tong (1989) in her book Feminist
Thought,
there are several types of feminist theory including liberal,
Marxist, radi-
cal, psychoanalytic, socialist, existentialist, and postmodern.
This study
would be concerned with liberal feminist theory, which states
that
because society believes that females are by nature less
intellectually and

or/physically capable than males, it excludes them from the
academy, the
forum, and the marketplace (Tong, 1989). As a result of this
exclusion, the
true potential of women goes unfulfilled (Tong, 1989).
Feminine Leadership Theory
A feminine-inclusive leadership theory can be defined as a
leadership
theory that takes into consideration the female perspective and
experi-
ence yet can be applied to both males and females. It includes a
leadership
and management style that reflects the current leadership
paradigm of
site-based decision making, collaborative arrangements,
empowerment
of employees, and strong interpersonal skills that promote a
shared vision
(Brown & Irby, 1994).
Androcentric
Androcentric is used to show a male-biased or man-centered
view of
the world (Shakeshaft, 1989). To label a theory or concept
androcentric is
an attempt to establish parameters for the consumer to
internalize and
apply the results as well as heighten the awareness of producer

and con-
sumer as to the limitations of a one-sided system of knowledge.
(Trautman, 2000, pp. 15–19)
Trautman’s dissertation was an initial test of a new gender-
inclusive
leadership theory, namely the synergistic leadership theory
(Irby, Brown,
& Trautman, 1999; see also, Irby, Brown, Duffy, & Trautman,
2002).
Therefore, it was necessary for her to provide rather extensive
definitions
of important concepts related to theories in general, leadership
theory, and
feminine theory. Most dissertations and master’s theses will not
require
such extensive definitions as presented here. Note that
Trautman (2000)
used appropriate citations to define her terms. This is standard
practice
when using technical terms.
THEORETICAL FRAMEWORK
A theory is an organized body of interrelated concepts,
assumptions, and
generalizations that systematically explains regularities in
behavior. Thus,
a sociological theory may attempt to explain the relationship
between
home conditions and child-abuse behavior of a parent. A
psychological
theory may identify factors affecting antisocial behavior. An
educational
theory may propose how teachers’ collective efficacy in a
school may affect

students’ academic achievement in that school. Theory can
provide a
framework to generate hypotheses or research questions. In
turn, they
guide significant parts of the research design, including the data
collection,
data analysis, and presentation and interpretation of the
findings.
Your task is to find a theoretical framework within which to
pursue
your study. One way to select an appropriate theoretical
framework for
your study is to review the related literature. As you revi ew the
literature,
ask yourself: Is there a theory, or a variation of some theory to
which my
study has reference? You may find theories, which you will test
empiri-
cally, revise, extend, or create. Make a note of these. You may
not find a
specific theory in the literature; rather, the comprehensive
review of the lit-
erature may need to serve as the theoretical framework.
If you are seeking direct answers to certain questions, it may
not be
necessary to state hypotheses and design your study to test
them. If you
believe, however, that relationships may exist between
variables, based on
theory, or if it appears that one variable may be the cause of
another, a

hypothesis may be the best way to state what you are attempting
to find.
We encourage you to consult with your dissertation or master’s
thesis
chair whether a given research problem might be better
approached
through formulating hypotheses or by posing research questions.
Many quantitative studies are designed to test a theory that has
been
developed to explain regularities in behavior. Such theory-
driven studies
will use hypotheses as the starting point for designing the
research
methodology. Qualitative research studies might be informed by
a theo-
retical or epistemological framework, but formal hypotheses are
not typi-
cally used to frame the study (Gall et al., 2007). An exception is
a type of
qualitative research that is framed within grounded theory
methodology.
In this qualitative research tradition, studies can be designed so
that data
are collected first, and then a theory is derived from those data.
The result-
ing theory is called grounded theory, because it is grounded in a
set of real-
world data (Denzin & Lincoln, 2005; Glaser, 1978).
MODELS
When you are concerned with a larger picture than the
relationship between

two variables in a quantitative study, and you begin to look at
interrelation-
ships among a set of variables, you are involved in the
construction of mod-
els. Thus, in an effort to synthesize separate pieces of a larger
picture, you
construct a representation of how each variable influences
and/or is influ-
enced by other variables. Usually this results in the construction
of a model
with arrows indicating direction of influence. Your task in a
quantitative dis-
sertation (or thesis) is to provide evidence of relationships,
direction, and
size. Most such dissertation models are quite simple; confirming
complex
relationships requires large-scale studies unlikely to be
undertaken by doc-
toral or master’s students (Krathwohl & Smith, 2005).
An example of theoretical framework from Vickie Basham’s
(1988) dis-
Example 6.6
With the emphasis on accountability in education today, school
administrators are faced with the challenge of developing and
imple-
menting educational systems that are effective and efficient.
Local, state,
and federal mandates are requiring increased justification and
documen-
tation of program results. Communities are questioning school

district
programs, policies, and procedures. Parents and other
constituencies are
demanding grater participation in school programs. Educational
admin-
istrators must respond by devising more effective methods of
administra-
tive management. Strategic planning is considered to be
important to
effective administrative management (Ivancevich et al., 1980, p.
52;
Huntsman, 1986, pp. 11–12).
For planning to be effective, it needs to eventually affect
students in some
way. In describing the first step in providing instructional
leadership, Wallace
(1985) asserted that “one must begin with an analysis of
standardized tests
results” (p. 7). No planning process in education is complete
unless a direct
attempt is made, through goal setting, to improve student
academic learning
(Lewis, 1983, p. 68). William Bennett (1988), former Secretary
of Education,
proclaimed the importance of student achievement when he
said, “And the
entire project of American education—at every level—remains
insufficiently
accountable for the result that matters most: student learning”
(p. 2).

While business has devoted a great deal of attention to strategic
plan-
ning (Camilus, 1986; Gardner et al., 1986; Pfeiffer, 1986;
Bryce, 1987; Below
et al., 1987; Morrissey et al., 1988), it is only recently that any
emphasis has
been placed on the study of strategic planning in school
settings. And the
investigation of the relationship between strategic planning and
student
achievement has been neglected. The research reported here
seeks, on a
modest scale, to begin to remedy that situation. More
specifically, this study
examined relationships among three broad sets of variables: (1)
strategic
planning in school districts; (2) school district achievement in
reading, lan-
guage arts, and mathematics; and (3) ten school district
financial and demo-
graphic factors including (a) current expenses, (b) pupil -teacher
ratio,
(c) cost of instruction, (d) dropout rate, (e) percent Local
Education Agency
(LEA) supplement for instructional salaries, (f) percent
economically
deprived, (g) local financial index, (h) attendance rate, (i)
assessed property
value per child, and (j) percent revenue from local sources.
No other study shows a direct tie-in between strategic planning
in
school districts and school district performance on standardized
achieve-
ment tests (or results management), which constitutes what

might be
referred to as an Interrelated Planning Process. A strategic
planning model
was devised to guide the research. (See [Figure 6.1].) The
strategic planning
model incorporates the three broad sets of variables, which were
examined
in this study. As shown in the model, strategic planning begins
with a mis-
sion statement consisting of beliefs and goals of the school
district’s per-
sonnel, which provide guidelines for conducting a critical
analysis of the
internal and external environments, preparing planning
assumptions,
selecting action goals, developing objectives and evaluation
procedures,
designing an action plan, and monitoring and reporting results.
As shown in [Figure 6.1], I have placed student achievement
and the
other ten selected demographic and financial school district
factors in the
context of a macroenvironment. Within that context these
variables repre-
sent an important consideration of the strategic planning
process and
actions initiated to effect school district plans by analyzing,
synthesizing,
and evaluating this environment. (Basham, 1988, pp. 11–13)
Basham’s dissertation is an example of the use of a model to
synthesize
the interrelationships among a large set of variables. You do not
necessar-
ily have to use a model when describing your theoretical

framework.
Figure 6.1 Strategic Planning Model from Basham (1988)
Strategic Planning Model
Statement of
Needs
Beliefs
Develop
a Mission
Conduct a
Critical Analysis
of External
Environment
% Local Revenue
Prepare Planning
Assumptions

Develop Evaluation
Procedures
Monitor And
Report Results
Person
Responsibile
Etc.CostTime-Line
Action
Strategies
Develop
Objectives
Design Activities
(Action Plan)
Select Action
Goals
Local Financial
Index
Assessed Property
Valuation

Attendance
Rate
% Local for
Supplement
instruction
salaries
%
Econo-
mically
Deprived
Pupil
Teacher
Ratio
Cost for
Instruction
Test
Scores
Dropout

Rate
Current
Expenses
Conduct a
Critical Analysis
of Internal
Environment
B
A
B
C
H
D
F
EG
Goals
Statement of
Needs

However, a model is useful if your study involves a number of
interrelated
variables.
RESEARCH QUESTIONS (OR HYPOTHESES)
Research questions and hypotheses are critical components of
the disser-
tation or master’s thesis. Teamed with a tightly drawn
theoretical frame-
work, the research questions or hypotheses become a
“directional beam
for the study.” In essence, the research questions or hypotheses
should
flow directly from the preceding theoretical framework and
identify ques-
tions that have not been addressed previously or remain
unanswered in
the literature. Your committee members should be able to
understand how
your theoretical framework has led to the research questions
(and, subse-
quently, the research hypotheses, if you choose to use them).
Your committee members should be able to learn a great deal
about your
study simply by reading the research questions or hypotheses.
For example,
committee members should be able to identify independent and
dependent
variables; whether the focus of the study is descriptive,
relationship, or dif-
ference oriented; what types of analysis will be used to examine

each
research question or hypothesis; the sample size that will be
needed to ade-
quately test the research questions or hypotheses; what
literature should be
reviewed; and forecast elements of the results and discussion
chapters.
As mentioned previously, qualitative studies might be informed
by a
theoretical or epistemological framework, but formal
hypotheses are not
typically used to frame the study (Gall et al., 2007). Moreover,
in some
qualitative traditions, studies can be designed so that data are
collected
first, and a theory is derived from those data (Denzin &
Lincoln, 2005;
Glaser, 1978). We now turn to the types of research questions
used in both
the quantitative and qualitative traditions.
Types of Research Questions
Technically, a research question, in the quantitative tradition,
explores
the relationships between and among variables (Heppner &
Heppner,
2004; Heppner, Kivlighan, & Wampold, 1999). For example, a
research
question could be: Is there a relationship between teachers’
collective effi-
cacy in a school and students’ academic achievement in that
school? Drew
(1980) identified three general categories of research questions:
descrip-

tive, relationship, and difference.
Descriptive Questions
Descriptive questions ask what some phenomena are like. With
descriptive questions, researchers collect information from
interviews,
questionnaires, observation, or document analysis. Such
questions can be
either quantitative or qualitative in nature. Rarely do
descriptive questions
involve experimental research. An example of a descriptive
question
might be: What are the coping processes ethnic, racial minority
students
use in response to the stressors of acculturation when attending
a pre-
dominantly White college or university? Such a question could
be exam-
ined using either a quantitative or qualitative methodolo gy. For
more on
this topic, see Creighton’s (2006) dissertation.
Relationship Questions
Relationship questions examine the degree to which two or
more vari-
ables are related to each other. The essential feature is some
kind of corre-
lation between two variables. Although this was not the major
focus of

Creighton’s dissertation, an example of a relationship question
with her
topic would be: Is there a relationship between stressors of
acculturation
and psychological distress of ethnic, racial minority students
attending a
predominantly White college or university?
Difference Questions
Difference questions examine whether differences exist between
or
within individual groups or participants. The essential feature in
differ-
ence questions is some type of comparison. Such questions can
be either
quantitative or qualitative in nature. Again, this was not the
major focus of
Creighton’s dissertation, but it was a component within her
study. An
example of a difference question with her topic would be: Are
there dif-
ferences in stressors of acculturation for ethnic, racial minority
students
attending a predominantly White college or university as
compared to
those attending a culturally diverse, urban college or university?
This
question could be examined using either a quantitative or
qualitative
methodology.
At this point, you must describe whether you will be asking a
descrip-
tion question, a relationship question, or a difference question.
Write down

the question. Do not worry about form right now. Identify each
type of
research question you write. It is possible for you to have all
three types of
research questions addressed in the same dissertation or
master’s thesis.
Writing research questions for a dissertation (or thesis) is not a
linear
process; instead it is an iterative process. As you become more
knowl-
edgeable about your topic, you will be able to write clear,
concise, and
meaningful research questions. It usually takes several rewrites
to develop
research questions that are optimally clear and concise. In
addition, differ-
ent types of research questions require different types of
research designs,
and different types of data collection and data analysis
procedures.
Specifying Research Hypotheses
Research questions ask about relationships among variables.
Hypotheses are more precise. They speculate about the nature of
these
relationships. Research hypotheses predict an expected
relationship
between variables. The following examples illustrate the point.

Research Questions:
1. Is there a relationship between teachers’ collective efficacy
in a
school and students’ academic achievement in the school?
2. Is there a relationship between external pressure from the
commu-
nity on their schools and students’ academic achievement in the
schools?
Research Hypotheses:
1. The greater the teachers’ collective efficacy in a school, the
higher
the level of students’ academic achievement in the school.
2. The greater the external pressure from the community on
their
schools, the higher the level of students’ academic achievement
in
the schools.
A few observations can be made about the aforementioned
examples of
research questions and research hypotheses. First, each research
question
and research hypothesis clearly and concisely describe a
relationship
between variables. Second, each research question and
hypothesis is
testable; that is, it can be measured empirically. Third, carefully
drafted
hypotheses are more precise statements than research questions.
Moreover,
research hypotheses can be grouped into the same three

categories as
research questions: descriptive, relationship and difference
hypotheses.
Some dissertations and theses are exploratory in nature.
Exploratory
studies are based on little, if any, theory or previous research.
For
exploratory studies, it is not always possible to formulate
hypotheses,
because theoretical support for the hypotheses cannot be
provided. You
should state hypotheses only for those problem statements for
which you
can provide a rationale from your theoretical framework.
Furthermore,
qualitative research studies might be informed by a theory, but
formal
hypotheses are not typically used to frame the study. However,
in qualita-
tive research framed within grounded theory methodology,
hypotheses
may be the outcome of the study rather than used to frame the
study at the
outset (Gall et al., 2007).
Write hypotheses in the present tense and as positive assertions.
Avoid
using the words significant or significance in a research
question or hypoth-
esis. The terms significant and significance usually refer to tests
of statistical
significance. Most empirical studies include these tests;
therefore, refer-
ence to them in hypotheses is not necessary. Your committee
members will

assume that the issue of statistical significance will be handled
in the
results chapter of a quantitative dissertation or master’s thesis.
Qualitative studies usually do not contain hypotheses derived
from
theory or previous research. Nevertheless, if your study is
qualitative, you
need to state a research question at the outset of your study,
even if the
question is quite general. Qualitative researchers typically
“follow the
data.” That means that during the course of their interviews they
may shift
directions based on initial respondents’ replies, change the
nature of the
questions for subsequent respondents, and as a result, they
might modify
their initial research question. In quantitative research,
however, the spe-
cific research design, including hypotheses or research
questions, is very
seldom modified during the course of the study.
Some dissertations contain both research questions and research
hypotheses. In this case, researchers use research questions as
more gen-
eral investigatory themes, which are then followed by specific
research
hypotheses that make predictions in a testable form. Two
dissertation
examples from our students in which both research questions

and research
hypotheses (or null hypotheses) were used follow.
The first example is from Vicki Stanfield’s (2000) dissertation.
(See
Example 6.7.)
Example 6.7
Research Questions
The following research questions were addressed to determine
which, if
any, of the 19 scales of the College Student Inventory
distinguish enrollment
status and academic success in students attending community
colleges:
1. What differences exist between students attending community
colleges who persist, (also called persisters), and nonpersisters
(those who
drop out)?
2. What differences exist between academically successful
students
and academically unsuccessful students attending community
colleges?
(Stanfield, 2000, p. 5)
Hypotheses
In order to determine whether the College Student Inventory
distin-
guishes enrollment status and academic success in community
college
students, the following two null hypotheses were tested.

Null Hypothesis 1. There is no difference between students who
per-
sist (persisters) and those who do not persist (dropouts) on all
19 scales of
the College Student Inventory.
Null Hypothesis 2. There is no difference between academically
suc-
cessful students and academically unsuccessful students on all
19 scales
of the College Student Inventory. (Stanfield, 2000, p. 62)
Another example of the use of both research questions and
research
hypotheses (or null hypotheses) comes from Sally Craycraft’s
(1999) dis-
sertation. (See Example 6.8.)
Example 6.8
Research Questions
The major question guiding this study was to discover what
patterns of
brain dominance are exhibited by school superintendents and
business chief
executive officers. The three specific research questions
addressed were:
Question 1. What are the brain dominance preferences of school
superintendents and chief executive officers?

Question 2. Which brain dominance patterns are most prevalent
among school superintendents and chief executive officers?
Question 3. What differences exist between dominance profiles
in
terms of (a) affiliation (business or education), (b) gender, (c)
years of
experience, and (d) age? (Craycraft, 1999, pp. 10–11)
In answer to these questions, it was important to determine
where
“pronounced” (statistically significant) differences exist
between
the various groups (school superintende nts and chief executive
offi-
cers and between males and females) followed by post hoc tests
and descriptive analyses to assess the basis of
dissimilarities/differences.
The hypotheses to be tested that provided key insight where
“pro-
nounced” differences exist between the brain profiles of the
various
groups were:
Null Hypothesis 1. There is no difference in brain dominance
prefer-
ences exhibited between school superintendents and chief
executive officers.
Null Hypothesis 2. There is no difference in brain quadrant
scores
between school superintendents and chief executive officers.
scores

between males and females in both groups of leaders.
scores due
to experience level.
Null Hypothesis 5. Age is not an influence in determining brain
quad-
rant scores. (Craycraft, 1999, p. 66)
Both students used research questions in Chapter One and null
hypotheses in Chapter Four of their dissertations when they
presented and
analyzed their data. You may wish to review the use of research
questions,
directional hypotheses, and null hypotheses, and their use,
discussed ear-
lier in this chapter.
Additional Examples of Research Questions
Because the development of research questions is so important
when
writing a dissertation or master’s thesis, examples of research
questions
extracted from four other dissertations follow. They have used
only
research questions. The first two examples were based on
quantitative
studies, and the following two examples were drawn from
qualitative
studies. From our experience in chairing doctoral dissertations,

we have
found that most doctoral students have a very difficult time
writing clear,
concise, and meaningful research questions. The examples that
follow may
help. (See Examples 6.9, 6.10, 6.11, and 6.12.)
Example 6.9
Quantitative Dissertations:
Research Questions
Following are the seven research questions, which guided this
study
of content reading area subjects of science and social studies in
17 third
grade Spanish/English transitional bilingual classrooms in an
urban dis-
trict in Southeast Texas:
1. Which language of instruction do teachers predominantly
use?
2. To what extent is the language of instruction used by teachers
associated with language content?
3. To what extent is the language of instruction associated with
lan-
guage mode?
4. To what extent does each level of language content occur
with
each activity structure?
5. To what extent do associations exist among the following

vari-
ables: language mode, language content, activity structure,
students’ lan-
guage of instruction, and teacher’s language of instruction?
6. Is there an association between classroom observations and
teachers’ perceptions of their language of instruction?
7. What are the associations between the four dimensions of the
Transitional Bilingual Protocol (TBP) theory and bilingual
student
achievement on the high-stakes assessment, Texas Assessment
of
Academic Skills (TAAS)? (Meyer, 2000, p. 8)
Example 6.10
Research Questions
Quantitative and qualitative data were collected through the
Thomas-
Kilmann Conflict Mode Instrument (Thomas & Kilmann, 1974),
an
open-ended protocol, and in conjunction with the Organizational
Leadership Effectiveness Inventory (OLEI) (Irby et al., 2002) to
answer the
following research questions:
1. Is there a difference in gender distribution among conflict
man-
agement modes of superintendents?

2. Is there a relationship between conflict management modes of
female superintendents and the Synergistic Leadership Theory?
3. Is there a relationship between conflict management modes of
male superintendents and the Synergistic Leadership Theory?
4. Is there a relationship among gender, conflict management
modes, and the four factors of the Synergistic Leadership
Theory.
(Truslow, 2004, p. 5)
Example 6.11
Qualitative Dissertations:
Research Questions
Four research questions guided my dissertation:
1. What are the leadership behaviors of the superintendent?
2. Is the organizational structure of each district aligned with
the
superintendent’s leadership behaviors?
3. How do the leadership behaviors of the superintendent impact
the
relations between the district and external forces?
4. Are the attitudes, values, and beliefs of the superintendent
aligned
to the attitudes, values, and beliefs of the school board member
and the
administrative team member? (Bamberg, 2004, p. 12)

Example 6.12
Research Questions
The following research questions guided my study:
1. What are doctoral students’, recent graduates’, and program
direc-
tors’ perceptions of positive and negative aspects of the cohort
structure
used in educational leadership programs in six universities?
2. What recommendations do doctoral cohort students, recent
grad-
uates, and program directors make to improve their respective
doctoral
programs?
3. What recommendations do doctoral cohort students, recent
grad-
uates, and program directors make to those people entertaining
a doctoral
endeavor?
4. What are the perceptions of doctoral cohort students, recent
grad-
uates, and program directors concerning the dissertation stage
of the doc-
torate within a cohort structure? (Miller, 2003, p. 3)
Note that we have provided examples of research questions
from

quantitative, qualitative, and mixed methods dissertations.
These can
serve as models for your own work.
LIMITATIONS
Limitations of a study are not under the control of the
researcher.
Limitations are factors that may have an effect on the
interpretation of the
findings or on the generalizability of the results. Limitations
may arise
from the methodology, data, or method of analysis. For
example, the find-
ings of a case study cannot be generalized to an entire
population. Data
derived from a study of drug use in an attendance area of a
large, urban
school district may not be representative of all school age
children in the
United States or in other school districts in the same state, or
even in other
schools within the same school district. A finding that school
size corre-
lates negatively with student behavior problems may not justify
the infer-
ence that small schools cause student behavior problems. You
need to state
the limitations of your study to avoid misinterpretation of the
findings as
illustrated in the aforementioned examples.
An example of limitations from Clark’s (2002) dissertation
follows.
(See Example 6.13.)

Example 6.13
Limitations
The study has the following limitations:
1. The financial information obtained in PEIMS comes directly
from
1,042 school districts in Texas. This information could vary
because of
variations in the local interpretation of expenditures.
2. The sample of school districts was drawn from a single state;
therefore, results may not be generalizeable to all states.
3. Many variables outside the control of the researcher could
impact
the student achievement and financial information. These
variables may
include: uncontrollable variations in the prices that school
districts
pay for various resources, ancillary services provided by the
district, the
quality of the school facilities, the quality of the instructional
programs,
parental involvement in the district, graduation rates, course-
completion
rates, honors program enrollments, and the diversity of course
offerings.
4. There are statistical and design problems inherent with
correlation
studies. (Clark, 2002, pp. 18–19)

DELIMITATIONS
Delimitations are self-imposed boundaries set by the researcher
on the
purpose and scope of the study. Studies in the social and
behavioral sci-
ences typically have many variables that could be affected by
circum-
stances of time, location, populations, or environment
(including both
physical and social conditions). For example, let’s examine the
dissertation
title of one of our doctoral students: “Principal Leadership,
Trust, and
Teacher Efficacy” (Azodi, 2006). The title is clear and concise,
and it
describes the three major variables examined in the study. We
have no
quarrel with the title. Using it to illustrate elements of a study’s
delimita-
tions, there is no indication therein of the characteristics of the
population
or sample. Is it restricted to school principals in the United
States, Texas,
or Harris County? Does the sample include elementary school
principals,
middle school principals, high school principals, or all three
levels? Does
the sample include urban, suburban, and rural schools or merely
big city
schools? Does the sample include ethnic, racial minorities and
gender
diversity consistent with the population? Is the time period over
a shorter

or longer duration—one month? Six months? One year? Five
years? The
point is that boundaries and qualifications are inherent in every
study,
which must be noted in this section of the dissertation or
master’s thesis.
An example of delimitations from Craig Coleman’s (2003)
dissertation
follows. (See Example 6.14.)
Example 6.14
Delimitations
The delimitations utilized by the researcher in this study were
deter-
mined by a desire to better gain an understanding of the
complete rela-
tionship that exists between the superintendent and the school
board, and
how that relationship affects student achievement. In order to
gain the
perspectives of superintendents and school boards, the
researcher only
sought participants in the study who were superintendents and
school
board members of public schools. This use of public schools in
this study
did not allow the researcher to gain the views of those
individuals
involved in private and charter schools located in the state.
A second delimitation used by the researcher was the use of
only school
board presidents as participants. Due to the nature of the board

presidents’
role, the president serves as the spokesperson for the Board of
Trustees.
The nature and closeness of the relationship that is required
between
the superintendent and school board president in fulfilling the
responsi-
bilities of the district, led the researcher to analyze only those
superinten-
dents and school board presidents that had a relationship of
working as a
leadership team for a period of three or more years. The
researcher
believed, based upon the work of Senge (1990) and Nygren
(1992), that
the process of creating a successful organizational system
required an
extended period of time, and, in order to gain a true picture of
the lead-
ership team, determined to only use those teams that had been
in their
current positions together for a period of three or more
continuous years.
(Coleman, 2003, pp. 13–14)
ASSUMPTIONS
Assumptions are postulates, premises, and propositions that are
accepted
as operational for purposes of the research. Assumptions

include the
nature, analysis, and interpretation of the data. For example,
“This sample
is typical of the total population” (nature); “this instrument will
measure
what it purports to measure when applied to these data”
(analysis); “this
criterion is valid when applied to these findings”
(interpretation).
Assumptions influence the entire research endeavor.
Specifically, delin-
eation of assumptions provides a basis for formulating research
questions
or stating hypotheses and for interpreting data resulting from
the study;
and assumptions provide meaning to the conclusions and lend
support to
the recommendations.
An example of assumptions from Scott Barrett’s (2000)
dissertation fol-
lows. (See Example 6.15.)
Example 6.15
Assumptions
This study included the following assumptions: (a) the selected
prin-
cipals responded to the survey accurately and indicated their
perceptions
in the use of a management information system (MIS); (b) the
selected
principals understood the vocabulary and concepts associated
with a
management information system and decision making; (c) the

data col-
lected measured the knowledge, skills, and perceptions of the
principal’s
involvement in the utilization of a MIS to support decision
making; and
(d) the interpretation of the data accurately reflected the
perceptions of
the respondents. (Barrett, 2000, p. 20)
Note how Barrett succinctly described the assumptions of his
study,
attending to one or more of the critical elements of assumptions
(nature,
analysis, interpretation) delineated above.
ORGANIZATION OF THE STUDY
Each chapter of a dissertation or master’s thesis contains three
main
sections: the introduction, where you tell your readers what you
are going to
tell them; the body, where you tell them; and the summary,
where you tell
them what you told them. However, because Chapter One is
titled
“Introduction,” it does not begin with the heading
“Introduction” like the
chapters of a dissertation or master’s thesis that follow; nor
does it end with
the heading “Summary.” Instead it begins with the heading
“Background of

the Study” and ends with the heading “Organization of the
Study.” This
section of a dissertation or master’s thesis describes the content
found in
subsequent chapters of the entire dissertation or master’s thesis.
An example of organization of the study from Donna Azodi’s
(2006)
dissertation follows. (See Example 6.16.)
Example 6.16
This research study is presented in five chapters. Chapter I
includes
the background of the study, statement of the problem, purpose
of the
study, significance of the study, definition of terms, theoretical
frame-
work, research questions, limitations, delimitations, and the
assumptions
of the study.
Chapter II presents a review of the literature, which includes
efficacy
theory, teacher efficacy, principal leadership, trust, and
principal trust.
Chapter III describes the methodology used for this research
study. It
includes the selection of participants, instrumentation, data
collection,
and data analysis procedures.
Chapter IV presents the study’s findings including demographic
information, testing the research questions, confirmatory factor

analysis,
and results of the data analyses for the three research questions.
Chapter V
provides a summary of the entire study, discussion of the
findings, impli-
cations of the findings for theory and practice,
recommendations for fur-
ther research, and conclusions. (Azodi, 2006, p. 17)
SUMMARY
In this chapter, we discussed the contents that should appear in
Chapter
One of a dissertation or master’s thesis. Chapter One contains
an orienta-
tion to your study. In Chapter One, you describe the background
of the
study, statement of the problem, purpose of the study,
significance of the
study, definition of terms, theoretical framework, research
questions or
hypotheses, limitations, delimitations, and assumptions, and you
conclude
the chapter with an organization of the study. We provided
dissertation
examples from some of our doctoral students to illustrate each
section of
the dissertation or master’s thesis we described. The next
chapter contains
advice on reviewing the literature and writing the literature
review chapter.

7
Writing the Literature
Review Chapter
The review of the literature will provide the basic rationale for
your
research from which will emerge the statement of the problem,
research questions or hypotheses, and design of your study.
Therefore, we
recommend that you begin work on Chapter Two, “Review of
the
Literature,” prior to writing Chapter One, for you will need to
know the
theory and previous research relevant to your problem.
The review of the literature can illuminate every aspect of a
research
problem by (a) providing a historical background for it, (b)
describing its
current status, (c) supporting the purpose of the study, (d)
identifying gaps
in the literature, (e) becoming aware of variables relevant to the
problem,
(f) understanding the seminal studies widely cited, (g)
identifying the
leading scholars relevant to the problem, (h) proposing useful
theoretical
constructs for the study, (i) understanding the application of
appropriate
methodological procedures, and (j) observing comparative
studies that
assist in analyzing your data and interpreting the results. Thus,
a properly

executed review of the literature becomes a potent unifying
element
through its interrelationships with other sections of the
dissertation or
master’s thesis.
In the first part of this chapter, we provide an introduction on
how to
systematically search and review the literature for a dissertation
or mas-
ter’s thesis. In the second part of the chapter, we provide you
with tech-
niques to help you write a clear and effective Review of the
Literature
Chapter. We conclude the chapter with several additional
strategies to help
137
you critically, synthesize empirical research reports, which will
constitute
the body of your literature review. We provide examples from
disserta-
tions and from published articles that you can use as models in
writing
your Review of the Literature Chapter.
SEARCHING THE LITERATURE
To conduct a thorough review of the literature, you need to
examine a
broad range of sources that are directly related to your problem.
These
sources include: (a) handbooks and encyclopedias, (b) annual

reviews,
(c) review articles, (d) abstracting and indexing services, (e)
government
documents, and (f) major public search engines. None of these
sources
alone will provide a thorough review of the literature. However,
by using
all of them together, you will ensure a reasonably
comprehensive review
of the literature. It is possible to search a wide variety of these
sources
online, providing your university library subscribes to the
various elec-
tronic databases. In addition to the aforementioned six sources,
many
of which contain empirical studies, utilize journals in your field
as well.
A discussion of the use of professional journals in your
literature search is
provided in Chapter 1 under the heading “Sources of Topics,”
subheading
“Professional Journals.”
Handbooks and Encyclopedias
Handbooks and encyclopedias provide an excellent place to
begin your
literature search. Handbooks and encyclopedias summarize
major research
contributions in specific areas. Specialized topics, contained in
these
sources, are written by experts in the field. Chapters contained
within these
sources provide extensive references related to important
research in a spe-
cialized area. You can follow the references later. Examples of

handbooks
and encyclopedias in education and related fields are the
Handbook of
Research on Teaching, Handbook of Research on Educational
Administration,
Handbook of Industrial and Organizational Psychology,
Handbook of Social
Psychology, Handbook of Qualitative Research, Encyclopedia
of Educational
Research, and Encyclopedia of Educational Leadership and
Administration. The
contents of handbooks and encyclopedias are usually not
provided online,
but instead are housed in your library’s reference section.
Annual Reviews
Annual reviews are published yearly and provide comprehensive
reviews of the literature on specific topics. These topical
reviews are writ-
ten by specialists in the field. Annual reviews can be excellent
resources if
your topic has been reviewed recently. Not only do these annual
reviews
provide comprehensive reviews on a specific topic, but also
they provide
prototypes for how to write a critical review of the literature.
Examples, of
annual reviews in education and related fields are the Review of
Educational
Research, Harvard Educational Review, Annual Review of

Anthropology, Annual
Review of Psychology, Annual Review of Sociology, and the
annual yearbooks
of the National Society for the Study of Education.
Review Articles
Another excellent source for a literature search are review
articles. Some
journals in education and related fields periodically devote
entire issues of
the journal to specific topics. These are comprehensive reviews
of research
on a specific topic. They include Educational Leadership,
Educational and Urban
Society, National Association of Secondary School Principals
Bulletin, Phi Delta
Kappan, Psychological Bulletin, and The Counseling
Psychologist. Some univer-
sities, such as the University of Minnesota, Pennsylvania State
University,
and others, offer a monograph series devoted to a specific topic.
For
example, one of the authors of this book used University of
Minnesota
Research Monographs Nos. 1, 2, and 3 devoted to organizational
climate and
Pennsylvania State University Studies No. 24 on pupil control
ideology as
background information for his dissertation: Organizational
Climate,
Dogmatism, and Pupil Control Ideology (Lunenburg, 1972).
Abstracting and Indexing Services
Abstracting and indexing services can assist you with your

literature
search. For example, the Educational Resources Information
Center (ERIC)
is a major clearinghouse for literature related to education.
PsycINFO
(Psychological Abstracts) and Sociofile (Sociological Abstracts)
respectively
contain abbreviated abstracts from psychological and
sociological jour-
nals, and indexes these abstracts by topic and author.
Dissertation
Abstracts International is a similar reference source for
dissertations.
Psych Books indexes chapters in books.
Another very useful abstracting and indexing service is Social
Scisearch (Social Science Citation Index). It covers the social
and behavioral
sciences literature and permits you to search who has cited a
particular
article since its publication. For example, if you were
examining the rela-
tionship between teachers’ collective efficacy and students’
academic
achievement, you would probably find that Bandura’s social
learning
theory is cited by numerous authors of research articles. You
will want to
locate the pivotal article written by Bandura that was published
several
years ago. By using Social Scisearch, you can identify other
literature in
which Bandura’s article has since been cited.
Also published by the Institute for Scientific Information (ISI),
Inc., are

two other citation indexes. The Science Citation Index covers
the current lit-
erature in more than 100 fields of science and technology. The
Arts and
Humanities Citation Index thoroughly covers the current
literature in the arts
139Writing the Literature Review Chapter
and humanities field. Using all three of these citations indexes,
you can
cover most of the journal literature you will probably care to
search.
Moreover, to keep you abreast of the very latest developments
in the field,
Current Contents: Social and Behavioral Sciences indexes the
most recent tables
of contents from the major journals in the social and behavioral
sciences.
You may also find Business Periodicals Index useful for
searching indus-
trial and organizational topics. Other useful abstracting and
indexing ser-
vices include CINAHL (nursing, allied health, biomedical, and
consumer
health), LLBA (linguistics and language behavior abstracts),
Medline
(nursing, public health, medicine), MLA (literature, language,
linguistics,
and folklore), NCJRS (corrections, drugs and crime, juvenile
justice, law
enforcement, statistics, and victims), PAIS International (social
sciences,

emphasis on contemporary society, economics, political issues,
and on
public policy), Social Sciences Abstracts (sociology,
psychology, anthropol-
ogy, geography, economics, political science, and law), and
Social Work
Abstracts (social work and related fields).
Table 7.1 shows the range of database resources available in the
social
and behavioral sciences in most research university libraries.
This list is
not exhaustive; in fact, many large research libraries will have
many more
research services than are listed in this table.
As you examine your sources, make a list of authors who have
been
important contributors to the literature in your area. Using a
variety of
search engines, search by author’s name. Most library databases
allow you
to conduct both topical and author searchers. OVID is one of
the broader
search engines that allow you to search by topics and authors.
Searching
documents by key authors may identify additional publications
that were
omitted from your previous searches.
Government Documents
Government documents are another useful source of
information.
Government documents provide a massive amount of
information that is

published regularly. Various government agencies also have
created Web
sites (see, e.g., http://www.firstgov.gov) on which they post
newly
funded projects. Two excellent search tools for government
documents are
the Monthly Catalog of Government Documents and the Index
to U.S.
Government Periodicals. Some university libraries may have an
online elec-
tronic database, such as Government Documents Index.
FedStats is a valuable source of statistical information on the
Web. At
http://www.fedstats.gov, you can access statistics from more
than 100
federal agencies. The FedStats search engine automatically
searches all
agencies for relevant links to federal statistics—you no longer
have to
search each government agency separately.
State and local governments (including public and private
universi-
ties) frequently post recent statistics on the Web. Thus, you can
obtain
more current statistical information from nonfederal government
sources.
Table 7.1 Selected Library Databases

Anthropological Abstracts
Arts and Humanities Citation Index
Business Periodicals Index
CINAHL
Current Contents: Social and Behavioral Sciences
Dissertation Abstracts International
ERIC
LLBA
Medline
MLA
NCJRS
PAIS International
PsycBOOKS
Psychological Abstracts
Science Citation Index
Social Science Citation Index
Social Sciences Abstracts
Social Work Abstracts

Sociological Abstracts
This is because the federal government collects data
periodically, with
years intervening in some cases (e.g., the national census).
Local agencies
must report timely information to their superordinates, such as
mayors
and city councils.
Major Public Search Engines
Major search engines used by the public at large can also
provide help-
ful information for use in literature reviews for dissertations
and master’s
theses. You can use search engines (browsers) such as Microsoft
Explorer,
Netscape, Opera, or Safari to search for information on a
variety of topics
using keywords, wherever the search engine has indexed the
Web.
AltaVista, Ask.com, Google, and Yahoo! are popular search
engines.
Furthermore, meta search engines such as Alltheweb.com,
Dogpile,
Metacrawler, and Vivisimo simultaneously submit your search
to multiple
search engines. Other useful sources for additional reading on
literature
searches are Heppner and Heppner (2004); Krathwohl and Smith
(2005);
and Reed and Baxter (2003).

When sorting through a long list of Web sites retrieved by a
search
engine, note the extensions in your Web searches. The extension
edu in a
URL means “education,” gov means “government,” org means
“organiza-
tion,” net means “network,” and com means “commercial.” You
should
begin your search with those URLs with extensions of edu and
gov, because
these will likely produce more relevant research information
pertaining to
your topic. The other extensions may be useful as well.
In sum, computerized searches using search engines can be very
help-
ful in conducting a literature search. Many search engines have
an accom-
panying thesaurus, such as the Thesaurus of ERIC Descriptors
for
educational topics or the Psychological Abstracts Thesaur us of
Psychological
Index Terms for psychological topics. Keywords must be used
to enter
online electronic databases such as those listed in Table 7.1.
Finding the right keywords is the key to a productive computer
search.
This is not easy. It may take considerable trial and error to
determine the
most appropriate keywords to use. Here are a few tips: Begin by
underlin-
ing the keywords in the working title of your dissertation or
master’s the-
sis. For example, the dissertation title of one of our doctora l

students is
Principal Leadership, Trust, and Teacher Efficacy (Azodi,
2006). Logical key-
words for her literature search would be principal leadership,
trust, and
teacher efficacy. (Brief titles help considerably when doing
computerized lit-
erature searches. We find that most dissertation titles are much
too long.)
Another method of locating keywords is to find abstracts of
journal arti-
cles pertinent to your topic. Words that surface frequently are
typically
good keywords to use in a literature search. Still another
method to
uncover keywords to use in a literature search is to write down
the inde-
pendent and dependent variables used in your study. These
probably will
be the same or similar to the words used in your title, and they
more than
likely will be words used in the research questions or
hypotheses of a
quantitative study. Finally, many journals now list keywords by
which the
study will be indexed. Nevertheless, a major problem with
computerized
searches is to believe that you have done a thorough literature
search using
this method alone. Using all of the six aforementioned methods
together
will likely result in a reasonably thorough literature search.
WRITING THE LITERATURE REVIEW
At this point, you have searched and reviewed the literature

pertaining to
your research problem. Now you are ready to begin writing the
chapter.
Chapter Two, “Review of the Literature,” has three distinct
parts: the intro-
duction, in which you tell your readers what you are going to
tell them; the
body, in which you tell them; and the summary, in which you
tell them
what you told them. Table 7.2 provides an example of the
contents of the
Review of the Literature Chapter of a dissertation or master’s
thesis. In
any given study, the headings and subheadings contained in the
body of
the chapter will usually be determined by the variables
investigated in the
study. Qualitative studies may be organized around themes or
participants
interviewed.
Table 7.2 Contents for Chapter Two of a Dissertation
CHAPTER II
REVIEW OF THE LITERATURE
Introduction

Headings and Subheadings (These will be determined by the
type of study
being conducted)
Summary
The Review of the Literature Chapter is often a lengthy chapter
in a
dissertation or master’s thesis. Therefore, is it important to
provide a struc-
ture that will help you organize and integrate the material you
present.
The following techniques can be used to organize your Review
of the
Literature Chapter: (a) organize your material in a funnel, (b) be
selective,
(c) make an outline, (d) write the introduction, (e) use headings,
(f) use
transitions, (g) write a summary, and (h) be careful not to
plagiarize.
Organize Your Material in a Funnel
A funnel, a structural writing tool, has a wide portion at the top
and a
narrow portion at the bottom (Heppner & Heppner, 2004;
Martin, 1980).
The funnel provides a good analogy for the shape of the review
of the lit-
erature. The idea is that the literature review is organized so
that more
general information is discussed first, and the information most
closely
related to the research reported in your thesis or dissertation is
discussed
last. Your literature review may have four or five sections, each

with a
heading. Each section (heading) may have one or more
subsections (sub-
headings). That is, divide your review into headings and
organize the
sections so that the most general material is discussed first,
followed by
more specific material related to your study. The subsections of
the review
should be organized in a funnel structure as well.
Specifically, articles of historical interest or the articles
utilizing differ-
ent types of participants or methods than those utilized in your
study are
presented first. The more recent articles utilizing participants
and methods
similar to those you are using in your study are then presented.
Each article should not receive the same degree of coverage. An
obvious sign
of a poorly conducted literature review is one in which all the
articles/research
reports are given the same coverage and something is said about
the partici-
pants, method, results, and discussion of each piece of research.
Here is a
technique you might use in organizing your review. Divide your
material
into three parts: (a) material that will be described in detail,
because it is
highly related to your study, (b) material that will be briefly
discussed, and

(c) material that is tangential to your study, which may or may
not be
included in your literature review depending on the amount of
information
available in the literature directly related to your topic.
A common problem of beginning writers is that of providing too
much
information on a topic and not enough on the specific area that
their thesis
or dissertation will cover (Heppner & Heppner, 2004). Another
common
problem of beginning writers is that they frequently switch from
the general
to the specific and back again to the general. As you are writing
your review,
think funnel. Look at the shape of your literature review
chapter. Do you see
the shape of a funnel? Do you begin with more general material
and then
move toward more specific information until you are providing
the research
questions or hypotheses of your thesis or dissertation? Do the
same for sub-
sections of your literature review, again starting from the
general and mov-
ing to the specifics. The funnel structure is depicted in Figure
7.1.
Less specificity
of coverage
More specificity

of coverage
Figure 7.1 The Funnel Structure
Consult models of appropriate writing style and organization.
Study
the well-integrated reviews of literature found in the Annual
Review of
Anthropology, Annual Review of Psychology, Annual Review
of Sociology,
Review of Educational Research, the annual yearbooks of the
National Society
for the Study of Education, and other such reviews. These
reviews should be
used as models of writing style and organization of your
literature review
and not as material for your own review of the literature. More
informa-
tion on the use of primary sources is provided later in this
chapter.
Be Selective
A good literature review needs to be selective. It is permissible
to draw
upon the research of others and incorporate them into your own
thesis
or dissertation. This is consistent with the cumulative process
that
characterizes the development of science (Kuhn, 1962).
However, a skillful
researcher draws on primary source material rather than relying

on review
articles and secondary sources. Do not rely on others’
descriptions of stud-
ies, often referred to as secondary sources (Cone & Foster,
2007). If you
cannot locate the original article, follow American
Psychological
Association (APA) guidelines to indicate the citation as a
secondary source
(e.g., Irby, 2003, cited in Lunenburg, 2008).
A common problem of novice writers is that they sprinkle
references
liberally around their literature reviews with insufficient
thought as to
how they fit into the theory or the theme of their study. Make a
habit of
consistently asking yourself: Why am I including this study or
reference?
In other words, what is this reference adding to the development
of my
theory; how does it follow the thread of my dissertation, and
how does it
relate to my research questions? Examine the reasons why each
reference
will illuminate or complement your thesis or dissertation. One
or more of
the following must apply (Swetnam, 2004, p. 77):
• It deals with theory that undergirds your study.
• It makes a definitive statement about an aspect of your study.
• It deals with your subject matter or overlaps it.
• It assists in the maintenance of a coherent argument.
• It puts your study into an external context.
• It defines the current state of research in your area.
• It shows your acknowledgement of the work of others.

As your literature review develops, references should become
more
specific to the topic, use more journals that are current, move
from the gen-
eral to the specific, and finish with a direct lead-in to your
research ques-
tions or hypotheses.
You need to adopt a critical perspective in reading and
referencing the
work of others. Sentences beginning with “Smith found . . .”
should be
kept to a minimum, because it shifts the focus of the review
from your own
argument to the work of others. A preferred strategy is to
develop a theme
and then cite the work of relevant authors to support your
argument or to
provide examples of your point. In addition, the overuse of
quotations
tends to limit your own authority. Restrict the use of quotations
to proven
luminaries on a given topic or that are stated in a unique way
that is diffi-
cult to recapture (Rudestam & Newton, 2007).
The literature review is not a compilation of facts or narrative,
but a
coherent argument that leads to a description of your study.
There should
be no mystery about the direction you are going. A good
question to
repeatedly ask yourself while writing the review of the
literature is: Where
am I going with this? State at the outset of the literature review

the goal of
the review and the structure of your argument. Similarly, each
sentence in
the dissertation must be there for a purpose. It should never
serve as a
filler (Rudestam & Newton, 2007). By the end of the literature
review, the
reader should be aware of the current state of the literature and
what you
will be doing to extend the state of that knowledge. Your
summation of
pertinent literature is a significant contribution to the
knowledge base.
Make an Outline
Make an outline of the major headings you plan to use in your
review
of the literature. Developing and following an outline while
writing will
accomplish two things: (a) It will produce focused, logical
prose, and (b) it
will help you avoid becoming overwhelmed by the sheer volume
of mate-
rial to be covered in a lengthy review of the literature. Example
7.1 shows
an outline of Linda Creighton’s (2006) Review of the Literature
Chapter.
Note that Creighton has divided her literature review chapter
into 44
headings and subheadings. This gives her chapter coherence.

Coherence is
how well a chapter holds together as a unified document.
Headings and
subheadings help to unify a document’s structure and keep the
reader
focused, as do transitions. (See Example 7.1.)
Example 7.1
Outline
I. Introduction
II. Retaining African American Students
A. Pre-Entry Attributes
B. Goals and Commitments
C. Institutional Experiences
D. Personal and Normative Integration
III. Issues Impacting Hispanic College Student Retention
A. Personal Factors
1. High grade point average
2. Academic self-concept
3. The family
4. Finances
B. Environmental Factors
1. Racial climates
2. Presence of an ethnic community
3. Working and living off campus
C. Involvement Factors
1. Faculty-student interaction
2. Mentorship
3. Participation in student organizations

D. Sociocultural Factors
1. Immigrant status
2. Ethnic identity development
3. Languages/ESL issues
4. Community orientation
5. The role of religion
IV. College Persistence and Asian-Pacific American Students
A. Individual Factors
1. Academic underpreparedness
2. First generation status
3. Language/ESL issues
4. Low-income backgrounds
5. Other family demands
6. Cultural adjustment
B. Institutional Factors
1. Marginalization on campus
2. Racial discrimination on campus
V. Retention of Native American Students
A. Cultural Discontinuity
B. Resistance Theory and Transcultural Hypothesis
1. Alienation from the institution
2. Disillusionment
3. Emotional rejection
4. Physical rejection
(Creighton, 2006, pp. 21–45)

Make an outline before you begin writing the chapter. It will
help you
structure a lengthy review of the literature.
Write the Introduction
The Review of the Literature Chapter begins with a brief
introduction,
usually between one and three pages. The introduction should
introduce
the research problem, including the key variables examined in
the study,
and describe the scope and organization of the review. Conclude
the intro-
duction with a paragraph that describes the sequence of the
literature you
will cover, often referred to as an advance organizer, using the
exact head-
ings that appear in the chapter. This format is illustrated in
Example 7.2
taken from Donna Azodi’s (2006) dissertation. We have
provided the first
paragraph of each section of Azodi’s Review of the Literature
Chapter to
illustrate the flow and function of the advance organizer
concept. This
advance organizer should flow from section to section of the
chapter,
beginning with a topic sentence to start each paragraph in a
section, and
ending each section with a transition that leads the reader to the
next
section in the chapter. (See Example 7.2.)
Example 7.2

Introduction
This chapter presents the rationale for conducting research on
rela-
tionships between principals’ and teachers’ perceptions of
principals’
leadership behaviors; principal trust in teachers, students, and
parents;
and the teachers’ sense of efficacy. Educational researchers
have studied
the constructs of teacher efficacy and leadership for several
decades. Both
principal and teacher efficacy have been reviewed within the
framework
of school improvement and student achievement in those
studies. My
study sought to build upon this body of research through the
combined
lenses of teachers and principals. I studied similarities and
differences in
the perspectives of these key contributors in the educational
organization.
The recognition of trust as a critical element in the school
environment
has become a more recent focus of educational researchers. In
the context
of societal pressures that create stress, even strife, within the
educational
community, the importance of trust within schools has assumed

increased
significance. Thus, this study sought to examine the relationship
of princi-
pal trust and leadership behaviors supportive of teacher
efficacy.
The following review of the literature represents the literature
pertinent
to my research study, namely, teacher efficacy, principal
leadership, and
principal trust in teachers, students, and parents. Specifically,
Chapter II is
organized into five sections: (a) efficacy theory, (b) teacher
efficacy, (c) prin-
cipal leadership, (d) trust, and (e) principal trust. (Azodi, 2006,
p. 18)
The following five sections contain the first paragraphs of the
five
sections of Azodi’s review of the literature to show the
implementation of
the advance organizer concept and the use of transitions.
Efficacy Theory
Bandura’s (1997) social learning theory provided the foundation
from
which the concept of teacher efficacy used in this study was
developed.
Bandura posited a central processor of efficacy information in
which
“people process, weigh, and integrate diverse sources of
information con-
cerning their capability, and they regulate their choice of
behavior and
effort expenditure accordingly” (p. 212). He defined two

concepts of effi-
cacy: efficacy expectations and outcome expectations. “An
outcome
expectancy is defined as a person’s estimate that a given
behavior will
lead to certain outcomes. An efficacy expectation is the
conviction that
one can successfully execute the behavior required to produce
the out-
comes” (p. 193). (Azodi, 2006, p. 19)
Teacher Efficacy
In what has become a seminal study on teacher efficacy,
researchers
for the Rand Corporation analyzed factors that contributed to
success in
raising reading scores in selected Los Angeles minority schools.
One of
the results of that study was the conclusion that a teacher’s
sense of effi-
cacy had an effect on student achievement. The students, as
well as their
commitment and morale, helped to determine how much
children learn
(Amor, Conry-Oseguera, Cox, King, McDonnell, Pascal, Pauly,
&
Zellman, 1976). This finding has been supported throughout the
years of
research on teacher efficacy (Ashton & Webb, 1986; Brownell
& Pajares,

1996; Corbett, Wilson, & Williams, 2005; Henson, 2001;
Smylie, Conley, &
Marks, 2002; Spillane & Louis, 2002; Tschannen-Moran, &
Hoy, 2001;
Tschannen-Moran & Hoy 2002; Tschannen-Moran,Hoy, & Hoy,
1998).
(Azodi, 2006, p. 21)
Principal Leadership
In his study of exceptional leadership within industrial
organizations,
Burns (1978) discussed effective leadership using the terms
“transforma-
tional and transactional.” In transactional leadership, leaders
deal with fol-
lowers by exchanging one thing for another, for example, work
for increase
in salary or promotion. A transformational leader seeks to
understand
motives of followers and strives to meet higher needs.
Transformational
leadership also has the potential to convert followers into
leaders. Burns
(1978) expressed the belief that relationships were an important
compo-
nent of power in the context of leadership. (Azodi, 2006, p. 27)
Trust
Most of the time people act in ways that are expected not
because they
are obliged to do so, but rather because on some unconscious
level they
intuitively know that they are part of a network of trust
(Solomon & Flores,

2001). However, we notice trust most by its absence. Baier
(1986) indicated
that “we inhabit a climate of trust as we inhabit an atmosphere
and notice
it as we notice air, only when it becomes scarce or polluted” (p.
234). Our
day-to-day interactions that make up our lives and support our
personal
growth and happiness are possible only insofar as we trust. Our
trust is
based upon perceived protection inherent in such devices as
reference,
brand names, reputations and in this age of electronic
commerce, the secu-
rity encryption codes in computer transactions (Solmon &
Flores, 2001).
Moreover, as our lives have grown more complex with the
increasingly
rapid changes in our society and economy based, in part, on
greater imme-
diacy of dissemination of information, we are beginning to pay
closer
attention to trust (Tschannen-Moran & Hoy, 2000). (Azodi,
2006, pp. 36–37)
Principal Trust
“Schools play a special role in society, and as such,
understanding
trust relationships in school is vital” (Tschannen-Moran & Hoy,
2000,
p. 551). Bryk and Schneider (2003) further explain that trust
among school
leaders, teachers, and parents not only refines the day-to-day
work of
schools but is also a critical resource for improvement. For this

reason,
principals need to understand how trust is developed and how it
is lost if
they are to gain the trust of stakeholders needed for building
successful
schools. Moreover, as the school’s leader, the principal holds
greater insti-
tutional power and thus must assume the responsibility for
building
and maintaining trusting relationships within the school.
Through their
behavior, principals either support or weaken trust within their
schools,
thus setting the tone of trust for their campus. Trust more and
more has
been identified as a critical element of leadership, in so doing
establishing
a need for greater understanding of the relationship between
trust and
leadership (Tschannen-Moran & Hoy, 2000; Tschannen-Moran,
2003,
2004). (Azodi, 2006, pp. 43–44)
Note how Azodi used the advance organizer concept in the last
para-
graph of her introduction to outline for readers the headings that
consti-
tute the body of her literature review. Also note how the
headings she
outlined in her advance organizer paragraph are worded exactly
as stated

in the advance organizer paragraph. This technique helps the
reader stay
focused on each topic of the literature review and provides
coherence to
the chapter. This is an especially useful technique when the
literature
review is very long.
Use Headings
Divide your literature review into useful major sections
(headings) and
subsections (subheadings). Judicious use of headings and
subheadings
serves two functions: (a) It helps readers understand the
organization of
your review, and (b) it helps readers follow your transitions
from one topic
or subtopic to another.
Note that Creighton (see Example 7.1) divided her Review of
the
Literature Chapter into five major sections (headings). She
further divided
her literature review into 12 subsections. She further divided
these 12 sub-
sections into 27 additional subsections. Azodi (see Example
7.2) divided
her Review of the Literature Chapter into five major sections
(headings).
Although difficult to quantify in the abstract, a general
guideline is that
there should be at least one heading or subheading for every six
para-
graphs of manuscript. This provides coherence and keeps the
reader

focused on the topic, particularly transitions from one topic to
another.
Use Transitions
Use transitions between paragraphs to provide connections
between
ideas. Use transitions within paragraphs to provide a change
from one
sentence to another or from one section of the paragraph to
another.
Example 7.3 shows transitions (italicized) both betw een and
within
paragraphs from Johnson and Fauske’s (2000) qualitative study.
(See
Example 7.3.)
Example 7.3
That organizations such as schools interact with their
environments is
the focus of little debate. Indeed, organizations influence and
are influ-
enced by the environments in which they exist (Hall, 1991;
Jacobs, 1974;
Katz & Kahn, 1978; Meyer & Scott, 1992; Scott, 1995, 1998).
On one hand,
the presence or absence of critical environmental resources and
informa-
tion constrains or facilitates organizational processes and the

realization of
organizational goals (Aldrich, 1979; Pfeffer & Salancik, 1978,
1995). On the
other hand, the environment is something that organization
participants
act on in a variety of ways and with varying degrees of
sophistication to
achieve organizational goals (Lawrence & Lorsch, 1967; Weick,
1978, 1995).
In either case, the environment is consequential for
organizations.
Effective organizations are those that act on their environments
in
strategic ways. More specifically, effective organizations are
those that cre-
ate bridges and buffers between themselves and the environment
(Aldrich,
1979; Thompson, 1967). Organizational leaders often develop
bridging or
buffering structures to mitigate environmental effects on the
organization
(Scott, 1998; Thompson, 1967). The nature of those bridging
and buffering
structures found in the public school organization have been
described in
some detail (Corwin & Wagenaar, 1976; Goldring, 1995;
Ogawa, 1996;
Rowan, Raudenbush, & Cheong, 1993). These studies
underscore the
importance of the environment for school and leadership
effectiveness.
The environment of the school organization is something that
vies for
the attention of school administrators. As such, it is subject to

strategic
and tactical manipulation (Selznick, 1957). Although there are
limits to
this manipulation, the fact that manipulation is possible
highlights an
important component of the managerial imperative, namely, the
manage-
ment of the external environment of the school.
Of specific interest here are questions regarding what principals
attend
to and act on (or react to) in their external environments and
how they
explain that interactive behavior. Principals are not equally
effective in
attending to or managing the environment of the school
organization. To be
sure, there are those who prove adept at mapping, managing,
and adapting
to a variety of environmental constraints and demands.
Likewise, there are
those who prove equally inept. Many known and unknown
factors account
for these differences—intelligence, years of experience,
socialization, the
richness of ones’ past experiences, and other contextual factors
(Greenfield,
1995; Parkay, Currie, & Rhodes, 1992; Weick, 1978).
Purpose and Rationale
Using these ideas as a guiding context, the purpose of this study
is to
examine and explore (a) how a group of principals enact or
structure the
internal and external environment of their school and (b) the

cognitive
processes guiding these attention and selection processes. The
article will
address two questions: What actions lead to principals’
development of
strategic environmental initiatives and what are the underlying
cognitive
processes for principals selectively attending to certain as pects
of the
environment while ignoring others (Cyert & March, 1963)?
(Johnson &
Fauske, 2000, pp. 159–160)
Note how effectively Johnson and Fauske have used both
internal and
external transitions in the five paragraphs presented above. For
example,
words and phrases such as indeed, on the one hand, on the other
hand, in either
case, more specifically, to be sure, likewise, as well as
seriations, such as (a), (b),
provide internal transitions. Beginning the fifth paragraph with
Using these
ideas as a guiding context connects (transitions) their fifth
paragraph with
the preceding four. Also note how they repeat several keywords
critical to
an understanding of their content: bridges and buffers, bridging
and buffering,
environment, and manipulation. This example can serve as a
model for your

own use of transitions in your review of the literature.
Write a Summary
We began this chapter by stating that each chapter in a
dissertation or
master’s thesis has three distinct parts: the introduction, in
which you tell
your readers what you are going to tell them (often referred to
as an
advance organizer); the body, in which you tell them; and the
summary, in
which you tell them what you told them. Your Review of the
Literature
Chapter should end with a brief summary of the literature you
reviewed.
Thus, the summary provides an overview of the historical,
theoretical, and
empirical literature that supports your investigation. It makes
the rationale
of your study and its research questions or hypotheses apparent
to the
reader. You should draw on this summary when you are writing
your
statement of the problem section in Chapter One and describing
the theo-
retical framework for your research questions and/or
hypotheses, or set-
ting the context of your study in a qualitative thesis or
dissertation. It is for
this reason that we recommend that you begin work on your
review of the
literature prior to writing Chapter One, for you will need to
know what
has been done and how it relates to your research problem.

Scott Barrett (2000) provided a good example of a summary of
the
Review of the Literature Chapter in his dissertation. (See
Example 7.4.)
Example 7.4
Summary
According to Lucas (as cited by Telem, 1995), research on the
imple-
mentation of information technology has evolved through four
genera-
tions. The first generation was characterized by the building of
models,
such as the Theory of Reasoned Action (Fishbein & Ajzen,
1975), the
Technology Acceptance Model (Davis, 1989), and the Theory of
Planned
Behavior (Ajzen, 1985). The characteristics of second-
generation research
focused on testing and extending the original models, such as
the
Extended Technology Acceptance Model (Morris, 1996), the
Decomposed
Theory of Planned Behavior Model (Taylor & Todd, 1995), and
the
Psychological Acceptance Model (Malhota, 1998).
Third generation characteristics of information technology
research
focused on factors associated with implementation, such as
Visscher’s
(1991a) framework and the Perceptions Model for Effective
Usage

(Barrett, 1999). The fourth generation of research examines the
develop-
ment of a project, dealing with issues of relationships between
designers,
users, and the impact of an MIS on an organization, such as the
Task
Technology Fit Model (Dishaw & Strong, 1999), the Integrated
Model of
Microcomputer Usage (Igbaria et al., 1997), and the Integrated
TAM/TTF
Model (Dishaw & Strong, 1999).
According to Johnson and David (1990), “MIS is not a well-
defined
terminology in the discipline of student affairs. The review of
higher edu-
cational journals led on numerous occasions not to MIS or other
com-
puter-based information systems, but to the uses and benefits of
the
computer and microcomputer” (p. 28). Based upon the review of
relevant
literature, the preponderance of research focuses on evaluating
and/or
predicting the acceptance and utilization of technology
applications out-
side of the educational administration arena. Telem (1990)
warned of the
potential dangers of relying on information system models
developed
outside the field of education.

It is evident that valid and reliable instruments to assess the
success
of information systems that measure, predict, and explain use
are increas-
ingly important in determining what causes people to accept or
reject
information technology. By gaining a better understanding of
how the
constructs of perceived ease of use, perceived usefulness,
external vari-
ables, and postadoption experiences impact system usage, a
more theo-
retical understanding of technology acceptance can be
determined.
Understanding these constructs can promote the successful
implementa-
tion of an MIS in the proper contextual environment to support
a willing-
ness to adopt and utilize these systems. (Barrett, 2000, pp. 64–
65)
Barrett’s summary provides an overview of different positions
researchers have taken on the empirical puzzle selected by the
author, and
explains how the author’s dissertation contributes to its
understanding.
His summary allows an outside reader to become acquainted
with a com-
plex and comprehensive review of the literature and how it
supports the
author’s current investigation.
Be Careful Not to Plagiarize
When writing a thesis or dissertation, it is often necessary to
include

extracts from journal articles, books, or research reports. These
extracts are
used to support your arguments and to help set the context for
your
research. It is important for you to give credit to the author(s)
of the work
you have extracted. Plagiarism is “to steal and pass off (the
ideas or words
of another) as one’s own” (Merriam-Webster’s Ninth New
Collegiate Dictionary,
1986, p. 898). Taking someone’s exact words without using
quotation marks
is obviously plagiarism. Closely paraphrasing another person’s
sentences
without giving credit to the person is plagiarism. Presenting
another
person’s ideas as though they were your own is plagiarism. The
plagia-
rism may not be intentional, but it is the responsibility of the
student to cite
others when appropriate. This is not only an ethical matter but a
legal issue
as well.
SYNTHESIZING THE LITERATURE
We’ve said that a review of the literature is more than a string
of annota-
tions. Annotations are brief summaries of the contents of
articles. For
example, “Smith found . . . ,” “Jones argued . . . ,” “Harris

stated. . . .”
Stringing together several annotations in the body of the
literature review
may describe what research is available in an area of study, but
it fails to
organize the material. A good review of the literature is a
critical synthe-
sis. That is, you describe and analyze the literature to show how
previous
studies are related to each other and to your study. You can do
this by
using one or more of the following strategies: (a) provide a
historical con-
text to help the reader understand a phenomenon, (b) point out
gaps in the
literature, (c) show how a study differs from a group of
previous studies,
(d) identify themes in a group of studies, (e) report practical
significance,
(f) reconcile conflicting theories concerning an important
variable, (g) use
tables to summarize research on a single variable, (h) clarify
inconsistent
findings, and (i) signal a continuing line of inquiry. We discuss
each of
these nine strategies in the following sections.
Historical Context
One way of analyzing a body of literature is to provide a
historical con-
text that may be helpful to your readers in understanding a new
construct.
The following article by Hoy, Tarter, and Hoy (2006) is a good
example.
(See Example 7.5.)

Example 7.5
Coleman startled educators with his finding that the
characteristics
of a school mattered little in explaining student achievement
(Coleman
et al., 1966). He argued that schools had only a negligible effect
on
student performance and that most of the variation in student
learning
was a product of differences in family background. Edmonds
(1979) was
one of the first to dispute Coleman’s conclusions. His familiar
list of
effective school characteristics—strong principal leadership,
high expec-
tations for student achievement, an emphasis on basic skills, an
orderly
environment, and frequent and systematic evaluation of
students—
seemed to refute Colman. Good schools were the product of
good
administrators. As simple as the connection seems, empirical
demon-
strations of direct administrative influences on student
achievement
have been elusive.
It is one thing to identify high-performing schools in
neighborhoods
of low socioeconomic status (SES) and attribute their

performance to
leadership characteristics or climate or an orderly environment,
any of
which may be present at those schools. It is quite another matter
to
demonstrate a priori that school leadership or other school
properties will
be directly and systematically related to student success in a
controlled
study involving a large sample. Although, administrators do not
perceive
this to be the case, the weight of the evidence suggests that
little or no
direct relationship exists between principal leadership and
student
achievement (Hallinger & Heck, 1996). In fact, it is difficult to
find school
properties that are consistently related to student achievement
when con-
trolling for the socioeconomic level of the school (for a notable
exception
in private high schools, see Byrk, Lee, & Holland, 1993).
Nevertheless,
educational leaders and policymakers alike have been reluctant
to con-
clude that schools have little or no effect on student
achievement. Instead,
the quest has turned to identifying school characteristics that
make a dif-
ference in achievement, in spite of student SES.
Coleman was not wrong; socioeconomic factors are powerful
shapers
of student performance. In fact, in large-scale studies such as
those of
Coleman et al. (1966) and Jencks (1972), SES overwhelms the

association
between school properties and achievement; the influence of
school fac-
tors vanishes after social factors have been controlled. But
Coleman was
not entirely right; there are a few school characteristics that
consistently
predict student achievement, even after controlling for
socioeconomic fac-
tors. Three organizational properties seem to make a difference
in student
achievement: the academic emphasis of the school, the
collective efficacy
of the faculty, and the faculty’s trust in parents and students.
We suspect
that there are other such school properties, but they have not
been read-
ily revealed despite continuing research.
Academic emphasis, collective efficacy, and faculty trust are
tightly
woven together and seem to reinforce each other as they
positively con-
strain student performance. We first examine the research on
each of these
three school prosperities, and then we explore the theory and
research that
link the three together as a single powerful force explaining
school perfor-
mance. We call this force academic optimism, which has been
demon-
strated to be a general latent construct. (Hoy et al., 2006, pp.
425–426)
Note how Hoy et al., provided a historical context for
understanding a

new construct, academic optimism, and used it to explain
student achieve-
ment. Such an introduction will help the reader to understand a
new con-
struct such as academic optimism. You can use this as a model
for your
own study if you are introducing a new construct.
Gaps in the Literature
Another way to analyze a body of literature is to point out gaps
in the
literature. The qualitative study by Brunner (2000) is a good
example. (See
Example 7.6.)
Example 7.6
With a foundation laid by Patricia Schmuck, Flora Ida Ortiz,
Charol
Shakeshaft, and others, a previously neglected area of study—
women in
educational administration—began to take shape, and questions
about
the dearth of women in administration were asked. Even with
this new
foundation, however, research focused on women
superintendents was
and is as scarce and scattered as the women themselves. To be
sure, after
approximately 75 years of extant scholarship on the
superintendency that

relied primarily on White, male participants, it is only during
the past 20
years that one finds research directed specifically to women
superinten-
dents (Tallerico, 1999, p. 290).
Certainly, when women find themselves in the position of
super-
intendent of schools, it is immediately apparent to them that of
their
colleagues the greatest number are men. In fact, during this
1999–2000
school year, approximately 13% of superintendents are women
(Glass,
Bjork, & Brunner, 2000). Furthermore, it is shocking to see that
when a line
graph is constructed using the percentages of women in the
superinten-
dency from each year over the past century, the graphed line is,
for all
practical purposes, flat (Blount, 1998). And with the additional
informa-
tion that more than half of the students in educational
administration
classes are women, one is left with obvious questions: Why are
there so
few women in the superintendency? What part does gender
inequality
play in this phenomenon? Is gender inequality an experience of
women
in the role? (Brunner, 2000, pp. 76–77)
Note how Brunner used her study of women superintendents’
experi-
ences of inequality to point out gaps in the literature and why
they exist.

You can use this format as a model in pointing out gaps in the
literature
related to your study.
Different Approach
Another way to analyze studies is to show how a study differs
from a
group of previous studies. The mixed methods study by Davis
(1998) is a
good example. (See Example 7.7.)
Example 7.7
Many educators maintain that the principalship is the most
demanding
of all public school administrative roles. Dwindling resources,
burgeoning
paperwork, crumbling facilities, increasing public criticisms
and expecta-
tions, growing numbers of students with special needs, and
increasing
demands by teachers and parents to participate in decision
making pose
serious challenges to principals at virtually all levels and in
nearly every
area of the country. At best, principals strive to provide vision
and unity of
purpose within challenging, dynamic, and highly political
settings.
At worst, they are faced with the unrelenting task of

maintaining structure
and order within increasingly hostile, unpredictable, and
conflict-laden
environments. Even the most skilled and experienced principals
run the
risk of failing in their jobs as a result of actions, events,
outcomes over
which they may not always have direct control (Deal &
Peterson, 1994;
Gorton & Snowden, 1993; Martin-Lucchesi, 1990; Sergiovanni,
1995).
The precarious nature of the principalship reinforces the need
for con-
tinued research on factors related to leadership behavior,
especially behav-
ior that may jeopardize a principal’s job security. Most studi es
about school
principals focus on the characteristics and outcomes of effective
leadership.
Few studies have fully examined factors relating to ineffective
school lead-
ership. Such knowledge in concert with information about
effective leader-
ship, can help principals assess and refine their own leadership
behavior
and more effectively navigate the increasingly complex and
challenging
contexts of public school administration. (Davis, 1998, pp. 58–
59)
Note that Davis pointed out that most studies about school
principals
focus on the characteristics and outcomes of effective
leadership. He sug-
gested that few studies examined factors relating to ineffective

leadership
and that benefits are to be gained from such an approach. You
can use the
“different approach” technique in reviewing the literature in
your disser-
tation or master’s thesis.
Trends and Themes
In your review of the literature, you may find a group of studies
that
employ a similar theme. The following article by Lunenburg
(2003b) is a
good example. Lunenburg identified a theme in our continuing
quest for
school improvement. (See Example 7.8.)
Example 7.8
Accountability for school improvement is a central theme of
state
policies. The No Child Left Behind Act of 2001 (Public Law
107–110) sets
demanding accountability standards for schools, school
districts, and
states, including new state testing requirements designed to
improve edu-
cation. For example, the law requires that states develop both
content
standards in reading and mathematics and tests that are linked
to the
standards for grades 3 through 8, with science standards and
assessments
to follow. States must identify adequate yearly progress (AYP)
objectives
and disaggregate test results for all students and subgroups of

students
based on socioeconomic status, race/ethnicity, English language
profi-
ciency, and disability. Moreover, the law mandates that 100
percent of
students must score at the proficient level on state tests by
2014.
Furthermore, the No Child Left Behind Act requires states to
participate
every other year in the National Assessment of Educational
Progress
(NAEP) in reading and mathematics.
Will schools, school districts, and states be able to respond to
the
demand? In an ideal system, school improvement efforts focus
educational
policy, administration, and practices directly on teaching and
learning. This
will require districtwide leadership focused directly on learning.
School
leaders can accomplish this by (a) clarifying purpose, (b)
encouraging col-
lective learning, (c) aligning with state standards, (d) providing
support, and
(e) making data-driven decisions (Lunenburg, 2002). Taken
together, these
five dimensions provide a compelling framework for
accomplishing sus-
tained districtwide success for all children. (Lunenburg, 2003b,
pp. 36–37)

Note how Lunenburg identified a theme in our continuing quest
for
school improvement. Using trends and themes is another
strategy you can
use to synthesize the review of the literature in your
dissertation.
Reporting Practical Significance
As you provide the reader with a review of literature and as you
look
for significance and potential problems related to your topic,
investigating
practical significance of the reported studies is very important.
You should
consider reporting the type of study, the number of participants,
and sig-
nificance with effect size if applicable.
Drees (2006) provided an example of reporting practical
significance in
his review of the literature. (See Example 7.9.)
Example 7.9
Balogun (1988) reaffirmed his conclusions from 1986. PPGPA
(r = 0.55,
p < 0.01) and mean AHPAT score (r = 0.50, p < 0.01) showed a
significant
relationship to academic achievement. Unlike the earlier study,
Balogun
found no significant relationship between the essay score and
academic
achievement. The single best predictor for clinical achievement
was found
to be the interview score (r = 0.59, p < 0.01). (Drees, 2006, p.

23)
Drees also reported a section of the review of literature in this
manner:
Platt, Torocy, and McGlumphy (2001) investigated whether the
Scholastic
Aptitude Test (SAT) scores and high school grade point average
(HSGPA)
had any ability to predict college grade point average (CGPA)
in six allied
health programs at a private, northeast university. Descriptive
data,
HSGPA, CGPA, SAT mathematics (SATM), SAT verbal
(SATV), and
SAT total were compiled for 373 students of six programs:
athletic train-
ing, health management, systems, occupational therapy,
perfusion tech-
nology, physician assistant, and physical therapy. Using
stepwise
regression analyses, HSGPA and SATV scores were
significantly predic-
tive, F (2, 370) = 31.32, p = .00, of CGPA when looking at all
373 allied
health students. (Drees, 2006, p. 16)
Note how Drees included specific information within his review
of lit-
erature as he reported specific studies. This also indicates that
Drees used
primary sources of data.

Reconciling Conflicting Theories
One way of analyzing a body of literature is to reconcile
conflicting
theories concerning an important variable. The study by Hoy
and
Sweetland (2000) is a good example. (See example 7.10.)
Example 7.10
Virtually all modern organizations are bureaucracies; that is,
they
have the classical bureaucratic properties (hierarchy of
authority, division
of labor, impersonality, objective standards, technical
competence, rules,
and regulations) described by Weber (1947) in his seminal
analysis of
organizations. In practice, the word bureaucracy takes on many
connota-
tions, most of them negative. But Weber claims that
“Experience tends to
universally show that the purely bureaucratic type of
administrative
organization . . . is, from a purely technical point of view,
capable of
attaining the highest degree of efficiency” (1947, p. 337). Yet,
contempo-
rary criticisms of bureaucracy are rampant. For example, Scott
(1998)
describes four bureaucratic pathologies—alienation,
overconformity,
unresponsiveness, and relentlessness—each of which is
pervasive and has
negative consequences for participants. Feminists attack

bureaucracy and
argue that it is fundamentally a male invention that rewards
masculine
virtues and values such as competition, power, and hierarchy
(Ferguson,
1984; Martin & Knopoff, 1999). And school executives criticize
state
bureaucracies for impeding local control of schools and
preventing them
from delivering educational programs that meet community
needs. What
most of these critics have in common is the human frustrati on
and aggra-
vation with hierarchy, technical procedures, and unfair and
restrictive
rules (Hirschhorn, 1997).
While it is true that bureaucracies frustrate participants, that is
only
half the story, because research also suggests they can improve
worker
satisfaction (Michaels, Cron, Dubinsky, & Joachmisthaler,
1998), increase
innovation (Craig, 1995; Damanpour, 1991), reduce role
conflict (Senatra,
1980), and lessen feelings of alienation (Jackson & Schuler,
1985). Indeed,
organizational research depicts two conflicting views of the
human con-
sequences of bureaucracy. The negative side suggests that
bureaucracy
alienates, fosters dissatisfaction, stifles creativity, and
demotivates
employees; the positive view maintains that bureaucracy
provides new
guidance, clarifies responsibility, reduces role stress, and helps

individu-
als feel and be more effective (Adler & Borys, 1996; Hoy &
Miskel, 2001).
The purpose of this research is twofold; first, to reconcile
theoretically
these two views, and second, to develop and test empirically a
new
construct—enabling bureaucracy. (Hoy & Sweetland, 2000, pp.
525–526)
Note how Hoy and Sweetland reconciled conflicting theories
concern-
ing bureaucracy. This is another technique that can be used to
synthesize a
body of literature for a dissertation or master’s thesis.
Using Tables
Another way of synthesizing a large body of research on a
single vari-
able is to use a summary table (see Example 7.11). Table 7.3
provides a sum-
mary of the major Organizational Climate Description
Questionnaire
(OCDQ) studies completed between 1963 and 1987. This
strategy should not
be used unless at least 10 studies examine the effects of a single
variable.
Example 7.11

Table 7.3 Summary Data from Major OCDQ Studies
Study Findings
Anderson (1964)
Andrews (1965)
Appleberry (1969)
Brown (1965)
Feldvebel (1964)
Flagg (1965)
Guy (1970)
Hale (1965)
Hartley & Hoy (1972)
Principals of open schools are more confident,
sociable, and resourceful than principals in closed
schools.
Climate type is not related to teacher characteristics,
satisfaction, principal effectiveness, and student
achievement, but is related to some climate
dimensions.

Schools with open climates are more humanistic in
pupil control ideology than schools with closed
climates.
Validation of the OCDQ resulted in seven discrete
climate categories rather than six found
by Halpin & Croft (1963).
Climate types are not related to SES or school
achievement, but climate dimensions are related to
principal behavior.
Climate type is not related to student achievement
but is related to school size, teacher turnover, and
principal characteristics.
Climate type is not related to student
achievement but to some dimensions of climate.
Language achievement (not mathematics and
reading) is related to some climate dimensions.
There is a relationship between climate openness
and the absence of student alienation.

Hoy & Clover (1986) A revised version of the OCDQ—the
OCDQ-RE—
was developed for use in elementary schools.
Kanner (1974) Teachers are more satisfied and loyal to
principals in
open schools.
Kottkamp, Mulhern, &
Hoy (1987)
A revised version of the OCDQ—the OCDQ-RS—
was developed for use in secondary schools.
Lunenburg (1969) Validation of the OCDQ revealed seven
discrete
climate types instead of six found by Halpin &
Croft (1963). Several climate dimensions and open
climates were related to school size but not SES.
Principal and teacher perceptions of climate are
incongruous.

Lunenburg (1972) Openness of climate (esprit plus thrust minus
disengagement) was related to humanistic pupil
control ideology but not to open-mindedness.
Maxwell (1967) Achievement is positively related to principal
“consideration” and negatively related to teacher
“disengagement.” Teacher and principal perceptions
of climate are incongruous.
Miller (1969) Climate type is related to school achieveme nt,
with
teacher dimensions more important than principal
dimensions.
Nicholas, Virgo, &
Wattenberg (1965)
SES, school size, and behavior problems are related
to climate type.
Null (1967) Teachers’ attitudes toward children are related to
climate dimensions.
Nwankwo (1979) Climate type is related to school discipline.
Plaxton (1965) Principal personality types are not related to

overall climate types but are related to four of the
eight climate dimensions: production emphasis,
aloofness, thrust, and hindrance.
Randles (1964) Open climate is related to favorable attitudes of
teachers on MTAI.
Rice (1968) Overall climate type is not related to student
achievement. Some dimensions of climate are
related to student achievement.
Sargeant (1967) Difference in climate type are related to teacher
satisfaction, school effectiveness, and role, but not
school department, size, and community
composition.
Watkins (1968) Climate type is related to school size,
accountability,
and role.
Wiggins (1972) Climate type is not related to principal behavior
but
remains stable with principal turnover.

Using a table like the one above is a very effective way to help
the
reader visualize a large body of research related to a singe
variable.
The studies reported in Table 7.3 may seem dated to some
readers, but the
study of organizational climate throughout the 1960s and later
had a
tremendous impact on the literature. Note that the same
measuring
instrument was used to assess organizational climate, the
OCDQ, in the
studies reported in Table 7.3. The information is provided as a
model for
your own review of the literature using other variables to
synthesize a
large body of research on a single topic.
Inconsistent Findings
Another way of analyzing a body of literature is to clarify
inconsistent
findings. The study by Grejda and Hannafin (1992) is a good
example. (See
Example 7.12.)
Example 7.12
Interest in the potential of a word processor to improve writing
has grown
substantially during the past decade. Various authorities have
lauded the
capability to increase writer productivity (Zaharias, 1983), to
reduce tedious-
ness of recopying written work (Bean, 1983; Daiute, 1983;

Moran, 1983), to
increase the frequency of revising (Bridwell, Sirc, & Brooke,
1985; Daiute,
1986), and to improve both attitudes toward writing (Rodriguez,
1985) and the
writing process (McKenzie, 1984; Olds, 1982). Some
researchers have argued
that word processors alter both individuals’ writing styles and
the methods
used to teach writing (Bertram, Michaels, & Watson-Geges,
1985).
Yet research findings on the effects of word processing have
proved
inconsistent (Fitzgerald, 1987). Some researchers have reported
positive
effects on writing (Dauite, 1985), whereas others have reported
mixed
effects (Wheeler, 1985). Although word processing seems to
increase the
frequency of revision, the revisions are often surface level and
do little to
improve the overall quality of composition (Collier, 1983;
Hawisher,
1987). In some cases, word processing has actually hampered
different
aspects of writing (Grejda & Hannafin, in press; Perl, 1979).
However, comparatively little has been demonstrated
conclusively.
Attempts to study word processing have been confounded by
both typ-
ing requirements and limited word-processing proficiency.
Inadequate
definition has also plagued word-processing research. Many
studies have

isolated only mechanical attributes of revision, such as
punctuation,
requiring only proofreading rather than sophisticated revision
skills
(Collier, 1983; Dalton & Hannafin, 1987). Other research has
focused only
on global writing measures, with little attribution possible for
observing
changes in writing quality (Boone, 1985; Woodruff, Bereiter, &
Scardamalia, 1981–82). Both mechanical and holistic aspects of
writing are
important, but they are rarely considered concurrently (cf.
Humes, 1983).
Although comparatively few studies have focused on young
writers,
the results have been encouraging. Dauite (1986) reported that
junior high
schools students using word processors were more likely to
expand their
compositions, as well as to identify and correct existing errors,
than were
paper-and-pencil students. Likewise, word-processing students
were more
likely than paper-and-pencil students were to revise their
language-experience
stories (Barber, 1984; Bradley, 1982). Boone (1985) reported
that the compo-
sitions of fourth, fifth, and sixth graders become increasingly
sophisticated
through revisions focusing on both mechanics and higher-level
organiza-

tions. In contrast, despite improving students’ attitudes, word
processing
has failed to improve overall compositions based upon holistic
ratings of
writing quality (Woodruff, Bereiter, & Scardamalia, 1981–82).
The purpose of this study was to examine the effects of word
pro-
cessing on the holistic writing quality and revision patterns of
sixth
graders. We predicted that word processing would improve both
the
accuracy of revisions as well as the overall holistic quality of
student writ-
ing. (Grejda & Hannafin, 1992, p. 114)
Note how Grejda and Hannafin pointed out inconsistent findings
in a
study of the effects of word processing on the accuracy and
quality of
students’ writing. This technique can serve as still another
model for syn-
thesizing the review of literature in a dissertation or master’s
thesis.
Continuing Line of Inquiry
In analyzing a body of literature, you may find an article that
started a
new line of research that has continued into the present, using
additional
measurement instruments. The review prepared by Miskel and
Ogawa
(1988) in their chapter for the Handbook of Research on
Educational
Administration is a good example. Miskel and Ogawa reviewed

the litera-
ture on the organizational climate of schools from 1963 to
around
1982. Lunenburg and Ornstein (2008) continued the review up
to 1997.
(See Example 7.13.)
Example 7.13
The literature on the organizational climate of schools is largely
rooted in four conceptualizations (Hoy & Miskel, 1982): Halpin
and
Croft’s (1963) concept of open and closed climates; Likert’s
(1961) concept
of managerial systems that range from exploitive-authoritative
to partici-
pative; Steinhoff’s (1965) and Stern’s (1970) needs-press
model; and
Willower, Eidell, and Hoy’s (1967) concept of pupil-control
orientation.
Each has led to the development of measurement instruments
and subse-
quent research on schools. These conceptualizations of climate
share two
important features: They are extensions of theories or
perspectives con-
cerning what Tagiuri (1968) has called the social-system
dimension of
climate, and they operationalize climate as the average of
organizational
members’ perceptions. (Miskel & Ogawa, 1988, pp. 290–291)

Two revised versions of the OCDQ were developed in the
1980s, one
for elementary schools—the OCDQ-RE—(Hoy & Clover, 1986)
and one
for secondary schools—the OCDQ—RS (Kottkamp, Mulhern, &
Hoy,
1987). Another instrument to assess the climate of the school is
the
Organizational Health Inventory (OHI) developed by Hoy and
Tarter
(1997). Whereas the OCDQ examines the openness/closedness
of teacher-
teacher and principal-teacher interactions, the OHI describes the
health of
the interpersonal relations in schools among students, teachers,
adminis-
trators, and community members.
Hoy and Tarter conceptualize organizational health at three
levels:
institutional, administrative, and teacher. The institutional level
connects
the schools with its environment. The administrative level
controls the
internal managerial function of the organization and learning
process. A
healthy school is one that keeps the institutional,
administrative, and
teacher levels in harmony, meets functional needs and
successfully copes
with disruptive external forces, and directs its energies toward
school
goals. (Lunenburg & Ornstein, 2008, p. 83)
Note how Miskel and Ogawa and Lunenburg and Ornstein

synthesized
a continuing line of inquiry on school climate. You may wish to
use this
strategy in reviewing your literature for a dissertation or
master’s thesis.
SUMMARY
In this chapter, we provided you with an introduction on how to
system-
atically search the literature. We discussed six sources for such
a search:
handbooks and encyclopedias, annual reviews, review articles,
abstracting
and indexing services, government documents, and public
search engines.
In addition, eight techniques to help you write a clear and
effective Review
of the Literature Chapter were provided. They include:
organizing your
material in a funnel, being selective, making an outline, writing
the intro-
duction, using headings, using transitions, writing a summary,
and being
careful not to plagiarize. We concluded the chapter by
discussing nine
additional strategies to help you critically synthesize the body
of literature.
Examples from dissertations and from published articles may
serve as
models for your own work. In the next chapter, we provide
advice for
writing the Methodology Chapter of your dissertation or
master’s thesis.

8
Writing the
Methodology Chapter
In a typical dissertation or master’s thesis, the Introduction and
Review
of the Literature Chapters are followed by a description of the
research
methodology. The chapter is usually titled “Methodology.”
Thus, in
Chapter Three you describe the specific steps you have taken to
address
the research questions or hypotheses that you presented in
Chapter One,
and that you connected to the prior research you reviewed in
Chapter Two.
You need to describe the methodology in enough detail so that
other
researchers may replicate your study.
The Methodology Chapter is typically divided into the
following
sections: (a) selection of participants, (b) instrumentation, (c)
data collec-
tion, and (d) data analysis. Although qualitative studies (case
study,
ethnography, ethology, ethnomethodology, grounded theory,
phenome-
nology, symbolic interaction, and historical research) tend to be
less formal

in nature and procedure than quantitative studies (descriptive,
correla-
tional, causal-comparative, and experimental), much of what is
suggested
here can apply to all dissertations and master’s theses. The
typical contents
for Chapter Three are provided in Table 8.1.
As discussed previously, each chapter has three distinct parts:
the
introduction, where you tell your readers what you are going to
tell them;
the body, where you tell them; and the summary, where you tell
them
what you told them. You will note from Table 8.1 that Chapter
Three
begins with an introduction and ends with a summary. The
headings in
the middle constitute the body of the chapter. A more detailed
description
of each of these sections follows.
165
E
Table 8.1 Contents for Chapter Three
CHAPTER III
METHODOLOGY

Introduction
Selection of Participants
Instrumentation
Data Collection
Data Analysis
Summary
INTRODUCTION
In the introduction, you provide an overview of the chapter. The
overview,
containing one or two paragraphs, provides the reader with the
structure
of the chapter, an advance organizer. This advance organizer
prepares the
reader for what is to follow. In some dissertations, the student
restates the
research questions or hypotheses that were presented in Chapter
One. In
others, the student simply makes reference to the research
questions or
hypotheses presented earlier.
An example from Melinda Wooderson-Perzan’s (2000)
dissertation fol-
lows. (See Example 8.1.)
xample 8.1
Introduction

The primary goal of this study was to test the research questions
that
relate to transformational leadership, student achievement, and
selected
school district financial and demographic factors as stated in
Chapter I.
Separate instruments to measure these variables were utilized to
this end.
The methodology employed to test the research questions is
presented in
this chapter. The chapter is organized into four sections: (a)
selection of
participants, (b) instrumentation, (c) data collection, and (d)
data analysis.
(Wooderson-Perzan, 2000, p. 58)
Note how Wooderson-Perzan, in her brief introduction to her
chapter,
provided her readers with an advance organizer of the contents
of the
chapter. Note also that she included all elements typically found
in a
Methodology Chapter. Additionally, you may add your research
design in
the introductory section of Chapter Three, or the heading
“Design of the
Study” may stand alone after the heading “Introduction.”
SELECTION OF PARTICIPANTS
In studies involving humans, the term participants is general ly
preferred to
the term subjects to communicate the active and consensual
relationship

the participant had in the study. The purpose of the “Selection
of
Participants” section is to describe (a) who participated in the
study,
including their characteristics (e.g., age, gender, race/ethnicity),
(b) how
the participants were selected, and (c) how many participated in
the study.
We discuss each element in turn.
Who Participated in the Study?
The reason for selecting a sample is to gain information
concerning
your target population. For example, if you were interested in
the effect of
conflict resolution training on the number of student discipline
referrals
for fifth-grade inner-city students, it would not be possible to
include all
fifth grade students in the United States in your study. It would
be neces-
sary for you to select a sample of the population. Because
inferences con-
cerning a population are made based on the behavior of a
sample, it is
important that the sample be representative, sufficiently l arge,
and free of
sampling error and bias. However, in qualitative studies,
samples tend to
be smaller, nonrandom, and purposive. Sampling in qualitative
research is
discussed later in the chapter.
The first step, then, in sampling in quantitative studies is to
define your

target population. The target population is the group of interest
to the
researcher, the group to which you would like the results of the
study to be
generalizable. Generalizability is the extent to which the results
of one study
can be applied to other populations. Examples of populations
are all princi-
pals in the United States, all elementary school gifted and
talented children in
Texas, and all first grade students in Harris County who have
attended
preschool. These examples illustrate two important things about
populations.
First, populations may be any size and cover any geographic
area. Second, the
entire group the researcher would like to generalize is rarely
available.
For example, suppose you decide to do a study of high school
princi-
pals’ attitudes about teaching mathematics using traditional
instruction
versus online instruction. Suppose that you wish to generalize
your results
to all high school principals in the United States. You quickly
realize the
difficulty of getting information from every high school
principal in the
United States, so you decide to obtain a representative sample
of high
school principals in the United States. But even this would be a
difficult,
time consuming, and expensive task. So instead, you decide to
study only
principals in a single state. By selecting from a more narrowly

defined
population, you would save time and money but you would be
losing
generalizability. Your results would be generalizable to all high
school
167Writing the Methodology Chapter
principals in that state only, but not to all high school principals
in the
United States. The key is to define your population in sufficient
detail so
that other researchers may determine how applicable your
findings are to
their study.
Regardless of what sampling approach is used, it is important to
describe
the characteristics of the sample. This description should
include the number
of participants in the sample and a description of the
demographics of the
sample (e.g., gender, race, age, experience, education). The
nature of the
demographic data varies with the sample; that is, the
demographic informa-
tion used to describe a sample of principals would be different
from that used
to describe a sample of teachers, students, or parents.
The number of characteristics you might use to describe the
partici-
pants is almost limitless; therefore, you must be selective in
deciding on

which sample characteristics to report. Following is an example
of a
description of participants from Linda Creighton’s (2006)
dissertation to
further illustrate issues pertaining to selecting and describing
participants.
A table such as the one in Example 8.2 makes it easy for readers
to scan
information that describes the participants. (See Example 8.2.)
Example 8.2
The sample was comprised of 63 universities within the
University
Council for Educational Administration (UECA) membership.
The UCEA
is a consortium of 72 major public and private doctoral -degree
granting
research universities in the United States and Canada. It
includes doctoral
granting universities with the Carnegie Classifications: Very
High
Research Activity (RU/VH), High Research Activity (RU/H),
and
Master’s Colleges and Universities Larger Programs (Master’s
L).
Very High Research Activity (RU/VH)
These institutions offer a wide range of baccalaureate programs
and
are committed to graduate education through the doctorate.
They award
50 or more doctoral degrees per year across at least 15

disciplines (The
Chronicle of Higher Education, 2005).
High Research Activity (RU/H)
These institutions offer a wide range of baccalaureate programs
and are
committed to graduate education through the doctorate. They
award at least
10 doctoral degrees per year across three or more disci plines, or
at least 20
doctoral degrees per year overall (Chronicle of Higher
Education, 2005).
Master’s Colleges and Universities Larger Programs (Master’s
L)
These institutions offer a wider range of baccalaureate programs
and
are committed to graduate education through the master’s
degree. They
award 40 or more master’s degrees annually (Chronicle of
Higher
Education, 2005).
The colleges and universities studied were selected from the
four
regions of the United States into which the U.S. News & World
Reports places
schools for ranking purposes: North, South, Midwest, and West.
Within

these regions were UCEA colleges and universities classified
as: (a) two uni-
versities located in small towns having an average total full-
time under-
graduate enrollment of 16,799 students; (b) 33 universities from
mid-size
cities having an average full-time undergraduate enrollment of
27,350
students; (c) three universities located in large towns having an
average full-
time undergraduate enrollment of 16,249; (d) 19 universities
with an average
full-time undergraduate enrollment of 32,096 located in or near
large cities;
and (e) six universities with an average full-time undergraduate
enrollment
of 14,453 located in or on the fringe of large cities. The
Carnegie
Classification for the universities is: (a) RU/VH, 50; (b) RU/H,
8; and
(c) Master’s L, 5. [Table 8.2] displays the participants included
in the study
based on region, location, student enrollment, and Carnegie
Classification.
Table 8.2 Participants Based on Region, Location, Enrollment,
and Carnegie
Classification
Carnegie
Region Participants Location Participants Enrollment
Classification* Participants

North 5 Small
Town
2 16,799 (RU/VH) 50
South 12 Large
Town
3 16,249 (RU/H) 8
Midwest 26 Midsize
Town
33 27,350 Master’s L 5
West 20 Large City 19 32,096
Fringe of
Large City
6 14,453
* This denotes the new Carnegie Classifications which took
effect in 2005.
(Creighton, 2006, pp. 48–50)
Note how comprehensively Creighton describes her sample of
UCEA
universities. She follows the textual description with a table,
which makes

it easy for readers to scan information quickly describing the
participants.
Sampling Procedures
In writing Chapter Three, your purpose is not to discuss all the
major
sampling procedures but instead to describe what method you
used to obtain
your sample. However, knowledge of the major sampling
procedures is a
prerequisite to developing an appropriate description of your
sample. One
desirable characteristic of a sample is representativeness.
Representativeness
enables results from the sample to be generalized to the
population. The
major sampling procedures that provide representativeness are
(a) simple
random sampling, (b) stratified random sampling, (c) cluster
sampling, and
(d) systematic sampling. Each procedure is discussed below.
Simple Random Sampling
Simple random sampling is the process of selecting a sample in
such a
way that all individuals in the defined population have an equal
chance of
being selected for the sample. Simple random sampling involves
defining
the population, identifying each member of the population, and
selecting

individuals for the sample on a chance basis using a table of
random
numbers. Simple random sampling is the single best way to
obtain a rep-
resentative sample.
An example of simple random sampling from Diane Trautman’s
(2000)
mixed methods dissertation follows. (See Example 8.3.)
Example 8.3
The target population of this study were all superintendents,
assis-
tant superintendents, secondary principals (grades 6–12), and
elemen-
tary principals (grades K–5), male and female, in the United
States. This
population included only those school leaders in public schools.
The
Market Data Retrieval Company (2000), a company of the Dun
and
Bradstreet Corporation located in Chicago, Illinois, was used to
deter-
mine the target population. The following is a breakdown of the
target
population for each group: 11,542 male and 2,060 female
superinten-
dents; 4,269 male and 2,267 female assistant superintendents;
24,591
male and 29,305 female elementary principals; and 21,230 male
and
7,929 female principals. This would make a total target
population of

103,193 members.
According to Krejcie and Morgan (1970), when the population
is
100,000, a sample size of 384 is recommended. The quantitative
sample
taken from the population of 103,193 was 800 educational
leaders ran-
domly selected by computer through the Market Data Retrieval
Company
(2000). This group provided the quantitative data for the study
obtained
from the Organizational and Leadership Effectiveness
Inventory. The
Market Data Retrieval Company randomly selected by computer
from
each of the above groups 100 females and 100 males giving a
total sample
of 800 or .007% of the total population. (Trautman, 2000, p. 84)
Technically, the example above from Trautman’s dissertation is
not a
simple random sample but a stratified random sample. The
target popula-
tion of superintendents, assistant superintendents, secondary
principals,
and elementary principals in her study were divided into equal
numbers
of males and females. Thus, she stratified her sample on the
basis of
gender, which results in a sample that is representative in terms
of this

characteristic. Stratified random sampling usually provides a
more repre-
sentative sample than strictly simple random sampling does and,
there-
fore, is usually the sampling procedure chosen.
Stratified Random Sampling
Stratified random sampling is the process of selecting a sample
in such
a way that identified subgroups in the population are
represented in the
sample in the same proportion that they exist in the population.
It can also
be used to select equalized samples from a number of subgroups
if sub-
group comparisons are desired. For example, equal-sized
samples would
be most useful if you wanted to compare the performance of
different sub-
groups. Suppose, for example, that you were interested in
comparing the
achievement of students of different ability levels (high,
average, and low)
being taught by two different methods of mathematics
instruction, tradi-
tional and computer. The steps in stratified sampling are similar
to those
in random sampling except that the selection is from subgroups
in the
population rather than the population as a whole.
An excerpt from Darlene Blair’s (2001) dissertation follows.
(See
Example 8.4.)

Example 8.4
There are 1,041 school districts in the state of Texas
represented by
7,395 schools. The population of this study consisted of the
principals of
the 6,367 public schools located in those districts that were
rated by the
Accountability Rating System of the Texas Education Agency
for the year
2000. A sample size of 364 was required for the given
population as sug-
gested by Krejcie and Morgan (1970). A stratified random
purposive sam-
ple (N = 400) was selected that represented a proportional
sample of the
principals of Exemplary, Recognized, Acceptable, and Low-
Performing
elementary and secondary schools. Based on Texas Education
Agency
(TEA) data for the 1999–2000 school year, of those randomly
chosen, 20
percent represented schools that were rated Exemplary, 30
percent repre-
sented schools that were rated Recognized, 43 percent
represented
schools that were rated Acceptable, and 5 percent represented
schools that
were rated Low-Performing (TEA, 2000). The sampling and
contact infor-
mation for the potential principal participants was obtained
from the
Texas Education Public School Directory. (Blair, 2001, pp. 39–

40)
Note that Blair stratified her sample based on school ratings of
“Exemplary,” “Recognized,” “Acceptable,” and “Low
Performing.” The
goals of her research were to (a) determine relationships
between princi-
pals’ leadership styles and student achievement as evidenced by
school rat-
ings, and (b) examine relationships between the amount of time
principals
spend on instructional leadership and management tasks and
school rat-
ings. Therefore, her sample of principals needed to be stratified
by school
ratings to determine the relationships tested.
Cluster Sampling
Cluster sampling is the process of selecting groups, not
individuals.
For example, instead of randomly selecting from all ninth
graders in a
large school district, you could randomly select all ninth grade
classrooms
and use all the students in each classroom. Examples of clusters
include
classrooms, schools, school districts, city blocks, and hospitals.
The steps
in cluster sampling are not very different from those involved in
random

sampling. The major difference is that random selection of
groups (clus-
ters) is involved, not individuals.
Just as it is highly unlikely that one randomly selected student
could
ever be representative of an entire population, it is also unlikely
that one
randomly selected school could be representative of all schools
in the pop-
ulation. To overcome this problem, be sure to select a
sufficiently large
sample of clusters.
Following is an example of cluster sampling from Linda
Schmidt’s
(1987) dissertation. (See Example 8.5.)
Example 8.5
Sample
The sample for the study consisted of 239 elementary and
secondary
teachers and their students from two elementary and four
secondary
schools in five school districts in a midwestern state. Although
the school
sample was not random, it was diverse and distributed among
urban, sub-
urban, and rural areas and spanned the entire range of
socioeconomic sta-
tus. Extremely small elementary schools were not included in
the sample;
in fact, only schools with 10 or more faculty members where
considered

candidates for the sample. Schools that participated ranged in
size from 468
to over 3,000 students, with a mean of 1,098 students. Two of
the secondary
schools were located in a large metropolitan city. Another high
school and
one elementary school were in its suburbs. The two remaining
schools, one
elementary and one secondary school, were located in another
area of
the same state and could be described as rural-small town.
Furthermore,
the sample represented a group of educators diverse in age,
race, gender,
experience, and educational level. The student sample was also
diverse in
grade level, racial composition, gender, and socioeconomic
status.
After the schools were selected, trained researchers personally
col-
lected data from faculty and students in each school. PCI data
were
obtained from virtually the entire upper grade level elementar y
faculty
and the entire secondary faculty in each school. However, only
teachers
who taught major subjects that met five periods per week were
asked to
participate. Each of the 293 teachers, 49 elementary and 190
secondary

teachers, completed PCI Forms and personal data sheets.
Similarly, PCB
and QSL data were collected from one class taught by each of
these
teachers by a researcher during regularly scheduled classes. In
elemen-
tary schools fifth through eighth grade classes were selected by
the throw
of a die for each grade level. In high schools 9th through 12th
grade
students were chosen by the throw of a die for each grade level.
Except for
a few, all of the students selected in this manner completed the
forms,
which were reverse ordered in approximately half of the classes.
A total of
5,172 students furnished usable PCB Forms and 5,199 students
completed
usable QSL Scales for an average of 26 students per classroom.
In all, more
than 5,000 faculty and students in 239 classes returned usable
question-
naires. (Schmidt, 1987, p. 38)
Cluster sampling was used to obtain the participants for
Schmidt’s dis-
sertation. Rather than drawing individual names at random and
then trying
to contact individual teachers and students who would be
scattered
throughout the schools, she found it more effective to draw a
random sam-
ple of school districts, schools, and eventually classrooms
within those
schools (i.e., clusters) and request the classroom teachers to
help gather the

data for her (i.e., ask them to help her distribute the student
questionnaires).
This method of sample selection resulted in the completion of
question-
naires from 239 teachers (100% return rate) and more than
5,100 students
(approximately 96% useable returns). Thus, all teachers
completed the
instruments. Except for those students who were absent on the
day the ques-
tionnaires were administered, virtually all students completed
the instru-
ments administered in the respective elementary and secondary
classrooms.
Systematic Sampling
Systematic sampling is sampling in which individuals are
selected from
a list by taking every nth name. What N actually equals depends
on the size
of the list and the desired sample size. For example, if N = 4,
selection
involves taking every 4th name, if N =10, every 10th name, and
so forth.
Following is an example from Scott Barrett’s (2000)
dissertation. (See
Example 8.6.)
Example 8.6
Systematic sampling. The total population of Texas public
school prin-
cipals that utilize Pentamation at the time of this study was 662.
This

number was determined by identifying the Texas public school
districts
that utilize Pentamation, then acquiring a directory of principals
from
those school districts from the Texas Education Agency Ask
TED (Texas
Educational Directory), which provides current directory and
mailing
information for Texas schools. To avoid any possibility of
periodicity, the
list of principals was sorted by the name of the city where the
school was
located. To ensure a sufficient sample size, as determined by
the Olejnik
chart of Minimal Total Sample Size (as cited in Gall et al.,
1996), every
fourth name was selected from the list for participation in the
study,
resulting in a pool of 165 candidates. (Barrett, 2000, pp. 81–82)
The representativeness of your participants is crucial to the
external
validity of the study. Representativeness of a sample is
jeopardized by
persons who refuse to participate in a study and using
volunteers.
Nonresponse can be a problem in studies using mailed and
online ques-
tionnaires. The seriousness of the problem depends on the
amount of non-
response. You can minimize nonresponse by following the

initial
questionnaires with follow up requests after two and three
weeks respec-
tively (see Appendixes A, B, and C for sample letters).
Differences between
volunteers and nonvoluntures with respect to the variables
examined in
the study will affect the representativeness of the sample.
Therefore, you
need to report not only differences between volunteers and
nonvolunteers
but also the percentage of nonresponse.
Selecting a Nonrandom Sample
Although random sampling techniques provide the best
opportunity
to obtain unbiased samples, it is not always possible for
researchers to use
random sampling. Nonrandom sampling approaches include (a)
conve-
nience sampling, (b) purposive sampling, and (c) quota
sampling.
Convenience Sampling
Convenience sampling involves including in the sample
whoever hap-
pens to be available at the time. Examples include the use of
volunteers
and the use of existing groups just because they are there. If you
will be
using a convenience sample, generalizations from it to a
population will
have to be made cautiously. It is best to address this i ssue
directly when

describing your sample.
If you must use a convenience sample, consider the possibility
of using
one that has diversity like Cindy Loeffler (2005) did in her
dissertation.
Her sample was drawn from several universities. Drawing your
sample
from several universities (groups) will make your study stronger
than if
you rely on participation from one institution (or group).
An excerpt from Loeffler’s dissertation follows. (See Example
8.7.)
Example 8.7
The second population was represented through a convenience
sam-
ple of graduate students at both the masters and doctoral level
currently
enrolled in participating professors’ (n = 72) education
leadership classes
during fall 2004. The national student sample for this study can
only be
estimated (n = < 500) since the information from each professor
indicated
an approximation of the student enrollment in online education
leader-
ship classes at the time of this study. Professors reported that
their classes

ranged from hybrid or blended to Web-enhanced to online
computer-
mediated asynchronous. The number of students surveyed and
their geo-
graphical distribution over 15 states enable the generalization of
this
study’s results to online graduate classes in education
leadership courses
and programs in public and private universities and colleges in
the conti-
nental United States, thereby strengthening the external validity
of this
study. (Loeffler, 2005, p. 38)
Note how Loeffler strengthened her convenience sample by
drawing it
from several universities in 15 different states. Obviously, the
broader the
sample, the more representative it tends to be.
Quota Sampling
Quota sampling is most often used in survey research when it is
not
possible to list all members of the population of interest. When
quota sam-
pling is involved, data collectors are given exact characteristics
and quotas
of persons to be interviewed (e.g., 30 unmarried teenage
pregnant girls
between the ages of 13 and 17).
Purposive Sampling
Purposive sampling involves selecting a sample based on the
researcher’s experience or knowledge of the group to be

sampled. For
example, if a researcher planned to study exceptional high
schools, she
would choose a group of schools to study based on her
knowledge of
exceptional schools. Clear criteria provide a basis for describing
and
defending purposive samples. For example, the selection of
exceptional
schools might be based on criteria such as high proportion of
students
going to four-year colleges, large numbers of AP students, high
passing
rates on state-mandated tests, extensive computer facilities, and
high pro-
portions of teachers with graduate degrees. Much of the
research in quali-
tative research is purposive. Purposive sampling is used in
quantitative
research as well.
Following is an example from Vicki Stanfield’s (2000)
quantitative dis-
sertation. (See Example 8.8.)
Example 8.8
In special situations the use of a purposive sample is chosen as
the form
of data collection (Neuman, 1997). In the current study, the
purposive
sample provided the means to investigate a specialized
population of

students attending community colleges in the United States. The
explo-
ration of the purposive sample allowed for the examination of
enrollment
data and students’ responses to the College Student Inventory
from com-
munity colleges only, which had not taken place prior to this
study. The
researcher sought and received approval from USA Group Noel-
Levitz, Inc.
to utilize the necessary components of their comprehensive
database for the
purposes outlined in this study. An agreement to preserve both
confiden-
tiality and anonymity of institutions and individuals was signed
by the
researcher and returned to Noel-Levitz prior to receiving the
raw data files
(see Appendix). Each institution supplied information regarding
each CSI
respondent’s cumulative GPA, terms of enrollment, current
enrollment sta-
tus, and reason for leaving college. A questionnaire containing
institutional
demographic information was included in the database.
For the purpose of this study, a total of 1,368 students at eight
com-
munity colleges were selected from the Noel-Levitz database.
The institu-
tions were locating in the northern, eastern, and western regions
of the

United States. Each of these institutions administered the
College Student
Inventory to incoming freshman students in the first semester of
college
between the years 1996 to 1999. Student information included
in the
College Student Inventory included the following attributes:
native lan-
guage, ethnicity, marital status, and mother’s and father’s
highest level of
education. (Stanfield, 2000, pp. 42–43)
Sampling in Qualitative Research
Sampling in qualitative research is almost always purposive.
Within
the domain of purposive sampling are a number of specific
approaches
useful in qualitative research including:
1. Intensity sampling: selecting participants who permit study of
differ-
ent levels of the research topic (e.g., good and bad students,
experi-
enced and inexperienced teachers, or teachers in self-contained
classrooms and teachers in departmentalized teams).
2. Homogeneous sampling: selecting participants who are very
similar in
experience or philosophy; this produces a narrow, homogeneous
sample and makes data collection and analysis simpler.
3. Criterion sampling: selecting participants who meet some
criterion (e.g.,
students who have been retained for two successive years,
teachers

who left the field to raise children and then returned to
teaching,
female administrators with more than 15 years of experience).
4. Snowball sampling: selecting a few people who can identify
other people
who can identify still other people who might be good
participants for
a study; this approach is most useful when a study is carried on
in a set-
ting in which possible participants are scattered or not found in
clusters.
5. Random purposive sampling (with small sample): selecting
by random
means participants who were purposively selected and who are
too
numerous to include all in the sample; for example, if 25
potential
participants were purposively identified by the researcher, but
only
10 participants could be studied, a random sample of 10 from
the 25
potential participants would be chosen; this approach adds
credibil-
ity to the sample but is still based on an initial sample that was
pur-
posively selected.
Qualitative research uses sampling techniques that produce
samples
that are predominantly small and nonrandom. This is in keeping

with
qualitative research’s emphasis on in-depth description of
participants’
perspectives and context.
As mentioned previously, the purpose of qualitative research is
to
obtain an in-depth understanding of purposively selected
participants
from their perspective. Thus, if you conduct qualitative
research, you
should purposively select participants who meet criteria that
will provide
a sample that is likely to yield the type of information you need
to achieve
your purpose. For example, suppose the purpose of your study is
to apply
a theory to the leadership experiences of superintendents
leading success-
ful school districts. The theory you have selected is the
Synergistic
Leadership Theory (SLT) (Irby, Brown, Duffy, & Trautman,
2002). The SLT
is a gender-inclusive theory containing four factors that impact
successful
leadership: (a) attitudes, values, and beliefs, (b) leadership
behaviors,
(c) organizational structure, and (d) external factors. Your
research goal is
to test the SLT theory with superintendents leading successful
school dis-
tricts. Specifically, you want to examine the alignment of the
four factors
of the theory with the leadership experiences of a national
sample of
school superintendents.

Obviously one criterion you will use is that the superintendent-
participants will need to be successful superintendents. Other
criteria
you might consider include: (a) Will you restrict the sample to
superin-
tendent self reports, or will you include other role incumbents
in your
sample, superordinates and subordinates to the superintendent
(i.e.,
school board members and associate/assistant superintendents,
respectively) to obtain their assessments of the superintendent?
(b) Will
you include superintendents and others from urban, suburban,
and
rural districts, or will you limit the sample to those in one type
of dis-
trict? (c) Will you include male and female superintendents in
your
sample? After thinking about such questions, you should
prepare a
statement of the criteria for the selection of participants to
include in
Chapter Three of your dissertation.
The following is an excerpt from Wanda Bamberg’s (2004)
qualitative
dissertation containing such a statement. (See Example 8.9.)
Example 8.9

The purposive (Gall, Borg, & Gall, 1996) sample for this study
included
15 participants. Five female superintendents who participated in
the origi-
nal Learning First Alliance (LFA) study conducted in 2001–
2002 comprised
one third of the sample and were the primary focus for the
study. They
were the leaders for the school districts chosen for the LFA
study.
Five districts where identified from among 60 districts
nationally, a
study conducted by LFA in 2001–2002 based on the following
criteria: (a) the
district’s ability to exhibit at least three years of improvement
in student
achievement in mathematics and/or reading across multiple
grades and
across all races and ethnicities; and (b) the district’s
representation of a cross
section of characteristics, including size, region, urbanicity, and
union affili-
ation (Togneri, 2003). After LFA researchers identified five
school districts,
they discovered that all five districts had female
superintendents.
The sample for my study included the five superintendents
identified
by LFA and also included central office administrative team
members and
school board members from each district for a total of 15
participants. The
administrative team members from the districts included top-

level admin-
istrators in positions with experience working directly with the
superinten-
dent to make the district successful. In each case, the
administrative team
member would be considered the second in command or the
second in
command responsible for instruction or strategic planning.
Board members
for the study included experienced members who had worked
with the
superintendent for several years and were judged by the district
contacts or
superintendents to be knowledgeable of the district work and
success over
three to five years. Some board members and administrative
team members
were also participants in the LFA study. (Bamberg, 2004, pp.
45–46)
Note that sample size in qualitative research is typically smaller
than
in quantitative studies. This is because you will be striving for
in-depth
information in qualitative research. It is more important for you
to spend
sufficient time with a small number of participants than to work
superfi-
cially with a large number.
How Many Participated in the Study?
One question students often ask when planning their
dissertation or
master’s thesis is: How many participants do I need? The
response from

committee chairs is likely to be: It depends. Researchers use
four major
ways of making the decision: (a) heuristics, (b) literature
review, (c) for-
mulas, and (d) power analysis.
Heuristics
A heuristic is a rule of thumb that can help the decision maker
find a
solution to a complex problem (Lunenburg, 2006). We offer
very rough
guidelines for sample sizes that might be used in studies of
different types:
(a) For qualitative research, use from 1 to 20 participants (on
the lower end
if you are using groups); (b) for survey research, drawn from
very large
populations such as superintendents, assistant superintendents,
sec-
ondary principals, elementary principals in the United States,
use 800 or
more participants (see Example 8.3); (c) for correlation
research, use 100 to
200 participants; (d) for causal-comparative research, use more
than
30 participants; (e) for experimental research, use 30 or more
participants;
(f) for factorial statistical designs, at least 15 participants is
required per
cell; and (g) for multiple regression statistical designs, use at

least 10 par-
ticipants for each variable.
Literature Review
Another way to determine sample size is to review the
literature. Look
at studies that are similar to yours. Pay attention to past studies
that
employed the same method of research as yours and that used
similar
variables to the study you are planning. Be cautious, however,
when using
such norms, because many published studies have used too few
partici-
pants to have sufficient statistical power, even those published
in the most
prestigious journals.
Formulas
A third method of determining appropriate sample size is to use
a for-
mula. Krejcie and Morgan (1970) generated a model of
appropriate sample
size (S) required for given population sizes (N). Their model is
based on a
formula originally developed by the U.S. Office of Education
(see Table 8.3).
As shown in Table 8.3, the following general guidelines are
helpful in
determining sample size (Gay, Miles, & Airisian, 2006).
1. The larger the population size, the smaller the percentage of
the

population required to get a representative sample.
2. For smaller populations, say, N = 100 or fewer, there is little
point in
sampling; survey the entire population.
3. If the population size is around 500 (give or take 100), 50%
should
be sampled.
4. If the population size is around 1,500, 20% should be
sampled.
5. Beyond a certain point (about N = 5,000), the population size
is
almost irrelevant and a sample size of 400 will be adequate. (p.
110)
Of course, these numbers or percentages are suggested
minimums. If
it is possible to obtain more participants, you should do so.
Table 8.3 Sample Sizes (S) Required for Given Population Sizes
(N)
N S N S N S N S N S
10
15
20

25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
10
14
19
24
28
32
36
40
44
48
52
56
59
63
66
70
73
76
100

110
120
130
140
150
160
170
180
190
200
210
220
230
240
250
260
270
80
86
92
97
103
108
113
118
123
127
132
136
140
144
148
152
155
159

280
290
300
320
340
360
380
400
420
440
460
480
500
550
600
650
700
750
162
165
169
175
181
186
191
196
201
205
210
214
217
226
234
242

248
254
800
850
900
950
1000
1100
1200
1300
1400
1500
1600
1700
1800
1900
2000
2200
2400
2600
260
265
269
274
278
285
291
297
302
306
310
313
317
320

322
327
331
335
2800
3000
3500
4000
4500
5000
6000
7000
8000
9000
20000
15000
20000
30000
40000
50000
75000
100000
338
341
346
351
354
357
361
364
367
368
370
375

377
379
380
381
382
384
SOURCE: From R. V. Krejcie and D. W. Morgan, “Determining
Sample Size for Research Activities,”
Education and Psychological Measurement, 30, p. 608,
copyright © 1970 Sage Publications, Inc.,
Reprinted by permission of Sage Publications, Inc.
Power Analysis
A fourth and more precise method of determining an appropriate
sam-
ple size for your research is to calculate a power analysis. The
purpose of
a power analysis is to determine how many participants you will
need to
detect how much of a difference your independent variable
makes in rela-
tion to the dependent variable. Thus, power analysis allows you
to esti-
mate how many participants you will need to detect small
effects, medium
effects, and large effects.
Cohen (1988) and Olejnik (1984) explain how to calculate
power analy-
ses. Cohen (1992) and Olejnik (1984) provide brief, very
readable discus-
sions of power analysis as well as tables you can use to estimate
the
number of participants you will need to detect small, medium,

or large
effect sizes at the p < .01, .05, and .10 levels. The tables cover t
tests, analy-
sis of variance, correlations, chi square, and multiple regression
analysis.
INSTRUMENTATION
Up to this point in Chapter Three, you introduced your chapter
under the
heading “Introduction.” This was followed by a description of
the popu-
lation and sample under the heading “Selection of Participants.”
Your next
heading in Chapter Three is “Instrumentation,” under which you
will
describe the psychometric adequacy of the instruments you have
used in
your study. Each description should include the following key
points:
(a) name of the instrument, (b) acronym, (c) author(s), (d) key
reference(s),
(e) purpose of the instrument (i.e., what it measures), (f)
number of items,
(g) subtests or subscales and their definitions, (h) response
format (e.g.,
Likert, multiple choice, yes/no, open-ended), (i) scoring of the
instrument,
(j) reliability, and (k) validity (Heppner & Heppner, 2004).
All of the aforementioned key points provide important
information
about the instrument(s), but reliability and validity are critical.

The mea-
surement instrument that you use must be both valid and
reliable. On the
one hand, validity is the most important characteristic of an
instrument.
On the other hand, reliability is necessary for validity; an
instrument that
does not provide reliable measures cannot provide valid ones.
Validity
Validity is the degree to which an instrument measures what it
pur-
ports to measure. There are three main types of validity: (a)
content valid-
ity, (b) criterion-related validity, and (c) construct validity.
Content Validity
Content validity is the degree to which an instrument measures
an
intended content area. Content validity is of particular
importance for
achievement tests. A test score cannot accurately reflect a
student’s
achievement if it does not measure what the student was taught
and
was expected to learn. For example, most standardized
achievement
tests have good content validity. Content validity is determined
by
expert judgment.
Criterion-Related Validity
Criterion-related validity has two forms, concurrent and

predictive.
Concurrent validity is the degree to which scores on one test
correlate to
scores on another test when both tests are administered at about
the same
time. Predictive validity is the degree to which a test can
predict how well
individuals will do in a future situation. Predictive validity is
important
for tests that are used to select individuals. Examples are the
use of the SAT
or ACT as a criterion for admission to colleges and universities,
and the
Graduate Record Examination (GRE) or Miller Analogies Test
(MAT)
scores to select students for admission to graduate school.
Construct Validity
Construct validity is the most important form of validity,
because it
deals with what the instrument is really measuring. As
mentioned earlier,
all variables derive from constructs, such as intelligence,
achievement, per-
sonality, and so forth.
Content and criterion-related validity are used in studies to
determine
an instrument’s construct validity. No single validation study
can establish

the construct validity of an instrument. Instead, a serious of
studies would
be required to establish construct validity. Construct validity
provides jus-
tification of the instrument being used in a research study and
the appro-
priateness of the intended instrument interpretations.
Reliability
Reliability is the degree to which an instrument consistently
measures
whatever it is measuring. There are five different types of
reliability, each of
which deals with a different kind of instrument consistency: (a)
test-retest
reliability, (b) equivalent-forms reliability, (c) internal
consistency reliability,
(d) alpha reliability, (e) split-half reliability, and (f) interrater
reliability.
Test-Retest Reliability
Test-retest reliability is the degree to which scores on the same
instru-
ment are consistent over time. The procedure for determining
test-retest
reliability is as follows: Administer the instrument to an
appropriate group.
After about two weeks has passed, administer the same
instrument to the
same group. Correlate the two sets of scores. Evaluate the
results. If the
resulting coefficient is high, the instrument has good test-retest
reliability.

Equivalent-Forms Reliability
Equivalent forms of an instrument are two instruments that are
identi-
cal in every way except for the actual items included. Examples
are equiv-
alent forms of a norm-referenced standardized achievement test.
Also, in
some research studies, two forms of an instrument are
administered to the
same group, one as a pretest and the other as a posttest. The
procedure for
determining equivalent-forms reliability is as follows:
Administer one
form of the test to an appropriate group. At the same session, or
shortly
thereafter, administer the second form of the instrument to the
same
group. Correlate the two sets of scores. Evaluate the results. If
the result-
ing coefficient of equivalence is high, the instrument has good
equivalent-
forms reliability.
Internal Consistency Reliability
Internal consistency is a commonly used form of reliability that
deals
with one instrument at a time. There are three different
approaches to
obtaining internal consistency reliability: split-half, Kuder-
Richardson,
and Cronbach’s alpha.

Kuder-Richardson and Cronbach’s Alpha Reliabilities
Kuder-Richardson (KR) and Cronbach’s alpha estimate internal
con-
sistency reliability by determining how all items on an
instrument relate to
all other instrument items and to the total instrument. An
internal consis-
tency coefficient of .80 is acceptable for an instrument
containing 40 items.
If an instrument contains subscales, internal consistency
coefficients
should be obtained for each subscale. Instruments containing
fewer items
and subscales will typically have smaller reliability
coefficients.
Split-Half Reliability
Split-half reliability involves breaking a single instrument into
two
halves. The procedure for determining split-half reliability is as
follows:
Administer the total test to a group. Divide the test into
comparable
halves, most commonly by dividing the instrument into odd and
even
numbered subtests. Compute each subject’s score on the two
halves; each
subject will have a score for the odd items and a score for e ven
numbers.
Correlate the two sets of scores. Apply the Spearman-Brown
correction
formula. Evaluate the results. If the coefficient is high, the

instrument has
good split-half reliability.
Interrater Reliability
Reliability must be investigated also when scoring potentially
subjec-
tive instruments. Essay tests, performance tests, projective
tests, and
observations—almost any instrument that requires more than a
one-word
response—raise concerns about the reliability of scoring. In
such situa-
tions, researchers are concerned with interjudge (interscorer,
interobserver,
interrater) reliability. Estimates of interjudge reliability are
typically
obtained by correlation techniques but can also be expressed as
a percent-
age of agreement.
A note of caution: When you discuss your instrument and the
related
reliability and validity, remember that reliability is related to
the scores or
the data derived from your instrument; therefore, it is the scores
that are
either reliable or unreliable. You would not say, “The
instrument is reli-
able.” Reliability is a function of the scores and is related
specifically to
your sample or the norming sample for a standardized
instrument. You
should also be cautious in stating that the test or instrument is
valid. The
items of the instrument have, for example, content, criterion, or

construct
validity and should be discussed under those conditions. You
may also
wish to discuss the items related to face validity.
Examples of instrument descriptions from two dissertations
follow.
The first example of instrumentation is from Victoria Cadavid’s
(1986) dis-
sertation. The second example is from Linda Schmidt’s (1987)
dissertation.
(See Examples 8.10 and 8.11.)
Example 8.10
Instrumentation
Maslach Burnout Inventory
The Maslach Burnout Inventory (Maslach & Jackson 1981),
which was
entitled Human Services to disguise the purpose of the study,
was used to
measure dimensions of teacher burnout. It consists of 22 items
forming three
subscales: Emotional Exhaustion, Personal Accomplishment,
and
Depersonalization and is rated on both frequency of feeling and
intensity of
the feeling on each subscale producing six dimensions. The
frequency scale
ranges from 1 (a few times a year or less) to 6 (every day). A

value of zero is
given if the respondent never experiences the described attitude
or feeling;
a separate box labeled “never” is used in this event. The
intensity scale
ranges from 1 (very mild, barely noticeable) to 7 (major, very
strong).
The Emotional Exhaustion subscale, consisting of nine items,
describes feelings of being emotionally overextended and
exhausted by
one’s work. The five items on the Depersonalization subscale
describe
unfeeling and impersonal responses to coworkers or recipients
of ser-
vices. The Personal Accomplishment subscale consists of eight
items
describing feelings of competence and success towards one’s
achieve-
ments. The higher mean scores of Emotional Exhaustion and
Depersonalization subscales correspond to greater degr ees of
experienced
burnout, whereas lower scores on Personal Accomplishment
correspond
to greater degrees of burnout. One final score for burnout is not
com-
puted; instead, six separate scores are derived for burnout.
Internal consistency of the MBI was estimated by Cronbach’s
alpha
(Cronbach, 1951) for two samples (n = 1316 for frequency) and
(n = 1789
for intensity). The reliability coefficients for the subscales were
as follows:
.90 for Emotional Exhaustion Frequency, .87 for Emotional
Exhaustion

Intensity, .79 for Depersonalization Frequency, .76 for
Depersonalization
Intensity, .71 for Personal Accomplishment Frequency, and .73
for
Personal Accomplishment Intensity (Maslach & Jackson, 1981).
Data on
test-retest reliability (n = 53) ranged from .53 to .89 for the six
dimensions
of the MBI and were significant beyond the .001 level (Maslach
& Jackson,
1981). Convergent validity was established by Maslach and
Jackson
(1981). Individual MBI scores were correlated with behavioral
ratings
made independently by persons who knew the individual well,
such as a
spouse or co-worker. Second, MBI scores were correlated with
the pres-
ence of certain job characteristics that were expected to
contribute to
experienced burnout. Third, MBI scores were correlated with
measures of
various outcomes that had been hypothesized to be related to
burnout.
All three sets of correlations provided substantial evidence for
the valid-
ity of the MBI. (Cadavid, 1986, pp. 54–55)
Locus of Control
The Internal-External Locus of Control Scale (Rotter, 1966),
called the
Social Reaction Survey for purposes of this study, was used to
measure
the extent to which faculty had an internal or external locus of
control. It

consists of 29 forced-choice items of which 23 are keyed and
six are fillers.
Respondents choose one statement out of each pair of 29
statements. It is
scored in the direction of externality such that a higher score
indicates
external orientation. A total score of 12 or less out of 23
assesses an indi-
vidual as internally controlled and a score of 13 or more
assesses one as
externally controlled. Test-retest reliability coefficients of .43
to .84 have
been reported by Hersch and Scheibe (1967). Internal
consistency of the
scale ranged from .65 to .79 (Rotter, 1966). (Cadavid, 1986, pp.
53–54)
Pupil Control Ideology
The Pupil Control Ideology Form (PCI) measures the pupil
control
ideology of educators on a humanistic-custodial continuum. It
consists of
20 Likert-type items. Responses are scored from 5 (strongly
agree) to 1
(strongly disagree); the higher the overall score, the more
custodial the
ideology of the respondent.
Prior studies by Willower, et al. (1973) using the PCI Form
deter-

mined split-half reliability coefficients in two samples of .95 (N
= 170) and
.91 (N = 55) with the application of the Spearman-Brown
formula. Validity
of the instrument was supported by principals’ judgments of
some of
their own teachers. Further evidence of validity was established
by a
comparison of PCI scores of personnel from schools known by
reputation
to be humanistic, with scores of personnel from other schools
that were
not humanistic at the same grade levels (Cadavid, 1986, pp. 57–
58)
Example 8.11
Instruments
Quality of School Life
The Quality of School Life Scale (QSL) is a standardized 27-
item
forced-choice, multidimensional measure of three basic aspects
of the
quality of school life. The initial validation study (Epstien &
McPartland,
1976) indicated the existence of three factors: satisfaction with
school
(SAT), commitment to classwork (COM) and reactions to
teachers (TCH).
The satisfaction with school subscale examines student’s
general reactions
to school. Because school is a major part of youngsters’ lives,
students
who are positive in their evaluation of life in school may be

more likely to
experience feelings of general well-being. They also may be
more likely to
behave in socially acceptable ways and help other students in
the school
setting. The commitment to classwork subscale deals with the
level of
student interest in classwork. Tasks and assignments are what
makes
school different from non-school settings. In short, the work is
what
makes school a school. Students who find class assignments and
projects
interesting and important may learn facts and concepts more
completely
and may develop more positive attitudes toward learning. The
reactions to
teachers subscale examines student evaluations of instructional
and per-
sonal interactions with teachers. Student-teacher relationships
may be the
key to student acceptance of educational goals, student
understanding of
school procedures, differences in students’ independent or
dependent
behavior and attitudes toward authority in and out of school.
The first factor (SAT) consists of 5 items and the other two
factors
(COM) and (TCH) include 11 items each. Items use positive and
negative

statements and several response patterns to minimize response
set.
Examples of items include the following: “I like school very
much” (SAT);
“Work in class is just busy work and a waste of time” (COM);
and “I feel
I can go to my teacher with the things that are on my mind”
(TCH).
Responses to the questionnaire are scored so as to yield
measures on each
of the three qualities of school life factors, and the sum of the
factor scores
represents a global measure of the quality of school life.
Substantial evi-
dence as to the validity of the QSL Scale has been reported. The
overall
Kuder-Richardson reliability for the QSL is .87 and .89 for
secondary and
elementary students (N = 4,266), respectively. For the subtests,
reliability
coefficients range from .79 to .81 for the SAT subscale, .72 to
.80 for the
COM subscale and .64 to .73 for the TCH subscale (Epstein &
McPartland,
1978). (Schmidt, 1987, pp. 48–49)
First, note how efficiently most of the 11 key points related to
psycho-
metric adequacy, discussed earlier, are addressed in the
instrument
descriptions from the dissertations of Cadavid and Schmidt.
Second, note
how the writers have provided key references pertaining to the
psycho-
metric properties of the instruments rather than attempting to
summarize

hundreds of investigations. Third, we recommend that you
include a copy
of each instrument in the appendix of your dissertation or
master’s thesis,
if possible. If the instrument(s) are copyrighted, you will need
to get per-
mission in writing not only to use the instrument but also to
publish it in
your dissertation. Both permissions must be clearly stated in the
letter for
inclusion in your dissertation.
There should be a close match between your research questions
and
hypotheses and the measuring instruments you use. When there
is no pub-
lished instrument that closely matches your research purposes,
there are
three alternatives: modify slightly an existing instrument;
combine exist-
ing instruments; or develop a new instrument. The first two
alternatives
may make the norms invalid and may affect both the validity
and reliabil-
ity of the instrument. Developing and assessing a new
instrument is a
valid dissertation topic in itself (e.g., see Lauren Black’s 2003
dissertation,
referenced earlier in this book).
If you develop a new instrument to be used in your study, you
will not
have information on the psychometric adequacy of the
instrument. In this
case, you will need to conduct a pilot study and report the
results of that

study. Your report should include the number and demographic
charac-
teristics of the participants, the criterion used to develop the
initial item
pool and final items, and information concerning the reliability
and valid-
ity of the instrument.
The following example from Vickie Basham’s (1988)
dissertation
shows how this might be accomplished. (See Example 8.12.)
Example 8.12
Instrumentation
Strategic Planning
Having defined the concept of strategic planning, the next step
was to
develop a measure of that concept. Since a validated instrument
had not
yet been developed, the need arose to devise an operational
measure of
strategic planning.
One of the early decisions that had to be made in an attempt to
mea-
sure strategic planning centered on the method of measurement
to be used.
It was decided that a descriptive questionnaire was the most
feasible and

appropriate for this initial attempt to map the domain of
strategic planning.
This decision was prompted by the success previous
investigators have
experienced using the descriptive questionnaire technique
(Stogdill &
Coons, 1957; Halpin, 1956; Halpin & Croft, 1963; Gross &
Herriott, 1965;
Punch, 1967; Willower et al., 1967). While there are criticisms
of this tech-
nique, especially those that have called into question
respondents’ percep-
tions as a measure of “true” behavior (Charters, 1964; Erickson,
1965, 1967),
it was deemed appropriate for this study. It was decided to
adopt the sym-
bolic interactionist viewpoint taken by a number of previous
developers
and users of description questionnaires; i.e., that the technique
is justified
“ . . . more because of than in spite of the susceptibility of these
descriptive
statements to projective distortion . . .” (Brown, 1967, pp. 62–
73). The meta-
physical problem is provided by assuming that how an
individual really
behaves is less important than the way he is perceived to
behave, since it is
perceptions of strategic planning behavior that determines the
perceivers’
own actions. Best (1981) states that “descriptive design . . .
describes and
interprets what is. It is concerned with conditions or
relationships that exist,
opinions that are evident, or trends that are developing” (p. 93).

As an operational measure of strategic planning, an instrument
called
the Strategic Planning in Kentucky Schools (SPKS) Scale was
developed.
The final form of this instrument consists of twenty items with
several
response categories in a Likert-type format.
Construction of the instrument was begun by writing 63
statements,
which described specific strategic planning concepts. These
statements
were based on a review of the literature, the authors’ experience
in admin-
istering public school districts, and items developed by
members in a
research methods class.
The items were then screened for ambiguity, wording, and
content
overlap. To insure content validity, the resultant pool of items
was
subjected to the scrutiny and evaluation of four experts: a
professor
of educational administration at the University of Louisville, a
superin-
tendent of schools, an independent planning consultant, and an
adminis-
trator in the Department of Planning and Development in the
Kentucky
Department of Education. As a result of this initial work with
the instru-
ment, a 37- item form emerged for further modification.
In order to gather more systematic data on the instrument, the
37-item

version of the SPKS Scale was then administered in 89 (urban,
suburban,
and rural areas) school districts outside of Kentucky. A total of
66 usable
returns were collected from the superintendents of the selected
districts.
As a result of this field test of the instrument, several
statements were
modified and a number of statements were omitted, leaving
twenty items
in the final form of the instrument. An alpha coefficient
(Cronbach, 1951)
of .59 supported the moderate reliability of the scale. From
these data, it
was determined that the SPKS Scale was a relatively valid and
reliable
measure of strategic planning. (Basham, 1988, pp. 58–59)
Note the thoroughness with which Basham described the
development
of the SPKS Scale. Also note that the scale went through
intensive scrutiny
by four experts in the field (construct validity) and pilot testing
by a rela-
tively large sample of 66 superintendents selected from 89
school districts
outside of the state in which the study was conducted.
Another example of the development of a new instrument, from
Lunenburg and Schmidt (1987), follows. A description of the
procedures

used in developing the instrument together with a copy of the
instrument
is provided. This study was funded by a grant. (See Example
8.13.)
Example 8.13
Method
Test Construction
Construction of the instrument was begun by generating items
from a
review of the literature and from suggestions from practicing
school
administrators. In addition, members of the first author’s
graduate classes
in research methods contributed statements as an exercise in
item con-
struction. The items, which were statements describing specific
pupil dis-
ruptive behavior, were placed in questionnaire format using an
eight-point
Likert scale ranging on a continuum from “no action” to
“expulsion.” The
items were then screened for ambiguity, wording, and content
overlap. To
insure content validity, the resultant poll of items was subjected
to the
scrutiny and evaluation of four experts: a high school principal,
an assis-
tant principal, a professor of education administration, and a
statistics pro-
fessor. The critics were individually instructed to determine
which items
represented school realties. If a majority of the judges agreed

(interrater reli-
ability), an item was retained. This process generated 64 items.
Subjects
Subjects were 227 full-time educators from three states: Illinois
(N =
106), Iowa (N = 83), and Wisconsin (N = 38). Included were
regular educa-
tion teachers (51%), special education teachers (21%), school
administra-
tors (16%), and school counselors (12%). The subjects had an
average of
15.2 years of experience and an average age of 43.4. Of the 227
subjects, 141
were male and 86 were female. All subjects were employed in
high schools.
Data Analysis
A principal components factor analysis, using an orthogonal
rotation
with varimax solution, provided the best solution. In order to
maximize
simple structure and theoretical clarity, items that exhibited
factor load-
ings whose absolute value was .40 or greater were used to
define a factor.
Application of the Scree Test (Cattell, 1966) clearly identified
three inter-
pretable factors. The three factors accounted for 45% of the

variance. The
35 items, which did not conform psychometrically or
theoretically to any
factor were eliminated. Alpha coefficients (Cronbach, 1951) of
.93 for the
total scale and for the respective factors (.91, .79, and .73)
supported their
moderately high reliability.
Results
The final form of the PDB together with their factor loadings
are pre-
sented in [Table 8.4].
Factor 1 had salient loadings on eleven items dealing with
general
indifference to school rules. Included were items about
unexcused
absences from school or class, tardiness to school or class,
smoking in
unauthorized areas of the school building, and minor acts of
insubordi-
nation and classroom distractions.
Factor 2 contained ten items, which appeared to be more serious
vio-
lations of school rules and, in some cases, violations of state
law. It was
comprised of items concerned with fighting, theft, profanity,
vandalism,
battery, and the possession, use, and sale of drugs.
The eight items loading on Factor 3 dealt primarily with minor
dis-
ruptions of school and classroom activity. Included were items

concerning
disobedience of posted school rules, inappropriate dress,
cheating on
examinations, and classroom interruptions.
Conclusion
The purpose of this present study was to develop an operational
mea-
sure of pupil disruptive behavior. The instrument empirically
identifies
incidents of disruptive behavior in schools and their
dimensions. While
more narrowly defined, the incidents of disruptive behavior in
the pre-
sent study seem closely related to those suggested in the
literature and by
practicing school administrators. The study suggests that the
PDB is a
short, direct, reliable measure useful at the high school level for
research
and evaluation. (Lunenburg & Schmidt, 1987, pp. 1082–1084)
Table 8.4 Factor Structure for the PDB Scale Items
Item Factor
Factor 1
58. A boy skips school and goes skiing. .746
19. A girl is observed smoking in the girls’ lavatory. .706
12. A boy is late for school and submits a forged note as an
excuse. .672
61. A girl skips a class and spends her time roaming the school

.669
corridors.
16. A girl refuses to participate in physical education activity.
.661
11. A boy is seen smoking in the school cafeteria. .635
(Continued)
Item Factor
Factor 1
09. A girl is late for school and uses the excuse that her car .604
wouldn’t start.
39. A boy is summoned to the assistant principal’s office and
arrives .599
one hour later without an explanation.
07. After being reprimanded by her teacher, a girl makes an
obscene .584
gesture using the middle finger of her right hand.
27. A girl tears a page out of a reference book in the library.
.561
54. A girl is observed setting a fire in a waste basket in the
girl’s lavatory. .555
Factor 2

44. A boy is observed stealing money from another boy’s
locker. .762
15. Two girls are observed fighting in the school cafeteria. .690
60. A girl is seen smoking marijuana in the school parking lot
shortly .641
before school.
32. A boy is discovered carving his initials in a desk during
class. .627
30. A boy is observed selling drugs to another student. .603
57. A boy is seen puncturing the tire of a teacher’s car. .595
51. After being reprimanded by his teacher, a boy shouts, “ki ss
my ass,” .564
to the teacher.
33. During an authorized search of a boy’s locker, drugs are
discovered .516
in the student’s locker.
46. A boy is observed throwing a rock through a window of the
school .508
building shortly after school.
52. After being reprimanded by his teacher, a boy strikes the
teacher in .469
the face with his fist.
Factor 3
35. A boy comes to school with an earring in his ear. .645
41. A boy is observed playing basketball in the gymnasium in
his street .620
clothes after being warned that such behavior is in violation of

gymnasium rules.
13. A girl walks across the grass on the school campus in an
area .547
marked “keep off.”
08. A girl is discovered copying answers from her own prepared
.522
“crib notes” during an examination.
18. A boy interrupts a classroom activity by shouting at a friend
in the .505
corridor.
42. A boy is observed riding his bicycle in the school corridor
shortly .502
after school.
23. A girl interrupts a class by laughing and talking while the
teacher is .440
lecturing.
55. A boy disrupts a class by throwing an eraser at another
student. .401
Response Categories:
No Mild Severe Detentions Detentions Suspension Suspension
Expulsion
Action Reprimand Reprimand 1 to 3 4 to 8 1 to 3 4 to 10
Hours Hours Days Days

You will note that the instrument Basham developed for use in
her dis-
sertation was a unifactor instrument. A factor analysis of the
instrument
was conducted as part of her dissertation. The second
instrument, devel-
oped by Lunenburg and Schmidt, was a multifactor instrument
consisting
of three distinct subscales. Note how content validity and
reliability (inter-
nal consistency) of the instrument and its subscales, using
Cronbach’s
alpha, were reported. All newly developed instruments as w ell
as all
existing instruments used in a dissertation must be subjected to
a factor
analysis using your research sample. Whether master’s theses
need to
follow the same procedure will depend on your committee’s
requirements.
We have described the psychometric adequacy of instruments
followed by examples of instrument descriptions from several
disserta-
tions. We have discussed also the development of new
instruments fol-
lowed by examples of them. Where do you look for such
measures?
Locating Instruments
Instruments are available to you, if you know where to look for
them.
The following published documents provide valuable
information about
instruments.

1. Mental Measurements Yearbooks (MMYs) represent the most
compre-
hensive source of test information available to educational
researchers.
2. Tests in Print (TIP) is a comprehensive bibliography of all
tests
that have appeared in preceding MMYs. It includes more
listings
than MMYs and is a good source for locating addresses and
jour-
nal articles relevant to a particular instrument. However, it is
rather dated and will not have listings for instruments available
after 1974.
3. Pro-Ed Publications provide information on over 3,000
instruments
intended for assessment in psychology, education, and business .
4. Educational Testing Service (ETS) Test Collection is an
excellent source
of test information, especially for recently developed tests. It
includes a library of 20,000 tests and other instruments from the
early 1900s to the present. The ETS Test Collection can be
accessed
via the Internet at: http://www.ets.org.
5. Professional journals regularly publish information of
interest to
researchers. Journals of interest to researchers include: Journal
of Applied Measurement, Journal of Consulting Psychology,
Journal of
Educational Measurement, Journal of Educational Psychology,
Journal of
Personnel Psychology, Educational and Psychological

Measurement, and
Psychological Abstracts.
6. Test publishers provide additional information on
instruments. For
example, publishers’ test manuals typically include: detailed
valid-
ity and reliability data, a description of the population for
whom the
instrument is intended, a detailed description of norming proce-
dures, conditions of administration, detailed scoring
instructions,
and scoring interpretation.
7. Dictionary of Behavioral Assessment Techniques provides
descriptions
of 286 devices and is a very good source of introductory
information
about an assessment measure. Reviews and references are
provided
to locate instruments used by researchers in the social and
behav-
ioral sciences.
8. Search features are available online in the ERIC/AE Test
Locator at
http://ericae.net/testcol.htm. (This is the same Web address as
that
of the ETS Test Collection Database.) These are the Test
Review

Locator and Buros/ERIC Test Publisher Directory. The Test
Review
Locator allows you to search for citations about a particular
testing
instrument in MMY and Pro-Ed directories. The Buros/ERIC
Test
Publisher Directory permits you to search for the names and
addresses of more than 900 major commercial test publishers.
Qualitative Instrumentation
Paper-and-pencil measurement devices are not the only data
collec-
tion instrumentation. Interviews, observations, and archival data
all
constitute valid sources of data collection for dissertations and
master’s
theses. Such types of instrumentation are frequently used by
qualitative
researchers.
Interviews
Qualitative researchers often use open-ended interviews (i.e.,
ques-
tions are asked but respondents are not provided choices to use
as
answers, such as Likert scales, multiple choice, yes/no).
Typically, these
are semistructured questions. That is, some questions are
developed in
advance. Follow-up questions are developed as the interview
progresses
based on participant responses.
The following excerpt from Wanda Bamberg’s (2004)

qualitative dis-
sertation shows how this might be accomplished. (See Example
8.14.)
Example 8.14
Instrumentation
The first instruments in my study were semi-structured, open-
ended
interviews, with 15 respondents from the five school districts.
Merriam
(1998) defined semi-structured interviews as interviews that
evolve from
inquiry composed of a mix of both structured and unstructured
ques-
tions. The unstructured questions were open-ended to allow the
respon-
dents (participants) more freedom and creativity to respond to
the
questions (Sowell & Casey, 1982).
I used semi-structured, open-ended interviews with the five
superin-
tendents and semi-structured, open-ended interviews with board
members and central office administrative team members. The
semi-
structured interview approach provided reasonably standard data
across
participants but also allowed me the flexibility to probe answers
more
deeply and gather more information than is found in a
structured inter-

view (Gall et al., 1996). To relate my study to previous work
with SLT,
questions from the revised Organizational and Leadership
Effectiveness
Inventory (OLEI) were reviewed to create the interview
questions. Three
sets of interview questions were developed: one set for the
superinten-
dents, one set for the board members, and one set for central
office admin-
istrative team members (see Appendixes A, B, and C for
interview
questions). All the interview questions were directly correlated
to the
research questions and based upon the four factors of the
Synergistic
Leadership Theory. Questions were designed to allow
participants an
opportunity to reflect upon the five superintendents’ leadership
styles
and how they relate to the SLT. (Bamberg, 2004, pp. 51–52)
Note how effectively Bamberg described her qualitative
instrumenta-
tion. Note also that there can be many variations to this
example. Your
objective is to write a description that is as specific as possible
while pro-
viding you with the flexibility consistent with qualitative
research.
Observations
Another measurement technique frequently used by qualitative
researchers is observation. Observation can take two forms:
participant

observer and nonparticipant observer. A participant observer is
one who
participants as a member of a group while making observations.
A nonpar-
ticipant observer is one who makes observations but is not
participating. If
you plan to use observations, consider asking yourself the
following ques-
tions to help you write this section of your dissertation or
master’s thesis:
(a) What will I be observing; (b) who will I be observing; (c)
when and where
will my observations take place; (d) how will I record the
observations;
(e) how long will I observe; and (f) will I solicit feedback from
my participants?
Archival Data
A third technique often used by qualitative researchers is
analysis of
records or documents. Qualitative researchers examine various
types of
documents, including archival documents (e.g., student records,
standard-
ized test scores, retention rates, minutes of meetings, newspaper
clippings);
journals; maps; videotapes; audiotapes; and artifacts.
In your section on instrumentation, you should address the issue
of
demographics (e.g., gender, race/ethnicity, age, years of

experience, edu-
cation level). Demographic data will provide your readers with
a picture
of your participants. Demographic information should be
included in both
quantitative and qualitative dissertations and master’s theses.
We mention
it here again because you need to decide how to attach a
“demographic
data sheet” to your measuring instruments. If you fail to gather
demo-
graphic data at the time the instrument(s) are administered, it is
unlikely
that you can obtain this important information later.
DATA COLLECTION
In the “Data Collection” section of your dissertation or master’s
thesis,
describe precisely the physical things you did to obtain data
from your
participants. Indicate what steps were taken before, during, and
after data
collection. Before collecting data, it may be necessary for you
to develop
materials, obtain informed consent from participants, acquire or
develop
questionnaires or other instruments, screen participants from a
list, mail a
precontact letter, train experimenters or assistants, conduct a
pilot study,
and comply with your university’s Human Subjects Committee.
During
data collection, describe what participants will be asked to do,
what inter-
ventions were used, the order instruments were administered,

precise
instructions given to each participant, time elapsed between
activities, and
how data were collected. When data collection procedures are
complex
and require multiple phases over multiple time periods, a flow
chart pre-
senting the procedures visually may be very helpful to the
reader. After
data collection, describe how participants will be debriefed. In
sum, this
section should be written in enough detail so that the procedures
could be
replicated by another investigator.
Following are examples from “Data Collection” sections from
students’ dissertations. The first example from Diane
Trautman’s (2000)
dissertation provides a model of a data collection section from a
mixed
methods (quantitative/qualitative) study. (See Example 8.15.)
Example 8.15
Data Collection
This study employed a qualitative and quantitative methodology
of
data collection and analysis. These two methodologies will be
explained
separately.
Quantitative
Data Collection. The first step in the quantitative data
collection

process included mailing to the 800 participants in the sample a
copy of
the Organizational and Leadership Effectiveness Inventory for
validation
of the Synergistic Leadership Theory. Address labels received
from the
Market Data Retrieval Company (2000) were numbered 1 to
800, and then
the inventories were coded accordingly in the same way in order
for each
participant to be identified by the researcher. A master list of
all 800 par-
ticipants was retained by the researcher and were recorded as
responses
were returned. This list was kept so that follow-up and reminder
letters
could be mailed to participants and also to select the purposeful
sample
for telephone interviews. Once participants were selected, the
numbers
and lists were destroyed.
The first mail-out included a cover letter explaining the study, a
copy
of the Organizational and Leadership Effectiveness Inventory,
and a
postage paid return envelope. Respondents were given two
weeks to
return the inventory, and only 73 out of 800 were returned by
that dead-
line giving a 9.12% return rate. A bright yellow post card

including a new
deadline of two more weeks was sent out reminding respondents
to
return the inventory. The second mail-out resulted in a return
rate of
16.25% with a total of 130 surveys returned. In the third and
final mail-
out, a cover letter explaining the importance of the study and a
new dead-
line of two additional weeks, a new copy of the inventory, and a
postage
paid return envelope was mailed out resulting in a final return
rate of 30%
with 243 of 800 surveys returned.
Qualitative
The qualitative method of data collection incorporated
telephone
interviews and open-ended responses from the further comments
area of
the OLEI to validate the theory. The following steps were taken
in the tele-
phone interviews:
1. Twelve names were chosen from eight separate lists
comprised of
male and female superintendents, assistant superintendents,
secondary
principals, and elementary principals. The level of management
repre-
sented was as follows: 2 superintendents (1 male selected from
71 names,
1 female selected from 75 names); 2 assistant superintendents (1
male
selected from 65 names, 1 female selected from 70 names); 6

secondary
principals (4 males selected from 55 names, 2 females selected
from 62
names); and 2 elementary principals (1 male selected from 74
names, and
1 female selected from 76 names).
2. These participants were telephoned and asked if they would
par-
ticipate in a telephone interview about the new theory. The
model and
description of the Synergistic Leadership Theory were faxed to
partici-
pants along with interview consent forms, and a request for a
convenient
time for the telephone interview.
3. During the telephone interviews, respondents were asked to
give
their perceptions of the Synergistic Leadership Theory and
examples of the
theory in action. The participant was encouraged to give
examples of situ-
ations, people, and organizations that fit their specific
perception of the
theory, making the interview more of an informal
conversational interview.
The strength of the informal conversational approach to
interviewing is that
is allows the interviewer to be highly responsive to individual
differences

and changes (Patton, 1987). One of the benefits of this type of
interview is
that questions can be individualized to establish in-depth
communication
with the person being interviewed (Patton, 1987). Interviews
were taped
and transcribed and lasted approximately ten to fifteen minutes.
4. Finally, the 243 inventories were sorted through to find those
that
were returned with open-ended responses in the further
comments area
of the OLEI. The 22 inventories that were returned with further
comments
were sorted demographically as well and included the
following: 3 female
and 4 male superintendents; 3 female and 1 male assistant
superinten-
dents; 3 female and 1 male secondary principals; and 6 female
and 1 male
elementary principals. (Trautman, 2000, pp. 89–93)
Note how Trautman described many of the details that should be
included in a “Data Collection” section using a mixed methods
research
design.
The following is a data collection section from Joan Slater’s
(2001)
qualitative study in which she used multiple sources of data
collection
divided into three separate phases. (See Example 8.16.)
Example 8.16
Data Collection

The study was conducted using multiple sources of
documentation:
questionnaires, focus groups, and student essays. This is
concordant with
Yin’s (1994) recommendation to use multiple sources of
evidence that con-
nect the questions asked to the data collected and conclusions
drawn. This
study employed a qualitative method of data collection that
helped the
researcher to analyze the motivation of at-risk ninth grade
students enrolled
in online courses and to generalize findings to the target
population.
Before data collection began, written consent from the school
district
administration office and from the Committee for the Protection
of
Human Subjects at Sam Houston State University was granted
to conduct
the study (see Appendix D). Three separate phases of data
collection were
included as part of this study.
Phase I
The first phase of data collection included the administration of
an
open-ended questionnaire of 10 questions to 24 ninth grade
student par-
ticipants who had been identified as being at-risk and who had
been
enrolled in both core and elective courses online. All
questionnaires were

administered to students at their respective campuses. Students
were
given an unlimited amount of time to answer the questions.
Lines pro-
vided on the questionnaire helped students to avoid answering
questions
with a few words or phrases. Students filled out all the lines
that were
provided on the questionnaire. Through this phase of data
collection,
emerging themes were identified, and additional ideas and
attitudes were
highlighted so that they could be incorporated into Phase II. I
adminis-
tered the questionnaire at one of the high schools, and the lead
teachers
for the initiative at each of the other two high schools
administered the
questionnaires at each of their respective schools.
Phase II
The second phase of the data collection employed four focus
groups, each comprised of six students who had been receiving
online
instruction for nine months in both core courses and electives.
Students
participating in the focus groups responded to questions that
were
based on data collected during Phase I. A scheduled and
designated

area was set up for the researcher to conduct focus groups at all
three
campuses. Each focus group lasted approximately 30 minutes,
and each
focus group interview was tape-recorded and transcribed with
the per-
mission of the participants and their parents. The focus group
was con-
ducted to gain information regarding the student’s perceptions
of the
impact of online instruction on their motivation. At each focus
group,
the researcher was accompanied by another central office
administrator
who was unknown by the student participants. This phase of
data col-
lection validated data collect in Phase I, and it resulted in the
emer-
gence of additional information.
Bogdan and Biklen (1998) noted that group interviews could be
useful
in bringing the researcher into the world of the subjects. In this
situation, a
number of people are brought together and encouraged to talk
about the
subject of interest. Group interviews are particularly useful if
adolescents’
perspectives on a particular issue are being studied. Young
people tend to
be stimulated to talk more expansively when others of their age
join them.
Phase III
The third phase of data collection included two focus groups,

com-
prised of six students each who had participated in both Phase I
and
Phase II of data collection. Questions asked in these interviews
were
based on questions previously asked since participants were
asked to val-
idate responses provided previously. These selected interviews
for
member checking also referenced the responses provided by the
student
essays that were administered during the first week of online
instruction.
This approach employed the strategy of member checking, the
process of
having research participants judge the accuracy and
completeness of
statements made in the researcher’s report (Gall, Borg, & Gall,
p. 763).
Students were shown a written summary of Phase I and Phase II
results.
Also, during this phase, participants were also encouraged to
add any
pertinent information that had not been included in the data.
During all phases of data collection, the students’ identities and
confidentiality were ensured. Names and student identification
numbers were not included in any summary information. (Slater,
2001,
pp. 63–65)
Note how thoroughly Slater describes the details of each of the

phases
of data collection she used in her qualitative study.
The following is a data collection section from Scott Barrett’s
(2000)
quantitative dissertation in which he used two different data
collection
methods: mailed survey and online survey. (See Example 8.17.)
Example 8.17
Data Collection
Data were collected by the administration of the MIS utilization
sur-
vey to school principals in two ways. The first method consisted
of mail-
ing the survey to selected principals. From the systematic
sample list of
principals, every second principal was selected to receive a
mailed survey.
A total of 82 survey packets were mailed, which included the
survey form
(survey on the front with a stamped, return address on the back)
and a
consent form. The second method consisted of distributing the
survey uti-
lizing electronic mail and a web-based technology to the
remaining prin-
cipals on the list. This strategy incorporated sending the consent
form to
the principals through electronic mail. Within the message was
the univer-
sal resource locator (URL) address of the on-line survey form
(http://www
.conroe.isd.tenet.edu/doctoral/misus.htm). The principal

provided consent
by visiting the specified URL and completing the on-line
survey. Once the
on-line survey was completed and submitted for processing, the
results
were automatically entered into an on-line FilmMaker database
running on
a Macintosh G3 server utilizing StarNine’s WebSTAR Internet
web server
software. The resulting data from the mailed survey and from
the on-line
survey were entered into a computer program that allowed for
aggregation
and disaggregation of the data according to the criteria
identified by the
study. (Barrett, 2000, pp. 82–83)
Although Barrett’s description of the data collection procedure
is not
as detailed as the previous models, he does walk the reader
through the
process just as the participant experienced it.
The following is a data collection section from a correlational
study.
The data collected was used to predict graduation rates at UCEA
public
universities using correlation and multiple regression analysis
(Creighton,
2006). (See Example 8.18.)
Example 8.18
Data Collection
The data used were the student and institutional factors that

could pre-
dict graduate rates at University Council for Educational
Administration
(UCEA) public universities. Data were collected on: (a)
students’ SAT
scores; (b) percentage of students graduating in the top 10% of
their high
school class; (c) percentage of students paying out-of-state
tuition; (d) per-
centage of students of underrepresented populations (i.e.,
African
American, Hispanic, Asian, and Native American); and (e)
percentage of
students age 25 and older. Data were also collected regarding:
(f) percent-
age of full time faculty; (g) the average salary of full time
faculty; (h) class
size (i.e., percentage of classes with less than 20 students;
percentage of
classes with 50 or more students); and (i) total student
expenditures (i.e.,
tuition and fees, instruction, research, student services). The
variation, if
any, in the educational outcomes for the 63 universities in the
sample group
is explained by the variation of the student and institutional
factors as a
result of a multiple regression analysis. (Creighton, 2006, pp.
53–54)
Note how succinctly Creighton described the 11 student and

institu-
tional factors that were used to predict graduation rates at all 63
UCEA
public universities in her sample.
The following data collection section is from a mixed methods
study
focused on the development of a new instrument for measuring
princi-
pals’ efficacy (Black, 2003). (See Example 8.19.)
Example 8.19
Data Collection
As an initial information gathering part of the study, an expert
panel
on educational leadership and efficacy reviewed the preliminary
model of
principal efficacy and provided feedback on strengths,
weaknesses, omis-
sions, and recommended changes. Experts of educational
leadership
came from the list members of the National Council of
Professors of
Educational Administration (NCPEA). Authors and researchers
who have
contributed to the body of research on self-efficacy and teacher
efficacy
were contacted for participation in gathering information on
factors that
contribute to principal efficacy and review of the preliminary
principal
efficacy model (see Appendixes A and B). All individuals were
contacted
via electronic mail to elicit participation. Follow up electronic

mail mes-
sages were sent to targeted participants in this phase of the
study (see
Appendix C). All participants were provided an explanation of
the
researcher’s purpose of the study and the intended outcome of
the
research process. Each participant was guaranteed anonymity
and
assured his/her feedback would be kept confidential.
Participants were
also provided the assurance that the option to remove
themselves from
the research process at any time was afforded.
A preliminary Principal Efficacy Scale was devised from the
body of
research on self-efficacy and teacher efficacy and on the role of
the princi-
pal. Additional information incorporated into the Principal
Efficacy Scale
was gleaned from analysis of current instruments that measure
self-efficacy
and input from the initial information-gathering phase described
above.
Upon completion of development, the Principal Efficacy Scale
was
presented to the stratified random sample derived in the
selection process
described previously (see Appendix D). The researcher chose

participants
randomly from every state’s department of education website
that lists
school districts and principals from district schools. Addresses
that were
returned as undeliverable by the Web server were replaced by
another
principal from the same region of the state as the original
individual
selected in the random sampling procedure. Contact was made
via elec-
tronic mail with a Web link to the Principal Efficacy Scale that
was
designed on an Internet survey website (see Appendix D).
General demo-
graphic questions were posed to obtain information on gender,
years of
experience as a principal, state, school geographic descriptor
(urban, sub-
urban, rural), and level of school (elementary, middle/junior
high, high,
alternative, other). Participants were provided the opportunity
to enter
their own electronic mail address to indicate an interest in
reviewing the
results of the study. A follow up request was made via
electronic mail two
weeks after the initial request for participation (see Appendix
E) to
encourage potential participants to respond. (Black, 2003, pp.
53–54)
Note how Black described the development of a new instrument,
the
Principal Efficacy Scale, and how she referred readers to five
separate

Appendixes for further information concerning instrument
development.
DATA ANALYSIS
The next section in the Methodology Chapter is titled “Data
Analysis.” In
this section, you describe the statistical tests that you used to
address your
research questions on hypotheses. You should carefully consider
each of
your research questions or hypotheses and determine the
respective sta-
tistical analysis that would be appropriate to test each one.
Following this
procedure will help you when you actually analyze your data.
Furthermore, a complete description of your analysis will
benefit other
researchers who wish to replicate your analysis.
Presentation of statistical analysis should include: the name and
description of each technique, the dependent and independent
variables,
the level of significance, and the research questions or
hypotheses
addressed by the analysis. If the type of statistical analysis is
commonly
employed by researchers in the field, it is not necessary to
include a
lengthy description of the analysis technique. However, a
reference, for-
mula, and a more detailed description should be provided for
established
types of analyses that are not commonly employed, as well as
techniques
that are unnamed or little known in the field.

Describe any subsequent analyses, commonly known as post hoc
analy-
ses, you need to perform should you find statistical significance
using
analysis of variance and chi square. There are six common
multiple com-
parison tests: Fisher LSD, Scheffé test, Turkey HSD, Newman
and Kewls
multiple range test, Duncan new multiple range test, and Dunn
Bonferroni
t statistics. Furthermore, the American Psychological
Association (2001)
and several research journals now require that investigators
report appro-
priate indicators that illustrate the strength or magnitude of a
difference or
relationship along with measures of statistical significance. This
require-
ment applies to dissertations and master’s theses as well. These
effect
magnitude measures, as they are called, are either measures of
strength of
association or effect size. Consult Field (2000) or Sprinthall
(2000) for a com-
plete treatment of post hoc tests, and Cohen (1988, 1992) and
Olejnik (1984)
for examples for calculating and reporting effect size.
The appropriate use of a given statistical technique should
depend ulti-

mately on the ability of the procedure to address your research
questions or
hypotheses, not on the complexity of the statistical analysis.
You should be
aware of the statistical techniques used in your field and where
to find infor-
mation about them. We believe that most graduate students are
capable of
mastering the statistical aspects of their dissertations or
master’s theses. We
devote an entire chapter in this book to statistical techniques
used in the
social and behavioral sciences (see Chapter 4, “Basic
Statistics”).
Careful, systematic attention to analysis of qualitative data is
required of
the serious qualitative researcher. If you are doing a qualitative
dissertation,
you should describe in detail which methods of analysis you
will employ
and the major steps you will follow. We devote an entire
chapter to qualita-
tive methods (see Chapter 5, “Qualitative Research Designs”).
In addition,
we have provided several examples of qualitative methods in
this chapter.
An example of data analysis from Diane Trautman’s (2000)
dissertation
follows. It is a mixed methods study. (See Example 8.20.)
Example 8.20
Data Analysis

The study employed a qualitative and quantitative methodology
of
data collection and data analysis. These two methodologies will
be
explained separately.
Quantitative
Quantitative analysis of the data included numerical ratings
obtained
from items 10–79 on the Organizational and Leadership
Effectiveness
Inventory. Responses ranging from 1 to 4 were input into SPSS
9.0 for each
of the 243 respondents along with their demographic data.
Data were analyzed by using the SPSS 9.0 program to run
statistical
tests. Tests of statistical analysis were performed to determine
theory val-
idation. The frequency and percentage of responses to items 10–
79 on the
inventory were displayed using descriptive statistics and tables.
A table
showing the demographic breakdown of the sample was
obtained from
the SPSS program. An item analysis indicated the mean
response of males
and females for each of the 70 items on the Organizational and
Leadership
Effectiveness Inventory. Then, a t test for independent means

was per-
formed on each item, 10–79, for males and females to determine
any dif-
ferences between male and female response on each item. A t
value of
−1.96 or less indicated statistical significance at p = .05. A two -
sample t test
for independent means was calculated to compare male and
female
responses on all 70 items for statistical differences as well.
Finally, mean responses were determined for females from each
of the
four levels of management, and then a one-way analysis of
variance was
conducted to determine any statistical differences between
female admin-
istrators at different levels of management. The level of
significance was
set at p = .05 for the ANOVA.
Qualitative
The qualitative analysis of data was comprised of analysis for
similarities
and differences, coding and categorizing, and constant
comparison. Twenty-
two of the 243 inventories returned included open-ended
responses under
the further comments section of the OLEI and were analyzed for
similarities
and differences. The 12 telephone interviews were taped and
transcribed
word for word. Using the constant comparative method,
interviews were
compared to each other as they were collected to determine

similarities and
differences. Categories were formed, coded, and triangulated
for both the
open-ended response data and the telephone interview data by
using a color
code representing different themes that emerged from the data.
Themes were
determined for each research question and those themes were
compared to
each other for further analysis. Additionally, themes from the
data were com-
pared to existing literature on leadership theory, characteristics
of feminine
leadership style, and the Synergistic Leadership Theory.
The constant comparative method is a research design for multi -
data sources similar to analytic induction in that the formal
analysis
begins early in the study and is nearly completed by the end of
data col-
lection (Bogdan & Biklen, 1998). This method of data
collection was
implemented after each phase of the data collection, constantly
analyz-
ing and comparing each new interview received to the
Synergistic
Leadership Theory.
Procedural Fidelity
Quantitative. An extensive review of the literature is a
characteristic
of procedural fidelity for quantitative studies. The review
should be
intensive and significantly affect the particular study (Gay &
Airasian,

2000). This study did conduct an extensive review of current
leadership
theories with an androcentric bias which did affect the study in
that it
illustrated a need for a new theory inclusive of the feminine
perceptive.
Additionally, the sample was randomly selected which also adds
to
the generalizability of the study. According to Gall, Borg, and
Gall (1998),
random selection shows an intent to select a large representative
sample
in order to generalize results to a population. This study
conducted a
random selection of a large, nationwide sample, which included
equal
numbers of males and females.
Measurement and analysis of quantitative data is standardized
and
numerical and gives greater objectivity to results (Gay &
Airasian, 2000).
In this study, numerical data was obtained from the
Organizational and
Leadership Effectiveness Inventory and was analyzed by a
computer
analysis program for greater objectivity. Additionally,
quantitative analy-
sis usually involves statistics, which can be used to make
inferences from

the findings (Fitzgibbon & Morris, 1987). Statistical tests of
significance
were conducted in this study such as t tests and an ANOVA to
provide a
value-free and objective result.
Qualitative. Triangulation (Lincoln & Guba, 1985) is a process
used in
qualitative studies to address questions of validity. The
researcher must
consciously utilize designs, which seek counter patterns as well
as con-
vergence if data are to be credible (Lather, 1991). Denzin
(1978) identifies
three basic types of triangulation: data triangulation,
investigator trian-
gulation, and theory triangulation (Denzin, 1978).
Data triangulation is the use of a variety of data sources or
subjects in
a study. This study employed data triangulation by interviewing
male
and female public school executives in secondary and
elementary levels
as well as positions in central office administration to obtain
their per-
spectives on leadership styles. Generalizability is promoted
when replica-
tion is done with other subjects matched as closely as possible
who share
the same problems (Gay & Airasian, 2000).
Investigator triangulation is defined as the use of several
different
evaluators, and the study accomplished this by letting the
subjects

become part of the investigation team in helping to validate the
theory
since the theory is built on their perspectives. Additionally, an
expert
group of university professors validated the Organizational and
Leadership Inventory and telephone interview questions for face
valid-
ity. Also, the researcher used a three-person team composed of
scholar
practitioners in several stages of data collection and
interpretation. The
team was composed of 3 male principals, one Hispanic and two
Anglo,
who reviewed and discussed the findings of this study with the
researcher and were given opportunity for feedback. The
feedback pro-
vided by the team reflected their experiences were the same as
the find-
ings and on no points did they differ from the researcher’s
findings.
Theory triangulation is the use of multiple perspectives/theories
to
interpret a single set of data, and the comparison of feminine
leadership
theory to 12 other leadership theories does exactly that. “Tying
emergent
theory to existing literature enhances the internal validity,
generalizability,
and theoretical level of the theory building . . .” (Eisenhardt,
1989, p. 545).
(Trautman, 2000, pp. 89–96)
Note how thoroughly Trautman described her quantitative and
quali-
tative data analyses. She included the name and description of

each
technique for both the quantitative and qualitative aspects of her
study.
Note also that she discussed procedural fidelity for both the
quantitative
and qualitative aspects of her study.
SUMMARY
The last section of Chapter Three is titled “Summary.” The
summary should
be substantive in nature; that is, it should repeat the contents of
the headings
that appeared in the body of the chapter (“Selection of
Participants,”
“Instrumentation,” “Data Collection,” and “Data Analysis”) but
in a con-
densed form. The summary should conclude with a transition
sentence such
as, “The following chapter contains a presentation and analysis
of data.”
An example from Rose Hernandez’s (2004) dissertation follows.
(See
Example 8.21.)
Example 8.21
Summary
This chapter restated the purpose of this research and presented

the
research questions. The participants were chosen through a
stratified ran-
dom sample of public school superintendents and their
respective school
boards in the United States. The selection of the 2,000
participant sample
from the target population was discussed. In addition, the
validity and
reliability of the instrument was presented, including the tests
of reliabil-
ity of the revised instrument. Data reliability tests (see Table II)
performed
on the pilot study of the revised OLEI indicated the instrument
was reli-
able. The data collection procedures and response rates were
also dis-
cussed in this chapter. Finally, the methods of data analysis for
each of the
research questions were presented, followed by a discussion of
statistical
power analysis. Results of the data analysis are presented in the
following
chapter. (Hernandez, 2004, p. 122)
Note how efficiently yet comprehensively Hernandez
summarized
Chapter Three of her dissertation, and how she provided the
reader with
a transition to the next chapter.
CONCLUSION
Chapter Three, “Methodology,” of the dissertation or thesis
contains a
detailed description of your research plan. In it, you describe

the selection
of participants, instrumentation, data collection, and data
analysis. We
provided details concerning these four sections contained in the
Methodology Chapter. The Methodology Chapter is extremely
important,
particularly at the proposal stage, because it defines how your
study will
be conducted. This is the section of your proposal where you
will receive
the most questions. In essence, it is the “blueprint” of your
study. We pro-
vided several excerpts from both quantitative and qualitative
disserta-
tions, which you may find helpful in writing your Methodology
Chapter.
After you have completed writing the first three chapters of
your dis-
sertation and have received approval from your dissertation
chair, some
universities require you to engage in a dissertation proposal
defense.
There is great variability among universities and departments
concerning
exactly how the dissertation proposal approval process works.
In some
institutions the proposal may consist of a description of your
research plan
consisting of 10 to 20 pages. In others the approval of your
research plan

may consist of the first three chapters of your dissertation. In
some schools,
the dissertation chair is the only person who must approve your
proposal.
In other schools you may be required to participate in a formal
oral
defense of your dissertation proposal before your entire
dissertation com-
mittee (see Chapter 11 for a description of both the proposal
defense and
the final oral defense of the dissertation). The following chapter
deals with
the presentation and analysis of your data.
9
Writing the Results
Chapter
pr
C hapter Four of a dissertation or master’s thesis is usually
titled
“Presentation and Analysis of Data.” It contains a clear and
com-
ehensive presentation of the results of your data analysis. Plan
the
presentation of your tables and figures before you begin writing
the
chapter.

In planning the outline of Chapter Four, concentrate on purely
objec-
tive analysis, without interjecting inferences and value
judgments.
Resisting the temptation to generalize at the same time data are
being ana-
lyzed makes the writing of your dissertation or master’s thesis a
more
manageable task, particularly for one who is inexperienced.
Judgments,
inferences, and questions will surface as you analyze your data.
You
should record these ideas for use in the subsequent chapter,
which will be
devoted to interpretation, comparison with other research
findings, and
recommendations. References to the literature and a restatement
of the
theoretical framework or methods are unnecessary and will
detract from
the purpose of the Results Chapter.
We are aware that there are alternative formats for writing
qualita-
tive dissertations. Some qualitative researchers recommend
combining
results and discussion chapters. Some dissertation chairs may
encour-
age students to present the results and discussion as one
interconnected
chapter. We acknowledge that such an approach may even
improve
206

the readability of a qualitative dissertation or master’s thesis.
And some
qualitative dissertations may exceed five chapters.
Nevertheless, we
recommend the general organizing principles for Chapter Four
on the
assumption that both quantitative and qualitative dissertations
will con-
tain separate results and discussion chapters, albeit the writing
in
Chapter Four may be quite different according to the
methodology of
choice (quantitative or qualitative).
Though the model we use is the traditional five-chapter
dissertation,
students and faculty chairing dissertations should feel free to
modify the
approach to reflect advising style, unique subject area, and
institutional
requirements. For example, Joan Slater (2001) and Teresa
Schlosberg
(2004) combined the results and discussion of their qualitative
disserta-
tions into one chapter, resulting in a four-chapter dissertation.
(See
Examples 9.1 and 9.2.)
Example 9.1
CHAPTERS
I INTRODUCTION
II REVIEW OF THE LITERATURE

III METHODOLOGY
IV RESULTS AND IMPLICATIONS
(Slater, 2001, pp. viii–x)
Example 9.2
CHAPTERS
I INTRODUCTION
III METHODOLOGY
IV RESULTS AND IMPLICATIONS
(Schlosberg, 2004, pp. vii–viii)
On the other hand, Judy Christensen’s (2003) qualitative
dissertation
contains nine chapters. She followed our five-chapter model for
the first
three chapters, then she organized her case study results into
four chapters
using themes. This was followed by a cross-case analysis
chapter. She con-
cluded the dissertation with a summary chapter. (See Example
9.3.)
207Writing the Results Chapter
Example 9.3
CHAPTERS
I INTRODUCTION

III METHODOLOGY
IV CASE A: MAGNETIC ATTRACTION
V CASE B: PREMEDITATED PROGRESS
VI CASE C: BUSINESS PARTNERSHIP
VII CASE D: DELIBERATE DESIGN
VIII CROSS-CASE ANALYSIS OF FINDINGS
IX SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS
(Christensen, 2003, pp. vii–x)
Wanda Bamberg’s (2004) qualitative dissertation contains nine
chap-
ters as well. Like Christensen, she followed our five-chapter
model for the
first three chapters. She organized her case study results into
five addi-
tional chapters focusing on each of the major participants of her
study and
concluded with a summary chapter. (See Example 9.4.)
Example 9.4
CHAPTERS
I INTRODUCTION
III METHODOLOGY
IV CASE STUDY SUPERINTENDENT A
V CASE STUDY SUPERINTENDENT B
VI CASE STUDY SUPERINTENDENT C
VII CASE STUDY SUPERINTENDENT D
VIII CASE STUDY SUPERINTENDENT E
IX SUMMARY AND CONCLUSIONS
(Bamberg, 2004, pp. viii–xii)

As shown in the previous four examples, qualitative
dissertations may
use alternative formats to the traditional five-chapter
dissertation model
we propose. Most quantitative dissertations and many
qualitative disser-
tations follow the traditional five-chapter model we have been
discussing
in this book.
For the sake of brevity, we will refer to Chapter Four as the
Results
Chapter. As mentioned previously, each chapter, including
Chapter Four,
has three distinct parts: the introduction, where you tell your
readers
what you are going to tell them; the body, where you tell them;
and the
summary, where you tell them what you told them. Table 9.1
contains
the typical headings found in the results chapter of a
dissertation or
master’s thesis.
Table 9.1 Contents of Chapter Four
CHAPTER IV
PRESENTATION AND ANALYSIS OF DATA

Introduction
Descriptive Statistics
Testing the Research Questions (or Hypotheses) (quantitative
study)
Research Question (or Hypotheses) 1
Etc.
(For a qualitative dissertation, themes, participants, or other
organizing
format may be used.)
Additional Analyses
Summary
A more detailed description of each of these sections follows.
INTRODUCTION
As in previous chapters, you begin the Results Chapter with an
intro-
duction, containing one or two paragraphs. In it, you provide an
overview of the chapter, an advance organizer. The advance
organizer
lays out the structure of the chapter; it prepares the reader for
what is
to follow.

The following is an example from Ying-Chiao Tsai’s (2006)
dissertation.
(See Example 9.5.)
Example 9.5
Introduction
This study intended to investigate the effects of student,
teacher, and
school factors on PIRLS reading achievement for both English-
language
majority and minority students, and it intended to investigate
school
and teacher factors that moderated the student effects including
the
language achievement gaps and students’ reading attitudes in
the
United States and Singapore. The purpose of this study was
achieved by
examining the explanatory power of the combined models with
student,
teacher, and school factors on the students’ reading achievement
in the
United States and Singapore. This chapter presents the results
of the data
analysis for the four stated research questions.
The descriptive statistics including univariate and bivariate
statistics
were first reported followed by the results of unconditional
models and

the HLM model overview for the change of students’ mean
reading
achievement and language achievement gaps across the five
HLM mod-
els. The presentation of the findings is arranged by the four
research ques-
tions. The HLM student background models (Model 2) and the
final
student models were used to answer research question one: “To
what
extent do student factors have a direct impact on students’
reading
achievement for both English-language majority and minority
students in
the United States and Singapore?”
The HLM school models (Model 4) provided the findings for
research
questions two and three: “To what extent do school and teacher
back-
ground variables influence students’ reading achievement for
both
English-language majority and minority students in the United
States and
Singapore?” and “To what extent do school and teacher
classroom
instructional factors have a direct impact on students’ reading
achieve-
ment for both English-language majority and minority students
in the
United States and Singapore?” The HLM full school models
were used to
answer research question four: “To what extent do school and
teacher
classroom instructional factors have differential effects on both
English-

language majority and minority students’ reading achievement
and on
students’ reading attitudes in the United States and Singapore?”
Univariate and Bivariate Statistics
(Tsai, 2006, pp. 75–76)
Note how Tsai identified each section of her Results Chapter.
Through
this advance organizer, she provided her readers with what was
to follow.
We included her next major heading following her introduction
to show
the sequence of headings following the introduction.
The next heading that typically appears in the Results Chapter
is titled
“Descriptive Statistics.” Here you summarize the univariate
statistics of
your study. The most frequently used statistics for this purpose
are the
mean (M), standard deviation (SD), median, mode, frequency,
and per-
centages (see Chapter 4, “Basic Statistics,” for a description of
these basic
descriptive statistics). When your data set contains many
variables, pre-
senting descriptive summary table(s) with an explanation of the
descrip-
tive nature of the variables is helpful to the reader.
DESCRIPTIVE STATISTICS
Descriptive statistics are often reported in the Results Chapter
to begin

the presentation and analysis of data. Following is an example
from
Keith Clark’s (2002) dissertation. (See Example 9.6.) We
display one
table only for each of the three variables (student achievement
vari-
ables, demographic variables, and expenditure variables) Clark
exam-
ined in his dissertation.
Example 9.6
Descriptive Statistics
Student Achievement Variables
The TEA Snapshot for the years 1999 and 2000 was used to
gather
student achievement information. These data on student
achievement are
utilized to study the achievement of the students in each district
included
in this study. Student achievement is defined in this study as the
percent-
age of a district’s total student population who passes the TAAS
test in
reading, writing, and mathematics across all grade levels tested.
Tables 1
and 2 report the mean percentage and standard deviation for
student
achievement by subject in all school districts included in this
study.

Table 1
Table 9.2 Percent of Students Passing TAAS by School Year
School Year
1998–1999 1999–2000
TAAS Subject n M SD n M SD
% Passed All Test 1,035 81.06 9.08 1,041 82.47 9.09
% Passed Reading 1,035 88.57 9.08 1,041 82.47 9.09
% Passed Writing 1,028 89.32 7.10 1,033 89.82 7.16
% Passed Mathematics 1,035 88.35 6.85 1,041 89.69 6.78
(Clark, 2002, p. 71)
Demographic Variables
Demographic information is another relevant data source related
to the
type of students who attend each of the school districts. These
data include
the demographic variables relating to the ethnicity of the
student population
of a district. Tables 4 and 5 demonstrate the percentage of total
African
American, Hispanic, White, or Other students in each district in
this study.
This information comes from data collected for the two years of
information
included in this study by the Texas Education Agency regarding
ethnicity.

Table 4
Table 9.3 Ethnicity of the Student Population of Texas School
Districts by
School Year
1998–1999 1999–2000
n M SD n M SD
% African American
% Hispanic
% White
% Others
1,035
1,035
1,035
1,035
7.95
27.38
63.82
1.03
11.82
26.55
26.46
1.97
1,041
1,041
1,041
1,041

8.07
28.15
62.91
1.03
12.16
26.80
26.84
1.88
(Clark, 2002, p. 72)
Expenditure Variables
District function level expenditures data was taken from the
Public
Education Information Management System (Texas Education
Agency, 2000).
Each of these expenditures is characterized by their functional
group. Each
are summarized into a per pupil expenditure. This amount is
calculated by
dividing the total functional expenditure by the total number of
students in
each given district. Tables 9 and 11 depict the mean per pupil
expenditures
and standard deviation for each of the eight functional
categories for all dis-
tricts in this study for the academic years of 1998–1999 and
1999–2000.
Table 11

Table 9.4 Total School District’s Functional Level Expenditures
1998–1999
through 1999–2000
Function N
1998–1999 and 1999–2000 School Years
Mean (in $) SD (in $) % Total
Instruction 2,076 4,071 1,101 60.23%
School Leadership 2,076 431 188 6.38%
Student Support 2,076 698 262 10.33%
General Administration 2,076 467 385 6.91%
Non-Student Services 2,076 849 406 12.56%
Ancillary Services 2,076 6 16 0.09%
Debt Services 2,076 64 152 0.94%
Facilities Acquisition 2,076 173 447 2.55%
(Clark, 2002, p. 76)
Testing the Research Questions
Descriptive and inferential statistics were used to investigate
the four
research questions of this study. To investigate the first

research question,
Analysis of Variance (ANOVA) was used to compare the
amount of variance
between group AEIS school district ratings of exemplary,
recognized, accept-
able, and low performing and the eight functional expenditure
categories
of (a) instruction and instructional related services, (b) school
leadership,
(c) student support services, (d) general administration, (e) non-
student ser-
vices, (f) ancillary services, (g) debt services, and (h) facilities
acquisition
resources per pupil. The other three research questions were
studied using
regression analysis. The first of three regressions tested the
correlation between
student scores on the TAAS test and each district’s functional
expenditures. The
last two research questions also compare the eight functional
groups to finan-
cial and demographic factors. The level of significance .05 was
used for each
statistical analysis used in this study. This is consistent with
commonly used
statistical practices (Gall, Borg & Gall, 1996). (Clark, 2002, pp.
70–77)
Note that Clark described student achievement variables, demo-
graphic variables, and expenditure variables using 11 tables (3
tables
reported here). We included the first paragraph of the next
heading of

Clark’s dissertation, “Testing the Research Questions,” which
typically fol-
lows the heading “Descriptive Statistics” in quantitative
dissertations.
Note how Clark provided an overview of his plan of analysis for
each
research question in this section.
TESTING THE RESEARCH
QUESTIONS (OR HYPOTHESES)
We recommend that you use research questions (or hypotheses)
to orga-
nize the presentation of quantitative results. Address one
research ques-
tion (or hypothesis) at a time. Thus, each research question (or
hypothesis)
would compose one subsection of the Results Chapter under the
major
section (heading) “Testing the Research Questions (or
Hypotheses).” This
main heading serves as the topic paragraph(s) for the analyses,
by each
research question (or hypothesis), to follow. Use narrative,
accompanied
by appropriate tables and figures.
Following is an outline of the way you might structure the
presenta-
tion of your results for a quantitative dissertation or master’s
thesis. Use
the following six steps to report the results of each research
question (or
hypothesis).

1. Restate the first research question (or hypothesis) exactly as
it
appeared in Chapter One of your dissertation or master’s thesis.
2. Reference the table or figure in the text that contains the
statistics
related to the research question (or hypothesis).
3. Discuss the highlights of the data contained in the table or
figure.
4. State the outcome of the analysis for research question or
hypothe-
sis one, together with the statistical procedure used.
5. State the disposition of the research question (or hypothesis).
6. State the next research question or hypothesis, and follow the
previ-
ous five steps until you have reported the results of each
research
question or hypothesis.
An example from Kimberly Truslow’s (2004) dissertation
follows. (See
Example 9.7.)
Example 9.7
Research Question One
Question 1: Is there a significant difference in gender
distribution
among conflict management modes of superintendents? The fir st
research

question examined the results of the TKI. On each of the 30
questions,
participants were asked to respond to a forced choice item to
indicate
how they would handle a conflict scenario. Each of the 30
questions
represented one of the conflict management modes and were
assigned
nominal values of 1 to 5. All 30 questions were analyzed to
answer
research question one using cross-tabulation and nonparametric
sta-
tistics. Chi square analysis and Cramer’s V were used to
identity if
statistically significant relationships existed between the
number of
males and females in each of the five conflict management
modes. The
magnitude of association for this research question was reported
using Cramer’s V.
Cramer’s V measures the strength of the relationship between
variables and reflects how closely variables are related (Gall,
Borg, &
Gall, 1996.) Cramer’s V, symbolized by V, is based on
measures of
association from 0 to 1. Rutgers (2003) established that when
the mea-
sure of V is between (a) 0 to .10, a weak relationship exists
between
variables; (b) .11 to .29, a moderate relationship exists between
vari-

ables; and (c) .30 to 1, a strong relationship exists between
variables.
Table 5 illustrates the cross-tabulation results of the number of
males
and females in each conflict management mode. The number of
males in
each conflict management mode was recorded as follows: 44 for
compet-
ing, 28 for collaborating, 56 for compromising, 28 for avoiding,
and 7 for
accommodating. The number of females in each conflict
management
mode was divided as follows: 8 competing, 25 collaborating, 95
compro-
mising, 22 avoiding, and 6 accommodating.
Table 5
Table 9.5 Fequencies of Gender Reponses for TKI Modes
Gender
TKI Modes
Competing Collaborating Compromising Avoiding
Accommodating Total
Male
Female
Total

44
8
52
28
25
53
56
95
151
28
22
50
7
6
13
163
156
319

A significant relationship χ2 (4, N = 319) = 35.826, p = .001,
was
found to exist between gender and conflict management modes
of
superintendents. No cells in the cross-tabulation analysis had
expected
counts less than five. The minimum expected count in each cell
was
6.36. The magnitude of the association of the relationship as
described
by Cramer’s V is V = .335 indicating a large effect size of
gender as
being a factor in conflict management modes (Rutgers, 2003).
Table 6
illustrates the results of the chi square test and Cramer’s V
degree of
relationship.
Table 6
Table 9.6 Analysis of Chi Square Test with Cramer’s V
Value df
Person Chi Square
Cramer’s V (Nominal)
N of Valid Cases
35.826

.335*
319
4
* p < .001
One-sample t tests were also performed on the TKI data to
analyze
the gender distribution of males and females in each of the
conflict man-
agement modes. Two of the conflict management modes,
competing and
compromising, had significant differences in gender
distribution. There
was a significant difference between the number of males and
females in
the competing conflict management mode, t (51) = −6.852, p =
.000. The
difference between the gender distribution of superintendents
with a
compromising conflict management mode was also significant, t
(150) =
3.274, p = .001. Table 7 illustrates the results of the one-sample
t tests for
each conflict management mode.
Table 7
Table 9.7 One-Sample t Test for Conflict Management Modes
Conflict Management Mode N t df Sig. Mean Diff.
Competing 52 −6.852 51 .000 −.3462

Collaborating 53 −0.409 52 .684 −.0228
Compromising 151 3.274 150 .001 .1291
Avoiding 50 −0.267 49 .402 −.0038
Accommodating 13 −0.267 12 .794 −.0038
Research Question Two
Question 2: Is there a relationship between conflict management
modes of
female superintendents and the Synergistic Leadership Theory?
To answer
research question two, a qualitative approach was employed. A
telephone
interview of five female superintendents who scored the highest
on each
of the five conflict management modes was conducted. Each
superinten-
dent was contacted to obtain permission to be interviewed by
telephone,
an audiotape of the interview was made and major themes were
identi-
fied. Participants also provided written permission to participate
in the
interview. Each interview was transcribed and returned to the
superin-
tendent for member checking. Corrections were made to each
transcript
where noted by the participant. Major themes were identified in
regards
to the four factors of the SLT. Each factor was coded a different
color on
the transcript as follows: organizational structure was coded
pink; lead-

ership behaviors were coded purple; beliefs, attitudes, and
values were
coded green; and external forces were coded yellow. These
phrases that
were indicative of each of the factors were loaded in the matrix
that
grouped them for discussion purposes. For ease of reading, the
narratives
that follow are organized by participant responses in each of the
four fac-
tors. (Truslow, 2004, pp. 62–65)
Note how efficiently Truslow followed all six steps we
recommend for
reporting quantitative results. We included the first paragraph
of her sec-
ond research question to show continuity. You will note from
Truslow’s
second research question that she has done a mixed methods
dissertation.
The method of analysis for research question two was
qualitative.
Assumptions of Statistical Tests
Sometimes it may be necessary for a student to discuss the
assumptions
of the statistical tests used, particularly when the data may not
be normally
distributed. The placement of this discussion should precede
each research
question (or hypothesis) to which the statistical procedure
applies.

An example from Linda Creighton’s (2006) dissertation follows.
(See
Example 9.8.)
Example 9.8
The first stage in any data analysis is to explore the data
collected to
get ideas of any patterns. In addition, it is important to see
whether the
data meet the criteria necessary for the statistical procedures
used (Field,
2000). Since many of the statistical tests used are parametric, it
is impor-
tant to check the assumptions required of parametric tests.
Using a para-
metric test when the data are not parametric increases greatly
the risk of
inaccurate decisions. Parametric tests are based on the normal
distribu-
tion and have four basic assumptions that must be met for the
test to be
accurate: (a) normally distributed data, (b) homogeneity of
variance,
(c) interval data, and (d) independence (Field, 2000).
Normally Distributed Data
In addition to using histograms to determine normality, my
study
extends the inspections of normality to include measures of
kurtosis and
skewness. All variables revealed normal distributions with the
exception of
two: (a) Hispanic graduation rates and (b) African American

graduation
rates. Both of these data sets were positively skewed, revealing
lower than
normal graduation rates. The Spearman’s rho correlation test,
which is
appropriate for non-parametric data, was used with these two
distributions,
and the Pearson correlation test was used with the normal
distributions.
Homogeneity of Variance
Variances should not change systematically throughout the data.
A
thorough analysis of the data revealed similar stable variances
across all
of the variables.
Interval Data
Data must be at the interval scale to be analyzed with
parametric
tests. Pearson correlation was used with all parametric data, and
variables
not meeting parametric assumptions were analyzed with
Spearman’s rho
correlations. All variables with significant relationships were
subjected to
multiple regression analysis.
Independence

This assumption was met in all data. Simply, to meet
independence, no
individuals can be counted in more than one variable or
influence the effect
on another participant. Each university and its students in this
study were
independent of other students and universities. (Creighton,
2006, p. 67)
Note how Creighton explored her data to see whether it met the
criteria
necessary for the statistical procedure she planned to use. She
examined the
four basic assumptions required to use parametric statistics:
normally dis-
tributed data, homogeneity of variance, interval data, and
independence.
Clarity and Consistency
The Results Chapter requires maximum clarity. One way to
achieve
clarity is to minimize variety in sentence structure when
presenting your
results. A good rule to follow is “Be monotonously repetitive”
(Cone &
Foster, 2007, p. 222). Decide on a particular sentence structure
that most
clearly presents the results of a particular statistical procedure,
and use
that structure for all similar results. For example, suppose you
wanted to
examine the influence of teachers’ collective efficacy and
community pres-
sure for academic achievement (two independent variables) on
students’

academic achievement in reading, writing, and mathematics
(three depen-
dent variables). You will use a 2 (high/low teacher efficacy) × 2
(high/low
community pressure) × 1 ANOVA that involves three dependent
variables
(student achievement in reading, writing, and mathematics).
Conduct
three separate ANOVAs, one for each dependent variable. To
achieve clar-
ity and consistency, you would present the results for each
dependent vari-
able (reading, writing, and math achievement) in exactly the
same way.
Thus, begin with your first independent variable (teacher
efficacy), and
present results for it first, followed by your results for your
second inde-
pendent variable (community pressure for achievement),
followed by the
interaction, if any. Use this order as you present the data for
each depen-
dent variable. Furthermore, if you discuss high teacher efficacy
first for the
first dependent variable, discuss it first for all dependent
variables.
Another way to achieve clarity is to place the statistics related
to a
result at the end of the sentence. For example, “Females were
more
humanistic in pupil control ideology than males (M females =
48.1;
M males = 53.4), F(1,924) = 6.84 < .01.” Compare this
statement with the
following: “The mean for the females on the PCI Form was

48.1, which
the ANOVA F(1,924) = 6.84, p < .01, revealed to be
significantly lower than
the males’ mean of 53.4.” Note how the first example is much
easier to read
than the second example. Moreover, in the first example,
reference is made
to the dependent variable (pupil control ideology) when
comparing
females and males, and not the instrument used to measure it. In
the sec-
ond example, the researcher would have to explain what “lower”
means.
Tables
Most dissertations contain one or more tables. If you have a
large
amount of data, which are also complex, tables can make it
easier for you
to present the data. Tables usually help organize both
descriptive data and
the results of statistical analyses.
Tables typically contain five major parts: (a) number, (b) title,
(c) head-
ings, (d) body, and (e) notes (Rudestam & Newton, 2007).
Number. Every table must be numbered with Arabic numbers in
the order
in which they first appear in the text (American Psychological

Association,
2001). Begin with Table 1, and continue throughout the
dissertation or
master’s thesis.
Title. Every table must have a title. Generally, a good title
provides the
name of the major variable(s) and type of analysis.
Headings. Every table must have a heading. Headings include
the vari-
ables and statistics presented in the table. Common
abbreviations are per-
missible: M (mean), SD (standard deviation), df (degrees of
freedom),
f (frequency), % (percent), N (number in sample). Avoid using
mnemonics
for variables and acronyms for describing instruments. Include a
note
indicating their meaning at the bottom of the table.
Body. The body of the table contains the data. Consider
readability; that is,
allow sufficient space between entries, align columns of
numbers on decimal
points, and do not present data beyond two decimal points.
Enter a dash (—)
when cells of a table are empty. Tables presented vertically are
less cumber-
some than tables presented horizontally. If you must use the
horizontal for-
mat, be certain the table appears along the binding side of the
page.
Notes. Two kinds of notes may appear at the bottom of a table:
general notes

and probability notes. General notes include the meaning of
symbols or abbre-
viations. Probability notes indicate the outcome of significance
tests. Asterisks
indicate the probability levels as follows: * p < .05. ** p < .01.
*** p < .001.
The generally accepted rules concerning how to present the
results of
statistical tests have been documented in the fifth edition of the
Publication
Manual of the American Psychological Association (2001). The
general rule
when presenting inferential statistics in the text is to present the
symbol of
the statistic followed by the degrees of freedom, such as, F(1,
924), then the
value of the statistic (e.g., 6.84), and finally the probability
level (e.g., p < .01);
thus, from the previous example: F(1, 924) = 6.84, p < .01.
When a chi
square is used, the degrees of freedom and sample size are
reported in
parentheses: χ2 (4, N = 319) = 35.83, p < .001. When
correlation is used, the
statistical statement indicates both the strength of the
relationship and its
significance: r (62) = .484, p < .01. The 62 in parentheses
following the r is
the degrees of freedom associated with the statistical test and is
one less

than the sample size (i.e., N − 1). The APA manual provides
more details.
Following are examples of tables taken from the dissertations of
Clark
(2002), Truslow (2004), and Creighton (2006) to illustrate
ANOVA, χ2, and
r, respectively. (See Examples 9.9, 9.10, and 9.11.)
Example 9.9
Table 9.8 Means and ANOVA Summary Table per Pupil
Expenditures for
Instruction
District Rating N M S.D. (in $)
Exemplary
Recognized
Academically Acceptable
Low Performing
Total
290
822
952

12
2,076
4,540
4,083
3,920
4,071
4,071
1,664
975
936
1,204
1,101
Source df SS MS F η2
Rating
Error
Total
3
2,072

2,072
85,800,422
2,428,554,379
2,514,354,801
28,600,141
1,172,082
24.4011* .04
Note. η2 = effect size.
*p < .001.
(Clark, 2002, p. 79)
Example 9.10
Table 9.9 Analysis of Chi Square Test with Cramer’s V
Value df
Person Chi Square
Cramer’s V (Nominal)
N of Valid Cases
35.826
.335*

319
4
* p < .001
(Truslow, 2004, p. 64)
Example 9.11
Table 9.10 Pearson Correlations between Top 10 % and
Graduation Rates
% in Top 10% 4-Year Rate 5-Year Rate 6-Year Rate
% in Top 10 %
4-Year Rate
5-Year Rate
6-Year Rate
1.00
.484**
.455**
.534**
1.00

.840
.886
1.00
.952 1.00
** Correlation is significant at the .01 level (2-tailed)
(Creighton, 2006, p. 67)
Statistical significance does not deal with the power of a
relationship
between variables (called effect size). Include measures of
effect size in all
tables when presenting the results of statistical tests. It is not
enough to
discuss effect size in the narrative preceding the insertion of the
table.
Reference each table by number in the text before inserting the
table in
the manuscript. Tables should be placed in your dissertation as
close to
where they have been described as possible. Alternate tables
with textual
description rather than placing tables consecutively one after
another, unless
the tables are clearly connected. Thus, considerable planning
should precede
the construction of tables. Think carefully about the number of
tables to
include in your document and their content. Also, think about
how to orga-

nize your tables throughout the chapter. Therefore, construct all
tables you
intend to include in your Results Chapter before writing the text
for the
chapter. This will help you with the placement of tables and the
avoidance of
white space at the bottom of the page in your dissertation or
master’s thesis.
Figures
Many dissertations contain figures. Figures include graphs,
charts,
photographs, drawings, and anything that is not text or a table.
Some
examples of when a figure would be helpful include the
following:
1. Trends in longitudinal data
2. Scatter plots in correlational analysis
3. Testing a moderating effect in a hierarchical multiple
regression
4. Cluster analysis
5. Path analysis
6. Structural equation modeling
Many of the qualities of good tables apply equally to figures.
Presenting the Results of Qualitative Research

If you have conducted a qualitative study, the organization of
your
Results Chapter may differ from that used for a quantitative
study (Long,
Convey, & Chwalek, 1985). We recommended using research
questions (or
hypotheses) to organize the presentation of results for a
quantitative study.
As with all research (quantitative or qualitative), the first
priority is to
adopt a well-organized strategy that makes sense of your data
and pre-
sents them clearly and comprehensively (Rudestam & Newton,
2007).
Qualitative researchers typically gather large amounts of data in
the
form of words and ideas rather than numbers and statistics. The
methods
of gathering such data in qualitative research include in-depth
interviews,
focus groups, participant and nonparticipant observation, and
document
analysis. Instead of using research questions (or hypotheses) to
organize
the presentation of results in your qualitative study, consider
using major
themes as headings in your Results Chapter. For example, in a
qualitative
study of 12 women superintendents’ experiences of inequality,
Brunner
(2000) identified five topics of talk that emerged from the
narrative data:
power, silence, style, responsibility, and people. These themes
became the

headings in her analysis. Each of these topics was examined for
settled and
unsettled taxonomies. (See Example 9.12.)
Example 9.12
Within the taxonomy of unsettled talk, there were five topics:
(1) power-understood as control or dominance, (2) overt
silencing,
(3) negative consequences for being direct, (4) barriers that
blocked their
ability to perform the verbal responsibilities of role, and (5) the
need to
use others as mouthpieces. Clearly, inequality because of
gender bias was
a consistent part of these women’s experiences. When the
women broke
the normative cultural rules governing gender-appropriate
behavior, they
faced negative consequences.
Within the taxonomy of settled talk, the women shared
experiences that
fell within the traditional normative behavior of women in
culture. The five
topics of talk within the settled taxonomy were: (1) power as
shared, (2) pos-
itive silence-listening, (3) positive consequences for having a
soft approach—
being a “lady,” (4) finding ways to be verbally responsible in
spite of barriers,
and (5) working for equality of voice for everyone. (Brunner,
2000, p. 106)
The stated purposes of Brunner’s qualitative research study
were (a) to

examine the women superintendents’ perceptions of their
experiences of
inequality within the contextual constraints of their positions,
and (b) how
they overcame these experiences. Rather than using research
questions to
organize the presentation of results, Brunner used themes (or
topics) to
organize her analysis of results.
Previously we provided a six-step strategy for reporting the
results
of quantitative analyses. Qualitative researchers also need to
devise a
well-organized strategy for wading through volumes of
qualitative data.
We suggest the following eight-step strategy:
1. Read and reread all of the data you have collected to get a
feel for
the whole (Creswell, 2007).
2. Summarize the data in the form of memos and reflective
notes.
3. Give participants copies of your summaries or transcripts
(called
member checks or reciprocity).
4. Reduce your data by developing codes for further sorting.

5. Sort your data into some kind of coherent pattern or themes.
6. Once you have determined your overall themes, give each
theme a
color code and attach a matching color tape flag to the page(s).
Include the narrative (quotations) and speaker for that theme,
and
create a file.
7. Continue the process for each theme.
8. For each major theme, find subthemes and affix smaller
color-
coded, Post-it notes to the larger tape flags.
Earlier in this chapter we mentioned four qualitative
dissertations that
deviated from the traditional five-chapter dissertation model we
have
been discussing in this book: Slater (2001), Schlosberg (2004),
Christensen
(2003), and Bamberg (2004). The first two dissertations were
four chapters
each, and the latter two were nine chapters each. Christensen’s
disserta-
tion was organized according to themes and Bamberg’s
dissertation was
organized according to participants. Both dissertations used the
case study
method. (See Examples 9.3 and 9.4.) Providing excerpts from
these chap-
ters would not do justice to the rich narrative contained therein.
Therefore,
we recommend that you read Christensen’s entire nine-chapter,
274-page
qualitative dissertation and Bamberg’s entire nine-chapter, 462-

page qual-
itative dissertation.
All four qualitative researchers—Slater, Schlosberg,
Christensen, and
Bamberg—analyzed their data by following the eight-step
strategy we rec-
ommended for organizing the results of qualitative data.
ADDITIONAL ANALYSES
This section of your dissertation or master’s thesis is reserved
for data not
covered by the research questions (or hypotheses) in a
quantitative study.
The heading for this section in your Results Chapter is typically
“Additional Analyses” (see Table 9.1). For example, in a study
of the effects
of teacher self-efficacy, locus of control, and pupil-control
orientation on
teacher burnout, you may have an interest in examining the
relationships
among the three independent variables (self-efficacy, locus of
control, and
pupil-control orientation) even though these relationships were
not the
subject of the research questions (or hypotheses). Such analyses
would be
placed in the section titled “Additional Analyses.”
We mentioned previously that all instruments used to collect

data for
a quantitative dissertation should be factor analyzed with your
own study
sample. Assume that you found that, as a result of your factor
analysis,
several items of the instrument(s) you used in your study
produced low
reliability and validity, or the items of a multidimensional
instrument did
not load as in previous research, and therefore you choose to
eliminate
those items from the questionnaire(s). You decide to retest the
research
questions (or hypotheses) using the modified paper-and-pencil
instru-
ment. You cannot place the results of these analyses in the
section titled
“Testing the Research Questions (or Hypotheses),” but you
place these
additional analyses in the section titled “Additional Analyses.”
Furthermore, suppose you decide to perform statistical analyses
of
demographic variables (e.g., gender, race/ethnicity, grade level
taught)
with each of the independent variables (i.e., self-efficacy, locus
of control,
and pupil-control orientation), but these variables were not the
subject of
the research questions (or hypotheses). As a result of these
analyses, you
found some interesting things. You found differences between
elementary
and secondary school levels on one or more of the independent
variables
(self-efficacy, locus of control, pupil-control orientation). The

placement of
the results of these additional analyses belong in the
“Additional
Analyses” section of the Results Chapter.
SUMMARY
The last major heading in the Results Chapter is “Summary”
(see Table 9.1).
Concluding the Results Chapter with a summary has two
advantages. First,
results can be generalized at a higher level in the summary than
that found
in the preceding text analysis. Now results extracted from tables
and fig-
ures in quantitative dissertations and from rich narrative in
qualitative dis-
sertations can be synthesized and summarized in ways that were
not
possible earlier because of the organization of the chapter.
Results that sup-
port or contradict each other can be identified and related to
each other.
Segmentation due to chapter organization (by research
questions/hypothesis,
themes, or participants) can be overcome in the summar y.
The second advantage is that the results have been generalized
to the
degree necessary for making comparisons with the findings of
other stud-
ies. The summaries of Chapter Two, “Review of the Literature,”
and
Chapter Three, “Methodology,” provide the supports needed for
develop-
ment of the Discussion Chapter to follow. Without such a basis

from which
to proceed, the next chapter invariably will be a repetition of
the informa-
tion found in the tables, figures, and narrative preceding it.
From our
experience, such repetition occurs with some frequency. It
makes the task
of writing the Discussion Chapter quite difficult. Furthermore,
it serves to
emphasize the importance of chapter summaries. The last
sentence of the
summary should introduce the following chapter.
The following comes from Diane Trautman’s (2000) mixed
methods
dissertation. (See Example 9.13.)
Example 9.13
Summary
In this chapter, an introduction was given regarding the analysis
and
statistical tests that were to be discussed and in which order
they would
be addressed. This was followed by a demographic analysis of
the sam-
ple, an item analysis with individual t tests of items 10–79 from
the
Organizational and Leadership Effectiveness Inventory, results
of a t test

for overall difference between groups, and results of an
ANOVA between
four levels of management.
Results from the first quantitative research question revealed
that
although there were slight statistical differences between males
and
females on how they perceived the leadership behavior factor of
the
Synergistic Leadership Theory, male and females perceived the
leader-
ship behaviors included in the leadership behavior factor of the
theory to
be statistically reflective or non-reflective of their leadership
styles thus
yielding construct validity to the factor. Similarly, female
leaders at dif-
ferent levels of management acknowledged the validity of the
leadership
behavior factor of the Synergistic Leadership Theory. Further,
the factor
was validated by a strong level of internal consistency of scores
on the
OLEI which measured the factor at ∝ = .928. The quantitative
findings
contradicted current literature that only females exhibit
feminine leader-
ship behaviors or that only males exhibit masculine behaviors.
The next chapter will present and analyze the qualitative data
obtained from the open-ended response section of the
Organizational and
Leadership Effectiveness Inventory and the telephone
interviews.
(Trautman, 2000, pp. 120–121)

CONCLUSION
In this chapter, we discussed the common sections and headings
that typ-
ically appear in a Results Chapter. We also included typical
examples
found in some qualitative dissertations. We provided a step-by-
step guide
to writing the Results Chapter, together with examples from
dissertations
on how to present each section of the chapter (both quantitative
and qual-
itative), including the use of tables and figures. In the next
chapter, we
provide advice on how to write Chapter Five, “Summary,
Discussion, and
Conclusions.”
10
Writing the Discussion
Chapter
the findings, (c) implications for practice, (d) r
Chapter Five of your dissertation or master’s thesis typically
contains
the following elements: (a) a summary of the study, (b)
discussion of
ecommendations for further

research, and (e) conclusions. These five elements comprise the
sections (or
headings) that are contained in Chapter Five (see Table 10.1).
Table 10.1 Contents of Chapter Five
CHAPTER V
SUMMARY, DISCUSSION, AND CONCLUSIONS
Introduction
Summary of the Study
Discussion of the Findings
Implications for Practice
Recommendations for Further Research
Conclusions
The concluding chapter of a dissertation or master’s thesis can
have any
number of titles: “Summary,” “Discussion,” “Implications,”
“Recommendations,”
and/or “Conclusions.” We prefer the title shown in Table 10.1.
For the sake
of brevity, we will refer to Chapter Five as the Discussion
Chapter. A more
detailed description of each of the above sections follows.
225

INTRODUCTION
As in previous chapters, you begin the Discussion Chapter with
an
introduction, which contains one or two paragraphs. In it, you
provide an
overview of the chapter, an advance organizer. The advance
organizer lays
out the structure of the chapter; it prepares the reader for what
is to follow.
The following is an example of an introduction from Donna
Azodi’s
(2006) dissertation. (See Example 10.1.)
Example 10.1
Introduction
In the preceding chapter, the presentation and analysis of data
have
been reported. Chapter V consists of a summary of the study,
discussion
of the findings, implications for practice, recommendations for
further
research, and conclusions. The purpose of the latter sections is
to expand
upon the concepts that were studied in an effort to provide a
further
understanding of their possible influence on leadership practice,
and to
present suggestions for further research targeting the
understanding of
trust and principal leadership behaviors and their impact on
teachers’

sense of efficacy. Finally, a synthesizing statement is offered to
capture the
substance and scope of what has been appempted in this
research. (Azodi,
2006, p. 347)
Note how Azodi, in the brief introduction to her chapter,
provides her
readers with an advance organizer of the contents of the
chapter. Note also
that she includes all elements typically found in a Results
Chapter.
SUMMARY OF THE STUDY
The next heading that typically appears in the Discussion
Chapter is titled
“Summary of the Study.” In this section, briefly restate the
problem and
purpose of your study, the theoretical framework, research
questions or
hypotheses, methodology, and findings. The findings are the
most impor-
tant part of the dissertation and should receive the heaviest
emphasis. The
summary of the study should be brief, containing two or three
pages.
We provide the summary from Diane Trautman’s (2000) mixed
meth-
ods dissertation as an example. (See Example 10.2.)
Example 10.2

This chapter begins with a summary of the purpose and
structure of
the study and is followed by the major findings related to the
Synergistic
Leadership Theory. Conclusions from the findings of this study
are dis-
cussed in relation to the definition, function, and characteristics
of a good
theory. Finally, implications for practice and recommendations
for further
research are presented and discussed.
The purpose of this study was to validate a feminine-inclusive
lead-
ership theory, the Synergistic Leadership Theory, by analyzing
the per-
ceptions of male and female educational administrators across
the United
States through quantitative and qualitative research.
The Organizational and Leadership Effectiveness Inventory
(OLEI), a
self-report inventory related to the four factors of the
Synergistic
Leadership Theory developed in a previous study by Irby et al.
(1999),
was revised for this study with face validity established by a
group of uni-
versity professors and educators. Participants were asked to
score items
10–79 of the leadership behavior factor of the OLEI because

this data
could most effectively measure agreement or disagreement with
the lead-
ership behavior factor of the theory. Reliability was establi shed
for the
leadership behavior factor studies at ∝ = .98. By scoring their
own leader-
ship behaviors and effectiveness, respondents indicated their
perceptions
of the leadership behavior factor of the Synergistic Leadership
Theory.
Scores from the OLEI were used as quantitative data and were
used to
determine theory validity.
Respondents also wrote comments to an open-ended question of
the
OLEI, which were included in the qualitative analysis.
Additionally, tele-
phone interviews were conducted with administrators to
determine their
perceptions of how the four factors of the Synergistic
Leadership Theory
interacted and whether they perceived the theory as valid.
Respondents
often related real-life experiences to illustrate how the four
factors of the
model interacted.
The study included 243 participants (65 superintendents, 67
assistant
superintendents, 52 secondary principals, and 57 elementary
principals)
randomly selected from a computer database compiled by the
Market
Data Retrieval Company (2000) for the quantitative sample. A

demo-
graphic breakdown was provided for gender, ethnicity,
management
level, and years of experience. Additionally, a qualitative
sample of 34
was composed of 12 educational leaders chosen from those who
did not
reply to the OLEI and 22 participants who added comments on
the OLEI.
This study included four research questions:
Quantitative
1. To what extent do differences exist between male and female
edu-
cational leaders regarding the leadership behavior factor of the
Synergistic Leadership Theory?
2. To what extent do differences exist among female educational
leaders at different management levels regarding the leadership
behavior
factor of the Synergistic Leadership Theory?
Qualitative
1. In what ways do male and female leaders perceive the four
factors
of the Synergistic Leadership Theory to be interactive?
227Writing the Discussion Chapter
2. How do female leaders at different levels of management
perceive
the interaction of the four factors of the Synergistic Leadership

Theory?
Questions one and two were answered quantitatively from the
data
obtained from participant scores on the leadership behavior
factor of the
OLEI. Question one was answered using the results from a t test
for inde-
pendent means comparing means of male and females’ scores
for each
item and for group differences. To answer question two, an
ANOVA was
performed comparing means of females in the four different
management
levels. The quantitative results addressed only one of the four
factors of
the Synergistic Leadership Theory, the leadership behavior
factor.
To answer the two questions to the qualitative phase of the
study, data
were obtained from telephone interviews as well as responses to
the
open-ended questions of the OLEI. Data were categorized,
coded, and tri-
angulated to determine levels of management. Where
quantitative
research questions addressed only one factor of the Synergistic
Leadership Theory, the qualitative questions addressed possible
interac-
tions of all four factors of the Synergistic Leadership Theory.
(Trautman,
2000, pp. 146–148)
Note how skillfully Trautman summarized her study up to the
find-

ings. Instead of summarizing findings in this section, she
summarized her
findings in the next section of her dissertation, titled
“Discussion of the
Findings,” incorporating a discussion of each finding.
The five elements may be positioned in a variety of ways, as
long as
each element is addressed in Chapter Five, or the concluding
chapter, if the
dissertation contains more or fewer than five chapters as some
qualitative
dissertations do.
DISCUSSION OF THE FINDINGS
In Chapter One, “Introduction,” you presented the theoretical
framework
undergirding your study. In Chapter Two, “Review of the
Literature,” you
presented other empirical studies that were related to your
study. In a
theory-driven study, you present your interpretation of your
results by
linking them with the theoretical framework and the existing
literature. As
you discuss your results, you evaluate what your results mean,
how they
fit with your theoretical framework and the literature you
reviewed ear-
lier, and what you can conclude about the research questions or
hypothe-
ses you posed at the outset. Of course, you will need to cite
some of the
relevant studies that you discussed previously in Chapter Two.

In many qualitative traditions, studies might be informed by a
theo-
retical or epistemological framework; but the researcher does
not typically
set out to test a theory by collecting empirical data. Instead
studies may be
designed so that data are collected first, and then a theory is
derived from
those data. In other words, the theory is grounded in the
particular data
that the researcher has collected. The usefulness of the theory
can be tested
in subsequent research (Denzin & Lincoln, 2005; Gall, Gall, &
Borg, 2007;
Glaser, 1978). This is the case with testing the usefulness of the
Synergistic
Leadership Theory (Irby, Brown, Duffy, & Trautman, 2002),
which was
developed using a qualitative methodology and tested in
subsequent
research by Bamberg (2004), Hernandez (2004), Holtkamp
(2001),
Schlosberg (2004), Trautman (2000), and Truslow (2004), and
which is cur-
rently being tested in other countries.
Regardless of the methodology you use, you will need to base
your
interpretations on the data (i.e., “Stay close to the data”). Thus,
when mak-
ing statements about what the data mean, you must be able to

reference the
specific data to support your statements. To write an effective
discussion
section, you will need a thorough knowledge of your data, your
theoretical
framework, or epistemological framework, and the existing
literature.
The discussion section in Chapter Five, or the concluding
chapter, may
be organized in many ways. Research questions (or hypotheses),
variables,
or themes can be used to organize this section. Present them in
the same
order they were presented in the Results Chapter. Following are
guidelines
that may help you structure the discussion section in Chapter
Five or your
concluding chapter.
GUIDELINES TO STRUCTURE
YOUR DISCUSSION SECTION
First, briefly restate each research question (or hypothesis),
variable, or
theme. Present the results that relate to it. Do not reiterate
technical details
of the analysis, such as F and p values. Analyses have already
been pre-
sented in the Results Chapter. Here you discuss, analyze, and
explain the
results of each research question (or hypothesis), variable, or
theme.
Examine the extent to which the data answered your research
questions,

supported/failed to support your hypotheses, showed functional
relation-
ships between variables, or made sense of the themes. Also
identify any
confounding or mediating variables that may have affected the
results,
and discuss their implications for your study.
Second, indicate how the results of your study support,
contradict, or
extend the knowledge base in your discipline. It requires you to
return to
the literature you reviewed in Chapter Two to integrate results
of your
study with other empirical studies that have examined the same
phenom-
ena. If your results support previous findings, highlight points
of similar-
ity. But if your results contradict what other researchers have
found,
discuss why your results differ from previous research. If your
study was
guided by a theoretical framework or epistemological
framework, discuss
your results in relation to that framework. However, if your
study was not
based on theory, discuss how your study related to theory in
your disci-
pline. Recall that in some qualitative traditions, theory may be
derived
directly from the immediate data that the researcher has

collected rather
than from prior research and theory (Gall et al., 2007).
Third, in some cases, your results may not have turned out as
expected. You must explain why this happened. Consider
several expla-
nations. Causes of unexpected results usually fall into three
major cate-
gories: (a) sampling, (b) instrumentation, and (c) research
design.
Sampling
One problem that may limit your results is sampling, including
inade-
quate sample size, low consent rates, high percentage of
nonresponse from
participants, participants dropping out of the study, use of
volunteers, and
improper sampling procedures. Lack of representativeness of a
sample can
bias the results of a quantitative study. You should examine
these issues to
determine if sampling has affected your results.
Instrumentation
Another problem that could affect the results of your study is
instru-
mentation. Perhaps the instruments you used did not adequately
opera-
tionalize the variables you measured. Other instrument issues
include lack
of validity and reliability of your instruments, unreliable ratings
or obser-
vations, missing data, and possible biases. Review the

appropriateness of
each instrument concerning these issues.
Research Design
Still another problem that may limit your results is your
research
design. The design of your study involves issues of internal
validity,
including confounding variables, and many others. Space does
not permit
a full discussion here. You can review specific issues
concerning internal
validity associated with particular research designs by
consulting
Campbell and Stanley (1963) and Cook and Campbell (1979).
They provide
detailed descriptions of internal validity issues for experimental
and
quasi-experimental designs. Bogdan and Biklen (2007),
Creswell (2007),
and Denzin and Lincoln (2005) provide information on issues of
research
design for qualitative studies.
An example of a discussion section taken from Linda
Creighton’s
(2006) dissertation follows. Creighton proposed nine research
questions
and regression models for student and institutional factors in
her dis-
sertation, which focused on predicting graduation rates at
University
Council for Educational Administration (UCEA) public
universities.
Due to space limitations, we present three of her research

questions and
a discussion of her regression models in the following example.
(See
Example 10.3.)
Example 10.3
Previous researchers (Astin, 2005; Goenner & Snaith, 2003;
Pascarella &
Terenzini, 2005) studied extensively what phenomena affect and
ultimately
predict a student’s decision to remain in school or not. The goal
of my study
was to predict what factors were statistically significant at 63
UCEA public
universities. This section discusses the implications of the
findings for each
of the nine research questions and two prediction models.
Research Question One
Is there a relationship between graduation rates at University
Council for
Educational Administration public universities and student SAT
scores?
The findings resulting from research question one indicate a
positive
and significant relationship between graduation rates for the
universities

in this sample at the four-, five-, and six-year graduation level
and student
SAT scores. This finding speaks to the selectivity of the
colleges and uni-
versities regarding their admission criteria and the curriculum
in which
the students are enrolled. Universities that are highly selective
in their
admission policies (e.g., accepting only those students
achieving higher
than average scores on the SAT test) may raise graduation rates.
An insti-
tution requiring an average SAT score greater than 1100 should
expect a
higher graduation rate than a less selective required SAT score
that is in
the range of less than 990.
Students who meet the admission requirements of highly
selective
universities already come possessing the attributes for academic
success;
thus, it is imperative that colleges and universities offer
academic pro-
grams and support services to ensure the continued success of
these
students. The graduation rate performance of students is an
indicator that
the university’s programs and policies are perhaps meeting the
needs of
students better than those colleges and universities evidencing
lower
graduation rates.
Research Question Two

Is there a relationship between graduation rates at Univer sity
Council for
Educational Administration public universities and students
graduating in the
top 10% of their high school class?
As in research question one, the findings for research question
two
revealed a significantly positive relationship between
graduation rates at
the sample universities and students graduating in the top 10%
of their
high school class. This finding is consistent with previous
research (Astin,
1991, 1993, 1997, 2005; Pascarella & Terenzini, 2005; Tinto,
1999) which indi-
cated that one of the most accurate predictors of graduation
rates was the
percentage of students enrolled in the university who had
graduated in the
top 10% of their high school class. The implication here is that
students, by
virtue of their high academic ranking in high school, are
equipped to han-
dle the separation from family and high school friends, make a
successful
transition from high school to college, and become successfully
integrated
into the society of the college. A note of caution is warranted in
interpreting
this finding: it may be that not all high schools maintain the

same standard
of rigor in their academic curriculum. In addition, some
students may have
achieved this status because of inflated grading procedures and
may not in
reality have the ability to assimilate into the society of the
college that this
ranking would imply; however, the results of the statistical test
used to ana-
lyze this factor indicates a positive relationship between
students’ high
school class rank and graduation rates.
Research Question Three
Is there a relationship between graduation rates at University
Council for
Educational Administration public universities and the
percentage of students
from underrepresented populations (i.e., African American,
Hispanic, Asian, and
Native American)?
As evidenced by the review of literature, the underrepresented
popu-
lations identified in this study do not graduate from college at
the same
rate as the majority population (U. S. Department of Education,
National
Center for Education Statistics, 2004). However, there were
approximately
11 UCEA institutions identified in the current study that
experienced grad-
uation rates for these underrepresented populations ranging
between 70%
and 93%. The literature (Agboo, 2001; Flowers, 2004;

Hernandez, 2000;
Hurtado, 2000; Nora, 2001) named numerous factors including:
(a) envi-
ronmental factors (i.e., racial climate, presence of an ethnic
community,
and working and living off campus); (b) involvement factors
(i.e., faculty-
student interaction, mentorship, participation in student
organizations);
and (c) social-cultural factors (immigrant status, ethnic identity
develop-
ment, community orientation). A further implication for the low
gradua-
tion rates of these underrepresented populations is that many of
the
students may not speak English as their primary language and
struggle
with language issues, resulting in the need for remediation
courses. As
earlier research indicated (Kerr, 2001), many minority students
experience
academic difficulties because of their language barrier. The
college envi-
ronment may be alienating for these students and places them at
risk of
dropping out. The implication here is that the 11 UCEA
institutions hav-
ing higher than average (i.e., 70%–90%) graduation rates for
underrepre-
sented populations may have programs in place to further their
academic
success. Perhaps they show a high regard for diversity and
foster a college
climate of genuine concern regarding the possible effects that
anticipatory
stress could have on the interaction of minority students. In

addition, it is
possible that the faculty and student affairs professionals are
acutely
aware of the varied effects that ethnic group membership have
on the
social encounters of these students and exert collaborative
effort to
actively address issues related to the social experiences of
racial-ethnic
group members.
The findings of my study also have implications for the
counseling of
minority students. The counseling programs at the 11 UCEA
institutions
cited as having high graduation rates for these students may
have programs
in place that help foster students’ self-efficacy. These schools
may have
professional counselors or graduate student advisers available to
assess and
evaluate students’ academic and personal needs. Taking
physical, economic,
social, and cultural environments into consideration, counseling
staff
members work to involve students in academic and
extracurricular activi-
ties that integrate them into the campus community and promote
personal
well being and success. These programs help and encourage
students to

maintain respective cultural values and simultaneously employ
strategies to
negotiate negative messages possibly perpetuated by the
dominant society
(Lesure-Lester & King, 2004). (Creighton, 2006, pp. 97–101)
Student Factor Regression Results
The regression model for student factors revealed a student’s
SAT
score and high school rank as the strongest predictors of
graduation rates.
Though universities commonly take these variables into
account, they are
mostly doing so for selection and admission purposes.
Administrators
and policymakers must shift their focus to consider both of
these vari-
ables for the purpose of increasing graduation rates. Selection
and admis-
sion are important, but the major objective for universities is to
graduate
students. For example, many of the UCEA institutions have high
SAT
scores and high-school rank requirements for admission, but
continue to
have graduation rates in the 30–40% range. This is problematic.
The point
is: universities can predict that a 100-point increase in SAT
requirements
will affect graduation rates by approximately 10%. For example,
if the
graduation rate of a university is 40% while average SAT scores
are 1100,
the prediction can be made that by increasing its SAT average
to 1200, its

graduation rate would increase from 40% to approximately 50%.
In addi-
tion, increasing the percentage of students admitted who are in
the top
10% of their graduating high-school class by 10% will likely
result in a
four percent rise in graduation rate. Combining these two
factors can
affect rates by nearly 15%, having powerful and significant
implications
for accomplishing the real goals of education—degree
completion.
The percentage of students enrolled over the age of 25 also had
an
effect on graduation rate, albeit a negative one. This issue is
very com-
plex with obvious confounding variables already identified in
research
studies (Berger, 2001; Caison, 2004; Carey, 2004). The
implication here is
that universities should not be quick to limit the enrollment of
this age
group. Evidence exists (Kuh, 2005; Mangold, Bean, Adams,
Schwab, &
Lynch, 2002; Pascarella & Terenzini, 2005; Schnell & Doetkott,
2002) that
indicates student support systems and activities focus mostly on
uni-
versity freshmen (e.g., First Year Experience programs). Rarely
are there
support programs and activities geared toward the student over
the age
of 25. Programs must be designed and implemented that include
sup-
port for this group of non-traditional students. For example, if

we find
that family emergencies interrupt a student’s program of study,
there
must be an alternative plan available to the student. Even in
doctoral
programs, students can take a semester off to attend to family
emergen-
cies, then return to their program the following semester. This
kind of
option must be made available to undergraduate students in the
over-25
age group.
Institutional Factor Regression Results
The regression model for institutional factors revealed the
percentage of
full-time faculty, percentage of classes with less than 20
students, and edu-
cational expenditures as the strongest predictors of graduation
rates. In
addressing these three factors several difficulties arise. First, in
a time of eco-
nomic constrictions, adding faculty, increasing expenditures,
and reducing
class size will not likely occur. Second, finding qualified
faculty and addi-
tional classroom space continue to be problematic. But the
implications here
can be encouraging. Rather than addressing these issues
directly, perhaps
universities can approach them more creatively and indirectly.

For example,
many universities are implementing two- and three-tier faculty
load status.
Faculty can elect to focus more on teaching with a reduction in
expectations
for research—resulting in faculty teaching additional sections of
courses. If
this practice is rewarded and encouraged, it seems that the
result would be
a reduction in class size, due to increased sections of courses.
The correlation between student expenditures and graduation
rates is
not surprising. Currently, there is much emphasis on such things
as
increasing expenditures for programs that assist students from
underrep-
resented populations, programs that provide more one-on-one
advising,
and programs that help at-risk students. The implication,
though, is that
universities must also focus on indirect effects of expenditures.
As an
example, if more funding is funneled to improving the
technological
infrastructure, the result may actually be more academic support
for
students, which in turn could have a positive effect on
graduation rates.
If library holdings are increased to support the addition of
undergraduate
programs and degrees, it seems that the result might be
increased gradu-
ation rates in the long run. (Creighton, 2006, pp. 107–109)
As recommended, Creighton discussed each research question in

the
same order she presented them in Chapter One, “Introduction”
and Chapter
Four, “Presentation and Analysis of Data.” She carried forth a
discussion of
her two regression models from Chapter Four. Note that
Creighton
refrained from repeating the technical details of her statistical
analyses,
which had already been presented in Chapter Four. Here she
discussed and
explained the results of each research question and regression
model.
As mentioned at the beginning of this chapter, the concluding
chapter
of a dissertation or master’s thesis can have any number of
titles, includ-
ing: “Summary, “Discussion,” “Implications,
“Recommendations,” and/
or “Conclusions.” And we have also mentioned elsewhere in
this book
that qualitative dissertations often deviate from the traditional
five-
chapter dissertation or thesis model we have been discussing
throughout
this book. Such is the case with Wanda Bamberg’s (2004)
qualitative dis-
sertation. Bamberg’s dissertation is nine chapters. After
following our
model for the first three chapters, she devoted five chapters to a
discussion
of her results: Chapters IV, V, VI, VII, and VIII, one chapter
each devoted
to the rich narrative she developed from interviewing five
female superin-

tendents, five school board members, and five administrative
team
members from the districts that participated in the 2001–2002
study
conducted by Learning First Alliance. The Learning First
Alliance identi-
fied the five female superintendents as being the “most
effective” superin-
tendents in the nation. This constituted Bamberg’s sample,
including
board members and administrative team members in these
superinten-
dents’ school districts. All were female superintendents.
Bamberg’s concluding chapter of her 2004 dissertation, Chapter
IX, is
titled “Summary and Conclusions.” In Example 10.3 we provide
the intro-
duction to her concluding chapter, followed by the remainder of
the
sections (headings) contained in her chapter. (See Example
10.3.)
Example 10.3
CHAPTER IX
SUMMARY AND CONCLUSIONS
Introduction

Five female superintendents have been presented in five case
studies
in Chapters IV through VIII. My study examined the
professional leader-
ship experiences of the superintendents as they related to the
Synergistic
Leadership Theory (SLT) (Irby et al., 2002). In each case study,
the data
were presented from the interviews, responses on the
Organizational
Leadership and Effectiveness Inventory (OLEI) (Irby et al.,
2000;
Hernandez, 2004; Holtkamp, 2001; Trautman, 2000; Truslow,
2004), the
LFA case studies, organizational charts, and newspaper and
journal arti-
cles to address the four research questions in the study. Each
case study
ended with a summary of the relationship between the
leadership experi-
ences of the superintendent and the alignment of the four
factors of the
SLT: attitudes, values, and beliefs; leadership behaviors;
organizational
structure; and external forces.
The chapter presents a summary of the cases of five
superintendents as
their leadership experiences related to the alignment of the four
factors in
their respective school districts. The chapter also offers
conclusions about
the superintendents and a description of theory into practice
through the
application of the SLT in implications for practice.
Recommendations for

further study are also included. (Bamberg, 2004, p. 410)
Below are the remaining headings in Bamberg’s concluding
chapter of
her dissertation.
Summary of the Five Superintendents
Factor 1: Attitudes, Values, and Beliefs
Factor 2: Leadership Behaviors
Factor 3: Organizational Structure
Factor 4: External Forces
SLT Leadership Experiences: Alignment Summary
Conclusions
Note that Bamberg included all but one of the elements,
“Discussion of
he Findings,” we recommend for inclusion in the concluding
chapter of a
issertation or master’s thesis. Nevertheless, Bamberg provided
an excel-
ent discussion of her findings, which she skillfully integrated
into
hapters IV, V, VI, VII, and VIII while discussing her results of
the inter-
iews she conducted. This is another technique many qualitative

esearchers use, the integration of results with discussion of
those results.
f course, the ultimate decision as to what sections (headings)
constitute a
iven chapter rests with the dissertation or thesis chair. We
recommend
hat you read Wanda Bamberg’s (2004) entire nine-chapter, 462-
page dis-
ertation in order to capture the rich narrative contained therein.
t
d
l
C
v
r
O
g
t
s
IMPLICATIONS FOR PRACTICE
The section on implications for practice in the Results Chapter
is your
opportunity to suggest how the results of your study may be
applied to
practice. Of course, some studies have more implications for
practice than
others. Typically, studies that involve education populations are
likely to
have a great many practical applications. Inferring those
implications for
practitioners can be challenging, but educators must be able to
make those
connections. For example, dissertations or master’s theses in

education
might include a section on implications for teachers, counselors,
depart-
ment heads, and administrators. You also can provide
implications of your
study for other researchers. Implications for research and
practice should
be drawn as your study evolves rather than waiting until it is
completed.
Of course, you can do both.
For example, Keith Clark (2002) discussed implications of his
study of
the relationship between the allocation of educational resources
and
student achievement. (See Example 10.4.)
Example 10.4
The era of school accountability in the United States started
several
years ago (Murphy, 1990). Many community members as well as
law-
makers have called for greater accountability for schools. State
and fed-
eral legislatures have answered the call and are investing large
amounts
of financial resources into schools. These agencies are closely
examining
how this investment in education is being utilized by the
country’s school

districts. Many reports have shown that this increase in funds
has not sig-
nificantly improved student achievement (Barry & Hederman,
2000;
Coate & VanderHoff, 1999).
The findings of this study have far-reaching implications for
many
persons interested in the United States educational system. This
study
identified several links between school district expenditure
patterns and
student achievement. Persons interested in school finance,
policy, and
research will find the evidence of links between expenditures of
educa-
tional resources and student achievement very useful.
For educational administrators, this study offers insight into
what
resource allocation strategies are more likely to positively
influence
student achievement. It will also give the administrator a good
idea of
which strategies may negatively influence student achievement.
In par-
ticular, this study suggests expenditures for instructional
purposes can
help boost student outcomes. Research questions one and two
demon-
strate these phenomena. In both research questions using
different
methodologies, instructional expenditures were identified as
significant

predictors of student achievement.
This study will also be useful to persons interested in school
finance
research and policy development. Many researchers have
indicated that
the money spent on schools does not matter when looking at
student
achievement. These studies, in most cases, looked at school
district spend-
ing in the aggregate. This study looked at the actual functional
expendi-
ture areas in which school districts expend their resources. The
information contained in this study indicates that the money
spent on
education does make a difference in student outcomes. What
really mat-
ters is how and to what activities or programs the resources are
directed.
Another important finding that relates to policymakers is the
discov-
ery that resource allocation strategies can positively or
negatively effect
student achievement. Many state school allocation systems are
designed
to equalize funding to school districts. These equalization-
funding
systems are designed to give every school district substantially
equal
funding in order to give every student the same level of
education. This
study implies that equalization strategies may not work to
enable
students to achieve at higher levels. The data suggests that even
though

school districts may receive the same funding, the way they
expend their
financial resources may not result in equal educational
environments for
students. This information should have a direct effect on the
way state
education agencies allocate resources. For example, the data
suggest that
the allocation of more money for instruction will increase
student
achievement. State agencies that want to increase student
achievement
could require specific percentages of state funds be used for
instructional
related activities. (Clark, 2002, pp. 128–130)
Note that Clark discussed implications of his research for
school
administrators, policymakers, and those persons interested in
school
finance research. Note also that Clark has observed our maxim
mentioned
earlier in this chapter: “Stay close to the data.” His discussion
of implica-
tions for research and practice are tied to his findings.
RECOMMENDATIONS FOR FURTHER RESEARCH
Recommendations for further research are frequently generated
during
the course of the study. Such recommendations can be valuable
to other

researchers, particularly other graduate students, who are
seeking ideas
for research topics. For example, when our doctoral students are
searching
for appropriate topics for their dissertations, we advise them to
examine
the final chapter of completed dissertations or discussion
sections of pro-
fessional journals to get ideas from other researchers about
areas that war-
rant further research.
In addition to providing ideas for researchable topics, the
recommenda-
tions section contains suggestions for ways that your study can
be improved
or extended. As you ponder how you might improve or extend
your study,
ask yourself the following questions: What additional
independent vari-
ables need further study? What important dependent variables
should be
studied? What methodological improvements are needed? What
questions
remain unanswered? These questions and others prompt
recommendations
for further research. Data gaps, such as these, may have
weakened your
study and may have caused qualifications in the support of some
of your
predictions. Thus, their resolution is important to the goal of
extending the
knowledge base in your area of study. Similarly, qualitative
researchers can
ask similar questions related to research designs in the
qualitative tradition.

Be certain that your recommendations are based on your results
and
not on opinion. Recommendations should not be generalized
beyond the
scope of your study. Adhere to the maxim we suggested earlier:
“Stay close
to the data.” Furthermore, be careful not to make
recommendations with-
out considering their possible consequences, or taking into
account their
practicality. Finally, be careful that your recommendations are
not stated so
they sound like conclusions.
For example, Clark (2002) discussed recommendations for
further
research in which he followed our suggestions. (See Example
10.5.)
Example 10.5
The goal of this study was to investigate the effect resource
allocation
strategies had on student achievement. Data was collected to
test four
research questions relating to this goal. The information was
studied and
many significant findings resulted from the examination of the
data. The
findings, although significant, have some limitations. One
limitation is
that the findings explain only a small proportion of the
activities that are

affecting any individual student’s academic achievement.
Another limita-
tion is the design of the study. The study focused on aggregate
functional
expenditures. By only using functional level expenditures, the
researcher
could not explain in any detail what actual expenditures caused
the
significant effects discovered by this study. Based on the
inadequacies of
the study design, as well as the lack of statistical evidence in
explaining a
large part of the variance in test scores, suggestions are made
for further
research. The first two research questions examined the
relationship
between a school district’s student achievement and their
allocation of
financial resources by functional expenditures. It was found that
higher
budget allocations for instructional purposes were significantly
related to
higher student achievement. It was also found that some
budgetary
expenditures have a negative effect on student achievement. The
problem
is that the statistical methods used to analyze the data could not
provide
complete answers to the complex relationships between budget
alloca-
tions and student achievement. Further research along these

lines should
use a variety of data collection methods. Quantitative analysis
can iden-
tify relationships between school district expenditure patterns
and
student achievement in reading, writing, and mathematics, as
well as
school district ratings. Qualitative studies can be used to map
specific
examples of relationships among the variables. A study in this
format
could go beyond simply discovering what expenditure functions
have an
effect on instruction. It would allow researchers who have
discovered a
relationship to delve far deeper into the factors causing the
relationship.
This could include case studies or interviews with school
personnel who
are intimately involved in the allocation of financial resources.
Future research into this subject should also include more
detailed
expenditure information. This study broke informatio n down
into expen-
diture functions. Functional level expenditure data is much
more detailed
than earlier studies looking only at total district expenditures.
The prob-
lem is that the information is not detailed enough. Future
studies should
break financial expenditure data down to the object level.
Object level
expenditure information is a disaggregate of functional
expenditures. It
provides detailed spending information at a more detailed level

than
expenditures at the aggregate functional level. At this level of
detail, the
researcher would be able to tell what instructional level
expenditure was
having the greatest effect on student achievement. For example,
if this
study had utilized functional level expenditure information, the
results
would be able to show more than the fact that instructional level
expen-
diture increased student achievement. It would also be able to
show if
higher teacher compensation or greater expenditures for
instructional
supplies were significant factors in this increase.
Another avenue of research could be to institute a case study of
sev-
eral successful districts to discover their unique allocation
practices. The
information from the case studies could be combined to
ascertain if there
are common practices successful school districts are using.
These prac-
tices could include financial as well as nonfinancial
information. This type
of research could also be used to study successful student
instructional
programs or strategies. The researchers could study successful
programs
that create structures allowing the researchers to report the
amount of
student academic gain for each unit of monetary resources.
This study has shown that it is not enough to simply assume that

increasing the dollars spent on education will improve the
existing
system. It has shown that the allocation of resources within a
school dis-
trict does have a limited impact on student achievement. The
one factor
that was shown to have a strong positive impact on student
achievement
was expenditures for instruction. This was shown to be the
single
strongest indicator of a district’s success in improving student
achieve-
ment. This is not to say that simply throwing money at school
districts
will not ensure increased student achievement. There are many
other fac-
tors that affect student learning. The data indicate that the
amount of
school district expenditures is not the only factor that is related
to student
achievement. In other words, the keys to excellence in student
achieve-
ment may lie outside of conventional measures of increasing the
overall
monetary investment in instructional expenditures. Money must
be tar-
geted toward specific programs that are designed to meet the
specific
needs of individual students. The data also strongly indicated
that other
factors not included in this study had a stronger impact on a

student’s
achievement than money. (Clark, 2002, pp. 130–133)
Two things are noteworthy from Clark’s recommendations for
further
research. First, he has aligned his recommendations with his
research
questions. Second, he has adhered to the maxim: “Stay close to
the data.”
His recommendations are based on his data.
CONCLUSIONS
Sometimes the “Conclusions” section is omitted. At other times,
conclu-
sions are incorporated into the discussion section. The
conclusions section
of the final chapter is very important, because it is the
summative state-
ment of the researcher’s study. Its function is similar to the
summaries pro-
vided at the end of each chapter, except that the conclusions
section is the
conclusion of the whole study. It provides closure to the entire
project.
Every dissertation and master’s thesis should end with
conclusions.
Conclusions are assertions based on the findings. By reading
your con-
clusions, readers discover to what extent research questions
posed have
been answered or to what extent hypotheses are sustained or not
sus-
tained. Thus, conclusions address issues that support or fail to
support

your theoretical framework. For example, if a hypothesis has
been sup-
ported by the data, then the researcher can conclude that the
data support
the validity of the theory, which was being tested in the study.
Qualitative
researchers may use trends or themes, which emerged in their
findings as
a basis for potential theory building. In this case, the
conclusions section
provides the researcher’s sense of how findings of the study
contribute to
the knowledge base.
A common error made by students is drawing more conclusions
than
the data warrant. Conclusions must not be generalized beyond
what the
data can support. Again, we call your attention to the maxim:
“Stay close to
the data.” Another common error found in the conclusions
section is that
what are supposedly conclusions are actually summaries.
Conclusions like
the following can be found in numerous dissertations and
master’s theses:
“Mean scores for males and females were not significantly
different.” The
statement merely recalls a finding derived from analyzing the
data. This
finding needs to be interpreted in a way that will lead to a

conclusion about
what it means in terms of your research questions or
hypotheses. Finally,
you may include supplemental findings in the conclusion
section not
addressed in your research questions or hypotheses, providing
you have
included the analyses in the Results Chapter. These analyses
will appear
under the heading “Additional Analyses” in Chapter Four. It is
inappro-
priate to introduce new data anywhere in the Discussion
Chapter.
Sally Craycraft (1999) presented a conclusions section in her
study of
brain dominance and leadership styles of school superintendents
and
business chief executive officers. (See Example 10.6.)
Example 10.6
Conclusions
The findings of this study expanded the work of previous
researchers
in the area of leadership style and brain dominance
characteristics. This
investigation revealed that persons in the top executive level
educational
administration position (school superintendents) are
characterized by a
brain dominance style, which prefers the logical, analytic,
organized, and
controlled processes of the left hemisphere. A further
assessment of the

brain dominance results found in this study showed school
superinten-
dents generally lacking intuitive, conceptualizing right-brained
skills
required of leaders administering in a setting characterized by
the need
for risk-taking and acceptance of change. Business chief
executive officers,
on the other hand, are more right brained, preferring more
intuitive, con-
ceptual, interpersonal, and holistic processes. Significant
differences were
also found between the brain dominance mean scores for male
and female
leaders, with each group showing a stronger preference for left
and right
hemispheres, respectively.
The management/leadership literature indicates that most
American
organizations are experiencing dramatic change processes due
to changes
in technology, financial resources, and retrenchment of
organizational
structures and the people who work in them. The literature
suggests a
need for executive leadership that is able to visualize images of
the future
and articulate that vision to the followers in ways that empower
them to
change. (Craycraft, 1999, p. 108)
Note that Craycraft provided a brief but comprehensive
discussion of
conclusions from her study. Note also that she has “stayed close
to the

data”; that is, her conclusions are based on her findings.
SUMMARY
The final chapter of your dissertation may have any one of the
following
titles or a combination thereof: “Summary,” “Discussion,”
“Implications,”
“Recommendations,” and/or “Conclusions.” The final chapter of
your
dissertation or master’s thesis needs to include all of these
elements. In this
chapter, we provided advice on the contents of each of these
elements. This
advice was supported with examples from completed
dissertations. We
also suggested that qualitative dissertations and theses may not
necessar-
ily follow this pattern. In qualitative dissertations and theses,
researchers
frequently integrate results and the discussion of those results
in one or
more chapters. In the next chapter, we provide advice on how to
prepare
for and defend your proposal and final document.
PART IV

The Defense and
Afterward
11
The Proposal and
Final Oral Defense
first meeting is the pr
Most universities require that you meet formally with a faculty
com-
mittee twice, during which you defend your research project.
The
oposal meeting. The second meeting is the final oral
defense. The proposal meeting is held before you begin to
collect your data.
The final oral defense is held after you have collected and
analyzed your
data and have written your entire dissertation or master’s thesis.
Our uni-
versity decided that proposals should consist of Chapters One,
Two, and
Three of the dissertation. This practice is becoming
commonplace at many
other institutions. However, universities use a variety of
formats for
preparing the dissertation proposal. These formats can range
from a 10- to
20-page description of your proposed study to the completion of
the first

three chapters of the dissertation. Thesis proposals are typically
less formal.
It is your responsibility to schedule the proposal meeting. You
should
schedule your proposal meeting at least three weeks in advance
of the pro-
posal defense. Submit a quality, hard-copy document to your
committee at
the time you set the defense date, with a reminder memorandum
indicat-
ing the date, time, and location of the defense.
You can take a number of steps to help ensure a successful
proposal
meeting: (a) prepare a well-written document, (b) know the
format of the
defense, (c) prepare your presentation, (d) practice your
presentation, and
(e) anticipate questions.
244
PREPARE A WELL-WRITTEN DOCUMENT
You can increase the chances that your proposal meeting will go
well if
you prepare a well-written document. Do not pressure your
chair or com-
mittee to meet with you if your written document requires
additional
work. Make sure your chair has read and approves the final
draft of your
proposal. In this way, you will have your chair’s support. Once

your final
draft is approved by your chair, you can submit the document to
commit-
tee members. If the manuscript you submit to your committee is
well writ-
ten and carefully prepared (i.e., no spelling errors or poor
grammar, no
missing references, tables and figures match text, clear printed
copies), you
will begin your proposal meeting with a favorable impression.
On the
other hand, a poorly prepared document is likely to be perceived
by your
committee as the mark of an imprecise researcher.
KNOW THE FORMAT
The committee chair usually begins the proposal meeting by
introducing the
committee and explaining the procedures to be followed. Then
you are
asked to introduce yourself, make a brief statement about your
professional
background, and present a 25- to 35-minute summary of your
proposal. This
summary should cover all the major aspects of your research
plan—the
problem, purpose, theoretical framework, research questions or
hypotheses,
and methodology. The presentation should be clear and concise.
Questions from the committee follow your presentation. These
ques-
tions and your responses occupy most of the meeting, which
normally
takes between one and two hours. Usually, questioning is

conducted in
one of two basic ways: Each member asks an uninterrupted
segment of
questions, with the committee chair acting as moderator; or
questions are
posed from any committee member on any aspect of the
document. The
round-robin format tends to break down in most cases after the
first hour
or so, with an increase in cross-fire questions from any member
of the com-
mittee on any aspect of the document.
After the committee has completed its questioning, the
committee
chair will call for further questions. It there are no further
questions, you
will be asked to leave the room. The committee will then
deliberate, con-
cerning both your performance during the proposal defense and
the ade-
quacy of your research plan. Finally, the committee will vote on
the
acceptability of your research proposal.
Committees typically use four options in deciding the result of a
pro-
posal defense: (a) approval as submitted (this option is rare);
(b) condi-
tional approval, pending minor changes; (c) conditional
approval, pending
245Proposal and Final Oral Defense

major changes; and (d) denied approval (this option is also rare,
but it does
happen occasionally). In the latter case, a waiting period is
usually
required before the proposal defense can be repeated. Typically,
only one
reassessment is permitted.
When the committee has reached a decision, you will be called
back
into the room. The chair of the committee will announce the
decision.
Changes required in the document are summarized orally by the
commit-
tee chair. An official approval form is circulated for committee
members’
signatures before the meeting is adjourned. Subsequently, the
committee
chair attaches a memorandum to the approval form, containing a
list of the
revisions agreed upon in the meeting. The chair will then meet
with you
and go over the changes agreed to in the proposal defense.
PREPARE YOUR PRESENTATION
If you are in a field where PowerPoint is used for presentations
(e.g., edu-
cation and business), consider doing a PowerPoint presentation.
PowerPoint will provide your presentation visually, so the
committee will
focus on the screen, not on you, and you can refer to the
monitor as you
speak. Provide committee members copies of your PowerPoint
slides. In
order to remain within the time allotted for your presentation

(25 to
35 minutes), do not exceed 40 slides. Make a backup of
transparencies from
your PowerPoint slides in case the equipment fails on the day of
your pre-
sentation. Nevertheless, we suggest you bring your own laptop
computer
and present your PowerPoint presentation from your own
equipment.
The presentation you give will set the tone for the rest of the
meeting.
After you are introduced by your committee chair, begin your
presentation
by thanking your committee by name (Dr. Jones, Dr. Smith . . .)
for all the
help they have given you. Smile and make eye contact with your
committee
members. Take the opportunity to stand. This puts you in a
dominant posi-
tion, while all others in the room are seated. Tell the committee
members how
you became interested in your study. Keep it brief and
professional. You
might say something like this: “For some time now, I have been
interested in
variables that might have an effect on student achievement. In
reviewing the
literature, I have come across three variables that may be
related to student
achievement, namely: academic emphasis, collective efficacy,
and faculty
trust. I have decided to pursue this line of inquiry in my
research.”
Then, use this introduction to begin briefly reviewing the

literature in
this area. Concentrate on the literature directly related to your
own study.
Briefly summarize previous studies. Emphasize major findings.
Then, lay
the groundwork for your theoretical framework. Briefly present
your the-
oretical framework. This brief discussion of your theoretical
framework
will lead to your research questions or hypotheses. State each
research
question or hypothesis (1, 2, 3, and so on).
246 The Defense and Afterward
Then move into your methodology. Describe your participants.
Tell
how you will acquire them. Describe your independent and
dependent
variables. Discuss the instrument(s) you will use. Mention
reliability and
validity of these measures. Describe your data collection
procedures (e.g.,
questionnaires, in-depth interviews, observation, and document
analysis).
Be specific here. Discuss how you will analyze your data for
each research
question (hypothesis), theme, or variable.
Conclude by thanking your committee members for their
interest in
your research and for the time they devoted to reading many
drafts of
your proposal document.

The following outline may help you to prepare your proposal
and final
oral defense (see Table 11.1). It follows the five-chapter
dissertation model
we have presented in this book.
Table 11.1 Sample Presentation Outline
I. Introduction
Greeting
Background of the study
Statement of the problem
Purpose of the study
Significance of the study
Operational definitions (list only)
Theoretical framework
Research questions (or hypotheses)
II. Literature Review
The literature review can be integrated with the elements above,
as suggested in the
aforementioned example.
III. Method
Participants
Instrumentation
Validity
Reliability
Data Collection
Data Analysis
Limitations
Delimitations
Assumptions

These three integrated sections typically constitute the proposal
defense.
IV. Results
V. Discussion, Implications, and Conclusions
All five sections constitute the final oral defense with emphasis
on the results and
discussion of those results.
PRACTICE YOUR PRESENTATION
As mentioned previously, you will be asked to give a 25- to 35-
minute pre-
sentation of your research plan. It is a good idea to practice this
presenta-
tion alone, as well as in front of an audience. Time your
presentation.
Students frequently underestimate the time it takes to make a
presenta-
tion. As a result, they end up rushing through the most
important part of
their research plan: the methodology, especially during the
proposal
defense. In addition to practicing and timing your presentation,
practice
responding to questions about your research that may be asked
by your
committee. You can do this with other students or alone. Law
students
routinely engage in numerous “mock trials,” as part of their
training,
before trying a case in front of a jury.

Find out where your proposal meeting will be held. Go and look
at the
room. Check to see if the equipment you will need is available,
such as a
blackboard, an overhead projector, a screen, or PowerPoint
equipment. If
any items you will need are absent, make arrangements for them
well in
advance of your proposal meeting. If possible, practice your
presentation
in the same room your proposal meeting will be held.
ANTICIPATE QUESTIONS
Questions from the committee follow your presentation. It is
difficult to
predict the questions committee members will ask in a proposal
defense.
However, the committee will almost certainly engage you in
detailed ques-
tioning about your methodology during the proposal defense.
This is
because the Methodology Chapter provides the specific steps
you plan to
take to carry out your study. When committee members sign the
approval
form at the end of your proposal meeting, they are in essence
supporting
your methodology. That is, they are affirming that your
methodology will
provide an adequate test of the research questions or hypotheses
you pro-
posed (Heppner & Heppner, 2004).
Therefore, you can expect that the committee will focus on
detailed

aspects of your methodology. Most research involves sampling
issues.
They may inquire about the manner in which you selected your
partici-
pants. They may ask about the extent to which your study can
be general-
ized to other populations. They may inquire about the
instruments you
plan to use and the validity and reliability of these measures. If
you plan
to create you own instrument, they are certain to ask about your
plans to
validate the instrument and details about your pilot study. They
may ask
you about any potential internal and external threats to validity
and steps
you will take to minimize these. You may be asked questions
about ethical
issues, or about strategies to ensure anonymity and
confidentiality of your
participants.
You should expect to be asked questions about your plans for
data col-
lection. The committee may ask how you plan to analyze your
data. We
have found that detailed questioning about statistical analysis is
usually
not a major focus in proposal meetings. This is because most
universities,
like our own, have what is called “a resident statistician.” He or
she

teaches the statistics course(s) and usually serves as a
committee member
on all quantitative studies. If not, the resident statistician
consults with the
students on their statistical analyses. Typically, many of the
finer points of
statistical analysis are intensified after the proposal has been
approved,
but prior to data collection, although a rough schema is usually
required
at the time of the proposal meeting. If your study is qualitative,
the com-
mittee will certainly question you on your data collection
procedures (e.g.,
in-depth interviews, focus groups, document analysis) and the
analytic
processes you will employ.
You must be able to answer detailed questions about your
methodol-
ogy. These are questions you can anticipate, and that you can
prepare to
answer prior to the proposal meeting. The detail with which
committee
members will question you on these issues will depend on the
thorough-
ness with which you have prepared your written document.
In sum, after your proposal has been approved and you obtain
clear-
ance from your institution’s Human Subjects’ Committee, you
can begin
collecting and analyzing your data. Having collected and
analyzed your
data, you will then write the remaining chapters. Once you have
finished

writing the remaining chapters and have received approval from
your
committee chair, you will schedule the final oral defense.
FINAL ORAL DEFENSE
The final oral defense is similar to the proposal meeting.
Therefore, much
of the advice provided thus far regarding scheduling and
presenting the
proposal applies here. That is, similar to the proposal meeting,
you will be
asked to present, in this case, a 30- to 40-minute summary of
your study.
In this meeting, you will describe all the major aspects of your
study—the
problem, purpose, brief literature review, theoretical
framework, research
questions or hypotheses, method, results, and interpretation.
However, in
the final oral defense, you will focus less on the literature,
rationale, and
method of the study and more on the results and interpretation
of those
results. Thus, in your final oral defense, committee members
will ask you
to describe what you did, what you found, and what it means.
When responding to questions, you should observe two basic
princi-
ples: (a) listen carefully and (b) respond both respectfully and
scientifically.
This means that you listen to and acknowledge every question
posed by a
committee member, and you respond to each question
professionally,

using a well thought out rationale. We recommend that you
prepare and
practice the 30- to 40-minute summary, as well as ways of
responding to
questions from the committee. A few examples follow.
It is important to listen carefully to every question. This will
ensure
that you answer the question asked. Answer questions
completely yet suc-
cinctly. Do not give a lecture.
If you do not understand a question, ask the committee member
to
rephrase the question. Or you might paraphrase the question
yourself to
make sure you heard it correctly.
Perhaps a committee member will say: “Are you familiar with
the
recent article by X and Y on . . . What did you think of their
findings in rela-
tion to yours?” Do not panic if you have not read the article.
Say, “No, I am
not familiar with their work. Could you please give me the
reference, and
I will read it.” X and Y’s study may be only remotely related to
yours, so
do not agree to any more than reading it. Missing a question or
two will
not result in a failed oral defense.

Suppose a committee member offers a suggestion for
strengthening
your study that implies another study. You might respond by
saying,
“That is a very interesting point, but I believe it is beyond the
scope of this
study.” Such a response is fine, but an even better response
would be:
“That is a very interesting point. I agree with you. I will add it
to my
section on recommendations for further research.” In the latter
response,
you have recognized the merit of the committee member’s idea
and have
agreed to include it in your document. This is what Stephen
Covey (1991)
would call a “win-win” situation. You have acknowledged the
committee
member’s idea, and the change to your document is minimal.
Does this mean that you have to agree with all committee
members’
suggestions? Of course, not. For example, suppose a committee
member
offers another procedural option that she feels will strengthen
your study.
The suggestion seems to be gaining momentum among other
committee
members during the meeting. You have listened openly to the
faculty
member’s suggestion, but you do not agree. Describe the
rationale you
used in making the decision. Say: “Here was my rationale for
selecting that
particular procedure . . . ” Give a relatively detailed explanation

of your
decision, supported by data and author references. You have
acknowl-
edged the faculty member’s suggestion, and you have provided
a scien-
tific response. Usually the strategy works. The issue is settled.
Again, you
have created another win-win situation.
Say another committee member questions one of your summary
data
tables, claiming that it does not contain the proper heading for
the statisti-
cal procedure used. This will be an easy change to make. Agree
with the
professor. Say: “I agree with you. It certainly clarifies the
table.” Your chair
will have the final decision in matters like these, and in this
case, the statis-
tician’s opinion will be sought. The point is, you have
responded respect-
fully during the meeting. This constitutes another win-win
situation.
Another committee member begins asking a long series of
questions
based on an earlier draft of your document. It is obvious to all
that he has
not read the final version of the document. Maintain your
composure. Very
skillfully and systematically respond to each question one-by-

one, in each
case referring to the page number in the revised document. For
example,
say: “I have added to that explanation on page 38; I have
revised that table
on page 144,” and so on. Resist the temptation to express your
true feelings
of frustration and disappointment at the faculty member’s lack
of attention
to details. Remember, he or she may be on numerous
dissertation commit-
tees. Always respond respectfully and professionally to all
questions.
Expect a certain amount of posturing. This behavior takes the
form of
mini-lectures or long convoluted, rambling comments, which
are not
really questions at all. This behavior by a faculty member is
intended to
impress other committee members with the speaker’s knowledge
about
your topic. Let the faculty member have his or her say. Do not
interrupt.
When he or she concludes, say: “That is an important point.”
Say nothing
else, and get ready for the next question. Once again, you have
created
another win-win situation. You respectfully acknowledged the
faculty
member’s ideas, which has resulted in no change to your
document.
TIPS FOR AVOIDING COMMON MISTAKES
Preparation is the key to a successful dissertation proposal and

final oral
defense. The proposal usually consists of the first three chapters
of the the-
sis or dissertation: introduction, review of the literature, and
methodology.
The final oral defense consists of an examination of the entire
document,
including the Results and Discussion Chapters. These two
important
meetings are the culmination of a long preparation process.
Throughout
the process, you have been guided by a dissertation or thesis
chair. By the
time of your final oral defense of your dissertation or thesis,
you have
thoroughly reviewed the literature, designed the methodology,
and con-
ducted the study under the guidance of your chair and
committee. The
dissertation or thesis was written with the advice and
consultation of the
entire committee. Every member of the committee has seen the
document
and has critiqued it prior to the final oral defense meeting. With
all this
preparation and scrutiny, the final defense should proceed
smoothly.
Nevertheless, it is not uncommon to encounter some problems at
this
stage of the process. Why does this happen? We provide a half-
dozen tips
on how to avoid such problems (Castetter & Heisler, 1984;
Mauch & Park,
2003; Newman, Benz, Weis, & McNeil, 1997; Sternberg, 1981).

1. You lack mastery of the substance of your dissertation. Your
chair has
been misled about how much of the document—the literature
review, the
research design, the data collection, and the data analysis—was
really
completed or understood by you. Because of this, serious
weaknesses sur-
face at the final oral defense, which are then pursued by the
committee.
Make certain that you have total mastery of the dissertation or
master’s
thesis and how each section of the document relates to the
knowledge
base. Although you pay someone to do your data analysis, you
will be
held accountable for understanding the results.
2. You convince your chair that you are ready for the final
defense. Your
chair, feeling pressured, allows you to come to the final defense
before
your document is thoroughly and carefully completed, and
before it rep-
resents a work of excellence. Get your chair’s approval of the
entire docu-
ment before setting the defense date, and do not pressure your
chair into
agreeing to a defense date until your document is ready.
3. You have failed to gain approval of successive stages and

chapters of the dis-
sertation from committee members. In some programs, the
dissertation
process is still so vague that the student can proceed with the
bulk of the
dissertation or thesis without being scrutinized by faculty. Set
up weekly
or monthly meetings with your chair and crucial committee
members.
Such a schedule will keep you and your examiners on the same
page
throughout the process.
4. Your dissertation is not well packaged for the final defense.
An often
neglected aspect of packaging is the two- or three-page abstract
that
University Microfilms International requires at the beginning of
all disser-
tations. The first substance item that catches the wary reader’s
eye is the
abstract. Make it a terse yet complete overview of the
dissertation, includ-
ing the purpose embedded in the knowledge base of the
discipline, a con-
veyance of its contribution, the research questions or
hypotheses
developed from the problem examined, the methodologies
employed in
collecting and analyzing the data, the major findings, discussion
of the
results, and implications for practice. Your abstract should
project the cen-
tral themes from each chapter of the dissertation or thesis,
condensed into
one or two paragraphs each.

5. The appearance of the final draft copy of your dissertation is
neglected.
This is another important aspect of packaging your dissertation
for the
final defense. Use a high-quality laser printer for the final draft
of the dis-
sertation that the committee receives. From our experience, the
Helvetica
font seems to produce darker, more distinct copies than other
fonts.
Helvetica is also easier to read than many other fonts. Take care
in prepar-
ing your tables symmetrically on each page and avoid white
space at the
bottom of the pages of your dissertation. This will require
careful planning
of all of your tables before you begin to write the chapter.
According to the
American Psychological Association, you may reduce the tables
to 10 point
and single space some items when space is needed.
6. You lack “informed consent” about the defense. Informed
consent is a term
used in medicine when surgeons inform patients about the
procedures or
risks of a particular operation to be performed. In the context of
an oral
defense, uninformed consent means that you have neither a clear
picture of

what will likely transpire during the defense nor what can go
wrong. In
such a state, you may experience predefense fear, during-
defense paraly-
sis, and postdefense depression. The best way to have informed
consent
about how dissertation defenses are conducted in your
department is to
attend one or more defenses. The final oral defense is typically
open to the
public, and doctoral students are encouraged to attend. If you
cannot
attend a defense at your institution, talk to someone who has
recently suc-
cessfully defended a dissertation in your department. We
provided a
detailed description of what is likely to transpire at a
dissertation defense
in this chapter, under the heading “Know the Format.” Most of
the sug-
gestions in that section are applicable to both the proposal
defense and the
final oral defense of the dissertation.
SUMMARY
The essence of the discussion in this chapter is that the best
preparation for
a dissertation or thesis defense is through meticulous
preparation of the
written document and the meeting itself. Thus, know what you
did, why
you did it, what you found, and what it means. The mark of an
excellent
researcher is one who becomes her or his own harshest critic.

In the next chapter, we provide advice on how to prepare your
disser-
tation or master’s thesis for wider dissemination, including
professional
presentations and print media.
12
Presenting and
Publishing Your
Dissertation
sertation may be consulted by students who ar
You have defended your dissertation, made any necessary
corrections,
and submitted the required copies to your university library.
Your dis-
e interested in your topic; it
may be requested by other universities through the interlibrary
loan
system; and it may be referenced in other dissertations, books,
or journal
articles. However, your research will not reach a wide audience
in this way.
Publishing your dissertation is the way you can get it into the
main-
stream of academic ideas, rather than having it sit on a shelf and

collect
dust in your university library. You can disseminate your
research to a
wider audience in a number of ways, including: (a)
presentations, (b) job
interviews, (c) academic journals, (d) books, (e) chapters in
books, (f) the
popular press, (g) Internet publishing, and (h) desktop
publishing.
PRESENTATIONS
Formats for presentations include department colloquia;
presentations at
state, regional, national, and international professional
conferences; and
presentations at job interviews. In many departments, graduate
students,
faculty, or invited guests have opportunities to present their
research in
254
colloquia (also called “brown bags”) to anyone interested in
attending.
You can reach a larger audience, however, if you present your
research at
state, regional, national, and international meetings. Presenting
at depart-
ment colloquia and state and regional meetings will give you
practice for
presenting at the more prestigious national and international
conferences.

If your career aspirations lean toward academia, presentations at
national and international meetings will count more heavily on
your cur-
riculum vitae than state and regional meetings. National and
international
conferences provide a number of different presentation formats.
They
include paper presentations, poster sessions, organized
symposia, work-
shops, round table discussions, panel discussions, fireside chats,
and invited
addresses. Research based on dissertations and master’s theses
are most
appropriate for the first two formats: paper presentations and
poster ses-
sions. Therefore, we will concentrate our discussion on these
two formats.
Paper Presentation
Abstracts and proposals must be submitted well in advance of a
national or international conference. For example, the American
Educational Research Association (AERA), which meets in
April annually,
requires that submissions be received by early August. The
University
Council for Educational Administration (UCEA), which meets
in
November each year, requires that submissions be received by
early May.
Thus, presenting your study at national meetings such as these
requires
advanced planning. You may wish to check specific
organizations’ Web
sites to determine deadlines for presentation submissions.

If you plan to present your research at one of the
aforementioned
national conferences, you will need to submit electronically a
50-word
abstract and a three-page proposal of your study. Other
organizations may
require a different submission format. Much of the groundwork
will have
been done, providing you have prepared a stellar abstract for
Dissertation
Abstracts International. The three-page proposal should contain
the follow-
ing items: problem, theoretical (or conceptual) framework,
method, results,
and implications. About two-thirds of the proposal should
describe the
method and results. Reviewers will want to know the most
important
details of the study. The abstract is a brief summary of the
proposal.
Following are examples of an abstract (see Example 12.1) and a
pro-
posal (see Example 12.2). Although a variety of session formats
are avail-
able to presenters, these examples represent submissions that
were
accepted for paper presentations at the annual meeting of the
American
Educational Research Association (AERA) in April 2007. These
submis-
sions to AERA were based on Allen Garner’s (2005) doctoral
dissertation.
They are typical submissions and may serve as models for your
own
submissions.

255Presenting and Publishing Your Dissertation
Example 12.1
Managerial Tasks of Superintendents in Large,
Medium, and Small School Districts
Abstract
Superintendents’ managerial tasks performed in large, medium,
and
small school districts was the focus of this study. Three
research questions
guided the investigation concerning level of involvement,
differences
of involvement, and priority of tasks performed. Glass’s (2004)
Superintendent Management Grid was administered to a
national sample of
2,000 school superintendents randomly selected by computer
through the
Market Data Retrieval Company. After three requests, 1,042
superinten-
dents returned usable instruments (52%). Inferential statistics
revealed 87
significant differences between large, medium, and small school
districts
with respect to Glass’s five management domains: financial
management,
facility management, personnel management, student personnel
and
special services, and support services. The study provides
implications

for the preparation of school superintendents.
Example 12.2
Managerial Tasks of Superintendents in Large,
Medium, and Small School Districts
1. Objectives
The managerial of tasks of school superintendents “differs
greatly
among districts of varying size, wealth, and program
configuration” (Glass,
2004, p. 17). The purpose of this study was to examine the
managerial tasks
of school superintendents in large, medium, and small school
districts.
Specifically, this superintendent study was intended to: (1)
examine the level
of involvement of school superintendents in large, medium, and
small school
districts with respect to Glass’s (2004) five areas of school
management:
financial management, facility management, personnel
management,
student personnel and special services, and support services; (2)
examine
the differences of involvement of school superintendents in
large, medium,
and small school districts with respect to Glass’s five areas of
school man-
agement; and (3) examine the priority of tasks performed by
school superin-
tendents with respect to Glass’s five areas of school
management.

2. Perspective(s)
The following managerial tasks are common to every school
district
regardless of size and wealth: financial management, facility
management,
personnel management, student personnel and special services,
and sup-
port services. Financial management includes the following
functions:
fiscal planning, budgeting/forecasting/demographics,
accounting/cash
management, purchasing/contracts, inventory management,
materials
distribution, risk management, salary/wage management, and
worker’s
compensation. Facility management includes replacement,
maintenance,
retrofit, technology, and equipment. Personnel management
includes the
following functions: needs assessment, recruitment, evaluation,
induction,
staff development, record keeping, payroll, fringe benefits,
safety/
environment, negotiations/contract management, and grievance
manage-
ment. Student personnel and special services include program
organiza-
tion and management, supervision of services,
assessment/compliance,

adjudication of problems, student health, and school attorney.
Support ser-
vices include student transportation, food services, and
contracting. They
are prescribed actions both in highly centralized or
decentralized systems.
Most of these management functions are closely monitored by
state
departments and other regulatory agencies. They constitute a
nonnego-
tiable managerial imperative for superintendents to supervise,
coordinate,
perform, and be held accountable by the school board and
community.
The level of superintendent participation in the preceding
management
areas is primarily determined by district size, not training,
experience, or
personal inclination. Larger school districts have larger central
office staffs
than smaller districts allowing the superintendent to assign and
delegate
many management responsibilities to others. In most of the
14,500 American
school districts, however, superintendents perform the tasks
themselves or
share them with one or two other administrators. We adopted
the four levels
of management devised by Glass (2004) in an earlier study
(Level 1—
Supervise: The superintendent has little if any direct
involvement; Level 2—
Coordinate: The superintendent has coordinator role and is
directly
involved; Level 3—Co-Worker: The superintendent is actually

performing
some of the work; Level 4—Do It: The superintendent performs
the task)
devised by Glass (2004). Also we adopted school district sizes
(large,
medium, small), which were used in each of the American
Association of
School Administrator (AASA) ten-year superintendent studies.
3. Methods
Participants:
A national sample of 1,042 superintendents participated in the
study.
The responding school superintendents’ responses were placed
in one of
the following three categories: (a) large for those with an
enrollment of
10,000 and over, totaling 86 school districts; (b) medium for
those with an
enrollment of 1,600 to 9,999 students, totaling 282 school
districts; and
(c) small for those with an enrollment of 1 to 1,599 students
totaling, 674
school districts.
Instrumentation:
A useful instrument for examining the managerial tasks of
school
superintendents is Glass’s superintendent management grid
(2004). The
instrument contains 36 management tasks divided into five
categories:
(a) financial management, (b) facility management, (c)

personnel manage-
ment, (d) student personnel and special services, and (e) support
services.
The instrument was modified slightly for this study: First,
numbers were
added to help clarify and organize constructs. Next, percentages
were
added to the four levels of superintendent involvement to help
provide
clarification for respondents which included the following: (a)
level 1
supervise at 25%, (b) level 2 coordinate at 50%, (c) level 3 co-
worker at
75%, and (d) level 4 do it all at 100%. Finally, section two of
the survey
instrument was added to include demographic information.
Reliability
and validity of the superintendent management grid is reported
else-
where (Glass, 2004). A Cronbach alpha was conducted with the
present
sample to measure reliability and internal consistency of the
instrument.
The Cronbach alpha was .96 (n = 1,042).
4. Data Sources
The superintendent management grid, together with a cover
letter and
self-addressed envelope, was mailed to a national sample of
2,000 super-

intendents randomly selected by computer through the Market
Data
Retrieval Company. All surveys were coded to a master list of
the sample
for identification in order to calculate return percentages. After
two addi-
tional requests, 1,042 superintendents returned usable
instruments (52%
return rate). The Statistical Package for Social Sciences (SPSS)
software was
used to analyze and describe the data. Data were coded and
inputted into
the computer on all responses from surveys returned. Size of
school dis-
trict (large, medium, and small) was used as the dependent
variable.
Combinations of descriptive and inferential statistics were
utilized to ana-
lyze the data. Descriptive analysis was used to characterize
superinten-
dent respondents with the following demographic information:
(a) gender,
(b) ethnicity, (c) age, (d) highest degree earned, (e) total years
in education,
(f) total years as superintendent, (g) number of superintendent
positions
held, and (h) school district size. Descriptive analysis was also
used to
identify the level of involvement (Level 1–4) superintendents
have in the
work tasks of large, medium, and small school districts in the
following
areas of school management: (a) financial management, (b)
facility man-
agement, (c) personnel management, (d) student personnel and
special

services, and (e) support services. Additionally, analysis
variance
(ANOVA) was used to compare differences of responses of
superintendent
management tasks with school district enrollment. Due to the
fact that sig-
nificant mean differences were evident between different sized
school dis-
tricts, a Turkey post hoc analysis was performed to determine
precisely
where the significant mean differences were found. Effect size
was also cal-
culated on all inferential statistics. Finally, descriptive analysis
was used to
rank order superintendent managerial tasks by school district
size.
5. Results
Analysis of variance (ANOVA) revealed 87 significant
differences of
superintendent involvement according to school district size.
First, super-
intendent involvement in financial management revealed 24
significant
differences among superintendents of large, medium, and small
school
districts. Subsequently, there were 11 significant differences
among super-
intendent respondents in facility management. In addition, the
study of

superintendent involvement in personnel management revealed
27 sig-
nificant differences. Furthermore, there were 18 significant
differences
among superintendent respondents in student personnel and
special ser-
vices. Finally, superintendent involvement in support services
revealed
seven significant differences in responses.
Descriptive methods revealed that financial management was
the
most important area of school management for superintendents,
followed
by personnel management, student personnel and special
services, facil-
ity management, and support services. The demographics of the
current
superintendent study were quite similar to the Glass, Björk, and
Brunner
(2000) national AASA study.
6. Educational Importance of the Study
Not only are current superintendent preparation programs
offered by
universities not uniform, but also certification programs vary
from state to
state. In the past, certification requirements have driven the
content of super-
intendent preparation. Certification or licensing codes generally
require uni-
versity coursework and passing a state exam. In approximately
30 states, the
certification or licensing code is closely or loosely based on
seven standards

developed by the Educational Leadership Constituent Council
(2002) for a
K–12 principal position. The application of generic principal
standards may be
due to the traditional structure of many university
superintendent programs
being mere extensions of principal preparation (Kowalski &
Glass, 2002).
This paper does not debate the appropriateness of existing
superin-
tendent preparation but has implications for developing a
superintendent
executive management program consisting of five domains of
manage-
ment: financial management, personnel management, student
personnel
and special services, facility management, and support services.
Each
domain can be developed into an instructional and performance
module
and should be a collaborative effort between the university,
professional
associations, and the state agency.
The abstract and three-page proposal was sent electronically,
single-
spaced, and in 12-point type to AERA. The documents included
as
Examples 12.1 and 12.2 are reproduced here exactly as they
were submit-
ted. The proposal was accepted as a paper presentation at the
annual meet-
ing of AERA in April 2007. These are the typical submissions
required by
national organizations such as AERA and UCEA, and many

others. Note
how Garner’s submission, which is based on his dissertation,
conforms to
our earlier recommendations concerning contents of proposals
submitted
to national meetings.
Preparing a Paper Presentation
In Chapter 11, we provided you with many suggestions for
preparing
a presentation for your proposal and final oral defense. Much of
that infor-
mation applies here. Following are a few additional suggestions
for
presenting a paper at a national or international conference.
1. Submit a copy of your paper to your session discussant at
least two
weeks prior to your presentation date.
2. Observe time limits set for presenters. Usually four
presenters per
session are allowed 10 to 15 minutes each, followed by
questions
from the audience.
3. Practice reading your paper to determine how much time you
will
need. Adjust the length accordingly. If you will be using
PowerPoint, adjust the number of slides appropriate for the time
allotted. We recommend that you bring your own equipment

(e.g.,
laptop computer). Make transparencies of your slides as a
backup in
case your PowerPoint does not work.
4. Familiarize yourself with the layout of the room pri or to your
pre-
sentation, and set up your PowerPoint prior to your
presentation.
5. When presenting your paper, maintain eye contact with your
audi-
ence periodically, enunciate, and show enthusiasm by varying
the
pitch and modulation of your voice.
6. Stay within your time limit. Look at your watch when you
begin
talking, or set a timer on the podium.
7. During the questioning portion of the presentation, answer
ques-
tions completely and succinctly.
8. Make a sufficient number of copies of your paper available
for your
audience.
If you follow these suggestions, it is likely that you will make a
suc-
cessful presentation.
Poster Session
The poster session is a relatively new innovation. Researchers
are

assigned positions in a room where each one displays key
elements of her or
his research and discusses it with whomever attends. For a
poster session,
you prepare a visual layout of your research that parallels the
content of a
paper presentation. The various sections of your paper, namely
problem (or
purpose), theoretical framework (or perspectives), method,
results, and
implications, are typed and attached to a 3-foot by 5-foot
poster. The type
should be readable at a distance of about two or three feet. As
to organiza-
tion of your paper, allocate one page for purpose, theoretical
framework,
and implications, two pages each for method and results, and
display no
more than three tables or figures. You can start by using the
three-page pro-
posal to develop your poster. Both formats (paper presentation
and poster
session) require a 50-word abstract and a 3-page single-spaced
proposal. The
poster display allows other researchers to stop by your poster,
read its con-
tents, and discuss it with you if they wish. Prepare a handout of
the contents
of your poster, which can be distributed to interested persons.
JOB INTERVIEW

Another means of disseminating your research to a wider
audience is a job
interview. Of course, in this case, your primary motive is to use
your dis-
sertation or master’s thesis to secure a position. Suppose, for
example, that
you are one of three finalists for a faculty position at a college
or univer-
sity. You have been invited to campus for two days to meet with
faculty,
administrators, and students. As part of the interview, you are
required to
make a one-hour presentation (including questions) on your
research
interests. How do you prepare for this event?
First, consider your audience. Your audience is likely to be
composed
of faculty, students, and administrators. Faculty will be
interested in your
potential as a teacher, scholar, and colleague. Students will be
interested
in your potential as their teacher and what they can learn from
you.
Administrators will be interested in all of the above. At this
point, all three
finalists are probably about even based on their paper
credentials, or they
would not have been invited to make a presentation. Therefore,
it is impor-
tant that you make a solid presentation. The presentation you
make will
set the tone for the rest of the interview.
Second, as mentioned in Chapter 11, practice your presentation

alone
and in front of a group. Perhaps you have already presented an
abbrevi-
ated version of your dissertation or master’s thesis at a local,
state, or
national conference. This has given you some practice. But such
presenta-
tions were only 10 to 15 minutes in length at those conferences.
You could
read your paper, if done skillfully, without losing your
audience. A 45-minute
presentation followed by 15 minutes of questions is different
and more
demanding. If you read your paper for 45 minutes, you will
surely lose
your audience. What you need to do is learn your content so
well that you
do not have to read your paper. However, if you cannot
memorize the con-
tent, then practice reading the paper with as much enthusiasm—
variation in pitch, tone, and modulation of your voice, as well
as periodic
eye contact with your audience—as possible. “The trick to
reading a
paper . . . is to appear not to be reading it” (Madsen, 1992, p.
134). An even
better approach is to do a PowerPoint presentation. This way
you can refer
to your paper off your laptop monitor. The audience will focus
on the
screen, and not on you. Provide slides of your PowerPoint, so
your audi-
ence can follow you using either the screen or your slides. We
recommend
that you work off your own laptop computer. Make
transparencies of your

PowerPoint slides as a backup and use an overhead projector
should your
PowerPoint equipment fail.
Third, what should be included in your presentation? Follow the
guidelines for the preparation of a conference paper or poster
session:
problem, theoretical framework (or perspectives), method,
results, and
implications. Provide a brief overview of the research problem.
Do not
spend much time reviewing the literature, but discuss theories
that
undergird your study. Simply state your research questions or
hypothe-
ses. Spend most of your time describing the methodology (e.g.,
in-depth
interviews, focus groups, document analysis, instruments, or
experi-
ments) and the results. Conclude by discussing implications of
the
results (i.e., what is the significance of your study). Thank
everyone for
attending, and wait for questions. Much of the advice we
provided in
Chapter 11 regarding questioning during the proposal and final
oral
defense applies here. Recall the advice we provided in Chapter
11 for
responding to questions: you should observe two basic
principles when
responding to questions: (a) listen carefully and (b) respond

both
respectfully and scientifically.
ACADEMIC JOURNALS
If you intend to pursue a career in higher education, you need to
consider
very carefully the requirements for tenure. If you want to
achieve tenure,
you will need to amass an appropriate number of important
articles in
high-quality, peer-reviewed journals. At the time of the tenure
decision,
most universities want to see a long list of quality published
articles. The
requirement for a research university is about three articles per
year.
Smaller colleges and universities, oriented toward teaching and
under-
graduate students, might require only one article per year.
Having completed the doctorate recently, your first step is to
deter-
mine how many articles can be published from your
dissertation. We
believe that any dissertation of seminal importance can produce
multiple
journal articles. For example, your literature review may be so
comprehen-
sive and of such high quality that it would be suitable for
publication in a
review journal. In addition, you may have developed a piece of
computer
software during the course of your study that could be described
in a spe-
cialized journal that focuses on computers. Perhaps you have

developed a
new statistical procedure or variant of an existing procedure
while con-
ducting your study that could be described in a short statistics
handbook.
Or, you might have discovered a different set of factors based
on a factor
analysis of the instrument(s) you used in your study, a
description of
which might be appropriate for publication in a journal that
specializes in
measurement issues.
The scheme outlined above—a review article, a computer
software
article, a statistical procedure article, and a measurement
article—derived
from your dissertation could constitute the beginning of a
publication plan
for tenure.
Note that we are speaking here about submitting multiple papers
derived from your dissertation that describes independent parts
of the total
document. We are not speaking about creating several
publications based on
the results from a single database. This practice constitutes
duplicate publi-
cation (Cone & Foster, 2007). Consult the American
Psychological Association
Publication Manual (5th ed., 2001) for further discussion of this

issue.
Select a Suitable Journal
Determining where to send your manuscript involves three
important
factors: (a) selecting a journal that matches your topic, (b)
selecting a jour-
nal that matches you career goals, and (c) selecting a journal
that is appro-
priate for your study (Heppner & Heppner, 2004).
First, find a journal that publishes the types of articles that
match
your dissertation topic. A particular discipline may have a
dozen or more
journals. Typically, each one will focus on a particular type of
article it
publishes, such as empirical articles, theoretical articles, or
practitioner
articles. For example, within educational administration,
Educational
Administration Quarterly publishes primarily empirical
research. The
Journal of Educational Administration publishes primarily
conceptual and
empirical studies. Educational Leadership and Phi Delta Kappan
publish
primarily applied articles on educational leadership and
education in
general, respectively, as well as some empirical articles with
definitive
implications for practice. Thus, select a suitable journal that
matches
your dissertation topic. Read the journal’s mission statement
and

its guidelines for manuscript submission. In addition, peruse the
con-
tents of the primary journals in your discipline to get a feel for
the type
of articles published in them. And, of course, consult with your
disserta-
tion chair.
Second, your career goals will determine the most suitable
journal for
your manuscript. For example, if you are interested in working
as a con-
sultant or as an administrator in a public school, then you
should choose
to publish in a journal that focuses on practitioner articles.
However, if
you intend to build a career in a research university, you should
submit
your work to a prestigious empirical journal. Academic
departments in
research universities will only favorably consider articles
published in
top-tier journals.
Following are a dozen Tier 1 journals in educational administra-
tion for those professors interested in building a career teaching
educational administration at a prestigious research university.
(See
Table 12.1.)
Table 12.1 Tier 1 Journals in Educational Administra tion

Educational Administration Quarterly Review of Research in
Education
Journal of Educational Administration Educational Evaluation
& Policy Analysis
Journal of School Leadership Harvard Educational Review
Educational Researcher Teachers College Record
American Educational Research Journal Educational
Management &
Administration
Review of Educational Research Journal of Educational
Research
Third, determine the most prestigious journal that is suitable for
your
manuscript. Many journals are available in any given discipline,
but not all
journals are of the same quality or scientific rigor. Academics
classify jour-
nals into a rough hierarchy as to quality as follows: excellent
(Tier 1), above
average to average (Tier 2), and below average to marginal
(Tier 3). This
rough classification system is based primarily on three factors:
refereeing
systems, acceptance rates, and citation scores. For example,
some journals
may not be refereed (i.e., they do not peer review manuscripts)
and may
have very high acceptance rates. Other journals have a
refereeing system
and may have a very low acceptance rate. Some journals have

high citation
scores, while other journals are cited less often. Generally, the
higher the
rejection rate, the higher the quality of the journal. However,
there are excep-
tions to this general pattern. In many social and behavioral
science disci-
plines, some prestigious journals do not operate on the basis of
a refereeing
system. In addition, some universities place importance on
citation scores.
This is a calculation of how often other researchers cite an
article from a spe-
cific journal. The more often a specific journal is cited, the
higher the quality
the journal is judge to be. Citation scores for most journals can
be found in
the Social Science Citation Index and the Humanities Citation
Index.
One good source of information on the characteristics of
journals is
Cabell’s Directory of Publishing Opportunities (2006). It lists
more than 4,000
specialized and professional journals by discipline. For each
journal it sup-
plies (a) submission addresses; (b) publication guidelines,
including man-
uscript length, copies required, computer submission
requirements,
format, and manuscript style; (c) review information, including
number of
external reviewers, acceptance rate, time required to review,
reviewer’s
comments, and invited articles; (d) circulation data, including
primary

audience, frequency of issue, copies per issue, publisher; (e)
manuscript
topics; and (f) manuscript guidelines.
Now that you have determined the level of quality of various
journals,
you should evaluate the level of scientific rigor of your
manuscript. If your
dissertation is a two-variable study using correlation, it is not
likely to be
published in a Tier 1, empirical journal. It may be more suitable
for a Tier 2
or Tier 3 journal. However, if your study was a test of a new
theory, used
an underrepresented population not traditionally used in the
past, or
employed an advance-level statistical procedure, like structural
equation
modeling, it might be accepted in a Tier 1, empirical journal.
Moreover, if
your study was qualitative, and your analyses were solid, it too
might be
accepted in a Tier 1 journal.
Prepare the Manuscript
After selecting a suitable journal, the next step is to prepare the
manu-
script for publication. Most APA-style journal articles include
the follow-
ing sections: abstract, introduction, method, results, and

discussion. These
sections parallel the sections (or chapters) of a dissertation or
master’s the-
sis. Pare down your dissertation so that these sections of your
document
are shorter and more focused, or build up the conference
proposal you
submitted to AERA or UCEA, or other national or international
annual
meeting “call for papers.” Read the guidelines for manuscript
submission
found in the journal you have selected, and follow them exactly.
Observe the following maxim when preparing your manuscript:
“Write,
print, edit, revise, polish, get feedback, and revise again.”
Repeat this cycle
as many times as it takes to get a publication-ready manuscript.
Write a first
draft concentrating on getting your thoughts in written form. Do
not worry
about how your ideas are organized at first. Print your writings
and edit
them on the hard copy. Avoid on-screen editing, which is
usually confined
to simple corrections. At the revision stage, focus on organizing
your ideas
into a clear argument sequence and linking closely related
points. Then pol-
ish the text using topic sentences, transitions, and closure
sentences, as dis-
cussed in Chapter 7. Get feedback on your polished text from
well-respected
and published colleagues. Based on the feedback you receive,
revise again.
Repeat this cycle until you are completely satisfied that your

manuscript is
ready for submission to the journal of your choice.
Submit the Manuscript
Submit the required number of copies to the address provided in
the
most recent issue of the journal you selected. Send the
manuscript Return
Receipt Requested. This postal method requires a signature
from a receiver
where the journal is housed. When you receive the receipt in the
mail, you
will know your manuscript arrived safely. Most editors will
acknowledge
receipt of your manuscript by letter. They will usually indicate
who is han-
dling your manuscript, typically an associate editor. If you have
not
received such an acknowledgment letter within four weeks of
submission,
contact the editor (by e-mail, telephone, or letter) to inquire
about the sta-
tus of your manuscript.
You will receive a decision on your manuscript. Journal editors
typically use four categories of response: (a) acceptance with no
changes;
(b) conditional acceptance pending changes; (c) rejection,
accompanied by
two or more anonymous reviews; and (d) revise and resubmit. In

the latter
case, the editor encourages the author to revise the manuscript
and resub-
mit it for additional consideration. The “conditional acceptance
pending
changes” and the “revise-resubmit” decisions are very common
nowa-
days. In the former case, you have an acceptance. It doesn’t get
much
better than that, for “acceptance with no changes” is very rare.
Thus, revise
the manuscript as soon as possible, following the suggestions
provided by
the reviewers. You do not have to make all suggestions
recommended if
you do not agree with them, but you must respond to each
suggestion. The
“revise-resubmit” decision is, in essence, no decision. You may
decide to
revise and resubmit, or submit the manuscript to another
journal, using
the suggestions from reviewers to improve the manuscript.
BOOKS
Books can be divided into two main types—trade books and
academic
books. Trade books are intended for the general public and ca n
be found
in commercial bookstores, public libraries, and publisher’s
catalogs.
Academic books, including textbooks and monographs, are
intended for
academic audiences, such as students and faculty in colleges
and univer-
sities, and practitioners in the field. Most social and behavioral

science
research is best suited for publication in academic books.
However, some
may appeal to the general public and qualify for publication in
trade
books.
Most dissertations or master’s theses are best suited for
publication as
research monographs. A monograph is a detailed study of one
particular
topic (e.g., organizational climate and pupil control ideology, as
men-
tioned in Chapter 7). It is at the opposite end of a continuum
from a best-
selling textbook. Sales for monographs total in the low
hundreds as
compared textbooks, which may sell in the thousands.
Why is it worth publishing a monograph? There are at least
three rea-
sons: (a) to disseminate your research to a larger audience, (b)
to add
accomplishments to your curriculum vitae, and (c) to improve
your cita-
tion score (i.e., how many times your work is cited in the
professional lit-
erature) (Dunleavy, 2003). The citation score is often used by
research
universities as one criterion in making tenure decisions. The
more often
your work is cited by other scholars the better, and the more
prestigious
the journal the better yet.
Possible publishers to approach with a book proposal include:

(a) major university presses, such as Teachers College Press,
Columbia
University; (b) major commercial publishers with monograph
lists; (c) less
well-known university presses; (d) smaller or lesser-known
commercial
publishers with monograph lists; or (e) vanity presses. The first
four
choices are legitimate publications, the first one being the best
choice. Do
not even consider vanity presses. A book published by a vanity
press may
impress your friends, but it cannot be used to build your
curriculum vitae
in your bid for tenure in most colleges and universities.
After considering possible publishers, your next task is to
prepare a
book proposal. Most book publishers want to know (a) about
your project,
(b) about the contents, and (c) about the market. Following are
book pro-
posal guidelines issued by Corwin Press. (See Example 12.3.)
Example 12.3
Part 1: About the Project
Rationale: Why are you writing the book? What will it
accomplish? Why
do people need help on the topic at this time? Is the topic of

increasing
importance rather than passing or declining importance? How
does the
book meet the need you have identified?
Knowledge Base: What is the research or experience base for
the informa-
tion in the book? Briefly describe any special studies or
previous work rel-
evant to the content.
Nature of Content: Do you provide a “broad brush” approach or
a
detailed, comprehensive treatment of the topic? How will the
book be
applicable to practice?
Alternative Title Possibilities: Along with your current working
title, please
suggest several alternative titles. We strive for titles that clearly
communi-
cate to all audiences the topic, purpose, and usefulness of our
publications.
Competing or Related Works: Please list the author, title, and
publisher of
the main competing or related books, and tell us how your book
would
differ or be superior.
Special Materials: Describe any elements in the book that will
help illus-
trate the textual discussions (for example, cases, flowcharts,
forms, sum-
maries, annotated bibliography, glossary, and so on.) How many
tables,

figures, and exhibits do you anticipate using?
Length and Schedule: How many double-spaced, typewritten
pages do
you anticipate the manuscript will have? Include special
materials, tables,
figures, and the reference section in the page count. When do
you expect
to have your manuscript ready? (Please be realistic in planning
your
schedule.)
Other Publishers: Which other publishers are considering your
project?
Author: What experience, background, or other qualifications
do you
have that make you uniquely qualified to undertake this project?
Please
attach your curriculum vitae or resume.
Part 2: About the Contents
Outline of Contents and Chapter Descriptions: Please provide an
outline
with all part and chapter titles indicated. Also, include a few
sentences
about the purpose and contents of each chapter along with
specific details
and examples.
Sample Chapters: Do not wait until the manuscript is completed

to sub-
mit the prospectus. Instead, send it with one or two sample
chapters.
When we learn of projects in the early stages, we can point out
potential
problems and offer editorial suggestions. Of course, if the
manuscript is
complete, or nearly so, then send copies of all that is avai lable.
Part 3: About the Market
Primary Audience: What is the primary market for the book?
Professionals in what specific field/subfield? What associations
or spe-
cific divisions of professional organizations or professional
journals
would provide highly targeted mailing lists for promoting your
book?
Secondary Audience: Are there other audiences that might be
interested in
the book, but to a lesser degree? Please be as realistic and as
specific as
possible in describing who will buy the book.
Uses: What would the book help the audience do, understand,
improve, carry
out, etc.? Distinguish between the use for the practitioner
audiences and the
uses for training workshops or whatever other distinction is
most meaningful.
Potential Course Adoptions: In addition to sales to individual
practition-
ers and academics, Corwin Press books are often used as
supplements in

college university courses, staff development workshops, and
other train-
ing settings. If your book has the potential for such use, please
provide as
much specific information as possible as to the types of courses
and
approximate enrollments.
(Corwin Press Book Prospectus Guidelines, 2000)
Send your book proposal, together with your curriculum vitae to
two
or three publishers on your first attempt. Precede your
submission with a
brief telephone conversation with the acquisitions editor for
your disci-
pline. Most publishers have a Web site on which publication
guidelines
can be found. Read the guidelines carefully.
A dissertation or master’s thesis is rarely ready for publication
as a
book. Dissertations and master’s theses are published as a book
only after
considerable editing and revision. Here are some general
suggestions for
making your dissertation suitable for publication as a book: (a)
delete
excessively repetitive text found in most dissertations; (b)
decrease sub-
stantially the number of tables and figures, references,

excessive lists, and
academic jargon; and (c) eliminate most of the literature review
(unless
your book will be a historical treatise).
CHAPTERS IN BOOKS
Abbreviated versions of your dissertation or master’s thesis may
be pub-
lished in an edited book. First, the editor of the volume selects
the authors
who will be asked to submit chapters for the edited book. Such
was the
case with The Changing World of School Administration—25
chapters
(Perreault & Lunenburg, 2002) and Shaping the Future—28
chapters
(Lunenburg & Carr, 2003). Second, papers presented at a
conference can
comprise an edited book’s contents.
POPULAR PRESS
The popular press includes magazines, newspapers, and
newsletters.
Compared with journal articles, books, and chapters in books,
they are
brief, free of jargon, and often suggest applications of the
research results
for lay people. An advantage of the popular press as compared
to the more
academic venues is that they may reach more readers. The
disadvantage is
that these are not refereed publications and, therefore, do not
carry much
weight in the academic community. Appearances on radio and

television
can also be included in the category of the popular press.
INTERNET PUBLISHING
Recently we have witnessed the World Wide Web as a
publishing venue.
The Web consists of millions of information servers and
millions of users
at more than a million sites. The growth of the Internet and
World Wide
Web has created a new outlet for reports of research. At the
present time,
researchers can distribute their research reports via the Internet
and World
Wide Web in two main ways. Both involve electronic
transmission to a spe-
cific site. For example, in the first case, an author can transmit
a document
to a user at their network address. One such site appropriate for
the receipt
of research reports is Connexions, a project of Rice University.
The research
report, after acceptance and any necessary editing by the author,
is placed
on the Connexions network by category of topic. The second
alternative is
for an author of a research report to transmit the manuscript via
the
Internet to the editor of a journal that issues its product on the
Internet.

One example is the online journal Advancing Women in
Leadership (see
http://www.advancingwomen.com).
DESKTOP PUBLISHING
Equipped with a word processing program, an author can create
a book
that can appear like a professionally typeset document. With the
use of a
copy machine, the same author can print copies of the
manuscript. Many
authors have typesetting equipment, but few have bindery
equipment.
Therefore, binding a book or journal pages would need to be
contracted
out to a professional bindery at the author’s expense. Of course,
desktop
publishing, as well as the popular press, are not considered
legitimate
publications in the academic community, and, therefore, would
carry little
weight regarding tenure decisions.
PLANNING THE WRITING PROCESS
One of the major problems beginning assistant professors face
in getting
ready for tenure is the shortage of time. Amassing a suitable
number of
important articles in top-tier, refereed journals is difficult for
some, espe-
cially with the competing demands of teaching, service, and
family. Once
you begin teaching, it will seem as though you cannot find the
time to write.

The keys to your writing success and publishing success are
planning and
discipline. Following are tips for organizing the writing process
(Dunleavy,
2003; Heppner & Heppner, 2004; Luey, 2004). (See Example
12.4.)
Example 12.4
Tip No. 1. Establish regular, predetermined writing times. For
example, some
authors find it helpful to set aside two or three half-days or one
full day (8–9
hours) each week for writing. Others write for two hours every
day. Still
others may prefer to reserve writing blocks of 6–8 hours one or
two times a
week. It doesn’t matter when, where, or what you write, at least
in the begin-
ning. Make certain that you establish regular, predetermined
writing times,
and make them inviolable. Writing regularly greatly increases
the level of
writing productivity, as well as the quality of the writing.
Tip No. 2. Just write. If you find it difficult to write during your
regu-
lar, predetermined writing times, do not stop writing, but
instead, just
write a first draft. Do not worry about how your first draft is
organized.
Concentrate instead on expressing your ideas. Then, later you
can
rearrange your ideas into a single, clear sequence of arguments.
For
example, you may decide that eight typed pages is a good level

of produc-
tivity for a full eight-hour day of writing. Then set a minimum
goal of
writing six typed pages in a full day of writing. Reward yourself
in some
way for reaching your writing goal.
Tip No. 3. The order of the writing task need not be linear. We
have sug-
gested that an APA-style journal article contains the following
sections:
abstract, introduction, method, results, and discussion. You do
not have to
write the manuscript from beginning to end, (i.e., write the
abstract page
and continue linearly through the manuscript). Start with the
easiest task
and progress to the more difficult tasks. Perhaps the method
section is the
easiest for you to write. Then begin there. When this task is
accomplished,
you will feel that you are making progress and will be ready to
focus on
the next task. Also, some sections may take longer to write than
others.
Thus, you might plan the writing tasks in relation to the amount
of time
you have available to write. For example, if you have a large
block of time
to write on a given day, it may be sufficient to write the first
draft of the

introduction, which usually takes the longest time to write.
However, if
you have a small block of time, you may wish to tackle a
smaller task.
Tip No. 4. Write, print, edit, revise, polish, get feedback, and
revise again. A
good draft of the manuscript is not ready to submit for
publication.
Follow the suggested cycle above. Write your ideas. Next print
out your
writings and edit them on your hard copy print out. Avoid on-
screen edit-
ing, which tends to be confined to simple corrections rather than
textual
improvements. Once you have edited the text, extend and clarify
the man-
uscript if needed. After you have made revisions and further
polished the
manuscript, ask several well-published colleagues to read it and
provide
critical, editorial feedback. Ask them specifically to provide
critical feed-
back. Then revise again based on the feedback you receive.
Tip No. 5. The best writing does not happen under pressure.
Successful
professional writing takes time: time to reflect, time to read,
time to
write, time to get feedback, time to evaluate the writing, and
time to
repeatedly revise the manuscript. “A manuscript should be
written and
revised at least five times, and more for inexperienced writers.”
This is
a tip that doctoral students should follow before submitting

their work
to their committee chair. One of the authors of this book had a
conver-
sation a few years ago with a doctoral student who was
attending a
major research university. She told him that she was going to
submit
her dissertation chapters to her dissertation chair the following
day “to
get his reaction.” The author of this book said to the student:
“Are you
completely satisfied that your dissertation chapters are the very
best
work you can do. If not, consider revising them until you are
absolutely
certain your chapters cannot be improved any further by
yourself.
Then, and only then, should you submit them to your chair.”
This is
sound advice to any writer. We find that many of our doctoral
students
submit their first draft to us “to get our reaction.” Ninety-nine
times
out of a hundred, we send it back for further revision.
Tip No. 6. Writing does not just happen. Writing should be
planned.
Authors can exercise control over the process. The production
of a quality
product requires sustained effort over time. If you follow the
aforemen-
tioned tips, your chances of delivering a quality product will
improve.

SUMMARY
In this chapter, we discussed many important issues regarding
disseminat-
ing your research to the professional community and lay public.
This
needs to be done as soon after your final oral defense as
possible.
Dissertations and master’s theses can and should be published
in one
venue or another depending on your career goals. We concluded
the
chapter with some tips on planning the writing process. The
mark of an
excellent writer is one who becomes his or her own harshest
critic.
APPENDIX A
Initial Letter Soliciting Participation
UNIVERSITY LETTERHEAD
College of Education
Address
Date
Principal’s Name
and Address

Dear ________________:
Two hundred principals have been selected to participate in a
basic
research project in educational administrati on being conducted
at XYZ
University. Enclosed is a questionnaire-type instrument, which
is
employed to secure:
1. Your opinions concerning certain aspects of principal -teacher
relationships
2. Your opinions on a number of important social and personal
questions
The forms do not take long to complete; the time required by
most indi-
viduals varies from ten to fifteen minutes. Since we are
collecting opinions,
there are no correct or incorrect answers. All that is necessary is
that you
give your frank opinion. Your responses will be strictly
confidential; all
replies are anonymous, and no individual or school will be
named in any
report of the research.
Please read the directions carefully and then proceed to answer
all the
questions. Some of the questions may be difficult to answer
with the
information given, but please respond to each statement as well
as you are
able, following the directions.

Your prompt attention and cooperation will be greatly
appreciated, as it is
a prerequisite to the success of this research. Thank you.
Sincerely,
John Smith Tom Jones, Ph.D.
Doctoral Student Professor
P.S.: You will receive a report of the research, probably early
next year, but
perhaps sooner.
Copyright © 2008 by Corwin Press. All rights reserved.
Reprinted from Writing a Successful Thesis or
Dissertation: Tips and Strategies for Students in the Social and
Behavioral Sciences, by Fred C. Lunenburg
and Beverly J. Irby. Thousand Oaks, CA: Corwin Press,
www.corwinpress.com. Reproduction
authorized only for the local school site or nonprofit
organization that has purchased this book.
273
APPENDIX B
First Follow-Up Letter
Address

Date
Principal’s Name
and Address
Dear ________________:
About two weeks ago, you were one of the 200 school principals
who
received a questionnaire-type instrument in connection with a
basic
research project in educational administration being conducted
at XYZ
University. These forms do not take long to complete; the time
required by
most individuals varies from ten to fifteen minutes.
Sincerely,
P.S.: If you have misplaced the questionnaire, please drop the
enclosed
coded post card in the mail and you will receive another copy of
the instru-
ment by return mail.

274
APPENDIX C
Second Follow-Up Letter
Address
Date
Principal’s Name
and Address
Dear ________________:
About three weeks ago you were one of 200 principals who
received a
questionnaire-type instrument in connection with a basic
research project
in educational administration being conducted at XYZ
University. These
forms do not take long to complete; the time required by most
individuals
varies from ten to fifteen minutes.
Enclosed is another copy of the questionnaire, in case you have

misplaced
the original. Your responses will be strictly confidential; all
replies are
anonymous, and no individual or school will be named in any
report of
the research.
Sincerely,
P.S.: If the individual to whom this letter is addressed is no
longer in the
position, the new principal should respond to the questionnaire.
You will
receive a report of the research, probably early next year, but
perhaps
sooner.
275

APPENDIX D
Dissertation Proposal Outline (Correlational)
PREDICTING GRADUATION RATES: AN ANALYSIS OF
STUDENT
AND INSTITUTIONAL FACTORS AT UNIVERSITY
COUNCIL FOR
EDUCATIONAL ADMINISTRATION (UCEA) PUBLIC
UNIVERSITIES
Problem:
1. To examine the relationship between graduation rates at
University
Council for Educational Administration public universities and
student SAT scores and students graduating in the top 10% of
their
high school class.
2. To examine the relationship between the graduation rates at
University Council for Educational Administration public
universi-
ties and students paying out-of-state tuition and students from
underrepresented populations (i.e. African American, Hispanic,
Asian, and Native American).
universi-
ties and the percent of full-time faculty and the average salary
of
full-time faculty.

universi-
ties and class size (i.e., percent of classes with fewer than 20
students; percent of classes with 50 or more students).
universi-
ties and total student educational expenditures (i.e., tuition and
fees,
cost of instruction, research, student services).
Literature:
Graduation Rates:
American College Test (1999); Braxton & Mundy (2001);
Caison (2004);
Chronicle of Higher Education (1999); Engle, Reilly, & Levine
(2003);
Goenner & Snaith (2003); Horn & Carroll (1998); Mangold,
Bean, Adams,
Schwab, & Lynch (2002); Pascarella & Terenzini (1991); Schuh
(1999); Tinto
(1993).
276
Student Factors:
Braustein & McGrath (1997); Credle & Dean (1991); Engle,
Reilly, &

Levine (2003); Gardner, Keller, & Protrowski (1996); Goenner
& Snaith
(2003); Gold (1995); Good, Halpin, & Halpin (1992); House
(1992); Payne,
Pullen, & Padgett (1996); Porter (2003); Rowser (1997); Schuh
(1999);
Schwartz & Washington (2002); Tinto (1987).
Institutional Factors:
Astin (1983); Chronicle of Higher Education (2005); Goenner &
Snaith
(2003); Jacoby & Garland (2004); Landrum (2001); U.S. News
& World
Report (2006).
Research Questions:
1. What is the relationship between graduation rates at
University
student SAT scores?
University
students graduating in the top 10% of their high school class?
University
the
percent of students paying out-of-state tuition?
University

the percent of students of underrepresented populations (i.e.
African American, Hispanic, Asian, and Native American)?
University
the
percent of full-time faculty?
University
the
average salary of full-time faculty?
University
class size (i.e., percent of classes with fewer than 20 students;
per-
cent of classes with 50 or more students)?
University
total student educational expenditures (i.e., tuition and fees,
cost of
instruction, research, and student services)?
277Appendix D
Design:

1. Sample:
Sixty-three public universities in the University Council for
Educational Administration (UCEA)
2. Data Collection:
All data for the sample are available in: (1) The Chronicle of
Higher
Education Almanac Issue 2005–2006, (2) U.S. News and World
Report
2006 Edition of America’s Best Colleges, (3) The U. S.
Department of
Education Graduation Survey retrieved at The Education Trust:
College
Results Online, and (4) Integrated Postsecondary Education
Data System
(iPEDS) Spring 2001–2004 Data File.
3. Data Analysis:
Research Question 1. A Pearson Correlation will be used to
determine if
there is a statistically significant relationship between
graduation rates at
universities and
student SAT scores.
determine if
graduation rates at
universities and
students graduating in the top 10% of their high school class.

determine if
graduation rates at
universities and
the percent of students paying out-of state tuition.
determine if
graduation rates at
universities and
the percent of students from underrepresented populations (i.e.
African
American, Hispanic, Asian, and Native American).
determine if
there is statistically significant relationship between graduation
rates at
universities and
the percent of full-time faculty.
determine if
graduation rates at
universities and
the average salary of full-time faculty.

determine if
graduation rates at
universities and
278 Writing a Successful Thesis or Dissertation
class size (i.e., percent of classes with fewer than 20 students;
percent of
classes with 50 or more students).
determine if
graduation rates at
universities and
total student education expenditures (i.e., tuition and fees, cost
of instruc-
tion, research, and student services)?
The Pearson Correlation describes and measures the linear
relation-
ship between two variables. However, correlation tells us
nothing about
the predictive power of variables. Multiple regression analysis
will be uti-
lized to predict values of the dependent variable (graduation
rates) from
the independent variables found to be significantly correlated
(student
and institutional factors). For example, if the Pearson
Correlation reveals a

strong and significant relationship between SAT scores and
graduation
rates, a regression analysis will help identify a model that
allows us to pre-
dict graduation rates based on students’ SAT scores. Thus,
highly ranked
colleges and universities can use the regression model to predict
that a
student entering college with an SAT score of 1400 should have
a 97%
chance of graduation.
Contribution:
1. To determine the relationship between graduation rates at
University Council for Educational Administratio n public
universi-
ties and student SAT scores and students graduating in the top
10%
of their high school class
universi-
ties and students paying out-of-state tuition and students from
underrepresented populations (i.e. African American, Hispanic,
Asian, and Native American)
universi-
ties and the percent of full-time faculty and student-faculty
ratio

universi-
ties and student tuition and fees
279Appendix D
APPENDIX E
Dissertation Proposal Outline (Analysis of
Variance and Regression)
THE RELATIONSHIP OF SUPERINTENDENT LEADERSHIP
STYLES TO STUDENT ACHIEVEMENT AND SCHOOL
DISTRICT
FINANCIAL AND DEMOGRAPHIC FACTORS
Problem:
1. To determine differences in superintendents’ leadership
styles and
student achievement as measured by Academic Excellence
Indicator
System (AEIS) school district ratings of exemplary, recognized,
acade-
mically acceptable, and low performing.

2. To examine the relationship between superintendents’
leadership
styles and student achievement as measured by TAAS scores in
reading, writing, and mathematics.
3. To examine the relationship between superintendents’
leadership
styles and 11 selected school district financial and demographic
fac-
tors, such as (a) percent of economically disadvantaged
students,
(b) percent of limited English proficient students, (c) percent of
minority populations, (d) percent of special education students,
(e)
percent of gifted and talented students, (f) percent of career and
technology students, (g) per pupil cost for instruction, (h)
percent of
operating expenses spent on instruction, (i) percent of operating
expenses spent on extracurricular activities, (j) amount of local
tax
value per pupil, and (k) pupil-teacher ratio.
4. To examine the relationship between student achievement as
mea-
sured by school district TAAS scores in reading, writing, and
math-
ematics and 11 school district financial and demographic
factors,
such as (a) percent of economically disadvantaged students, (b)
per-
cent of limited English proficient students, (c) percent of
minority
populations, (d) percent of special education students, (e)
percent of
gifted and talented students, (f) percent of career and

technology
students, (g) per pupil cost for instruction, (h) percent of
operating
expenses spent on instruction, (i) percent of operating expenses
spent on extracurricular activities, (j) amount of local tax value
per
pupil, and (k) pupil-teacher ratio.
280
Literature:
Transformational Leadership:
Bass (1981, 1985); Bass & Avolio (1990, 1993, 1994, 1995);
Bass, Avolio, &
Goodheim (1987); Bennis & Nanus (1985); Bright (1987); Buck
(1989); Burns
(1978, 1984); Byrd (1987); Downton (1973); Hampton,
Summer, & Webber
(1987); Hoover (1987); House (1977); King (1989); Koh (1990);
Orr (1990);
Roberts (1985); Rouche (1989); Sergiovanni (1990, 1992);
Smith (1982); Stogdill
(1963); Tichy & Devonna (1986); Tichy & Ulrich (1984);
Zaleznik (1989).
Student Achievement:
Educational Economic Policy Center (1994); Firestone (1991);
Goals
2000: Educate America Act (1994); Heck, Larsen, &
Marcoulldes (1990);
Heller (1994); Johnson, Lein, & Ragland (1997); Mace-Matluck

(1987);
McCord (1996); National Commission on Excellence in
Education (1983);
Newmann, King, & Rigdon (1997); Shavelson (1991); Texas
Education
Agency (1997); Vornberg (2000); Zigarelli (1996).
Accountability and the Superintendent:
Alpin & Daresh (1984); Buck (1989); Cawelti (1981); Clark,
Clark, &
Campbell (1993); Collier (1987); Collier, Sclafani, & Estes
(1987); Cuban
(1988); Duigan (1980); Ford (1999); Fullan (1993, 1998); Geery
(1997);
Lunenburg & Ornstein (2000); Marks (1981); Mintzberg (1973);
Palmaffy
(1998); Pankratz & Petrosko (2000); Sergiovanni (1992);
Schlechty (1990,
2000); Siccone (1997); Wallace (1985).
Financial and Demographic Factors:
Brookover & Lezotte (1979); Coleman (1966); Crisfield (1999);
Educational Economic Policy Center (1994); Golden (1999);
Tomlinson
(1980); Turner (1999).
Research Questions:
1. What differences exist in superintendent leadership styles and
student achievement as measured by Academic Excellence
Indicator
System (AEIS) school district ratings of exemplary, recognized,
acade-
mically acceptable, and low performing?

2. What is the relationship between superintendents’ leadership
styles
and student achievement as measured by school district TAAS
scores in reading, writing, and mathematics?
3. What is the relationship between superintendents’ leadership
styles
and 11 selected school district financial and demographic
factors,
281Appendix E
such as (a) percent of economically disadvantaged students, (b)
per-
cent of limited English proficient students, (c) percent of
minority
populations, (d) percent of special education students, (e)
percent of
gifted and talented students, (f) percent of career and
technology
students, (g) per pupil cost for instruction, (h) percent of
operating
expenses spent on instruction, (i) percent of operating expenses
spent on extracurricular activities, (j) amount of local tax value
per
pupil, and (k) pupil-teacher ratio?
4. What is the relationship between student achievement as
measured
by school district TAAS scores in reading, writing, and
mathematics
and 11 selected financial and demographic factors, such as (a)
percent

of economically disadvantaged students, (b) percent of limited
English proficient students, (c) percent of minority populations,
(d) percent of special education students, (e) percent of gifted
and
talented students, (f) percent of career and technology students,
(g) per pupil cost for instruction, (h) percent of operating
expenses
spent on instruction, (i) percent of operating expenses spent on
extracurricular activities, (j) amount of local tax value per
pupil, and
(k) pupil-teacher ratio?
Design:
1. Sample:
207 school districts in Texas
207 superintendents and 3 principals from each school distr ict
(one
representing each school level: elementary, middle, high school)
420,000 students (approximately)
2. Instrumentation:
Multifactor Leadership Questionnaire (Bass & Avolio, 1985)
TAAS tests in reading, writing, and mathematics (TEA Web
site)
Financial and demographic factors (PEIMS-TEA Web site)
3. Data Collection:
a. MLQ (self-report version) administered to the superintendent

and
the MLQ (principal version) administered to three principals in
the
207 school districts
b. TAAS scores in reading, writing, and mathematics retrieved
from
the TEA Web site
c. Eleven financial and demographic factors retrieved from the
TEA
Web site
4. Data Analysis:
Research Question # 1: 12 ANOVAs computed for each of the
12 sub-
scales of the MSQ to determine differences in superintendents’
leadership
styles among the four school district ratings.
Research Question # 2: Three stepwise multiple regression
analyses com-
puted to determine MSQ predictors of school district TAAS
scores in read-
ing, writing, and mathematics.
Research Question # 3: 12 forward stepwise multiple regression
analyses
computed to determine financial and demographic factor
predictors of the
12 subscales of the MLQ. For those scales significantly related

to financial
and demographic factors, backward stepwise multiple regression
analyses
computed to determine financial and demographic factor
predictors of the
MSQ subscales.
Research Question # 4: Three forward stepwise multiple
regression
analyses computed to determine financial and demographic
factor predic-
tors of school district TAAS scores in reading, writing, and
mathematics.
Contribution:
1. To determine the influence of superintendent leadership on
school
district ratings
2. To determine the influence of superintendent leadership on
student
achievement as measured by TAAS scores in reading, writing,
and
mathematics
3. To determine relationships between 12 MSQ subscales and 11
school
district financial and demographic factors
4. To determine relationships between 11 school district
financial and
demographic factors and TAAS scores in reading, writing, and
mathematics

283Appendix E
284
APPENDIX F
Dissertation Proposal Outline
(Multivariate Analysis of Variance)
THE RELATIONSHIP BETWEEN BRAIN DOMINANCE
AND LEADERSHIP STYLE OF SCHOOL
SUPERINTENDENTS
AND BUSINESS CHIEF EXECUTIVE OFFICERS
Problem:
1. To explore the prevailing brain hemisphere characteristics
and self-
perceived leadership styles of school superintendents and
business
chief executive officers.
2. To investigate the effects of intervening variables such as
affiliation
(business or education), gender, experience level, and age on

brain
hemisphere characteristics and leadership style.
Literature:
Brain Research:
Agor (1983, 1984, 1985); Andrews (1980); Begley & Carey
(1981);
Blakeslee (1980); Bogen (1977); Bogen & Gogen (1969);
Bunderson (1988);
Bunderson, Olsen, & Herrmann (1984); Carnine (1990); Cassell
(1978);
Chall & Mirsky (1978); Cicchette (1991); Cohen (1979); Cone
(1981, 1982);
Coulson & Strickland (1983a, 1983b, 1996); DeBono (1971,
1985); Eccles
(1973); Edwards (1979, 1982); Epstein (1974, 1978, 1984);
Fagan (1979); Finn
(1983); Ford (1987); Freeman (1995); Garrett (1976); Gaylean
(1981); Gaylin
(1976); Garmon (1985); Gazzaniga (1967, 1983, 1985a, 1985b);
Geschwind
(1979); Goldstein (1985); Goleman (1977); Gorovitz (1982);
Gowan (1981);
Grefe (1982); Hardyck & Haapanen (1979); Hart (1975, 1978,
1981, 1983);
Hatcher (1983); Herrmann (1981a, 1981b, 1981c, 1982a, 1982b,
1988, 1989,
1996); Hutson (1984); lronson (1984); Jaynes (1976); Kane &
Kane (1979);
Kanter (1983); Katz (1978); Kerensky (1983); Kimura (1973,
1978, 1985);
Kinsbourne (1975, 1982); Leaffer (1980); Levitt (1963); Levy
(1972, 1978,
1980, 1983); Levy & Trevarthen (1976); Lynch (1986); Maguire

(1990);
McCallum & Glynn (1979); McKenney & Keen (1974); McLean
(1978);
McQueen (1984); Mintzberg (1976); Nebes (1974); Norris
(1984, 1985a,
1985b, 1985c, 1986, 1988); Oates (1979); Ornstein (1972,
1978); Ornstein &
Brandwein (1977); Ornstein & Thompson (1984); Owens
(1986); Perelle
& Ehrman (1983); Piatt (1983); Reitz (1986); Restak (1979);
Reynolds &
Torrance (1980); Riggs (1991); Ross (1990); Samples (1980);
Satz (1979);
Schkade & Potvin (1981); Scott-Soler (1991); Segalowitz
(1983); Sperry
(1964, 1973, 1975); Springer & Deutsch (1990); Spruill (1986);
Suojanen
(1976); Torrance (1981); Trevarthen (1980); Trotter (1976);
Wallace (1992);
Wenger (1982); Wessman (1987, 1988); Witelson (1977);
Wittrock (1977);
Youngblook (1981); Zelinski & March (1976); Zemke (1984).
Leadership:
Anasse (1984); Argyris (1962); Artise (1984); Barnard (1938);
Barnard
(1968, 1976); Barrett & Yoder (1980); Blake & Mouton (1964);
Burke (1980);
Cleveland (1985); Coulson & Strickland (1983); Cuban (1988);
Culbertson
(1976); Davidson (1987); DeBono (1971, 1985); Drucker
(1982); Dunigan

(1980); Dwyer (1984); ERS (1986); Farkes (1979); Fielder
(1969); Freeman
(1995); Graham (1980); Greenleaf (1977); Griffiths (1966);
Guglielmino
(1979); Henderson & Nutt (1980); Hersey (1986); Hersey &
Blanchard
(1977, 1979, 1982); Hersey & Stinson (1980); Heyberg (1966);
Huckaby
(1980); Jung (1923); Katz (1974); Kouzes & Posner (1990);
Levitt (1963);
Lynch (1986); Macoby (1981, 1982); McGregor (1960);
Mesenburg (1983);
Mintzberg (1976); Paulsen (1982); Peters & Waterman (1982);
Pitner &
Ogawa (1981); Pondez (1978); Reitz (1986); Riggs (1991);
Roethlisberger
(1941); Ross (1990); Rowan (1986); Scott-Soler (1991);
Selznick (1957);
Sergiovanni (1979); Snyder & Anderson (1987); Spruill (1986);
Stodgill
(1948, 1970); Stodgill & Coons (1957); Sweeney (1982);
Tannenbaum &
Schmidt (1958); Taggert, Daniel, Roby & Kroeck (1985);
Taggert & Roby
(1981); Turner (1979); Vecchio (1981); Walter, Caldwell, &
Marshall (1980);
Weisenberg & Grunfield (1966); Wessman (1987); Wonder &
Donovan
(1984); Zaleznick (1964, 1977).
Research Questions/Hypotheses:
Research Questions:
1. What are the brain dominance preferences of superintendents
and

chief executive officers?
2. Which brain dominance patterns are most prevalent among
super-
intendents and chief executive officers?
3. What differences exist between brain dominance profiles in
terms of
(a) affiliation (business or education), (b) gender, (c) years of
experi-
ence, and (d) age?
Hypotheses:
1. There is no significant difference in brain dominance
preferences
exhibited between superintendents of schools and business chief
executive officers.
2. There is no significant difference in brain quadrant scores
between
superintendents and chief executive officers.
285Appendix F
between
males and females.
due to
experience level.
5. Age is not a significant influence in determining brain

quadrant
scores.
Design:
1. Sample:
707 subjects from a database compiled by Herrmann
International
(1988–1998)
408 (599) Chief Executive Officers and 299 (366)
Superintendents
610 males and 97 females
2. Instrumentation:
Herrmann Brain Dominance Instrument (HBDI)—Thinking
Styles
Assessment
3. Procedure:
Analysis of data elements obtained from the administration of
the
HBDI
a. Profile Codes—numeric representation of preference for the
use of
each of the four quadrants
b. Right/Left and Cerebral/Limbic Dominance Raw Scores—
used to
determine differences among the groups
c. Four Quadrant Scaled Scores—used to determine differences
among the groups

d. Key Descriptors, Work Elements, Twenty Questions—
provided
descriptive information about the work characteristics and
leader-
ship style of the groups (represent their behavioral preferences
for
decision making and interaction with others)
4. Statistical Analysis:
a. Multivariate Analyses
– MANOVA (multivariate analysis of variance)
– MANCOVA (multivariate analysis of covariance)
b. Analysis of Covariance (ANCOVA)
Contribution:
1. Influence the content and design of future educational
administration
training programs.
2. Have implications for the recruitment, selection, and training
of
educational administrators.
3. Guide individuals into the most appropriate course of study
within
the administration field.
4. Provide specific areas of focus for personal choices for

professional
development of practicing administrators.
5. Assist in selecting advanced-degree candidates for
educational
administration programs.
6. Provide initial information on the cognitive styles of
educational
administrators that may reflect on barriers to educational
change
and innovation.
287Appendix F
288
APPENDIX G
Dissertation Proposal Outline (Qualitative)
THE MOTIVATIONAL IMPACT OF ONLINE ELECTIVE
COURSES: AN ANALYSIS OF THE MOTIVATIONAL
ORIENTATION OF AT-RISK NINTH-GRADERS ENROLLED

IN
TRADITIONAL AND ONLINE ELECTIVE COURSES
Problem:
1. To examine the differences in the intrinsic goal orientation of
at-risk
ninth-grade students enrolled in online elective courses, as com-
pared to at-risk ninth-grade students enrolled in traditional
elective
courses.
2. To examine the differences in the extrinsic goal orientation
of at-risk
elective
courses.
3. To examine the differences in the task value of at-risk ninth-
grade
students enrolled in online elective courses, as compared to at-
risk
ninth-grade students enrolled in traditional elective courses.
4. To examine the differences in the learning control beliefs of
at-risk
elective
courses.
5. To examine the differences in the self-efficacy for learning
and per-
formance of at-risk ninth-grade students enrolled in online
elective

courses, as compared to at-risk ninth-grade students enrolled in
tra-
ditional elective courses.
6. To examine the differences in the test anxiety level of at-risk
ninth-
grade students enrolled in online elective courses, as compared
with
students enrolled in traditional elective courses.
7. To examine student perception of online instruction and
traditional
instruction as a means of developing motivational strategies.
Literature:
At-Risk Students:
Bransford et al. (1999); Brophy (1998); Conley (1992); Diem &
Katims
(1998); Egan & Gibb (1997); Ferguson (2000); Gaustad (1991);
George &
McEwin (1999); Jones (1988); Jones & Watson (1990); Jukes
(2000); Levine
& Ornstein (1993); Malone et al. (1997); Marshall (1999);
McCombs (2000);
National Assessment of Educational Progress (1990); SCANS
(1991);
Renchler (1993); Ruff (1993); Shank & Jona (1999); Texas
Education Agency
(1996, 2000); Thornburg (1999); Vacha & McLaughlin (1992);
Wehlage,
Rutter, & Turnbaugh (1987); Yellen (1998).

Attribution Theory:
Anderman & Midgley (1998); Raffini (1993); Stage, Muller,
Kinzie, &
Simmons (1998); Stipek (1988); Weiner (1985).
Goal Theory:
Condry & Chambers (1978); Herzberg (1959); Keller (1987);
Lepper
(1988); Maehr & Midgley (1991); Maslow (1954); McInerney
(1997);
Midd1eton & Midgley (1997); Ryan, Hicks, & Midgley (1997);
Small (1997);
Wlodkowski (1981).
Self-Determination Theory:
Deci & Ryan (1985); Deci, Vallerand, Pelletier, & Ryan (1991);
Midgley
& Feldlaufer (1987); Salisbury-Glennon, Gorrell, Sanders,
Boyd, & Kamen
(1999).
Technology and Online Courses:
Andrews & Marshall (2000); Burnham, Miller, & Ray (2000);
Bornas
(1997); Carroll (2000); Gershon & Bergstrom (1991); Guan,
Wang, Young,
Owen, & Andrew (1999); Jones, Valdez, Nowakowski, &
Rasmussen
(1994); Means (1997); Richardson (1973); Ruppert & Smith
(1996);
Southwest Educational Development Laboratory (2000); Taylor,

McKay,
CuIp, Baumann, & Elinich (1997); Sherman (1998);Wendt-
Keswick (1999);
Young (1996).
Research Questions:
1. What is the difference in the intrinsic goal orientation of at-
risk
elective
courses?
2. What is the difference in the extrinsic goal orientation of at-
risk
elective
courses?
3. What is the difference in the task value of at-risk ninth-grade
risk
ninth-grade students enrolled in traditional elective courses?
289Appendix G
4. What is the difference in the learning control beliefs of at-
risk ninth-
grade students enrolled in online elective courses, as compared
to
at-risk ninth-grade students enrolled in traditional elective
courses?

5. What is the difference in the self-efficacy of at-risk ninth-
grade
risk
6. What is the difference in the test anxiety levels of at-risk
ninth-grade
risk
7. What are the perceptions of students enrolled in online
elective
courses compared to students enrolled in traditional elective
courses, regarding the elective course’s role as a means of
develop-
ing motivational strategies?
Design:
1. Sample:
a. 169 at-risk ninth-grade students enrolled in online elective
courses
b. 169 at-risk ninth-grade students enrolled in traditional
elective
courses
c. All participants are enrolled in one large suburban district
north of
Houston, Texas
2. Instrumentation:

a. The Motivated Strategies for Learning Questionnaire (MSLQ)
(Pintrich, Smith, Garcia, & McKeachie, 1991)
b. Three student focus groups
c. Sixty open-ended student questionnaires
3. Data Collection:
a. MSLQ administered to the 169 at-risk ninth-grade students
enrolled
in online elective courses and to 169 at-risk ninth-grade
students
enrolled in traditional elective courses.
b. Student responses collected in three separate focus groups of
at-risk
ninth-grade students enrolled in online elective courses and
ninth-
grade students enrolled in traditional elective courses.
c. Student responses derived from open-ended interview
questions
administered to 30 at-risk ninth-grade students enrolled in
online
elective classes and to 30 at-risk ninth-grade students enrolled
in
traditional elective classes.
4. Data Analysis:

Research Question # 1:
t test for independent samples to determine differences in the
level of
intrinsic goal orientation between at-risk ninth-grade students
enrolled in
online elective courses and at-risk ninth-grade students enrolled
in tradi-
tional elective courses.
Research Question #2:
t test for independent samples to determine differences in the
level of
extrinsic goal orientation between at-risk ninth-grade students
enrolled in
online elective courses and at-risk ninth-grade students enrolled
in tradi-
tional elective courses.
t test for independent samples to determine differences in task
value
between at-risk ninth-grade students enrolled in online elective
courses
and at-risk ninth-grade students enrolled in traditional elective
courses.
t test for independent samples to determine differences in
learning
control beliefs between at-risk ninth-grade students enrolled in
online
elective courses and at-risk ninth-grade students enrolled in

traditional
elective courses.
t test for independent samples to determine differences in self-
efficacy
courses
courses.
t test for independent samples to determine the level of test
anxiety
courses
courses.
a. Three student focus groups designed to elicit more
information on
the relationship between enrollment in online elective courses
and
the development of motivational strategies, which include:
(a) intrinsic goal orientation, (b) extrinsic goal orientation, (c)
task
value, (d) learning control beliefs, (e) self-efficacy, and (f) test
anxi-
ety. Focused interviews will last from 45 to 60 minutes, and
they will
be recorded and transcribed in order to permit identification of
emergent themes and triangulation.

291Appendix G
b. Open-ended interviews will be conducted with 30 at-risk
ninth-
grade students enrolled in online elective courses and with 30
at-
risk ninth-grade students enrolled in traditional elective
courses.
Open-ended interviews will last from 30 to 45 minutes, and they
will be recorded and transcribed in order to permit
identification of
emergent themes and triangulation.
Contribution:
1. To determine the relationship between the enrollment of at-
risk
ninth-grade students in online elective courses and the develop-
ment of motivational strategies, which include: (a) intrinsic
goal ori-
entation, (b) extrinsic goal orientation, (c) task value, (d)
learning
control beliefs, (e) self-efficacy, and (f) test anxiety, as
measured by
student responses on six separate MSLQ subscales
2. To determine the perceptions of at-risk ninth-grade students
regard-
ing the relationship between enrollment in online elective
courses
and the development of motivational strategies, which include:
(a) intrinsic goal orientation, (b) extrinsic goal orientation, (c)
task

value, (d) learning control beliefs, (e) self-efficacy, and (f) test
anxi-
ety, as measured by student responses provided in focus groups
and
open-ended interviews
3. To refocus school reform efforts so that the impact of online
technology on motivational strategies is considered
4. To provide a model of instruction that would help to motivate
at-risk ninth-grade students through the use of online
instruction
APPENDIX H
Qualitative Research Critique
1. Bibliographic Information (APA Style): State the title of your
attached study in APA format.
2. Problem: What is the problem or need? (State it.) Is it stated
clearly?
(Explain.) Is it logical? (Explain.) Is the problem convincing?

(Explain.)
3. Purpose: What is the purpose? (State it.) Does it focus the
research?
(Explain.) Does the purpose follow the problem statement logi -
cally? (Explain.) Has the researcher convinced you that this
study
is worthwhile? (Explain.)
4. Theoretical Framework: On what is the theoretical framework
based? (Explain it.) If none is stated, please indicate that and
try to
surmise what it might be from the literature presented.
5. Prior Research or Literature Review: What previous work has
been
done leading up to this study? (You will need to do a search in
dis-
sertations and journals to determine if any major research piece
is
missing: for example, a name of a researcher that keeps
appearing.)
Is there any major body of research missing?
6. Method Research Question(s): What is/are the research
question(s)? (List them.) Are the research questions specific and
clear? Why or why not? Are the research question(s) related to
the
purpose? (Explain.)
7. Method—Data Collection and Analysis: Are participants
(How
many?), sampling techniques (How were they selected and was
this technique appropriate and accurate—answer both: appropri-
ate for the study and accurate?), and context for the study
explicit

and appropriate? (Explain.) Is the research design clear?
Explain
the design and whether it is appropriate for the study; was
some-
thing else more appropriate? Are the collection and analysis
based
on solid referenced methods? (Explain.) Are the methods the
best
choice in the study? (Explain.) What improvements would you
suggest?
8. Definition(s): Which terms are defined? Are the definitions
opera-
tional? Are the definitions included within the introduction or
within the methods section of the report? If there are no terms,
then
did the researcher include those in the written text? What terms
are
appropriate for this study?
293
9. Credibility and Reliability Issues in Design: What does the
author
say regarding issues of credibility and/or generalizing ability
internal and/or external validity? Respond to each of the inter -
nal/external validity issues as referenced in qualitative research.
10. Method Instrumentation: Describe the instrument(s) for
inter-
views, focus groups, and so forth. Were they preexisting, or
specially created? How were they related? How was reliability
established?

11. Ethics: Does the author discuss ethical issues? Do you see
any eth-
ical issues in the study? Are the ethical issues properly taken
care
of?
12. Limitations and Delimitations: What
limitations/delimitations are
identified? How do these limit generalizability, and does it
matter?
To what extent do the limitations/delimitations (stated or
unstated) affect the value of the research?
13. Results and Discussion: Are the research questions
answered?
How and sufficiently? Is existing literature brought into the dis -
cussion? Are supportive representative statements used from the
data when appropriate?
14. Implications and Recommendations: What are the
implications
from the research for practice and what recommendations for
theory, further research, and/or practice are presented?
SOURCE: © Beverly J. Irby. Used with permission.
295
APPENDIX I
Agreement: Guidelines for Chairing a Dissertation

The following are guidelines that I want all doctoral students to
con-
sider as they determine whether to ask me to serve as chair of
their
dissertation.
Role of the Dissertation Chair
1. The dissertation chair’s job in completing the dissertation is
to assist in
facilitating the process, giving direction, and providing quality
control.
2. The dissertation chair and the student work together to
determine
the committee members. The student must clear all committee
members with the dissertation chair.
3. All work on the dissertation must be approved by the
dissertation
chair before the student will be permitted to forward chapters of
the
dissertation to other committee members. That is, as chapters of
the
dissertation are approved by the dissertation chair, but not
before,
the chair will direct the student to forward completed chapters
to
the committee members.
4. The dissertation chair and the student will complete a
realistic time-
line for the completion of the dissertation; however, it is not the
responsibility of the dissertation chair to keep the student on the
timeline.

Function of the Proposal
1. The dissertation proposal must be thoroughly understood by
the
student, from the statement of the problem through the review
of
the literature to the methodology, before defending the
proposal.
2. The dissertation proposal must be delivered to all committee
members two weeks prior to the proposal defense.
3. Once the proposal has been approved, be aware that it is not a
con-
tract; rather, it is a guide. The actual dissertation may be altered
slightly as the dissertation chair and committee members work
through the process of completing the dissertation with the
student.
4. The student will PROOFREAD thoroughly the dissertation
proposal
and assure that the proposal is free of typographical and
grammat-
ical errors, and obvious omissions.
Preparation of the Dissertation
1. Each student must become familiar with the dissertation
format by
reviewing my publication, Writing a Successful Thesis or
Dissertation:
Tips and Strategies for Students in the Social and Behavioral
Sciences, and
other dissertations on the student’s topic.

2. Each student must become familiar and must be sufficiently
knowl-
edgeable of the research design, including the specific
statistical
tests or the qualitative method or combination of the two, and
table
headings and format. The student must do this by reading
research
and statistics books and by reviewing dissertations or research
stud-
ies using the particular method. The student must find those
disser-
tations, theses, research studies, or books on his or her own.
3. The student should expect to write as many drafts of the
disserta-
tion proposal and the dissertation as is necessary to render it in
acceptable form for wider distribution. However, the first draft
of
each chapter the student submits to the dissertation chair should
be
in as perfect form as is humanly possible. The dissertation chair
will
ask for explanations of the statistical tests, why the student
chose
the particular test, or, in the case of qualitative method, why the
spe-
cific method(s) were selected.
4. The student should expect to hire an external editor and/or a
statistician.
5. Before submission to the chair and subsequent distribution to
com-
mittee members, the student will PROOFREAD thoroughly each

chapter for typographical and grammatical errors, and obvious
omissions. Use, but do not rely exclusively on, spell check to
detect
errors. Proofreading means reading the dissertation word for
word
from the front matter through to the vita.
Dissertation Defense
1. The chair will not authorize the student to schedule the
dissertation
defense date until the chair has received and corrected all
chapters
of the dissertation.
2. After approval by the chair, all chapters of the dissertation
must be
delivered to all committee members at least two weeks prior to
the
dissertation defense.
3. The student must have the entire dissertation completed by
the dis-
sertation defense date. This includes all front pages (title page,
sig-
nature page, dedication, abstract, acknowledgements, table of
contents, list of tables, list of figures) and back pages
(references,
appendixes, and vita).
4. The student will PROOFREAD thoroughly again the entire
disser-

tation, take personal responsibility for its contents, and assure
that
the entire dissertation is free of typographical and grammatical
errors, and obvious omissions. It is expected that the
dissertation
will be in publishable form at the time of the dissertation
defense.
Graduation
1. The student may project a target date for graduation
(December,
May, or August); however, if the student is not ready according
to
standards set forth by the dissertation chair or other committee
members, then the student will not graduate at that particular
date.
2. The completion of the graduation process involves the
routing of
the completed dissertation as follows: Dissertation Committee,
Director of the Doctoral Program, Graduate School, Library,
Department Chair, Education Dean, and Registrar. Therefore,
dead-
lines for the dissertation defense must be enforced.
3. For December graduations, defense of the dissertation must
occur at
or prior to October 1.
4. For May graduations, defense of the dissertation must occur
at or
prior to March 1.
5. For August graduations, defense of the dissertation must
occur at or

prior to June 1.
Required Copies of the Dissertation
Required copies of the completed dissertation for doctoral
students in edu-
cation are as follows:
1. Library 2 (acid free, 20% cotton bond)
2. Dean, College of Education 1
3. Chair, Dissertation Committee 1
4. Committee Members 4 (one for each member)
5. Center for Doctoral Research 2
6. Bell & Howell/UMI 1
Total 11
Headings
Boldface type is not permitted in headings. Most dissertations
will use
three levels of headings. For three levels of headings, use the
following
format:
297Appendix I
CENTERED UPPERCASE HEADING (All chapter headings)

Centered Uppercase and Lowercase Heading (Level 1)
Flush Left, Italics, Uppercase and Lowercase Side Heading
(Level 2)
Indent, italics, lowercase paragraph heading ending with a
period. (Level 3)
For use of more than three levels of headings, consult the most
recent
edition of the Publication Manual of the American
Psychological Association.
An example follows:
CHAPTER III
METHODOLOGY
Instrumentation (Level 1)
The Multifactor Leadership Questionnaire (Level 2)
Reliability. (Level 3)
Validity. (Level 3)
____________________ _____________________________
_____________________________
Dissertation Chair Student’s Signature, Date
and Fred C. Lunenburg.

299
APPENDIX J
Checklist for Dissertation Quality
Following is a list of questions to consider when evaluating the
quality and content of
each chapter and section within each chapter of your
dissertation. Not all questions are
appropriate for all studies, and not all institutions, departments,
and committees will be
expecting the same content in each chapter. However, we have
structured this checklist in
accordance with the recommended content of a five-chapter
dissertation.
CHAPTER I: INTRODUCTION
Have you described how your study fits into the larger context
of
theory and practice? ______
Is adequate background presented for all the variables or
concepts in
the study? ______
Is the background of the problem presented clearly (e.g.,
educational
trends related to the problem, unresolved issues, social

concerns)? ______
Have the majority of the references cited been published within
the
past five years? ______
Is adequate information presented for an understanding of the
problem? ______
Is the problem clearly stated? ______
Is there a description of what has already been done related to
the problem? ______
Is the relationship of the problem to previous research made
clear? ______
Is there a logical transition that leads directly to the purpose of
the study? ______
the
Is the purpose of the study related to the statement of the
problem? ______
Is the purpose of the study stated clearly and succinctly?
______
the
Is it clear how this study will add to the body of knowledge
(theory
or practice)? ______
Does the study have the potential of being presented at a

national
conference? ______
Appendix J
Does the study have the potential of being published in an
academic
journal? ______
the
Definition of Terms
Are the terms used in the study adequately defined so that their
usage
is clearly understood? ______
Have you provided a brief description of the relevant
theory(ies) to
be utilized in approaching the problem? ______
Have you reviewed the major variables or concepts and their
interrelationships derived from the existing theoretical work?
______
Have you described a connection between these theoretical
constructions and your research questions or hypotheses?
______
Have you utilized theory as a heuristic device to examine and
interpret
qualitative data? ______
Research Questions

Are the research questions related to the problem and the
purpose? ______
Are the research questions well stated; e.g., (a) all variables
clarified,
(b) avoidance of “how” or “why” questions, (c) theoretical
constructs
clarified (achievement, success, intelligence)? ______
Is the kind of measurement obvious (e.g., description,
relationship,
differences)? ______
Is each research question testable? ______
Limitations
Are limitations well defined (methodology, data, analysis)?
______
Delimitations
Are the delimitations well defined (e.g., timeframe, location,
sample,
criterion)? ______
Assumptions
Are your assumptions clearly stated? ______
Is there a transition to Chapter Two and the chapters following?
(Typically Chapter One ends with an overview of the entire
dissertation.) ______

CHAPTER II: REVIEW OF THE LITERATURE
Introduction
Does Chapter Two begin with an introduction using an advance
organizer
to prepare the reader for the content of the chapter? ______
Body
Is the review of the literature comprehensive? ______
Does the review of the literature cover all variables or concepts
in
the study? ______
Does the review of the literature contain at least 50 references?
______
Are all references cited relevant to the problem? ______
the
Are references cited completely and accurately? ______
Are authors who make the same point combined in a single
citation? ______
Is there an overabundance of direct quotations? ______
Is each major section of the review of the literature summarized
without a summary heading (e.g., in sum, in summary, in
short)? ______
Have you searched all available sources in your review of
literature? ______
Summary
Does the Review of the Literature Chapter conclude with a
summary

of the literature review and a transition introducing the next
chapter? ______
CHAPTER III: METHODOLOGY
Introduction
Does the chapter begin with an introduction, using an advance
organizer
to prepare the reader for the content of the chapter? ______
Are the size and major characteristics of the population studied
described? ______
Are the procedures and criteria for selecting the sample clearly
described? ______
Is the sample size large enough for the method of research
used? ______
Are the size and major characteristics of the sample described?
______
Instrumentation
Is a rationale given for the selection of the instruments used?
______
Are the purpose and content of each instrument described?
______
Is the validity of each instrument described? ______
Is the reliability of each instrument described? ______
301Appendix J
Are the instruments appropriate for measuring the variables

studied? ______
If an instrument was developed specifically for the study, are
the
procedures involved in its development and validation
described? ______
If an instrument was developed specifically for the study, are
administration, scoring, and interpretation procedures fully
described? ______
If an instrument was developed, was a pilot test conducted with
a
different sample than was used in the study? ______
If interviews were used, were procedures described for
determining
interviewer bias? ______
When necessary, were interobserver or interrater reliability
assessed? ______
Data Collection
Are the procedures for collecting data described in sufficient
detail
to permit replication by another researcher? ______
If a pilot study was conducted, are its execution, results, and
effect on
the current study described? ______
If the study was qualitative, were internal validity strategies
described
(e.g., triangulation, member checks, peer examination)? ______
Data Analysis
Were the statistics used appropriate for the study? ______
Are the appropriate statistics reported for each test? ______
For statistical tests, are sufficient statistics presented (e.g.,
mean,
standard deviation) ______

In a qualitative study, are the themes and patterns appropriately
labeled? ______
Summary
Does the chapter conclude with a summary and a transition to
Chapter Four? ______
CHAPTER IV: PRESENTATION
AND ANALYSIS OF DATA
Introduction
organizer to
prepare the reader for the contents of the chapter? ______
Demographic Descriptive Statistics
Are demographic descriptive statistics of the sample presented
in
table form? ______
Are appropriate headings used to help the flow and organization
of the
chapter (e.g. research questions (hypotheses), themes, or
variables?) ______
Research Question (or Hypothesis) One
Research Question (or Hypothesis) Two

Etc.
Are the tables and figures (if any) well organized and easy to
understand? ______
Are the data in each table and figure described in the text?
______
Are tests of significance properly cited using appropriate
degrees of
freedom; e.g., [F(2,71) = 14.87, p < .001 or F(2,71) = 0.14, p >
.05]? ______
Were post hoc tests calculated when appropriate? ______
Were effect sizes reported when appropriate? ______
Is this chapter free of interpretation? (In qualitative studies,
factual
and interpretive information is sometimes intermingled to
sustain
reader interest.) ______
In a qualitative study, is there a balance of direct quotations and
description to enhance the meaning of themes and patterns?
______
Is there a summary of key findings in table form when
appropriate? ______
Summary
Does the chapter end with a summary of the entire chapter and a
transition to Chapter Five? ______
CHAPTER V: SUMMARY, DISCUSSION,
AND CONCLUSIONS
Introduction
organizer

to prepare the reader for the contents of the chapter? ______
Is there a brief summary of the entire study? ______
Are the findings discussed within the framework of previous
studies,
the theoretical rationale, and the review of the literature?
______
Are the implications derived from the findings? ______
Are implications made for theory and practice? ______
303Appendix J
Are recommendations for further research clearly implied from
the study? ______
Conclusions
Do the conclusions highlight the important aspects of the study?
______
Are the conclusions derived from the findings? ______

305
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Index
AAUW (American Association of University Women), 14
Abstract:
guidelines for, 252
for paper presentation, 255, 256
Abstracting and indexing services, 139–140
Academic books, 266–269
Academic journals. See Journals, professional

Acceptance, of manuscript, 265–266
Accessibility, 17–18
Achievement tests, 181
Adams, D. J., 233
Additional analyses, 222–223
Adler, P. S., 159
Advance organizer:
in Discussion Chapter, 226
examples of, 147–150
function of, 147
in Methodology Chapter
introduction, 165
Results chapter and, 209–210
Advancing Women in Leadership (online journal), 270
AERA. See American Educational Research
Association
Agboo, S. A., 232
Agreement: Guidelines for Chairing a Dissertation,

295–298
AIR (Association for Institutional Research), 14
Airasian, P.:
on inferential statistics, 67, 68
on qualitative research, 7
research methodology, 19
American Association of University Women
(AAUW), 14
American Educational Research Association (AERA):
Dissertation Grants Program, 14
paper presentations and, 255, 259
American Psychological Association:
effect magnitude measures, 70
statistical significance requirement
of, 201
on tables, 252
American Psychological Association Publication Manual,
218–219, 263

Analyses, additional, 222–223
Analysis, statistical, 79, 200–204, 249
Analysis of covariance (ANCOVA),
47, 74–75
Analysis of variance (ANOVA):
in causal-comparative research, 47
Dissertation Proposal Outline (Analysis of Variance
and Regression), 280–283
factorial, 74
test of null hypothesis, 72–73
Anderson, D. P., 160
Andrews, J. H. M., 160
Androcentric, 122
Annotations, 154
Annual Review of Anthropology, 5
Annual Review of Psychology, 5
Annual Review of Sociology, 5
Annual reviews, 138–139

ANOVA. See Analysis of variance
Anthony, W., 120, 121
Appleberry, J. B., 160
Approach, 119
Archival data, 193–194
Arnold, S. K., 48
Arts and Humanities Index (Institute for Scientific
Information), 139–140
Association for Institutional Research (AIR), 14
Assumptions, 135
Astin, A. W., 231
Attitude, 28
Audience, 268
Azodi, D.:
delimitations of study by, 134
introduction to Discussion Chapter, 226
introduction to Review of the Literature Chapt
147–150

organization of study, 136
b weight (regression weight), 82
Background of study, 113–114
Bamberg, W.:
dissertation format, 208
on instrumentation, 192–193
qualitative research presentation of, 222
research questions, 132
selection of participants, 177–178
SLT testing, 229
SLT validation, 7
Summary and Conclusions chapter
of, 234–236
Bandura, A., 7, 116, 148
Barrett, S.:
assumptions in study, 135
data collection methods of, 198
significance of study, 117–118

summary of Review of the Literature Chapter,
152–153
systematic sampling by, 173–174
Barry, J., 237
er,
Basham, V.:
instrumentation section of dissertation, 186–188
Kentucky strategic planning, 8
product-moment correlation, 67
theoretical framework of dissertation, 123–125
Baxter, P. M., 141
Bean, L. G., 233
Bean, M., 39–40
Benz, C. R., 251
Berger, J. B., 233
Best, J. W., 187
Beta weights (B), 82

Between subjects design, 55
Biklen, S.:
definition of terms, 119
on group interviews, 197
internal validity, 230
Bivariate correlation, 37
Björk, L. G., 156
Black, L.:
data collection methods of, 199–200
self-efficacy theory and, 7
statement of problem, 115–116
Blair, D.:
dissertation topic of, 9
purpose of study, 116–117
selection of participants, 172
Blanchard, K., 6
Bogdan, R.:
definition of terms, 119

on group interviews, 197
Bollen, K. A., 55
Book proposal, 267–269
Books:
book proposal guidelines, 267–269
chapters in, 269
publication of, 266–267
Borg, W. R.:
on hypotheses, 112
on member checking, 197
on null hypothesis, 68–69
on theory and findings, 229
on theory development, 120
Borys, B., 159
Brause, R. S., 22
Bridgewater, S. R., 33–34

Brown, A. F., 187
Brown, G.:
on feminist leadership theory, 122
Synergistic Leadership Theory, 6, 177, 229
Brown, R. J., 160
Brunner, C. C., 155–156, 221
Burns, J. M., 149
Buros/ERIC Test Publisher Directory, 192
Business Periodicals Index (citation index), 140
Byrne, B. M., 86
Cabell’s Directory of Publishing Opportunities, 264
Cadavid, V., 84, 184–185
Caison, A., 233
Campbell, D. T.:
on experimental research design, 54
nonequivalent control group design, 49–50
Canonical correlation, 40–41, 83–84

Canonical Correlation Analysis (Thompson), 84
Canonical correlation coefficient, 84
Career goals, 262, 263–264
Carey, K., 233
Carr, C., 269
Case study research design, 96–99
Casey, R. J., 193
Castetter, W. B., 114, 251
Cattell, R. B., 86
Causal model, 84–85
Causal-comparative research design, 45–49
Central tendency, measures of, 63–64
Chair:
295–298
committee composition and, 20–21
final oral defense and, 252
submission of dissertation to, 271

Chair, selection of:
accessibility, 17–18
attitudes towards methodology, 19–20
desirable student for, 23–25
expertise, 17
feedback, 18
overview of, 25
personality styles, 19
research on committee members, 21–23
role of chair, 16
success, 18–19
The Changing World of School Administration (Perreault
& Lunenburg), 269
Chapters, in books, 269
Characteristics, of participants, 168
Charters, W. W., 187
Charts. See figures
Checklist for Dissertation Quality, 299–304

Chi square, 77–78
Chi square score, 69
Choi, J. W., 43
Christensen, J. M., 58, 207–208, 222
Christopher, F. P., 107–108
Chung, W., 56–57
Chwalek, A. R., 221
Citation index, 139–140, 141, 264
Citation score, 266
Clandinin, D. J., 100
Clarity, 217–218
Clark, K.:
descriptive statistics of, 210–213
implications for practice, 236–237
limitations of study, 133
recommendations for further research, 238–240
statistical results publication of, 219

Clark, S. L., 51–52
Classification, of journals, 264
Clover, S. J., 161
Cluster sampling, 172–173
Coate, D., 237
Coefficient of determination (R2), 36, 82
Cohen, J.:
Cohen’s f 2, 39
on effect size index, 70, 201
on power analysis, 180
Cohen’s d, 47
Cohen’s f 2, 39
Coleman, C., 134–135
Coleman, J. S., 154, 155
Columba, L., 80
Committee:

final oral defense and, 249–251
mistakes, tips for avoiding, 251–253
presentation preparation for, 246–247
proposal meeting, format of, 245–246
questions from, 248–249
research questions/hypotheses and, 126
Committee, selection of:
composition, 20–21
desirable student for, 23–25
importance of, 16
overview of, 25
research of members, 21–23
role of, 21
Comparison groups, 46
Compensation strategy, 38–39
Conclusions, 31
“Conclusions” section, 240–241
See also Summary, Discussion, and

Conclusions Chapter
Concurrent validity, 181
Cone, J. D., 145, 217, 263
Conferences, national/international, 254–255
Connelly, F. M., 100
Connexions, 269
Consistency, 217–218
Constitutive definition, 118, 119
Construct validity, 103, 181–182
Content validity, 181
Continuing line of inquiry, 163–164
Control group, 54
Control variables, 118–119
Convenience case, 96
Convenience sampling, 174–175
Conversation analysis:
in ethnographic research, 100
for phenomenological study, 91, 94

Convey, J. J., 221
Cook, T. D.:
on experimental research design, 54
Cook, C. L., 34–35
Coon-Carty, H. M., 44–45
Corbin, J. M., 102–103
Correlated samples, 71
Correlation coefficients, 36
Correlation matrix, 85–86
Correlational research:
data collection, 198–199
Dissertation Proposal Outline (Correlational),
276–279
Correlational research design:
bivariate correlation, 37
canonical correlation, 40–41

cross-lagged panel, 44–45
discriminant analysis, 41–42
factor analysis, 42
multiple regression, 39–40
overview of, 35–37
path analysis, 42–43
regression, prediction, 38–39
Correlational statistics:
measures of relationship, 67
multivariate, 80–87
Corwin Press, 267–268
Council of Graduate Schools, 2
Covariate variable, 75
Covey, S., 250
Craycraft, S.:
conclusions of, 241
research questions, 130

Creighton, L.:
data collection methods of, 198–199
discussion section, 230–234
outline for Review of the Literature Chapter,
146–147
research questions of, 127, 216–217
Creswell, J. W.:
on qualitative research presentation, 222
Criteria:
for grounded theory research, 103–104
for topic selection, 10–14
Criterion sampling, 176
Criterion variable:
in discriminant analysis, 41

in multiple regression, 80–83
in multiple regression correlation, 39
Criterion-related validity, 181
Critical case, 96
Criticism, 19
Critique, 293–294
Croft, D. B., 10
Cromby, J., 104
Cron, W. L., 159
Cronbach, L. J., 86
Cronbach’s alpha reliability, 183
Cross, L. H., 85
Cross-lagged panel correlation, 44–45
Cross-sectional descriptive study, 33–35
Crown, W. H., 59
Culture, 99–102
Culver, S. M., 85
Curran, P. J., 55

Current Contents: Social and Behavioral Sciences
(citation index), 140
Cuttance, P., 114
Daresh, J., 119–120
Data:
for case study research design, 96–97
criteria for topic selection, 11–12
for descriptive research, 32
statistical test, 216–217
Data analysis:
in causal-comparative research, 47
importance of, 62
inferential statistics, 67–68
See also Statistics
“Data Analysis” section:
guidelines for, 200–201
Data collection, 99, 249

323Index
“Data Collection” section, 194–200
Database, existing, 8–10
Database resources, 140, 141
Davis, S. H., 156–157
Definition of terms, 118–122
Degrees of freedom (df), 71–73
Delaney, D., 6
Delimitations, 134–135
Demmin, P. E., 59
Demographic data, 194
Denzin, N. K.:
on focus groups, 92
on grounded theory, 123
on qualitative research, 7, 126
research methodology, 19–20

Department colloquia, 254–255
Dependent samples, 71
Dependent variable:
ANCOVA, 75
MANOVA and, 76
power analysis and, 180
Descriptive cluster study, 33
Descriptive questions, 126–127
Descriptive research design:
cross-sectional studies, 33–35
data, 32
instrumentation, 31–32
longitudinal studies, 32–33
phenomenological research design and, 90
Descriptive statistics:

central tendency, measures of, 63–64
definition of, 63
demographic variables, 211
normal curve, 65
overview of, 210
relationship, measures of, 66–67
relative position, measures of, 65–66
research questions, 212–213
variability, measures of, 64–65
Desktop publishing, 270
df (degrees of freedom), 71–73
Dictionary of Behavioral Assessment
Techniques, 192
Difference:
ANCOVA and, 75–76
ANOVA calculation of, 72–73
post hoc comparisons, 73–74
tests of statistical significance, 69

Difference questions, 127
“Different approach” technique, 156–157
Direct observation, 92–94
Discourse analysis, 94
Discriminant analysis, 83
Discriminant analysis correlation, 41–42
Discriminant function, 83
Discussion:
of findings, 228–229
See also Summary, Discussion, and
Conclusions Chapter
Dissertation:
for committee member research, 22–23
qualitative, 206–207
source of topic, 5–6
See also Methodology Chapter; Results Chapter;
Review of the Literature Chapter; Summary,

Discussion, and Conclusions Chapter
Dissertation, Introduction Chapter:
assumptions, 135
background of study, 113–114
components of, 112–113
definition of terms, 118–122
delimitations, 134–135
organization of study, 135–136
research questions/hypotheses, 126–133
theoretical framework, 122–126
Dissertation, presenting/publishing:
academic journals, 262–266
books, 266–269
desktop publishing, 270

formats for presentations, 254–255
Internet publishing, 269–270
job interview, 261–262
paper presentation, 255–260
popular press, 269
poster session, 260–261
summary of, 272
writing process, 270–271
Dissertation Abstracts International, 6, 23
Dissertation proposal:
for paper presentation, 255, 256–259
for poster session, 260
practice of, 248
preparation of, 246–247
requirements for, 3
See also Proposal meeting

Dissertation Proposal Outline (Correlational), 276–279
Dissertation Proposal Outline (Multivariate Analysis of
Variance), 284–287
Dissertation Proposal Outline (Qualitative), 288–292
Document, well-written, 245
Document analysis, 94
Doetkott, C. D., 233
Dorin, H., 59
Drafts, 24–25
Drawings. See Figures
Drees, W. F., 158–159
Drew, C. F., 126
Dubinsky, A. J., 159
Duffy, J., 6, 177, 229
Duncan’s new multiple range test, 73–74
Dunleavy, P., 270
Duplication publication, 263

Editing, 265, 271
Edmonds, R., 154
Educational Administration: Concepts and Practices
(Lunenburg & Ornstein), 4
Educational Administration Quarterly (journal), 263
Educational Leadership Constituent Council, 259
Educational Leadership (journal), 263
Educational Resources Information Center (ERIC), 139
Educational Testing Service (ETS) Test Collection, 191
Effect magnitude measures, 70, 201
Effect size:
for multiple regression, 39
statistical results and, 220
test in causal-comparative research, 47–48
Effect size index, 70

Eight-step strategy, 221–222
Employment, current, 8
Encyclopedia, 138
Encyclopedia of Educational Leadership and
Administration, 5
Encyclopedia of Educational Research, 5
Epistemological reflexivity, 104
Epstein, J. L., 185, 186
Equivalent time series designs, 56
Equivalent-forms reliability, 182
ERIC (Educational Resources Information Center), 139
ERIC/AE Test Locator, 192
Ericksen, J. B., 105–106
Erickson, D. A., 187
Ethnographic research design:
Evans, S. A., 42

Ex post facto research, 35
See also Causal-comparative research design
Experience, 90
Experimental research design, 54–58
Experimentation, 49–54
Expertise, 17
Exploratory studies, 128
Extensions, 142
External validity, 103–104
Extreme case, 96
F score, 69, 72–73
Factor analysis, 85–86
Factor analysis correlation, 42
Factorial analysis of variance, 74
Factorial experiment, 54–58
Factors, 85
Fauske, J. R., 150–152
FedStats, 140

Feedback, 18
Feldvebel, A. M., 160
Feminist leadership theory, 120–122
Feminist Thought (Tong), 121
Ferguson, K. E., 159
Field, A., 201
Figures, 220
Final oral defense:
timing of, 244
Findings:
Discussion Chapter guidelines, 229–236
discussion of, 228–236
in summary of study, 226
synthesizing literature, 162–163
See also Results
First draft, 270–271

First Follow-Up Letter, 274
Fisher’s LSD, 73–74
Flagg, J. T., 160
Flores, F., 149
Flowers, M. A., 232
Focus groups, 92, 197
Follow-through, 23–24
Follow-Up Letter, First, 274
Follow-Up Letter, Second, 275
Fonts, 252
Format:
for presentations, 254–255
of proposal meeting, 245–246
of Results Chapter, 206–209
Forms. See Resources
Formulas, 179–180
Foster, S. L., 145, 217, 263
Freeman, A., 119

Frequency counts, 77–78
Friedman two-way analysis of variance test,
77, 80
Funding, 13–14
Funnel organization, 143–144
Gabel, D., 59
Gall, J. P.:
on hypotheses, 112
member checking, 197
Gall, M. D.:
on hypotheses, 112, 128
interview approach, 193
member checking, 197

on qualitative research, 7, 123, 126
on research findings, 230
Gaps in literature, 155–156
Garner, A., 255–259
Gay, L. R.:
Gilbert, D., 119
Gitonga, S. K., 95–96
Glaser, B. G.:
on grounded theory, 102, 123
on qualitative research, 8, 112, 126
Glass, T. E., 156, 256, 257–258, 259
Government documents, 140–141
Grants, 13–14

Graphs. See Figures
Grejda, G. F., 162–163
Grounded theory:
from qualitative research, 88
theoretical framework and, 123
Grounded theory research design:
categories of, 103
concepts of, 102–103
propositions of, 103
research criteria, 103–104
Group interviews, 197
Gustafson, T. J., 118
Guy, R. M., 160
H value, 79
Hale, J., 160
325Index

Hallinger, P., 114, 155
Halpin, A. W., 10
Handbook of Qualitative Research, 5
Handbook of Research on Educational Administration, 5
Handbook of Research on Teaching, 5
Handbooks, 138
Hannafin, M. J., 162–163
Hartley, M., 160
Headings, 146–147, 150
Heck, R., 114, 155
Hederman, R., 237
Heisler, R. S., 114, 251
Helvetica font, 252
Heppner, M. J.:
on funnel structure, 143, 144
on instrumentation points, 181
on journal selection, 263
on literature searches, 141

proposal meeting, 248
on research questions, 126
on writing process, 270
Heppner, P. P.:
on funnel structure, 143, 144
on instrumentation points, 181
on journal selection, 263
on literature searches, 141
proposal meeting, 248
on research questions, 126
on writing process, 270
Hernandez, J. C., 229, 232
Hernandez, R. M., 7, 235
Hersey, P., 6
Heuristics, 178–179
Hierarchical linear modeling (HLM), 55–56
Hierarchy, 29, 30
Hinojosa, M. A., 48–49

Hirschhorn, L., 159
Historical context, 154–155
Hodge, B., 120, 121
Hoke, M. C., 38–39
Holtkamp, L. W., 7, 229, 235
Homogenous sampling, 176
Hoy, A. W.:
historical context example, 154–155
on structural equation monitoring, 87
on trust, 149–150
Hoy, W. K.:
conflicting theories, reconciliation of, 159–160
historical context example, 154–155
OCDQ study, 160, 161
on principal efficacy, 115, 116
self-efficacy theory and, 7
on structural equation monitoring, 87
Huberman, A. M., 97

Humanities Citation Index, 264
Hurtado, S., 232
Husserl, E., 90
Hypotheses:
in Introduction Chapter, 112, 126–133
nonparametric tests of, 77
null hypothesis, 68–69
statistical analysis presentation and, 200
theoretical framework and, 122–123
theories and, 7–8
See also Research questions
Igbaria, M., 117–118
”Implications for Practice” section, 236–237
Inconsistent findings, 162–163
Independent samples, 71
Independent variable:
in experimental research design, 54–55

factorial analysis of variance, 74
power analysis and, 180
Index to U.S. Government Periodicals, 140
Inferential statistics:
analysis of covariance, 75–76
definition of, 67–68
discriminant analysis, 83
effect size, 69–70
factor analysis, 85–86
multivariate analysis of variance, 76
nonparametric tests, 76–80
null hypothesis, 68–69
one-way analysis of variance, 72–73

post hoc procedures, 73–74
statistical analysis, 70
structural equation modeling, 86–87
t test, 70–72
tests of statistical significance, 69
Informed consent, 252–253
Initial Letter Soliciting Participation, 273
Inquiry, 163–164
Institute for Scientific Information (ISI), Inc., 139–140
Instrumentation:
in “Data Collection” section, 194
for descriptive research, 31–32
questions in proposal meeting, 248
results and, 230
“Instrumentation” section:
contents of, 180–181
locating instruments, 191–192
qualitative instrumentation, 192–194

reliability, 182–191
validity, 181–182
Integrity, personal, 24
Intellect model, 31
Intensity sampling, 176
Interaction, 74
Interest, 9–10
Internal consistency reliability, 182–183
Internal validity, 103, 230
Internal-External Locus of Control Scale, 184–185
Internet publishing, 269–270
Interrater reliability, 183
Interval data, 63
Interview:
for data collection, 197
in ethnographic research, 99
for phenomenological research design, 91–92

as qualitative instrument, 192–193
telephone interviews, 195–196
Introduction:
to Discussion Chapter, 226
to Methodology Chapter, 166
to Review of the Literature Chapter, 147–150
Introduction Chapter:
assumptions, 135
background of study, 113–114
components of, 112–113
definition of terms, 118–122
delimitations, 134–135
organization of study, 135–136
research questions/hypotheses, 126–133

theoretical framework, 122–126
Irby, B. J.:
on feminist leadership theory, 122
OLEI, 235
SLT and, 6, 177, 229
ISI (Institute for Scientific Information), Inc.,
139–140
Jackson, S., 159
Jackson, S. E., 184–185
Joachimisthaler, E. A., 159
Job interview, 261–262
Jodry, E. O., 105
Johnson, B. L., 150–152
Johnson, R. B., 106–107
The Journal of Educational Administration, 263
Journals, professional:

for instrument information, 191
publication in, 262–266
source of topics, 4–5
writing process and, 271
Kanner, L. T., 161
Kentucky Department of Education, 8
Kerlinger, F. N., 59, 118
Kerr, C., 232
Key terms, definition of, 118–122
Keywords, 142
Kim, J., 86
King, N., 233
Kivlighan, D. M., 126
Knopoff, K., 159
Knowledge, 12, 29
Kottkamp, R. B., 161
Krathwohl, D. R., 123, 141
Krejcie, R. V., 170, 179–180

Kreuger, R. A., 92
Kruskal-Wallis one-way analysis of variance, 77, 79
Kuder-Richardson (KR) reliability, 183
Kuh, G. D., 233
Kuhn, T., 144
Kvale, S., 91
Leadership theory, 6–7, 121
Learning First Alliance, 234–235
Lesure-Lester, G. E., 233
Letters:
Second Follow-Up Letter, 275
Lewis, J. A., 53
Li, Yu Fen Lin, 12
Life history, 96
Limitations of study, 133
Lincoln, Y. S.:

on focus groups, 92
on grounded theory, 123
on qualitative research, 7, 126
research methodology, 19–20
Line of inquiry, continuing, 163–164
LISREL (Analysis of Linear Structural Relationships), 86
Literature:
gaps in, 155–156
review for sample size decision, 179
See also Review of the Literature Chapter
Literature, searching:
abstracting and indexing services, 139–140
annual reviews, 138–139
government documents, 140–141
handbooks, encyclopedia, 138
review articles, 139

search engines, 141–142
Literature, synthesizing:
conflicting theories, 159–160
continuing line of inquiry, 163–164
“different approach” technique, 156–157
gaps in literature, 155–156
historical context, 154–155
inconsistent findings, 162–163
practical significance, 158–159
strategies for, 154
summary table for, 160–162
trends, themes, 157–158
Loeffler, C., 174–175
Loehlin, J. C., 85, 86
Long, T. J., 221
Longitudinal studies, 32–33
Luey, B., 270
Lunenburg, F. C.:

books edited by, 269
dissertation of, 10–11
Educational Administration: Concepts and Practices, 4
factor analysis study, 86
on heuristic, 178
instrument development by, 188–191
on leadership theory, 121
multiple regression in relationship study, 80
OCDQ study, 161
path analysis study, 84–85
product-moment correlation, 67
review articles and, 139
on t test, 71
theme in review of literature, 157–158
Lutz, D., 8–9
Lynch, S. M., 233
Mackinnon, D. P., 44

Macro organizational behavior, 120
Madsen, D., 261
Mailed surveys, 198
Manageability, 13
Management theory, 121
Mangold, W. D., 233
Mankowsky, S. A., 84
Mann-Whitney U test, 77, 79
MANOVA (multivariate analysis of variance),
76, 284–287
327Index
Manuscript, 265–266
Market, for book, 268
Martin, J., 159
Martin, R., 115, 143
Maslach, C., 184–185
Maslach Burnout Inventory, 184–185

Matched t test, 71
Matched-pairs designs, 56
Mauch, J. E., 251
Maxwell, R. E., 161
McCoy, V. A., 40–41
McFadden, D. M., 102
McNeil, K., 251
McPartland, J. M., 185, 186
Mean:
ANOVA and, 72–73
in inferential statistics, 68
measures of central tendency, 63
in normal curve, 65
t test, 70–72
Measures of central tendency, 63–64
Measures of relationship, 66–67

Measures of relative position, 65–66
Measures of variability, 64–65
Median, 63, 64
Menconi, J. P., 98–99
Mental Measurements Yearbooks (MMYs), 191
Merriam-Webster’s Ninth New Collegiate Dictionary, 153
Methodology Chapter:
contents of, 165–166
data analysis, 200–204
data collection, 194–200
instrumentation, 180–194
introduction, 166
participants, selection of, 167–180
statistical procedures listed in, 62
Meyer, D. J., 131
Meyertons, J. E., 95
Michaels, R. E., 159

Micro organizational behavior, 120
Miles, R., 120, 121
Miles, M. B., 97
Miller, H. E., 161
Miller, W. D., 133
Mills, G. E.:
Mishler, E., 100
Miskel, C. G., 159, 163–164
Mixed methods dissertation, 224
Mixed methods research design:
data collection for, 199–200
description of, 106–107
Mode, 63, 64
Models, 119, 123–126

Monograph, research, 266–267
Monthly Catalog of Government Documents, 140
Morgan, D. W., 170, 179–180
Morgen, S., 121
Mueller, J., 86
Mulhern, J. A., 161
Multiple correlation coefficient (R), 81–82
Multiple regression, 80–83
Multiple regression correlation, 39–40
Multiple time series design, 52
Multiple-case design, 96
Multivariate analysis of variance (MANOVA),
76, 284–287
Multivariate correlational statistics:
description of, 80
discriminant analysis, 83
factor analysis, 85–86

structural equation modeling, 86–87
Multivariate tests, 76
Murphy, J., 236
Narrative, 99, 100
Narrative analysis, 100, 102
National Assessment of Educational Progress
(NAEP), 32–33
National Clearinghouse of Educational Statistics
(NCES), 9
National Science Foundation, 14
Newman, I., 251
Newman-Keuls multiple range test, 73–74
Newton, R. R.:
on quotations, 145
on tables, 218

Nicholas, L. N., 161
Nightingale, D., 104
No Child Left Behind Act, 157–158
Nominal data, 63
Nonequivalent control group design, 49–52
Nonparametric tests:
chi square, 77–78
Friedman two-way analysis of variance test, 80
Kruskal-Wallis one-way analysis of variance, 79
Mann-Whitney U test, 79
use of, 70
Wilcoxon signed rank test, 79
Nonprofit organization, 38–39
Nonrandom sample, 174–176
Nora, A., 232
Norm referencing, 65–66
Normal curve (bell-shaped curve), 65

Normally distributed data, 216–217
Null, E. J., 161
Null hypothesis:
ANOVA to test, 72–73
t test, 70–72
Nwankwo, J. J., 161
Oblique solution, 86
Observation:
participant, 92–94, 99–102
for qualitative research, 193
Ogawa, R., 163–164
Olejnik, S. E., 180, 201
One-dimensional chi square, 78

One-group interrupted time-series
design, 52
One-group-posttest-only design, 50
One-group-pretest-posttest design, 50
Online surveys, 198
Onwuegbuzie, A. J., 106–107
Operational definition, 118
Oral defense. See Final oral defense
Ordinal data, 63
O’Reilly, R. R., 84
Organization:
of Introduction Chapter, 135–136
of Review of the Literature Chapter, 143–144
Organizational and Leadership Effectiveness
Inventory (OLEI), 195, 227–228, 235
Organizational behavior, 120
Organizational climate, 9–10
Organizational Climate Description Questionnaire

(OCDQ), 9–10, 160–162
“Organizational Climate, Dogmatism, and Pupil
Control Ideology” (Lunenburg), 11
Organizational theory, 120
Originality, 2
Ornstein, A. O.:
Educational Administration: Concepts and Practices, 4
on leadership theory, 121
Orthogonal solution, 86
Outcome variable, 39
Outline:
276–279
Dissertation Proposal Outline (Multivariate
Analysis of Variance), 284–287

for proposal presentation, 247
for Review of the Literature Chapter, 146–147
Ozawa, Y., 97–98
P values, 69–70
Packaging, 252
Paper presentation:
guidelines for, 255
preparation of, 259–260
Paragraphs, 150–152
Parameters, 76
Parametric procedures, 76–77
Parametric test, 70
Park, N., 251
Participant observation, 92–94, 99–102
Participants:

in summary of study, 227
Participants, selection of:
nonrandom sample, 174–176
number of participants, 178–180
sampling in qualitative research, 176–178
sampling procedures, 169–174
who participated in study, 167–169
Pascarella, E. T., 231, 233
Pascarella, S. V., 6, 71
Path analysis, 42–43, 84–85
Patterns, 89
Patton, M. Q., 94, 96, 196
PCI (Pupil Control Ideology Form), 185
PDB Scale, 188–191
Pearson r. See Product-moment correlation
coefficient (r)
Percentile ranks, 65–66
Permissions, 186

Perreault, G., 269
Personal integrity, 24
Personal reflexivity, 104
Personality styles, 19
Phenomenological case study, 96
Phenomenological research design:
description of, 89–90
techniques, approaches, methods, 90–94
phi coefficient, 45
Phi Delta Kappan, 263
Photographs. See Figures
PIRLS (Progress in International Reading Literacy
Study), 9
Plagiarism, 153–154
Plan, for writing process, 270–271
Plaxton, R., 161
Playground aggression, 37

Playko, M., 119–120
Politically important case, 96
Popular press, 269
Population. See Participants, selection of
Post hoc analysis, 200–201
Post hoc comparisons, 73–74
Poster session, 260–261
Posttest scores, 75
Posttest-only design with nonequivalent comparison
groups design, 50
Potter, J., 94
Power, 75–76
Power analysis, 180
PowerPoint presentation:
for job interview, 261
for paper presentation, 260
for proposal, 246
Practical significance, 158–159

Prediction, 38–39
Predictive validity, 181
Predictor variable:
in discriminant analysis, 41
in multiple regression, 80–83
“Presentation and Analysis of Data”.
See Results Chapter
Presentations:
format for, 254–255
paper presentation, 255–260
poster session, 260–261
See also Dissertation, presenting/publishing
Pretest scores, 75
Principal Efficacy Scale, 199–200
“Principal Leadership, Trust, and Teacher Efficacy”
(Azodi), 134

Probability level (p level), 71
329Index
Problem, statement of, 114–116, 152
Processes, of research, 29
Product moment correlation, 36
Product-moment correlation coefficient (r), 67, 80
Products, 29, 30
Pro-Ed Publications, 191
Professional journals. See Journals, professional
Progress in International Reading
Literacy Study (PIRLS), 9
Proportions, 77–78
Proposal. See Dissertation proposal
Proposal meeting:
format of, 245–246
guidelines for, 244
practice of presentation, 248

presentation preparation, 246–247
questions after presentation, 248–249
well-written document for, 245
Psych INFO, 139
Public search engines, 140, 141–142
Publication:
in academic journals, 262–266
books, 266–269
desktop publishing, 270
Internet, 269–270
popular press, 269
See also Dissertation, presenting/publishing
Publication Manual of the American Psychological
Association, 218–219
Publishers, 266–269
Pupil Control Ideology Form (PCI), 185
Purpose of study, 116–117
Purposive sampling:

description of, example of, 175–176
in qualitative research, 176–178
Qualitative dissertations, 206–207, 212
Qualitative instrumentation, 192–194
Qualitative research:
chair selection criteria and, 19–20
data analysis for, 202
data collection for, 195–198
hypotheses and, 128–129
instrumentation for, 192–194
presenting results of, 221–222
Qualitative Research Critique, 293–294
sampling in, 176–178
statistical analysis for, 201
Qualitative Research Critique, 293–294
Qualitative research designs:

case study research design, 96–99
ethnographic research design, 99–102
grounded theory research design, 102–106
mixed methods research design, 106–108
overview of, 88–89, 109
phenomenological research design, 89–96
selection of research method, 29
theoretical framework and, 123
Quality:
of dissertation, 23
Quantitative research:
chair selection criteria and, 19–20
data analysis for, 201–202
data collection for, 194–195, 198
Quantitative research designs:
attitude of researcher, 28

causal-comparative research design, 45–49
correlational research design, 35–45
descriptive research design, 30–35
experimental research design, 54–58
in mixed methods research design, 106–108
quasi-experimental research design, 49–54
selection of method, 29
theory development, 58–61
Quartile deviation, 64
Quasi-experimental research design:
time series design, 52–54
Questionnaire, 196–197
Questions:
in final oral defense, 249–250
in phenomenological research interview, 91–92
in proposal meeting, 246, 248–249
for qualitative interview, 192–193

for significance of study, 117
See also Research questions
Quon, G., 100–101
Quota sampling, 175
Quotations, 145
Randles, H. E., 161
Random purposive sampling, 177
Range, 65
Rank-difference correlation (rho), 67
Ratio data, 63
Reading, 261
“Recommendations for Further
Research” section, 238–240
Reed, J. G., 141
Refereeing system, 264
References, 144–146
See also Review of the Literature Chapter
Reflexivity, 104

Regression:
institutional factor regression results, 234
multiple regression correlation, 39–40
regression and prediction correlation, 38–39
student factor regression results, 233
Regression weight (b weight), 82
Relationship:
correlational research design, 35–45
measures of, 66–67
questions, 127
Relative position, measures of, 65–66
Reliability:
of grounded theory research, 104
of instrumentation, 181, 182–191
Representativeness, 169–170, 174
Research, recommendations for

further, 238–240
Research designs, 230
See also Qualitative research designs;
Quantitative research designs
Research methodology:
attitudes towards, chair selection criteria, 19–20
in proposal presentation, 247
questions in proposal meeting, 248, 249
Research monograph, 266–267
Research questions:
in Discussion Chapter, 229, 231–233
in Introduction Chapter, 112
research hypotheses, 127–133
statistical analysis presentation
and, 200
in summary of study, 227–228

types of, 126–127
Research questions, testing:
clarity/consistency, 217–218
figures, 220
format examples, 213–216
overview of, 213
qualitative research, presenting results of, 221–222
statistical tests, assumptions of, 216–217
tables, 218–220
Resident statistician, 21
Resources:
295–298
276–279

Dissertation Proposal Outline (Multivariate
Analysis of Variance), 284–287
for instruments, 191–192
Results:
in Discussion Chapter, 229–236
in final oral defense, 249
Results Chapter:
analyses, additional, 222–223
descriptive statistics, 210–213
research questions, testing, 213–222
statistical procedures listed in, 62
summary, 223–224

Return receipt requested, 265
Review articles, 5, 139
Review of literature, in proposal presentation, 246
Review of the Literature Chapter:
in general, 137–138
headings, 150
searching the literature, 138–142
synthesizing literature, 154–164
writing, 142–154
Review of the Literature Chapter, writing:
contents of chapter, 142–143
funnel organization, 143–144
introduction, 147–150
outline, 146–147
plagiarism, avoidance of, 153–154
selectiveness of, 144–146
summary, 152–153
transitions, 150–152

“Revise-submit “ decision, 266
Revisions, 271
Rice, R. K., 161
Robbins, S., 120
Rodriguez, P. A., 50–51
Rotter, J. B., 184–185
Rudestam, K. E.:
on quotations, 145
on tables, 218
Salvato, J. L., 34
Sample forms. See Resources
Sampling:
in case study research, 96
cluster sampling, 172–173
for ethnographic research, 99

participants, selection of, 169–174
participants in study, 167–169
in qualitative research, 176–178
results and, 230
sample size, 178–180
simple random sampling, 170–171
stratified random sampling, 171–172
systematic sampling, 173–174
Sargeant, J. C., 161
SAS (Statistical Analysis System), 62
Scheduling, proposal meeting, 244
Scheffé test, 73–74
Schlosberg, T.:
dissertation formatting of, 207
SLT and, 7, 229
Schmidt, L. J.:

factor analysis study, 86
instrument development by, 188–191
on instrumentation, 185–186
Schuler, R. S., 159
Schwab, W. A., 233
Scores, 183
Scott, W. R., 159
Search, of literature, 138–142
Search engine, 140, 141–142
Secondary sources, 145
Segmentation, 23
“Selection of Participants” section:
number of participants, 178–180
sampling in qualitative research, 176–178
sampling procedures, 169–174

who participated in study, 167–169
Selectiveness, 144–146
Self-efficacy theory, 7
SEM (structural equation modeling), 55, 86–87
Senatra, P. T., 159
Sentence structure, 217–218
Shakeshaft, C., 122
Shaping the Future (Lunenburg & Carr), 269
Siemers, E. E., 37
Significance:
criteria for topic selection, 11
practical, 158–159
of study, 117–118
use of term, 128
331Index
Simple random sampling, 170–171
Single-case design, 96

Size. See Effect size
Skills, 12–13
Slater, J.:
data collection by, 196–198
dissertation formatting of, 207
Slavin, R. E., 29
SLT. See Synergistic Leadership Theory
Smith, N. L., 123, 141
Snowball sampling, 176
Social Scisearch (Social Science Citation Index), 139, 264
Sociofile, 139
Software, 62
Solomon, R. C., 149
Sources of topics:
broad topic areas, 3–4
current employment, 8
dissertations, 5–6

existing database, 8–10
professional journals, 4–5
textbooks, 4
theories, 6–8
Sowell, E. J., 193
Spearman-Brown correction formula, 183
Spearman’s rho (p), 45, 67, 80
Spencer Foundation, 14
SPKS (Strategic Planning in Kentucky Schools) Scale,
187–188
Split-half reliability, 183
Sprinthall, R. C., 201
SPSS (Statistical Package for the Social Sciences),
62, 258
Spuck, D. W., 117, 118
Standard deviation, 64
Standard scores, 65, 66
Stanfield, V., 129, 175–176

Stanford, J., 121
Stanines, 66
Stanley, J. C., 230
Statement of problem, 114–116, 152
Statistical analysis:
in “Data analysis” section, 200–204
parametric/nonparametric tests, 70
Statistical Analysis System (SAS), 62
Statistical Package for the Social Sciences (SPSS),
62, 258
Statistical significance, 69, 201
Statistics:
definition of, 62
descriptive statistics, 63–67
results, presentation of, 218–220
Stepdown multiple regression, 82

Stepup multiple regression, 82
Stepwise multiple regression, 82
Sternberg, D., 10, 11–12, 251
Stoner, J., 119
Strategic Planning in Kentucky Schools
(SPKS) Scale, 187–188
Stratified random sampling, 171–172
Strauss, A. L., 102–103
Structural equation modeling (SEM), 55, 86–87
Stubbs, M., 94
Students, 22–-25
Success, 18–19
Summary:
“Conclusions” section, 240–241
in final oral defense, 249
of Results Chapter, 223–224
of Review of the Literature Chapter, 152–153
of study, 226–228

Summary, Discussion, and Conclusions Chapter:
conclusions, 240–241
discussion of findings, 228–229
elements of, 225, 242
introduction, 226
recommendations for further research, 238–240
summary of study, 226–228
Summary table, 160–162
Surveys, 32, 198
Sweetland, S. R., 159–160
Swetnam, D., 145
Synergistic Leadership Theory (SLT):
data collection process, 195–196
development of, 6–7
interview questions based on, 193
professional leadership and, 235

sampling in qualitative research, 177
Synthesizing literature. See Literature, synthesizing
Systematic sampling, 173–174
t score, 69
t test, 70–72
Tables:
for results chapter data presentation, 218–220
size of, 252
for synthesizing literature, 160–162
Tallerico, M., 156
Target population, 167–168
Tarter, C. J., 87, 154–155
Taylor, S., 94
Telephone interviews, 195–196
Tenure, 18, 262–263
Terenzini, P. T., 231
Terms, definition of, 118–122

Test publishers, 192
Test Review Locator, 192
Test scores, 183
Test-retest reliability, 182
Tests:
nonparametric, 76–80
of null hypothesis, 68–69
parametric/nonparametric tests, 70
of research questions/hypotheses, 213–222
t test, 70–72
of theory, 60–61
Tests in Print (TIP), 191
Textbooks, 4
Thanasui, P. L., 41–42
Themes, 157–158
Theoretical framework:
conclusions and, 240

example of, 123–126
findings linked to, 228–230
in Introduction Chapter, 122–126
in proposal presentation, 246
selection of, 122–123
Theory:
from case study, 97
conflicting theories, 159–160
development of, 58–61
grounded theory research design, 102–106
source of topics, 6–8
Thielemann, H., 9
Thompson, B., 36, 84
Thompson, L. C., 53–54
Thompson, P. B., 101

Time:
of committee, 24
manageability of topic and, 13
writing time, 270
Time series design, 52–54
Tinto, V., 231
Titles, 234
Tong, F., 57–58
Tong, R., 121
Topic, journal selection and, 263
Topic, selection of:
criteria for, 10–14
dissertation proposal, 3
originality and, 2
overview of, 15
recommendations for further research and, 238
sources of topics, 3–10
Trade books, 266

Transitions, 150–152
Trautman, D.:
data analysis by, 201–204
data collection section, 194–196
definition of terms by, 119–122
mixed methods dissertation, 224
OLEI, 235
SLT and, 6, 7, 229
Treatment group, 54, 55
Trend descriptive study, 32
Trends, 157–158
Triangulation, 104
Truslow, K.:
OLEI, 235
research questions of, 213–216
SLT and, 7, 229

testing of theory, 60–61
Trust, 149
Tsai, Y.-C.:
background of study, 114
results chapter of, 209–210
Tschannen-Moran, M., 7, 149–150
Tukey’s HSD, 73–74
Two-dimensional chi square, 78
Two-group pretest-posttest design using untreated
control group, 50
Typical case, 96
U value, 79
Uninformed consent, 252–253
Univariate tests, 76
University Council for Educational Administration
(UCEA):

data collection for study on, 198–199
discussion section of study on, 230–234
paper presentations and, 255
University Microfilms International, 252
University of Minnesota, 139
U.S. Department of Education Office of Educational
Research and Improvement, 14
“A Validation of the OCDQ in Elementary Schools”
(Lunenburg), 10–11
Validity:
for experimental research, 54
of grounded theory research, 103–104
of instruments, 181–182
quasi-experimental research and, 49
representativeness of sample and, 174
Van Eden-Moorefield, B. M., 59–60
VanderHoff, J., 237

Vanity presses, 266–267
Variability, measures of, 64–65
Variables:
communication of, 217–218
in correlational research design, 37–45
in experimental research design, 54–58
longitudinal studies, 32–33
See also Dependent variable; Independent variable
Variance. See Analysis of variance
Virgo, H. E., 161
Visscher, A. J., 117, 118
Wallace, R. C., 124
Wampold, B. E., 126
Watkins, J. F., 161
Wattenberg, W. W., 161
Web sites:
for committee member research, 23
search engines for literature search, 141–142

Weber, M., 159
Weis, D., 251
Wetherall, M., 94
Wiggins, T. W., 161
Wilbur, C. E., 108
Wilcoxon signed rank test, 77, 79
Wilk’s lamda, 76
Willig, C., 104
Willower, D. J., 185
Win-win situation, 250, 251
Within subjects (repeated measures)
design, 55
Wolfe, L. M., 85
Wooderson-Perzan, M., 9, 165
Woolfolk, A. E., 7
World Wide Web, publishing on, 269–270
Writing:
process, guidelines for, 270–271

Review of the Literature Chapter, 142–154
Yates, S. J., 94
Yearbook of the National Society for the Study of Education
(NSSE), 5
Yin, R. K., 196
Z score, 66
333Index
CORWIN
PRESS
The Corwin Press logo—a raven striding across an open book—
represents
the union of courage and learning. Corwin Press is committed to
improving
education for all learners by publishing books and other
professional
development resources for those serving the field of PreK–12
education. By
providing practical, hands-on materials, Corwin Press continues
to carry out the

promise of its motto: “Helping Educators Do Their Work
Better.”
CoverContentsPrefaceAcknowledgmentsAbout the AuthorsPart I
- Getting Started Chapter 1 - Selecting a Suitable TopicSources
of TopicsTextbooksProfessional
JournalsDissertationsTheoriesCurrent EmploymentExisting
DatabaseCriteria for Topic
SelectionInterestSignificanceAvailable DataKnowledge and
SkillsManageabilityFundingSummaryChapter 2 - Selecting a
Chair and CommitteeCriteria to Consider in Selecting a
ChairExpertiseAccessibilityFeedbackSuccessPersonality
StylesAttitudes Toward MethodologyComposition and Role of
the CommitteeCompositionRoleResearch Prospective
Committee MembersYour Own ExperienceOther
StudentsDissertationsWeb SitesThe Desirable StudentQuality
ProductFollow-ThroughThe Committee’s TimePersonal
IntegrityPolished DraftsSummaryPart II - What You Need to
KnowChapter 3 - Quantitative Research DesignsDescriptive
Research DesignInstrumentationDataLongitudinal StudiesCross-
Sectional StudiesCorrelational Research DesignBivariate
CorrelationRegression and PredictionMultiple
RegressionCanonical CorrelationDiscriminant AnalysisFactor
AnalysisPath AnalysisCross-Lagged PanelOther Correlation
CoefficientsAdvantages and DisadvantagesCausal-Comparative
Research DesignWeakness ControlledQuasi-Experimental
Research DesignNonequivalent Control Group DesignTime
Series DesignExperimental Research DesignFactorial
ExperimentTheory DevelopmentSummaryChapter 4 - Basic
StatisticsDescriptive StatisticsMeasures of Central
TendencyMeasures of VariabilityThe Normal CurveMeas ures of
Relative PositionMeasures of RelationshipInferential
StatisticsThe Null HypothesisTests of Statistical
SignificanceEffect SizeStatistical AnalysisThe t TestOne-Way
Analysis of VariancePost Hoc ProceduresFactorial Analysis of
VarianceAnalysis of CovarianceMultivariate Analysis of
VarianceNonparametric TestsChi SquareOther Nonparametric

TestsMultivariate Correlational StatisticsMultiple
RegressionDiscriminant AnalysisCanonical CorrelationPath
AnalysisFactor AnalysisStructural Equation
ModelingSummaryChapter 5 - Qualitative Research
DesignsPhenomenological Research
DesignTechniques/Approaches/MethodsCase Study Research
DesignEthnographic Research DesignGrounded Theory
Research DesignMixed Methods Research DesignSummaryPart
III - The Dissertation ChaptersChapter 6 - Writing the
Introduction ChapterBackground of the StudyStatement of the
ProblemPurpose of the StudySignificance of the
StudyDefinition of TermsTheoretical
FrameworkModelsResearch Questions (or Hypotheses)Types of
Research QuestionsSpecifying Research
HypothesesLimitationsDelimitationsAssumptionsOrganization
of the StudySummaryChapter 7 - Writing the Literature Review
ChapterSearching the LiteratureHandbooks and
EncyclopediasAnnual ReviewsReview ArticlesAbstracting and
Indexing ServicesGovernment DocumentsMajor Public Search
EnginesWriting the Literature ReviewOrganize Your Material in
a FunnelBe SelectiveMake an OutlineWrite the IntroductionUse
HeadingsUse TransitionsWrite a SummaryBe Careful Not to
PlagiarizeSynthesizing the LiteratureHistorical ContextGaps in
the LiteratureDifferent ApproachTrends and ThemesReporting
Practical SignificanceReconciling Conflicting TheoriesUsing
TablesInconsistent FindingsContinuing Line of
InquirySummaryChapter 8 - Writing the Methodology
ChapterIntroductionSelection of ParticipantsWho Participated in
the Study?Sampling ProceduresSelecting a Nonrandom
SampleSampling in Qualitative ResearchHow Many Participated
in the Study?InstrumentationValidityReliabilityLocating
InstrumentsQualitative InstrumentationData CollectionData
AnalysisSummaryConclusionChapter 9 - Writing the Results
ChapterIntroductionDescriptive StatisticsTesting the Research
Questions (or Hypotheses)Assumptions of Statistical
TestsClarity and ConsistencyTablesFiguresPresenting the

Results of Qualitative ResearchAdditional
AnalysesSummaryConclusionChapter 10 - Writing the
Discussion ChapterIntroductionSummary of the
StudyDiscussion of the FindingsGuidelines to Structure Your
Discussion SectionSamplingInstrumentationResearch
DesignImplications for PracticeRecommendations for Further
ResearchConclusionsSummaryPart IV - The Defense and
AfterwardChapter 11 - The Proposal and Final Oral
DefensePrepare a Well-Written DocumentKnow the
FormatPrepare Your PresentationPractice Your
PresentationAnticipate QuestionsFinal Oral DefenseTips for
Avoiding Common MistakesSummaryChapter 12 - Presenting
and Publishing Your DissertationPresentationsPaper
PresentationPoster SessionJob InterviewAcademic
JournalsSelect a Suitable JournalPrepare the ManuscriptSubmit
the ManuscriptBooksChapters in BooksPopular PressInternet
PublishingDesktop PublishingPlanning the Writing
ProcessSummaryAppendix A: Initial Letter Soliciting
ParticipationAppendix B: First Follow-Up LetterAppendix C:
Second Follow-Up LetterAppendix D: Dissertation Proposal
Outline (Correlational)Appendix E: Dissertation Proposal
Outline (Analysis of Variance and Regression)Appendix F:
Dissertation Proposal Outline (Multivariate Analysis of
Variance)Appendix G: Dissertation Proposal Outline
(Qualitative)Appendix H: Qualitative Research
CritiqueAppendix I: Agreement: Guidelines for Chairing a
DissertationAppendix J: Checklist for Dissertation
QualityReferencesIndex
Project # 1 – Matrix Addition and Matrix Multiplication
Task 1: Basic Matrix Addition
For this task, you will develop a complete CUDA program for
integer matrix addition. You will add two two-dimensional
matrices A, B on the device GPU in parallel. After the device
matrix addition kernel function is invoked, and the addition

result will be transferred back to the CPU. Your program will
also compute the sum matrix of matrices A and B using the
CPU. Your program should compare the device-computed result
with the CPU-computed result. If it matches, then it will print
out "Test PASSED" to the screen before exiting.
The pseudo code for matrix addition on the CPU is as follows:
void add_matrix_cpu(int *a, int *b, int *c, int N)
{
int i, j, index;
for (i=0;i<N;i++) {
for (j=0;j<N;j++) {
index =i*N+j;
c[index]=a[index]+b[index];
}
}
}
void main() {
.....
add_matrix(a,b,c,N);
}
The pseudo code for matrix addition on the GPU device is as

follows:
CUDA C program
__global__ void add_matrix_gpu(int *a, int *b, int *c, intN)
{
int col =blockIdx.x*blockDim.x+threadIdx.x;
int row=blockIdx.y*blockDim.y+threadIdx.y;
int index =row*N+col;
if( row<N && col <N)
c[index]=a[index]+b[index];
}
void main() {
dim3 dimBlock(blocksize, blocksize,1);
dim3 dimGrid( ceiling (double (N) /dimBlock.x), ceiling
(double (N) /dimBlock.y), 1 );
add_matrix_gpu<<<dimGrid, dimBlock>>>(a,b,c,N);
}
Use the following pseudo code for matrix initialization.
int *a, *b, *c;
A=malloc(sizeof(int)*N*N; //N is the size
//then malloc for b and c

Int init =1325;
For (i=0;i<N;i++){
For (j=0;j<N;j++){
Init=3125*init%65536;
A[i][j]=(init-32768)/6553;
B[i][j]=Init%1000;
}
}
Use the following matrix size and thread block size (the number
of threads in each block) to test your cuda program.
Matrix Size
Size of Thread block
8*8
4*4 (For debugging purpose)
128*128
16*16
500*500
16*16
1000*1000

16*16
Task 2: Matrix Multiplication
For this task, you will develop a complete CUDA program for
matrix multiplication. You will multiply two two-dimensional
matrices A,B on the device GPU in paralell. After the device
matrix multiplication kernel function is invoked, and the
multiplication result will be transferred back to the CPU. Your
program will also compute the product matrix of matrices A and
B using the CPU. Your program should compare the device-
computed result with the CPU-computed result. If it matches,
then it will print out "Test PASSED" to the screen before
exiting.
The pseudo code for matrix multiplication on the CPU is as
follows:
void MatrixMulOnHost(int* M, int* N, int* P, int Width)
{
for (int i = 0; i < Width; ++i)
for (int j = 0; j < Width; ++j) {
int sum = 0;
for (int k = 0; k < Width; ++k) {
int a = M[i * Width + k];
int b = N[k * Width + j];
sum += a * b;
}
P[i * Width + j] = sum;
}

}
void main() {
.....
add_matrix(a,b,c,N);
}
The pseudo code for matrix addition on the GPU device is as
follows:
CUDA C program
__global__ void MatrixMulKernel(int* M, int* N, int * P, int
Width)
{
int Row = blockIdx.y*blockDim.y+threadIdx.y;
int Col = blockIdx.x*blockDim.x+threadIdx.x;
if ((Row < Width) && (Col < Width)) {
int
Pvalue = 0;
for (int k = 0; k < Width; ++k)

Pvalue += M[Row*Width+k] * N[k*Width+Col];
d_P[Row*Width+Col] = Pvalue;
}
}
void main() {
dim3 dimBlock(blocksize,blocksize,1);
dim3 dimGrid( ceiling (double (N) /dimBlock.x), ceiling
(double (N) /dimBlock.y), 1 );
add_matrix_gpu<<<dimGrid,dimBlock>>>(a,b,c,N);
}
Use the following pseudo code for matrix initialization.
int *a, *b, *c;
A=malloc(sizeof(int)*N*N; //N is the size
//then malloc for b and c
Int init =1325;
For (i=0;i<N;i++){
For (j=0;j<N;j++){

Init=3125*init%65536;
A[i,j]=(init-32768)/6553;
B[i,j]=Init%1000;
}
}
Use the following matrix size and thread block size (the number
of threads in each block).
Matrix Size
Size of Thread block
8*8
4*4 (For debugging purpose)
128*128
16*16
500*500
16*16
1024*1024
16*16
Requirements:
1. In order to use the cuda compiler environment installed under
the cs unix server, fry.cs.wright.edu, you need to connect to this
unix server remotely using a secure shell client, such as putty.
You can remotely connect to this unix server, fry.cs.wright.edu,
on campus from a Wright State computer or use your own
laptop connecting to the WSU wifi network named “WSU-

Secure”. Note that you cannot remotely connect to this
computer using ssh using computers outside Wright State
University without installing VPN or use the campus
“WSU_EZ_CONNECT” wifi network. If you want to connect to
this server remotely off campus, you need to install VPN on
your computer first. If you want to edit your cuda source
programs under windows, download notepad++. Then edit your
source programs using notepad++. After you finish editing the
cuda source programs, using the secure file transfer client
(WinSCP, you can download it online, and install it on your
personal computer) to transfer your cuda source programs to
fry.cs.wright.edu.
2. You must submit an ELECTRONIC COPY of your source
program through Pilot before the due date. If for some reason
Pilot is unavailable, submit your source code by email to
[email protected]
3. Submit all your source codes, a README file, a report, and
any other required files. It is required that you explain how to
compile and run your programs clearly in the README file. In
the report, please report whether your programs have all the
functionalities required in the project description. In your
report, please state clearly any functionalities not implemented
in your program. If your program works correctly, please
include screenshots in your report. Your submitted file name
should use your last name as part of the file name, for example,
Liu_Project1.cpp, Liu_Project1_Report, Liu_Project1_ReadMe,
etc. All the submitted project files should have: Course Number
/ Course Title, your name, group member’s name, prof.’s name,
date, and the project name. If you did not include these required
contents in your submitted files, then 5 points will be deducted.
4. The grader or the instructor will test your programs under
CUDA environment, on the linux server, fry.cs.wright.edu.
Before you submit your program, please connect to this server
using your campus ID to test your program (I have demoed how
to compile and execute a cuda program on this server. If you
have questions, let me know).

5. The programming assignment is individual. You must finish
the project by yourself. If you allow others to copy your
programs or answers, you will get the same punishment as those
who copy yours.
How to use CUDA on fry.cs.wright.edu
First using putty or other secure shell clients to connect to
fry.cs.wright.edu using your campus id (for example, w123abc),
then run the following command:
srun -p a100 --gres=gpu:1 --pty bash
This command will request access to a gpu node and launch a
bash shell on it.
Then you can compile a cuda program vectadd.cu using the
following command under the directory where your source cuda
program is located.
nvcc vectadd.cu -o vectadd
Then you can execute vectadd using the following command
under the directory where the generated executable file (of your
cuda source program), vectadd, is located.
./vectadd
Last week you should have reviewed the IRB process and
started to think more about the related requirements. This
information should have led to questions in your mind about
your research design and how you will conduct your study. This
week we are taking a step back to Chapter One and taking a
closer look at developing your research questions. We will also
consider sources for gathering data as you work on your
research design.
Objective:

Discuss possible reseach design issues of qualitative and
quantitative research studies.
Learning Materials - Read Chapters 3 and 5
Week 4 Lecture Handouts (Add copies of Week 4 handouts
here)
===============================================
===============
===============================================
===============
4.1 - Week 4 Discussion
Select the appropriate question based on the type of dissertation
you are completing:
For a quantitative study, your chosen research design must
address reliability and validity issues. How will you insure the
measures you are using are valid and reliable. What will you put

in your research design to insure validity and reliability?
For a qualitative study, there are also issues your research
design has to address, such as credibility, transferability,
dependability, and confirmability. Address how these factors
you would address in your qualitative study.
Your initial posting is to be a detailed, well-organized response
to the posed question. In addition to your initial posting,
respond to one other classmate's thread.

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