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Surviving Your Dissertation
4
2

Surviving Your Dissertation
A Comprehensive Guide to Content and Process
4
Kjell Erik Rudestam
Fielding Graduate University
Rae R. Newton
Fielding Graduate University
3

FOR INFORMATION:
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Printed in the United States of America
Library of Congress Cataloging-in-Publication Data
Rudestam, Kjell Erik.
Surviving your dissertation : a comprehensive guide to content and process / Kjell Erik Rudestam, Fielding Graduate University, Rae R.
Newton, Fielding Graduate University.—Fourth edition.
pages cm.
Includes bibliographical references.
ISBN 978-1-4522-6097-6 (pbk. : alk. paper)
1. Dissertations, Academic—United States. 2. Report writing. 3. Research—United States. I. Newton, Rae R. II. Title.
LB2369.R83 2015
378.2—dc23 2014002905
4

This book is printed on acid-free paper.
14 15 16 17 18 10 9 8 7 6 5 4 3 2 1
Acquisitions Editor: Vicki Knight
Editorial Assistant: Yvonne McDuffee
Production Editors: Laura Barrett, David C. Felts
Copy Editor: Paula L. Fleming
Typesetter: C&M Digitals (P) Ltd.
Proofreader: Sarah J. Duffy
Indexer: Joan Shapiro
Cover Designer: Rose Storey
Marketing Manager: Nicole Elliott
5

Contents
Preface
About the Authors
Part I: Getting Started
1. The Research Process
2. Selecting a Suitable Topic
3. Methods of Inquiry: Quantitative and Qualitative Approaches
Part II: Working With Content: The Dissertation Chapters
4. Literature Review and Statement of the Problem
5. The Method Chapter: Describing Your Research Plan
6. Presenting the Results of Quantitative Research
7. Presenting the Results of Qualitative Research
8. Discussion
Part III: Working With Process: What You Need to Know to Make the Dissertation Easier
9. Overcoming Barriers: Becoming an Expert While Controlling Your Own Destiny
10. Writing
11. How to Complete Your Dissertation Using Online Data Access and Collection
12. Guidelines for the Presentation of Numbers in the Dissertation
13. Informed Consent and Other Ethical Concerns
References
Name Index
Subject Index
6

Preface
We are pleased to present the fourth edition of Surviving Your Dissertation. As with previous editions, we have
sought to answer questions that students and faculty have at every stage of the dissertation process. In past
editions, we have illustrated the challenge of engaging in such a notable project with book covers that depict a
bridge leading into an impenetrable jungle and life buoys close at hand to negotiate the stormy seas. The cover
of this edition offers a different, but equally relevant image: the pride of victory achieved by scaling a lofty
peak.
In many ways, these images also reflect our own experience in writing the book. The field of research in the
social and behavioral sciences has expanded rapidly over the past several years, and we have frequently felt as if
we are scrambling to keep up. This edition reflects our experience. We have maintained the overall structure
of the book, which has been well received so far, while updating content on those topics that are indispensable
to the dissertation process: the selection of an appropriate research topic; the review of the literature; the
description of the methodology and research design; the collection and analysis of data; and the interpretation,
presentation, and discussion of the results and implications of the study. Within this updated material, we
have once again attempted to provide sufficient detail to enable the reader to know exactly what goes into each
section and chapter of the dissertation and how to format that information. In addition, we continue to
include topics that are not always present in sources of this kind: the many types of quantitative and
qualitative research models and approaches that are available to the student, the principles of good scholarly
and academic writing, suggestions for how to select and work with committees, and tips for overcoming task
and emotional blocks that may impede progress. Throughout, we have replaced older references with newer,
more contemporary ones, including many new dissertation examples taken from our students and colleagues.
We have also added significant new content to the fourth edition. We note that the traditional null hypothesis
significance-testing model is being challenged and augmented by an emphasis on clinical or practical
significance and a corresponding use of measures of effect size and confidence intervals. We have explained
and illustrated this new approach to the presentation of statistical results. Similarly, we have acknowledged an
emerging emphasis on theoretical “models” and the influence of model building on the design and
presentation of research. We provide several examples of studies that incorporate this approach. We are
increasingly impressed by the implications of the Internet for the entire research enterprise. We have
expanded our discussion of the Internet as a source of data, an opportunity for data collection, and a vehicle
for data analysis, as well as providing recommendations of potentially helpful websites and software programs
that may be unfamiliar to the average reader. We also acknowledge the parallel expansion of available data
sources in all their varied forms, including data archives, social media, and what is currently known as “big
data.” Finally, we have expanded the sections on qualitative and multimodal methods of research, which have
an inductive, theory-building focus. With respect to all these topics, we have tried to explain the concepts,
illustrate them with new tables and figures, and, in many cases, provide very specific details about how to
incorporate them into a research study.
7

We believe that this book is suitable for a large academic and professional audience. Of course, it is primarily
directed at the graduate student who envisions or is involved in writing a research dissertation. Thus, there is
significant focus on material that is best suited for the beginning doctoral student—for example, how to
develop a research question, how to construct a table or figure, how to report a statistical finding, how to use
American Psychological Association formatting conventions, and so on. However, there is also content
directed at the more advanced student—for example, how to conceptualize and illustrate a mediation model,
how to report multiple regression findings, and how to code text for a grounded theory study. Moreover, we
have become increasingly aware that Surviving Your Dissertation serves also as a resource for researchers and
practitioners who have either forgotten important details or are motivated to keep abreast of evolving research
practices in their fields. Perhaps more important, we view the book as a convenient source of information for
faculty who are currently supervising graduate students’ dissertations or research projects.
We remain deeply indebted to our own students, who continue to thrill us with their creativity and force us to
keep learning in order to stay a step ahead. We hope that they, and you, find this volume a helpful and steady
companion in your research and writing endeavors.
A large number of individuals have contributed to the completion of this project. We called upon many
faculty colleagues to nominate student dissertations that exemplify high levels of scholarship and have
sprinkled references to these dissertations throughout the book to illustrate important principles and
recommendations. We are very appreciative of these relatively recent graduates for allowing us to share their
first major research endeavors in this venue. We also benefitted from the critical reflections and insight of the
following reviewers of the third edition of Surviving Your Dissertation: Anne J. Hacker, Bernie Kerr, Karin
Klenke, Kaye Pepper, and Udaya R. Wagle. Their observations and suggestions were both reinforcing and
helpful in crystallizing changes for this edition.
We are also grateful to be part of the SAGE family, a collaborative, dedicated group of professionals who have
facilitated our writing careers in so many ways. The following individuals were notable contributors: Vicki
Knight, publisher and senior editor, has always provided us with a balance of inspiring leadership and
nurturing support. Her editorial assistant, Jessica Miller, has been consistently responsive to our frequent
requests for assistance. Laura Barrett and David Felts, project editors, have gracefully guided the editorial
process from start to finish. And Paula Fleming, our copy editor, is truly a paragon in her craft. Her
grammatical acuity, common sense, and work ethic cannot be overestimated. Thank you all.
Finally, we must thank our partners in life, Jan and Kathy, for their continuing patience and support as we
have devoted our energy and attention to four editions of this volume.
8

About the Authors
Kjell Erik Rudestam
is Professor of Psychology at Fielding Graduate University, Santa Barbara, California, where he served
as Associate Dean of Academic Affairs for many years. He was previously a psychology professor at
York University, Toronto, and Miami University, Oxford, Ohio, after receiving his PhD in Psychology
(Clinical) from the University of Oregon. He is the author of Your Statistical Consultant: Answers to Your
Data Analysis Questions, 2nd edition (also with Rae R. Newton), Handbook of Online Learning, 2nd edition
(with Judith Schoenholtz-Read), and eight other books, as well as numerous articles in professional
journals on topics including suicide, psychotherapy, and family and organizational systems. He is a
Fellow of the American Psychological Association (Division 12), a Diplomate of the American Board of
Examiners in Professional Psychology (Clinical), a Diplomate of the American Academy of Experts in
Traumatic Stress, and holds an Honorary Doctorate of Science from The Professional School of
Psychology.
Rae R. Newton
is Professor of Sociology Emeritus at California State University, Fullerton. He recently joined the
faculty of the School of Psychology at Fielding Graduate University where he serves as a research
consultant and statistical advisor to doctoral students and faculty. He received his PhD in sociology
from the University of California, Santa Barbara, and completed postdoctoral training in mental health
measurement at Indiana University. His primary interests include longitudinal modeling of outcomes for
high risk youth and foster care populations, family violence and statistics education. He is author, with
Kjell Erik Rudestam, of Your Statistical Consultant: Answers to Your Data Analysis Questions, now in its
second edition and numerous articles in professional journals on topics including family violence, child
maltreatment, and measurement. In semi-retirement he enjoys traveling with his wife in their RV and
surfing throughout Mexico and Central America.
9

Part I Getting Started
The Research Process
Selecting a Suitable Topic
Methods of Inquiry: Quantitative and Qualitative Approaches
10

1 The Research Process
There is a story about a Zen Buddhist who took a group of monks into the forest. The group soon lost its
way. Presently one of the monks asked the leader where they were going. The wise man answered, “To the
deepest, darkest part of the forest so that we can all find our way out together.” Doctoral research for the
graduate student in the social sciences is often just such an experience—trekking into a forest of impenetrable
density and making many wrong turns. Over the years, our students have used various metaphors to describe
the dissertation process, metaphors that convey the feeling of being lost in the wilderness. One student
compared the process to the Sisyphean struggle of reaching the top of a hill, only to discover the presence of
an even higher mountain behind it. Another student experienced the task as learning a Martian language,
known to the natives who composed her committee but entirely foreign to her. A third student offered
perhaps the best description when she suggested that it was like waiting patiently in a seemingly interminable
line to gain admission to a desirable event, then finally reaching the front only to be told to return to the rear
of the line.
One reason that students become more exasperated than necessary on the dissertation journey is that they fail
to understand the procedures and practices that form the foundation for contemporary social science research.
Many students who are attracted to their field of interest out of an applied concern are apprehensive about
making the leap from application to theory, an indispensable part of the research enterprise. What may not be
so evident is that many of the skills that go into being a consummate practitioner are the same ones demanded
of a capable researcher. It is well known that curiosity and hypothesis testing are the bedrock of empirical
research. In a similar fashion, experienced psychotherapists, to take an example from clinical psychology, are
sensitive and keen observers of client behavior. They are persistent hypothesis testers. They are curious about
the relationship between family history variables and current functioning. They draw on theory and experience
to help select a particular intervention for a particular client problem or moment in therapy.
Dispassionate logic and clear and organized thinking are as necessary for effectiveness in the field as they are
for success in research. In fact, the bridge between research and just plain living is much shorter than most
people think. All of us gather data about the world around us, wonder what will happen if we or others behave
in particular ways, and test our pet hunches through deliberate action. To a large extent, the formal research
enterprise consists of thinking systematically about these same issues.
The procedures outlined in this book are intended to assist the doctoral student in planning and writing a
research dissertation, but the suggestions are equally applicable to writing a master’s thesis. In fact, there is
considerable overlap between these two challenging activities. For most students, the master’s thesis is the first
rigorous research project they attempt. This means that, in the absence of strong, supportive faculty
consultation, the student often concludes the thesis with considerable relief and an awareness of how not to do
the study the next time! With a doctoral dissertation, it is generally expected, sometimes as an act of faith,
that the student is a more seasoned and sophisticated researcher. The consensus opinion is that dissertations
are generally longer than theses, that they are more original, that they rely more heavily on theoretically based
11

arguments, and that they make a greater contribution to the field.
In most graduate programs, the prelude to conducting a dissertation study is presenting a dissertation
proposal. A research proposal is an action plan that justifies and describes the proposed study. The completion
of a comprehensive proposal is a very important step in the dissertation process. The proposal serves as a
contract between the student and his or her dissertation or thesis committee that, when approved by all
parties, constitutes an agreement that data may be collected and the study may be completed. As long as the
student follows the steps outlined in the proposal, committee members should be discouraged from
demanding significant changes to the study after the proposal has been approved. Naturally, it is not
uncommon to expect small changes, additions, or deletions as the study progresses because one can never
totally envision the unpredictable turns that research can take.
There is no universally agreed-on format for the research proposal. To our way of thinking, a good proposal
contains a review of the relevant literature, a statement of the problem and the associated hypotheses, and a
clear delineation of the proposed method and plan for data analysis. In our experience, an approved proposal
means that a significant percentage of the work on the dissertation has been completed. As such, this book is
intended to help students construct research proposals as well as complete dissertations.
12

The Research Wheel
One way to think about the phases of the research process is with reference to the so-called research wheel
(see Figure 1.1). The wheel metaphor suggests that research is not linear but is rather a recursive cycle of
steps. The most common entry point is some form of empirical observation. In other words, the researcher
selects a topic from the infinite array of possible topics. The next step is a process of inductive logic that
culminates in a proposition. The inductive process serves to relate the specific topic to a broader context and
begins with some hunches in the form “I wonder if . . . .” These hunches typically are guided by the values,
assumptions, and goals of the researcher, which need to be explicated.
Figure 1.1 The Research Wheel
Stage 2 of the research wheel is a developed proposition, which is expressed as a statement of an established
relationship (e.g., “the early bird is more likely than the late bird to catch the worm”). The proposition exists
within a conceptual or theoretical framework. The role of the researcher is to clarify the relationship between
a particular proposition and the broader context of theory and previous research. This is probably the most
challenging and creative aspect of the dissertation process.
Theories and conceptual frameworks are developed to account for or describe abstract phenomena that occur
under similar conditions. A theory is the language that allows researchers to move from observation to
observation and make sense of similarities and differences. A conceptual framework, which is simply a less-
developed form of a theory, consists of statements that link abstract concepts (e.g., motivation, role) to
empirical data. If not placed within such a context, the proposed study has a “So what?” quality. This is one of
the main objections to the research proposals of novice researchers: The research question may be inherently
interesting but ultimately meaningless. For instance, the question “Are there more women than men in
graduate school today?” is entirely banal as a research question unless the answer has conceptual or theoretical
implications that are developed within the study.
Although a study may be worthwhile primarily for its practical implications (e.g., “Should we start recruiting
13

more men into graduate schools?”), a purely applied study may not be acceptable as a dissertation. Kerlinger
and Lee (1999), authors of a highly respected text on research methodology, noted that “the basic purpose of
scientific research is theory” (p. 5). Generally speaking, a research dissertation is expected to contribute to the
scholarly literature in a field and not merely solve an applied problem. Thus, developing a proposition for
one’s dissertation typically involves immersing oneself in the research and theoretical literature of the field to
identify a conceptual framework for the study.
Having stated our position on the role of theory in dissertation research, we now need to take a step back. As
a psychologist and a sociologist, respectively, we are most familiar with research conventions within these two
disciplines. Other branches of the social sciences have their own standards of what constitutes an acceptable
dissertation topic. We have attempted to keep this book as generalizable as possible and to infuse it with
examples from other fields. Ultimately, of course, you will need to follow the rules and conventions that
pertain to your discipline as well as to your university and department.
For example, a few major universities allow a doctoral student to submit one or more published articles as the
equivalent of a dissertation. Many others encourage studies that consist of secondary data analyses derived
from national databases, such as U.S. Census data or the General Social Survey, or data obtained from a larger
study. Some fields—notably social work, education, policy evaluation, and professional psychology—may
encourage dissertations that solve applied problems rather than make distinct theoretical contributions.
Studies that evaluate the effectiveness of programs or interventions are a case in point because they sometimes
contribute little to validating a theory. Political science and economics are examples of fields that are diverse
enough to accommodate both theoretically based studies and purely applied studies. Within the subspecialty
of international relations, for instance, one could imagine a survey and analysis of security agreements of
European nations after the unraveling of the North Atlantic Treaty Organization (NATO) that rely on
interviews with foreign policy makers and are largely descriptive and applied. In contrast, a study of the role of
a commitment to ideology in the success of political parties in the United States, based on an analysis of
historical documents and voting records, might be grounded in a theory of how ideology attracts or alienates
the voting public.
Moving forward along the research wheel, the researcher uses deductive reasoning to move from the larger
context of theory to generate a specific research question. The research question is the precisely stated form of
the researcher’s intent and may be accompanied by one or more specific hypotheses. The first loop is
completed as the researcher seeks to discover or collect the data that will serve to answer the research question.
The data collection process is essentially another task of empirical observation, which then initiates another
round of the research wheel. Generalizations are made on the basis of the particular data that have been
observed (inductive process), and the generalizations are tied to a conceptual framework, which then leads to
the elucidation of further research questions and implications for additional study.
The kinds of skills called for at the various points of the research wheel are reminiscent of the thoughts about
learning presented by Bertrand Russell many years ago. Russell noted that there are two primary kinds of
knowledge acquisition: knowledge by description and knowledge by acquaintance. Knowledge by description
14

is learning in a passive mode, such as by reading a book on how to change the oil in one’s car or hearing a
lecture on Adam Smith’s theory of economics. This type of learning is especially well suited to mastering
abstract information; in other words, it is better for learning about economics than about changing the oil.
Knowledge by acquaintance, on the other hand, is learning by doing—the kind of skill training that comes
from practicing a tennis serve, driving an automobile, and playing with a computer. This is concrete
knowledge acquisition, oriented to solving problems.
The research process demands both skills. First, the researcher needs to apply clear, logical thinking to
working with concepts and ideas and building theories. It is our impression that many graduate students,
particularly those who have experience as practitioners in their fields, are weaker in this abstract
conceptualization, and honing this skill may be the major challenge of the dissertation. Second, the researcher
must engage in the practical application of ideas, including by systematically planning a study and then
collecting and analyzing data. The ability to focus, problem solve, and make decisions will help bring the
study to completion.
15

2 Selecting a Suitable Topic
The selection of an appropriate topic is the first major challenge in conducting research. In many academic
settings, this task is simplified by working with a faculty mentor who is already familiar with an interesting
area of study, may have an extensive program of research in that area, and may even have defined one or more
researchable questions. It is quite common for students interested in a particular area of research to not only
select their doctoral institution but also select their dissertation chair, with the goal of joining the research
program of a noted scholar in that field.
On the other hand, you may not be blessed with a faculty role model who is actively engaged in research in an
area of interest to you. There are no simple rules for selecting a topic of interest, but there are some
considerations with respect to appropriateness. It is generally unwise to define something as important as a
dissertation topic without first obtaining a broad familiarity with the field. This implies a large amount of
exploring the literature and studying the experts. Without this initial exploration, you can neither know the
range of possibilities of interesting topics nor have a clear idea of what is already known. Most students obtain
their research topics from the loose ends they discover in reading within an area, from an interesting
observation they have made (“I notice that men shut up when a beautiful woman enters the room; I wonder
what the effect of physical attractiveness is on group process?”), or from an applied focus in their lives or
professional work (“I have a difficult time treating these alcoholics and want to discover how best to work with
them”). In short, there is no substitute for immersing oneself in a field of study by having conversations with
leading scholars, advisers, and peers; critically reading the existing literature; and reflecting upon the
implications of professional and personal experiences.
16

Some Guidelines for Topic Selection
Here are some guidelines for deciding whether a topic is appropriate as a dissertation subject.
1. A topic needs to sustain your interest over a long period of time. A study on learning nonsense syllables
under two sets of environmental conditions may sound appealing in its simplicity, but remember Finagle’s first
law of research: If something can go wrong, it will go wrong! Dissertations usually take at least twice as much
time as anticipated, and there are few worse fates than slaving for hour after hour on a project that you abhor.
Remember, too, that all dissertations are recorded and published by the Library of Congress, and you will
always be associated with your particular study.
2. At the other extreme, it is wise to avoid a topic that is overly ambitious and overly challenging. Most
students want to graduate, preferably within a reasonable period of time. Grandiose dissertations have a way
of never being completed, and even the best dissertations end up being compromises among your own
ambition, the wishes of your committee, and practical circumstances. It is not realistic for a dissertation to say
everything there is to say about a particular topic (e.g., the European Union), and you need to temper your
enthusiasm with pragmatism. As one student put it, “There are two types of dissertations: the great ones and
those that are completed!” Sometimes it makes sense to select a research topic on the basis of convenience or
workability and use the luxury of the postgraduate years to pursue more esoteric topics of personal interest.
3. We suggest that you avoid topics that may be linked too closely with emotional issues in your own life. It
always makes sense to choose a topic that is interesting and personally meaningful. Some students, however,
try to use a dissertation to resolve an emotional issue or solve a personal problem. For example, even if you
think you have successfully overcome the personal impact of the death of your child, this is a topic to be
avoided. It will necessarily stir up emotional issues that may get in the way of completing the dissertation.
4. A related issue is selecting a topic in which you have a personal ax to grind. Remember that conducting
research demands ruthless honesty and objectivity. If you initiate a study to demonstrate that men are no
damned good, you will be able neither to allow yourself the sober reflections of good research nor to
acknowledge the possibility that your conclusions may contradict your expectations. It is much better to begin
with a hunch (“I’ve noticed that men don’t do very well with housekeeping; I wonder if that has something to
do with being pampered as children”) and to regard the research as an adventurous exploration to shed light
on this topic rather than as a polemical exercise to substantiate your point of view.
5. Finally, you need to select a topic that has the potential to make an original contribution to the field and
allow you to demonstrate your independent mastery of subject and method. In other words, the topic must be
worth pursuing. At the very least, the study must generate or help validate theoretical understanding in an
area or, in those fields where applied dissertations are permissible, contribute to the development of
professional practice. Some students are put off when they discover that a literature review contains
contradictory or puzzling results or explanations for a phenomenon. However, such contradictions should be
taken not as reasons to steer away from a topic but rather as opportunities to resolve a mystery. When people
17

disagree or when existing explanations seem inadequate, there is often room for a critical study to be
conducted. An opportunity to design a study that resolves theoretical contradictions within a discipline should
not be overlooked.
18

From Interesting Idea to Research Question
Let us assume that you have identified a general area of research and that your choice is based on curiosity and
may involve resolving a problem, explaining a phenomenon, uncovering a process by which something occurs,
demonstrating the truth of a hidden fact, building on or reevaluating other studies, or testing some theory in
your field. To know whether or not the topic is important (significant), you must be familiar with the
literature in the area. In Chapter 4, we present a number of suggestions for conducting a good review and
assessment of the literature. In the meantime, we have noticed that many students have difficulty
transforming an interesting idea into a researchable question, and we have designed a simple exercise to help
in that endeavor.
Researchable questions almost invariably involve a relationship between two or more variables, phenomena,
concepts, or ideas. The nature of that relationship may vary. Research studies generally consist of methods to
explicate the nature of the relationship. Research in the social sciences rarely consists of explicating a single
construct (e.g., “I will look at everything there is to know about the ‘imposter phenomenon’”) or a single
variable (e.g., voting rates in presidential elections).1 Even the presence of two variables is apt to be limiting,
and oftentimes it is only when a third “connecting” variable is invoked that an idea becomes researchable. As a
caveat, however, we acknowledge that research questions that are qualitative rather than quantitative in nature
might not be as focused on the relationship between variables as on “how” processes develop or are
experienced. We will have more to say about this distinction in forthcoming chapters.
An example might help to demonstrate how the introduction of an additional variable can lead to the birth of
a promising study. Let us assume that I am interested in how members of a younger generation perceive the
elderly. At this level, a study would be rather mundane and likely to lead to a “So what?” response. So far, it
implies asking people what they think of the elderly, perhaps using interviews or tests or even behavioral
observations. But we really won’t learn much of value about the nature of perceptions of the elderly in
contemporary society and what influences those perceptions. Introducing a second variable, however, can lead
to a set of questions that have promising theoretical (as well as practical) implications: I wonder what the role
of the media is in shaping social perceptions of the elderly? I wonder if living with a grandparent makes any
difference in how the elderly are viewed? I wonder how specific legislation designed to benefit the elderly has
changed our perception of them? I wonder if there is a relationship between how middle-aged adults deal with
their aging parents and how they view the elderly? The new variables introduced in these potential research
questions are, respectively, the slant of the media, presence or absence of a grandparent, type of legislation,
and treatment of one’s own parents. These variables impart meaning to the research because they offer
suggestions as to what accounts for variability in perceptions of the elderly.
As an example of generating a research question using three primary variables, let’s say that you have inferred
that many women lose interest in sexual relations with their husbands after the birth of a child. At this level,
the proposed study would consist of checking out this hunch by assessing the sexual interest of women
(Variable 1) before and after childbirth (Variable 2). But what would this finding mean? The introduction of a
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third variable or construct could lead to a much more sophisticated and conceptually meaningful study. An
investigator might ask, “I wonder if the partner’s involvement in parenting makes a difference? What’s the role
of his sexual initiative? How about childbirth complications? Father’s involvement in the birthing? The length
of time they have been married? Presence of other children in the home? Mother’s level of fatigue? Her body
image?” There is no end to the number of interesting questions that can be raised simply by introducing
another variable into the proposed study. This variable would then help to explain the nature of the
relationship between the primary variables. In fact, one could brainstorm a whole list of third variables that
could contribute to a better understanding of the relationship between childbirth and sexuality.
Note that the precise function of the third, or connecting, variable depends on the logic of the conceptual
model or theory underlying the study. In this regard, a distinction can be made between two terms, mediator
and moderator, which play important roles in research questions. A moderator variable pinpoints the
conditions under which an independent variable exerts its effects on a dependent variable. Strictly speaking, a
moderator effect is an interaction effect in which the influence of one variable depends upon the level of
another variable (Frazier, Tix, & Barron, 2004). One commonly employed moderator variable is gender,
which has two levels, male and female. The relationship between provocation and aggression, for example,
may be very different for men and women. The role of context can also be conceptualized as a moderator
variable. The famous Kinsey report on sexual behavior would certainly have generated very different results if
the interviews with participants about their sex lives had taken place in the presence of family members.
Identification of relevant contextual variables has important implications for the design of a study because
such variables will affect the generalizability of research findings.
A mediating variable, on the other hand, tries to describe how or why rather than when or for whom effects will
occur by accounting for the relationship between the independent variable (the predictor) and the dependent
variable (the criterion). The mediator is the mechanism through which the predictor affects the outcome.2 As
such, one can think of mediators as process variables. For example, in the counseling psychology field,
maladaptive perfectionism can be regarded as either a moderating variable or a mediating variable (Wei,
Mallinckrodt, Russell, & Abraham, 2004). Conceptualized as a moderator, attachment anxiety could be seen
to exert negative effects on depressive mood only under conditions of high maladaptive perfectionism (i.e.,
there is a statistical interaction between maladaptive perfectionism and attachment anxiety). Conceptualized
as a mediator, maladaptive perfectionism acts as an intervening variable between attachment anxiety and
depressive mood (i.e., there is an indirect relationship between anxiety and depression). As Wei et al. stated,
It is possible for maladaptive perfectionism to serve as both an intermediate link in the causal chain
leading from attachment insecurity to depressive mood (i.e., as a mediator) and as a variable that alters
the strength of association between attachment insecurity and depressive mood (i.e., as a moderator). (p.
203)
The diagram in Figure 2.1 captures the distinction between moderating and mediating variables in a
theoretical model. In the case of mediation, the mediating variable (maladaptive perfectionism) is placed
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between attachment anxiety and depressed mood. In the case of moderation, the arrow from maladaptive
perfectionism points to another arrow, that from attachment anxiety to depressed mood, indicating that the
relationship between attachment anxiety and depressed mood depends on the level of maladaptive
perfectionism.
Figure 2.1 The Distinction Between Moderator and Mediator Variables, Represented in a Causal Diagram
Figure 2.2 Classification Plot Representing Moderation of Relationship Between Attachment Anxiety and
Depressed Mood, Moderated by Maladaptive Perfectionism
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Source: Author created using data from Wei, W., Mallinckrodt, B., Russell, D., & Abraham, W. T.
(2004). Maladaptive perfectionism as a mediator and moderator between adult attachment and
depressive mood. Journal of Counseling Psychology, 51(2), 201–212.
We have illustrated one potential moderated outcome in the classification plot shown in Figure 2.2. Note that
under conditions of low maladaptive perfectionism, there is only a small difference in depression between
those with low and high attachment anxiety; however, under conditions of high maladaptive perfectionism,
those with high levels of attachment anxiety are more likely to be depressed than those with low levels. In
other words, maladaptive perfectionism moderates the relationship between attachment anxiety and
depression, or, put another way, maladaptive perfectionism and attachment anxiety interact.
One research study is not likely to establish and verify all of the important elements of a complex conceptual
model. As one of our colleagues puts it, you would need a video camera to capture the entire Grand Canyon
on film, whereas the dissertation is more like a snapshot, perhaps of a mule and rider descending one small
section of one canyon trail. Yet the proposed model can provide a useful context for current and future
research studies. Most ambitious research studies rely heavily on just such theoretical models.
As you might imagine, a researcher is in no position to test a model of this scope in a single study. For
example, Gerald Patterson and his colleagues (Patterson, DeBaryshe, & Ramsey, 1989) spent many years
developing and testing a model to explain aggressive and deviant behavior among young males. The model
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hypothesizes that such antisocial behavior can be causally linked to disrupted parental discipline and poor
family management skills. Moreover, the relationship between these two sets of variables is not direct but is
mediated by a network of other variables. The process is thought to begin with parents “training” a child to
behave aggressively by relying on aversive behaviors in both punishment and negative reinforcement
contingencies. The inability of the parents to control coercive exchanges among family members constitutes
“training for fighting,” which leads, in turn, to aggressive behavior and poor peer relationships. This lack of
social skills generalizes to antisocial behavior in the classroom, which makes it next to impossible for the youth
to obtain basic academic skills, thus preparing him poorly to cope with life outside school. Ultimately, this set
of factors leads to high rates of delinquent behavior. An abbreviated summary of one version of the model is
shown in Figure 2.3.
Figure 2.3 A Model of Antisocial Behavior
Source: From Patterson, G. R., DeBaryshe, B. D., & Ramsey, E. A developmental perspective on
antisocial behavior. American Psychologist, 44. Copyright © 1989, American Psychological Association.
Reprinted with permission.
Over the years that Patterson and his colleagues (Patterson et al., 1989) spent elaborating the nature of these
relationships, they conducted numerous studies that each constituted a “snapshot” of one aspect of this
complex model, perhaps focusing on a particular set of relationships. For instance, the investigator might ask
whether a relationship exists between physical fighting and poor peer relationships. Each variable would have
to be operationalized, probably by obtaining more than one measure of both fighting and peer relationships.
In Patterson’s work, he asked mothers, peers, and teachers to rate levels of physical fighting because their
perspectives might differ. Likewise, peers, teachers, and self-reports are used to obtain measures of peer
relations. The objective of the study—that is, to determine the nature and form of the relationship between
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the primary variables—determines the research method that is employed. In the early years of his career,
Patterson focused on the relationships among contextual variables, parental beliefs, parenting practices, and
child outcomes. He concluded that parenting practices—such as discipline, monitoring, problem solving,
involvement, and positive reinforcement—serve as mediating variables between parenting beliefs and attitudes
and the child’s behavior. Once this model was supported by sufficient data, Patterson proceeded to establish
links between children’s behavior problems and subsequent chronic juvenile and adult offending (Reid,
Patterson, & Snyder, 2002).
Whether or not a particular dissertation is designed to test a theory or model derived from the research
literature, we believe that the creation of a visual model, which shows how the network of relevant variables
and constructs may be related to one another, can serve as a powerful tool for guiding the study. Arranging
your ideas spatially helps to organize your thinking, which in turn helps position your proposed study within a
larger framework.
Research models are developed to account for the relationships among variables at a conceptual level and then
used to guide the construction of research designs by which the relationships will be tested, usually, but not
always, using contemporary statistics. The process is iterative so that the models are modified on the basis of
data and then reevaluated in further studies. Two primary types of relationships can be identified and explored
within a causal model (Jaccard & Jacoby, 2010): predictive relationships and causal relationships. A predictive
relationship implies that an association or correlation exists between two (or more) variables without assuming
that one causes the other. For example, we may determine that traveling frequently as a child is related to
(predicts) being more proficient in languages as an adult, without knowing whether travel actually causes
improvement in language skills. Above, we used the terms independent variables and predictor variables more or
less interchangeably. Strictly speaking, however, when the issue is prediction, the relationship is between one
or more predictor variables and a criterion variable (Jaccard & Jacoby, 2010).
Causal relationships imply that one variable “causes” another; that is, changes in the primary variable, usually
referred to as the independent variable, elicit changes in the second variable, the dependent variable or outcome
variable. Although the principle of causation is the foundational bedrock for the experimental method in
social science research, philosophers of science have argued for centuries whether causality can ever be truly
demonstrated. Jaccard and Jacoby (2010) made a persuasive argument that the concept of causality is
ultimately a heuristic that enables us to maintain an organized view of our world and of human behavior. By
inferring causality, we can identify systematic relationships between variables and produce socially significant
changes by manipulating some variables to influence others. Whether or not causality can be definitively
demonstrated, much of contemporary research is conducted to give us confidence in theoretical models that
purport causal relationships.
There are several types of causal relationships, and each type can play a role in developing a causal model.
How causal models can be employed and evaluated in dissertations is a subject for a later chapter (Chapter 6).
How to think about and construct causal models as a way of describing research ideas constitutes much of
Jaccard and Jacoby’s (2010) very useful book, and the following discussion is stimulated by their work. Jaccard
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and Jacoby observed that most researchers begin by identifying an outcome variable that they want to
understand better. An example might be the level of concern people express for the environment, including by
engaging in behaviors that are environmentally sensitive, such as recycling waste or reducing pollution. The
next challenge is to identify some variables that could potentially influence or relate to environmentally
sensitive behavior. One could imagine a study, for example, that seeks to determine which interventions would
increase the motivation to recycle trash (or, more modestly, just to understand differences between those who
readily recycle and those who do not). Perhaps we predict that having a neighborhood trash collection system
that mandates sorting trash into recyclable and nonrecyclable categories will directly affect environmentally
pro-social behavior. Of course, not all studies begin by identifying a dependent variable; it is also possible to
choose an independent variable and speculate about its effects. For instance, a study might address the
implications of working in a highly polluting industry for health, socioeconomic status, and social
relationships.
Indirect causal relationships have an effect through the influence of an intermediary variable, which we have
referred to above as a mediating variable. Moderated causal relationships are a third type of causal relationship,
again defined earlier in the chapter. Most causal models contain a combination of the various types of
relationships. Models can get very complex because of the number of variables and their subtle relationships
with one another. Thus, simple path diagrams evolve into sophisticated theoretical networks. Many of these
models have been developed over the course of a career by dedicated researchers, such as Gerald Patterson,
who started with studies that explored subsets of variables within a model that then evolved in complexity.
Because computer software allows for the manipulation of multiple variables simultaneously and relatively
effortlessly, researchers usually begin by proposing one or more theoretical models and evaluating them
empirically.
One example of a research model comes from the dissertation of Bill MacNulty (2004), one of our doctoral
students. MacNulty generated this model from existing research literature and then tested it empirically using
a number of well-validated self-report scales. The study employed the schema-polarity model of psychological
functioning to assess how self-schemas (cognitive representations of self and others) influence the experience
of gratitude and forgiveness and whether these variables mediate relationships between self-schemas and
physical health and well-being. The model is summarized in Figure 2.4. The plus and minus signs refer to the
direction of the hypothesized relationships among the variables. Although the results supported most of the
initial hypotheses, the proposed model needed to be amended to accommodate the data. This is typical of the
research enterprise, in which theories and conceptual models are continually tested and refined to serve as
increasingly sophisticated representations of real-life phenomena.
Another dissertation example comes from Jenny Knetig’s (2012) study of active duty military personnel who
are at risk of experiencing symptoms of posttraumatic stress disorder (PTSD). Knetig speculated that being
psychologically minded (a component of what is referred to as having a mentalizing capacity) allows some soldiers
to perceive and interpret cognitive and affective states of themselves and others in a way that might facilitate
resilience and help-seeking behaviors, which, in turn, mitigate how they are affected by severe stress. At the
outset of her study, on the basis of the available literature and her own experience, Knetig postulated that the
25

relationships among these variables might look something like the diagram in Figure 2.5.
Figure 2.4 A Theoretical Framework Presented as a Causal Diagram
Source: From Self-Schemas, Forgiveness, Gratitude, Physical Health, and Subjective Well-Being, by W.
MacNulty, 2004, unpublished doctoral dissertation, Fielding Graduate University, Santa Barbara, CA.
Copyright 2004 by W. MacNulty. Reprinted with permission of the author.
At the conclusion of the study, after collecting her data and performing a number of statistical analyses
(canonical correlation analysis), Knetig amended her proposed model, as shown in Figure 2.6, to reflect the
experiences of her participants more accurately.
Figure 2.5 Proposed Mediational Model Relating Psychological Mindedness to PTSD
Source: Knetig, 2012, p. 52. Reprinted with permission of the author.
In short, the data suggested that soldiers who are more psychologically minded are less apt to conceal their
thoughts and feelings. It also suggested that the relationship between psychological mindedness and
symptoms is mediated by self-concealment.
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Figure 2.6 Final Mediation Model Relating Psychological Mindedness to PTSD
Source: Knetig, 2012, p. 54. Reprinted with permission of the author.
27

Generating Researchable Questions
To help students generate researchable questions from their interesting ideas, we use a brainstorming exercise
that begins with labeling one or two variables and generating a second or third. Brainstorming consists of
openly and noncritically listing all possible ideas in a given period of time. Later you can return to a more
critical analysis of each idea and delete those that are uninteresting, not meaningful, or impractical.
Ultimately, of course, it is contact with the literature that determines whether or not a research question is
viable, because the literature houses the scholarly inquiry that goes beyond the limits of your own knowledge.
We suggest that you do this brainstorming exercise in a small group so that the person receiving the
consultation merely serves as a scribe to record the ideas thrown out by the other group members (see Box
2.1). After 5 or 10 minutes, move on to the next person’s partially formed research topic. We generally use
this exercise in groups of three or four so that group members can frequently shift groups and draw on the
spontaneous reactions of a larger number of peers, uncontaminated by prior ideas or a particular mind-set.
The exercise involves suspending critical thinking and allowing new ideas to percolate. It should especially suit
divergent thinkers, who will find the demand to be expansive in their thinking exciting and creative.
Convergent thinkers may experience the exercise as a bit overwhelming, but they will find fulfillment in other
stages of the research process that demand compulsivity, care, and precision. Every chapter of a dissertation
contains both divergent and convergent elements.
Note that not all worthwhile research studies focus on three (or more) primary variables. Many studies look at
the relationship between two variables or concepts, and a few descriptive studies make do with one variable or
construct. The latter generally occurs in the early stages of research in an area, when little is known about a
topic. Some investigators are pathfinders in terms of opening up new topics of research by trying to
understand as much as possible about a phenomenon and generating more informed hypotheses for others to
test in the future. Nevertheless, we believe that most students underestimate what is currently known about
most topics and that the most interesting, practical, and theoretically meaningful studies are likely to consider
relationships among several variables.
Our exercise for generating research questions is only one option. Jaccard and Jacoby (2010) have listed 26
different heuristics for how to think creatively about questions or phenomena that may be of interest to you.
Items on their list include analyzing your own experiences, using case studies, interviewing experts in the field,
role-playing, and conducting thought experiments. The adoption of thought experiments might be
particularly helpful. Jaccard and Jacoby viewed these as experiments that you conduct in your mind as if you are
really obtaining the data and analyzing the results. You can then play with the possibilities of adding new
variables or introducing different contingencies into the situation. One contingency is the introduction of so-
called counterfactuals into the thought experiment (Tetlock & Belkin, 1996). Counterfactuals refer to “what-
might-have-been” scenarios, such as what might happen if parents rather than administrators ran the public
schools. Researchers can use this strategy to address theoretical alternatives that might otherwise not be raised
or appreciated.
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We conclude this chapter with the outline in Box 2.2, which asks you to look at the kinds of issues that need
to be considered and responded to during the course of developing the research proposal. By and large, your
dissertation committee will need to be convinced of three things to be comfortable with your proposal:
1. Is the question clear and researchable, and will the answer to the question extend knowledge in your
field of study?
2. Have you located your question within the context of previous study such that you have demonstrated a
mastery and consideration of the relevant background literature?
3. Is the proposed method suitable for exploring your question?
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Box 2.1 Brainstorming Exercise
Begin by defining one or two variables (or constructs) of interest. Then generate a list of additional variables (or constructs) that in
some way amplify the original variables or illuminate the relationship between them. The new variables you list may be independent
variables, dependent variables, moderating variables, or mediating variables in the research questions you eventually select. After
brainstorming this list, go back and eliminate those variables that do not interest you or do not seem promising to pursue. Finally,
see if you can now define one or more research questions that speak to the relationship among the two or three variables (or
constructs) you have specified. Ultimately, each of these variables will need to be operationally defined as you develop your research
study.
Here are some examples of the application of this brainstorming exercise to topics from different disciplines.
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Political Science
Begin with an interest in citizen participation in city council meetings. List variables or phenomena that might influence, be
influenced by, or be related to this variable. A sample research question is “What is the impact of citizen participation in city council
meetings on legislative decision making?”
31

Education
Begin with an interest in single mothers who return to school while receiving Aid to Families with Dependent Children (AFDC).
List variables or phenomena that might influence, be influenced by, or be related to this variable. A sample research question is
“What is the effect of the availability of child care on whether single mothers receiving AFDC return to school?”
32

Criminal Justice
Begin with an interest in the relationship between neighborhood crime watch programs and robbery rates. List variables that might
influence or amplify the relationship between these two variables. A sample research question is “What is the effect of neighborhood
crime watch programs, in both urban and rural environments, on the rate of burglaries?”
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Psychology
Begin with an interest in the relationship between physical attractiveness and self-esteem. List variables that might amplify or
influence the relationship between these two variables. A sample research question is “What is the role of body image and physical
attractiveness in self-esteem?” Another sample research question is “What is the role of body image in mediating the relationship
between physical attractiveness and self-esteem?”
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Selecting a Suitable Topic: Student Suggestions
Over the years, our students have provided many useful suggestions for completing a dissertation. Here are some of the suggestions
they have offered to one another that pertain to the earliest stages of developing a dissertation. Other suggestions are noted at the
appropriate places in subsequent chapters.
1. Start a computer file where you can store good ideas for future reference. Use the file for noting books and articles to get
from the library, good quotations, inspirations for future studies, half-baked notions that might be useful in the future, and
so on.
2. Think of your topic as a large jigsaw puzzle with a piece missing. That piece is what you want to research to fill in the gap in
your field. To discover which piece is missing, you must read as much of the literature as possible in your field.
3. Before you begin, read several well-written dissertations recommended by your chairperson.
4. As you progress through the dissertation process and your question shrinks due to the necessity of maintaining a manageable
project for one person, don’t lose heart. Even very small questions can serve much larger purposes.
5. To keep the perspective of meaningfulness throughout, keep imagining an audience of individuals who would want to know
the results of your work. Even if you can imagine only 25 people in the world who would care, keep that group alive in front
of your eyes.
6. A few students and I have found that beginning dissertation work early on and setting “mile marker dates” is very helpful.
This has helped us keep each other accountable by regular “check-ins” with each other. Sharing these dates with friends and
family as well as posting them in a conspicuous place so you see them pretty much daily has helped as well.
7. Make contact with researchers who may be of interest to you. It cannot hurt, and it might be quite helpful.
8. Travel to at least one professional meeting where research in your area of interest is being presented. I went to a paper
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presentation in my area of family violence. I realized that I knew more about my topic than I thought and shared my research
ideas with one of the presenters. I came home reinvigorated and anxious to complete my dissertation.
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Box 2.2 Outline of Issues for a Student Researcher to Complete in the
Development of the Research Proposal
37

Review of the Literature
The classic, definitive, or most influential pieces of research in this area are . . .
The journals that specialize in the kind of research in which I propose to engage are . . .
The body(ies) of research to which I wish to add is (are) . . .
The experts in the field of my research are . . .
38

Statement of the Problem
The intellectual problem(s) I may help solve through this research is (are) . . .
The moral, political, social, or practical problem(s) I may help alleviate through this research is (are) . . .
39

Method
The method I propose to use to answer my question, prove my point, or gain more detailed and substantive knowledge is . . .
An alternative way to do this study would be . . .
Three important research studies that have been carried out using the method I propose are . . .
The reason(s) this method is a good one for my question, proposition, or subject is (are) . . .
Possible weaknesses of this method are . . .
The skills I will need to use this method are . . .
Of these skills, I still need to acquire . . .
I propose to acquire these skills by . . .
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Notes
1. A construct is a concept used for scientific purposes in building theories. Constructs (e.g., self-esteem), like
concepts, are abstractions formed by generalizing from specific behaviors or manipulations. When constructs
are operationalized in such a way that they can be “scored” to take on different numerical values, they are
referred to as variables.
2. There has been extensive discussion in recent literature regarding the definition of, appropriate statistical
analyses for, and interpretation of moderating and mediating effects. We recommend the following
discussions: Frazier, Tix, and Barron, 2004; Hayes, 2009; Jaccard and Jacoby, 2010; Kazdin, 2007; Kim, Kaye,
and Wright, 2001; and Preacher and Hayes, 2008.
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3 Methods of Inquiry Quantitative and Qualitative Approaches
The principal characteristic of scholarly and scientific inquiry—as opposed to informal, intuitive kinds of
inquiry—is the use of rationally grounded procedures to extend knowledge that a community of scholars
regards as reliable and valid. The dissertation process is a ritual of socialization into that community of
scholars, so it is necessary for you, as a student, to master the scholarly procedures within your discipline. The
specific methods chosen to attack a problem will depend on your discipline and the nature of the specific
problem. There is no universally accepted approach within the social sciences, although there are rich research
traditions that cannot be ignored, as well as a common understanding that chosen methods of inquiry must
rest on rational justification. This means that scientific methods differ from more informal methods of inquiry
by their reliance on validated public procedures that have been determined to produce reliable knowledge.
Currently, there are many disagreements in the social sciences regarding what constitutes knowledge and the
procedures for gaining it. One way to think about how research generally contributes to the knowledge base of
a discipline is in terms of the following three-level hierarchy of knowledge, suggested by our colleague
Marilyn Freimuth.
Axiologic/Epistemic Level.
This is the underlying level of basic world hypotheses that form the foundation for content and method
within a field of inquiry. Epistemology refers to the study of the nature of knowledge, whereas axiology refers
to the study of ethics, values, and aesthetics. Examples of constructs at this level include the explanatory
principle of cause and effect and the notion of open systems.
Theoretical Level.
This is the level of models and theories. Theories are premises to account for data or, more informally,
explanations of how things work based on data. Examples are the theory of loss aversion in economics
(Tversky & Kahneman, 1991) and the five-factor theory of personality in psychology (McCrae & Costa,
2003). The distinction between theories and models is murky because these terms are often used
interchangeably within the social sciences. At the most basic level, both theories and models refer to
relationships between concepts. For our purposes, the term model refers to a higher-order theory, that is, a
representational system at a higher level of abstraction that can inform and be informed by alternative
theories. (This concept is similar to the framework or worldview that guides researchers, identified as a
“paradigm” by Thomas Kuhn [1996].) Thus, psychoanalysis could be seen as a model, a wide lens with which
to view and understand the mysteries of human behavior. Each model carries with it certain sets of
assumptions. In the case of psychoanalysis, these assumptions include the unifying importance of causal
determinism and unconscious motivation. Note that this use of the term model differs somewhat from that in
the discussion of working models in Chapter 2.
Empirical Level.
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In the field of epistemology, empiricism refers to a commitment to obtaining knowledge through sense
experience (literally, “based on experience” in Greek). Empiricism is frequently contrasted with rationalism,
which refers to knowledge derived purely through thought and reason, and to more natural philosophical and
religious traditions of reaching conclusions. In the present context, the empirical level includes hypotheses and
methods and data of scientific research. Hypotheses are tentative answers to questions, generally based on
theory.
The primary role of research within this three-level schema is to link the theoretical and the empirical.
Theories need the support of data to remain viable, whereas methods carry assumptions that are theoretical in
nature. Note that research findings do not contribute directly to the axiologic/epistemic level or even to basic
models. Those levels reflect fundamental value commitments and personal preferences that are rarely modified
on the basis of additional data, especially the kind of data generated by scholarly research. It is hard to imagine
a psychoanalyst becoming a behaviorist or a Republican joining the Democrats without a significant shift in
values unlikely to be compelled by the accumulated wisdom imparted by a series of research studies. Because
most researchers strongly identify with particular values and carry many personal preferences into their work,
it becomes especially important to learn to discriminate between beliefs and opinions, on the one hand, and
verifiable, data-inspired support for ideas, on the other hand.
A brief look at the history of science is a humbling experience that should put to rest the misguided notion
that research discovers truth. Drilling holes in the skull (trephining) used to be an acceptable way to dismiss
the demons responsible for mental illness, and it wasn’t that long ago that the sun was thought to circle the
earth. One wonders what remnants of contemporary scientific truth will be regarded as equally ludicrous
tomorrow. Instead, what research contributes is a series of thoughtful observations that support or question
the validity of theories, which are in turn based on a set of largely untestable beliefs and assumptions. Every
once in a while, at opportunistic moments of scholarly upheaval, a new paradigm appears that seems to do a
better job of explaining the available data and guiding further inquiry.
Each social science discipline and set of investigators seems to have its own favored approach to generating
knowledge. For instance, public opinion studies usually rely on survey research methods, psychoanalytic
studies of infants make use of observational techniques, studies of organizational effectiveness may employ
action research methods and case studies, historical investigations of political and social events rely on archival
records and content analysis, and laboratory studies of perceptual processes stress experimental manipulation
and hypothesis testing. Within your chosen field, it is important to ask how a piece of research acquires
legitimacy as reliable knowledge. No doubt part of the answer comes down to underlying epistemological
assumptions and values. Certainly research strategies will differ in terms of the problems they address and the
outcomes they produce. As we later show, one important distinction in the choice of method seems to be the
nature of the relationship between the researcher and the topic of study.
We would argue that researchers in the social sciences have generally been myopic in defining the kinds of
studies that might legitimately lend themselves to research dissertations. Most students in the social sciences
are taught early on about the difference between independent and dependent variables and how experimental
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research implies active manipulation of independent variables to observe a subsequent impact on dependent
variables. This basic and time-honored strategy has an earthy history in the systematic evaluation of fertilizers
for agricultural productivity (Cowles, 2000). It remains a cornerstone in conducting social science research
with human subjects. Yet it is certainly not the only way to conduct research.
The only universal in scientific knowledge is a general commitment to using logical argument and evidence to
arrive at conclusions that are recognized as tentative and subject to further amendment. Good scientists in
action often deviate from an “official” philosophy of science and a prescribed methodology. William Bevan
(1991), former president of the American Psychological Association, noted,
If you want to understand what effective science making is about, don’t listen to what creative scientists
say about their formal belief systems. Watch what they do. When they engage in good, effective science
making they don’t, as a rule, reflect on their presuppositions; they engage in a practical art form in which
their decisions are motivated by the requirements of particular problem solving. (p. 478)
The key to evaluating a completed study is to assess whether the selected method is sufficiently rigorous and
appropriate to the research question and whether the study is conceptually and theoretically grounded. The
more familiar you are with the full range of alternative research strategies, the more enlightened and
appropriate your choice of a particular method is apt to be. Too often, students become so enamored with an
approach to research that they choose the method before determining the question. Unless the dissertation is
designed to illustrate the use of a promising and innovative methodology, this is putting the cart before the
horse. In general, the method needs to evolve out of the research question and be determined by it.
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Quantitative Methods
The epistemological foundation of most social science inquiry throughout the 20th century was logical
positivism, a school of thought that maintains that all knowledge is derived from direct observation and
logical inferences based on direct observation. To a great extent, the notion of objectively studying human
beings is derived from a love affair that social scientists have had with the natural sciences, which sought to
understand nature by isolating phenomena, observing them, and formulating mathematical laws to describe
patterns in nature. Current research in the social sciences is deeply steeped in the empirical and quantitative
traditions.
Statistical methods are especially useful for looking at relationships and patterns and expressing these patterns
with numbers. Descriptive statistics describe these patterns of behavior, whereas inferential statistics draw on
probabilistic arguments to generalize findings from samples to populations of interest. Kerlinger (1977)
focused on the inferential process when he defined statistics as
the theory and method of analyzing quantitative data obtained from samples of observations in order to
study and compare sources of variance of phenomena, to help make decisions to accept or reject
hypothesized relations between the phenomena, and to aid in making reliable inferences from empirical
observations. (p. 185)
Note that the focus in the natural science model of research is the study of average or group effects, not of
individual differences. The kinds of inferential statements that derive from this model of research refer to
groups of people or groups of events; that is, they are probabilistic (e.g., “Surveys find that most people believe
that police officers use excessive force in dealing with criminals,” or “Emotional expressiveness is related to
coping effectively with natural disasters”).
In experimental research, quantitative research designs are used to determine aggregate differences between
groups or classes of subjects. Emphasis is placed on precise measurement and controlling for extraneous
sources of error. The purpose, therefore, is to isolate a variable of interest (the independent variable) and
manipulate it to observe the impact of the manipulation on a second, or dependent, variable. This procedure is
facilitated by the “control” of extraneous variables, thus allowing the researcher to infer a causal relationship
between the two (or more) variables of interest.
Methodological control is generally accomplished by two procedures that rely on the principle of randomness.
One is random sampling, which uses subjects that have randomly been drawn from the potential pool of
subjects so that each member of the population has an equal chance or known probability of being selected.
Random selection of subjects permits the researcher to generalize the results of the study from the sample to
the population in question. The second procedure is randomization, which assigns subjects to groups or
experimental conditions in such a way that each subject has an equal chance of being selected for each
condition. Subject characteristics are thus randomly distributed in every respect other than the experimental
45

manipulation or treatment, allowing the researcher to infer that resultant differences between the groups must
be the result of the isolated variable in question.
Unfortunately, these efforts at experimental control are often impractical in social science research with
human subjects. Psychology, for instance, has an honorable tradition of laboratory research using tight
experimental designs, but research in the clinical or social arena may not permit the kind of control stipulated
by the experimental method. This dilemma is equally prominent in field studies in disciplines such as
sociology, education, and political science. Jared Diamond (2005), Pulitzer Prize–winning geographer and
biologist, for example, conducted quantitative “natural experiments” to investigate the problem of
deforestation on Pacific islands. He and his colleague Barry Rolett numerically graded the extent of
deforestation on 81 Pacific islands and statistically predicted this outcome from a combination of nine input
variables, such as the amount of rainfall, isolation from human populations, and restoration of soil fertility. In
a different context, one cannot practically conspire to rear children using two distinct parenting styles, nor can
one ethically inflict child abuse to study its immediate impact in a controlled fashion. Researchers can,
however, study analogs of these variables using pure experimental designs (e.g., one can ask parents to use
specific interventions at the onset of particular child behaviors). Change studies, in which a treatment or
program is being evaluated for its effectiveness, may also lend themselves well to experimental designs. Even
so, it may not be possible to randomize subjects into groups that receive a treatment or intervention and those
that do not. A number of ingenious solutions have been proposed to deal with the ethics of denying treatment
to those who need it, including the use of placebos and waiting-list controls (Kazdin, 2002).
More typically, the research method of choice in the social sciences seems to be a quasi-experimental design
that compromises some of the rigor of the controlled experiment but maintains the argument and logic of
experimental research (Kline, 2009; Shadish, Cook, & Campbell, 2001). This kind of research uses a
systematic, empirical approach in which the investigator does not employ a control group or does not
randomly assign subjects to conditions because events have already occurred or cannot be sufficiently
manipulated. So-called causal statements become correlational statements in quasi-experimental research,
although it is often possible to infer a sequence of events in causal form. That is one reason why it is crucial to
have a theoretical model as a foundation for an empirical study. The model itself informs your attempt to
meaningfully interpret the results of the study. However, because it is difficult to ascribe causality with
confidence from quasi-experimental designs that lack true experimental manipulation, independent variables
are often termed “predictor” variables in these studies (Kline, 2009).
Caution is also needed in interpreting the meaning of results whenever subjects assign themselves to groups. A
colorful example is the apparent negative correlation that exists between the numbers of mules found in the
various states and the number of PhDs living there. The fact that states that have a lot of mules don’t have so
many PhDs, and vice versa, is an empirical observation that can be statistically expressed in the form of a
correlation coefficient. A researcher would be hard-pressed to argue a causal relationship between these two
variables unless he or she drew on an underlying theoretical model that links the two variables through a third
(mediating) variable, such as the degree of urbanization. Note that this simple correlational study could, at
least theoretically, be transformed into an experimental study by, for example, flooding some states with mules
46

to see if the PhDs leave or wooing the PhDs across state lines to see if the number of mules in the new state
of residence decreases.
This is not a book on research design, but the adoption of a particular research strategy will affect the final
form of your dissertation. Whether a study employs a true experimental design, a quasi-experimental design,
or a cross-sectional survey design, the most popular strategy in the social sciences is a comparison between
groups. That is, independent (randomly assigned) groups of subjects are used for each experimental or control
condition. The best-known variant of this strategy, the pretest-posttest control group design, uses two
equivalent groups of subjects that both receive pretests and posttests and differ only in the experimental
treatment that is given to one group (see Table 3.1).
In this design, it becomes possible to evaluate the impact of an intervention because the control group offers a
baseline for comparison. One could use this design to evaluate, for example, whether the inclusion of spouses
in an aftercare program for heart bypass surgery patients encourages greater compliance with medical
regimens. Or one could design a study to evaluate the effect of introducing air bags in automobiles on the rate
of physical injury to passengers. Some automobiles of a given make would receive air bags, some would not,
and the change in types and rates of injuries would be the dependent measure.
The straightforward pretest–posttest–control group design makes it possible to attribute the effect of
experimental interventions to those interventions rather than to some extraneous variable. The interpretation
of results of studies using this design may be compromised, however, if the subjects have not been assigned to
conditions in a truly random manner. In the proposed air bag study, for example, if automobiles and drivers
are not randomly assigned to conditions, inherently safer drivers may well choose automobiles with better
safety features. Because randomization is not always possible, it becomes crucial to argue for the “equivalence”
of the two groups, even if they do not derive from the identical population of subjects. One way in which
researchers attempt to make this argument is by matching the groups on key variables that are critical to the
understanding of the study, such as age, sex, symptomatology, or, in the current example, the previous driving
records of the participants.
The basic pretest–posttest–control group design does not adequately control for any effect that the pretest
evaluations might have on the subjects. Some assessments can sensitize subjects by making them aware that
they are now participating in a study or by providing a practice experience that contaminates the validity of
posttest results. A simple posttest-only design may get around this difficulty and is probably underused
(Campbell & Stanley, 2005). In any case, choosing a basic research design does not eliminate the need for you
47

to think carefully and creatively about potential sources of error and alternative explanations to account for
findings.
Most experimental designs are variants of the treatment and control group format described earlier.1 Such
designs permit the researcher to make causal inferences regarding relationships among the variables. In
contrast, correlational (or observational) studies do not generally enable the researcher to demonstrate causal
relationships among variables. Any conclusions regarding causality must be inferred from the underlying
theory rather than from the results of the study.
Studies built around experimental or correlational designs generate data that are subsequently analyzed using
appropriate inferential statistics. Statistical techniques that are used to evaluate the effectiveness of an
intervention or a difference between groups, such as an analysis of variance (ANOVA) or t test, compare the
size of between-group differences (e.g., the treatment effect) with the size of within-group differences due to
individual variability. These techniques express the experimental tradition. The logic of the correlational
paradigm is quite different (Cronbach, 1975). Correlations depend on comparing two distributions of scores
that are broadly dispersed along two dimensions, such as longevity and alcohol use. Statistical techniques that
emerged from this tradition, such as multiple regression, are especially popular in social science research that
relies on questionnaires, surveys, or scales and the relationship between continuous variables. Because
correlational studies typically cannot randomly assign subjects to groups, we have a second major type of
control in social science research—statistical control. Statistical control attempts to use complex statistical
procedures to remove variability from measures of group difference or relationship that could be attributed to
variables other than the major independent variables of interest. Be aware, however, that it is the design of the
study and not the choice of statistical method that principally governs the types of statements that can be
made about the relationships among variables.
Both experimental and correlational traditions have a rightful place in the evaluation of quantitative data, and
a detailed comparison of them goes beyond the scope of this book. It is important to remember that although
statistics is an indispensable tool for scientific inference, the appropriate application of statistics cannot make
up for a faulty research design. In many instances, statistical methods drawn from both the experimental and
correlational paradigms are equally legitimate choices. In fact, the same data usually can be analyzed in
multiple ways. If you are looking at the relationship between locus of control and frequency of medical visits
for preventive health, for example, you could express this relationship using a correlation coefficient or by
dividing your sample into two or more subgroups on the basis of the personality construct of locus of control
and comparing the resulting groups on medical visits. Generally speaking, it is not a good idea to “throw
away” data (you are throwing away data if you arbitrarily reduce a continuum of locus of control scores to two
or more discrete values, such as internal or external categories), but these kinds of decisions require statistical
expertise and theoretical grounding.
Table 3.2 summarizes the methodological and statistical methods of controlling for extraneous factors in a
research design.
We wish to make two additional points regarding the use of quantitative research. One is that there is a
48

tendency in the social sciences to overemphasize the importance of statistically significant findings and to
underemphasize the importance of clinically or socially significant findings. In other words, simply because a
difference is significant at a certain probability level (typically,.05 or.01) does not mean that the difference is
meaningful in practical terms. For instance, a difference of 5 points on a depression scale might be statistically
meaningful but may not be meaningful clinically. Too often, students assume that the object of research is to
achieve statistical significance rather than to make meaningful inferences about behavior. The primary reason
that Jacob Cohen (1990), the father of power analysis, was drawn to correlational analyses is that they yield an
r, a measure of effect size. That is, unlike probability (p) values, correlation coefficients can straightforwardly
indicate the magnitude of the relationship between variables, which may be far more informative than the
presence or absence of statistical significance. Cohen went on to note that researchers sometimes learn more
from what they see than from what they compute, and he argued for an increased use of the graphic display of
data, using simple scatter plots and so-called stem-and-leaf diagrams before or instead of performing
complicated statistical analyses. (We have more to say about this topic in Chapter 6, our discussion of
strategies for presenting results.)
49

Second, as you consider the kinds of designs and controls that are available to the social science researcher, we
urge you to be aware of a fundamental dilemma. Good research is a constant balancing act between control
and meaningfulness. At one extreme is an emphasis on controlling the observation and measurement of a
variable by eliminating the influence of as many confounding variables as possible. What results might be a
tight laboratory study in which the findings inspire confidence but are not particularly interesting. At the
other extreme is the observation of complex human behavior in the field, without invoking any controls, so
that the results seem fascinating but are highly unreliable and difficult to replicate. The fashion in social
science research has moved back and forth between these poles of emphasizing precision of measurement and
generalizability of findings versus emphasizing depth of coverage and description of context. Today the
pendulum seems to be swinging in the direction of meaningfulness, hastened by the availability of a greater
number of permissible research strategies together with a reevaluation of research epistemology.
50

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