Fundamentals of Research Methodology

Fundamentals of Research
Methodology
Dr.S.Saravanan
M.Com., M.Phil., MBA.,MBA., PGDCA., Ph.D
Associate Professor
Post Graduate and Research Department of Commerce
with Computer Applications
Dr.N.G.P Arts and Science College
Coimbatore-641048
Tamilnadu.
Vanmathi Publishers
Kannan Nagar
Tiruppur – 641 604.
Tamil Nadu
vanmathipublishers@gmail.com

First edition 2011
Vanmathi Publishers
Kannan Nagar, Tiruppur - 641604. Tamil Nadu
This book or any part thereof may not be
Reproduced in any form without the
Written permission of the publisher
ISBN : 978-81-920808-0-2

Preface
The basic purpose of this book is to assist the readers to develop a
scrupulous understanding of the various concepts in a systematic way.
Although there are good number of standard books on “Research
Methodology” there are difference in the adoption and approaches of
the methods of applying the concepts and emphasis on lucidity.
The whole book has been written very carefully and each chapter has
been discussed in detail in order to meet the requirement of the
students. I sincerely hope that the students, learned teachers, research
scholars and other readers will find the book useful. However I shall
be grateful if the mistakes and deficiencies are pointed out by the
readers.
Constructive criticisms and suggestions for improvement of the book
are most welcome from researchers and students for further
development of the subject content as well as the presentation of this
book.
I fall short of words to express thank my family members and dear
ones, who have stood beside me while I immersed in writing this
book, oblivious of their needs for my time and attention.
I am extremely thankful to my students and learned colleagues in the
college for providing the essential stimulus for writing this book.
I am grateful to all those persons whose writings and works have
helped me in the preparation of this book. I am indebted to the

reviewer of this book whose invaluable inputs have made extremely
enhancing the value of the content.
My sincere gratitude is due to M/s Vanmathi publishers for their
tireless endeavor in bringing out this book well in time.
Last I record my gratitude to god almighty for giving me the power to
pen down this manuscript in its present shape.
Dr.S.SARAVANAN.

Dedicated to all my
Beloved family members

About the author
Dr.S.Saravanan M.Com., M.Phil., MBA.,MBA., PGDCA., Ph.D.,
is a Associate professor in Post Graduate and Research
Department of Commerce with Computer Applications at
Dr.N.G.P. Arts and Science College, Coimbatore, Tamilnadu,
India.
He has more than 10 years of experience in teaching and
research. An avid researcher having profound knowledge in SPSS
statistical research tools, he has published number of research
papers in various reputed national and international journals and
presented research papers in various conferences. He is also an
editor for the Journal of Commerce and Management Research.
His research interest has focused on Finance, Marketing and HR.

i
Chapter- I
Introduction 1-16
Definition of research
Types of research
a. Exploratory/ Formulative Research
b. Descriptive Research
c. Explanatory Research
d. Basic Research
e. Applied Research
Types of Applied Research
i) Action research
ii) Impact Assessment Research
iii) Evaluation Research
The Time Dimension in Research
a. Cross-Sectional Research
b. Longitudinal Research
i. Time series research
ii. The panel study
iii. A cohort analysis
Empirical research
Qualitative research
Quantitative research
Conceptual Research
Conclusion-oriented
Decision oriented Research
One-time Research
Diagnostic Research
Exploratory Research
Historical Research
Characteristics of Research

ii
Chapter- II
Research design 17-32
Definitions of research design
Steps in planning the research design
(1) Determining work involved in the project
(2) Estimating costs involved
(3) Preparing time schedule
(4) Verifying results
Importance / utility of research design
Feature of good research design
Steps of the research process
1: Identify the Problem
2: Review the Literature
3: Clarify the Problem
4: Clearly Define Terms and Concepts
5: Define the Population
6: Develop the Instrumentation Plan
7: Collect Data
8: Analyze the Data
Types of Research Design
Quantitative Research Designs
Qualitative Research Designs

iii
Chapter-III
Types of Variables 33-48
Independent Variable Definition
Dependent Variable Definition
Continuous variables
Discontinuous variables
Moderating Variables
Extraneous Variables
Intervening variable
Continuous or Quantitative Variables
Interval - scale Variables:
Continuous Ordinal Variables
Ratio - scale Variables
Qualitative or Discrete Variables
1. Nominal variables
2. Ordinal variables
3. Dummy variables from quantitative variables
4. Preference variables
5. Multiple response variables

iv
Chapter- IV
Sampling 49-86
Sample
Definition of sampling
Purpose of Sampling
Sampling Terminology
Population or Universe
Census
Precision
Bias
Frame
Design
Random
Unit
Attribute
Variable
Statistic
Steps in Sampling Process
1. Defining the target population
2. Specifying the sampling frame
3. Specifying the sampling unit
4. Selection of the sampling method
5. Determination of sample size
6. Specifying the sampling plan
7. Selecting the sample
Sampling Methods and Techniques

v
Probability Sampling
1. Simple random sample
2. Systematic random sample
3. Stratified sample
4. Cluster sample
Non-Probability Sampling
1. Convenient sample
2. Judgment sample
3. Quota sampling
Sample Size
Bias and error in sampling
Sampling error
The interviewer’s effect
The respondent effect
Knowing the study purpose
Induced bias
Error
Random Sampling Error
Non-Sampling error
Basic Concepts in Hypothesis Testing
Characteristics of Hypothesis
Important factors should be considered while frame hypothesis
1. Hypothesis should be clear and specific
2. Hypothesis should be competent of being tested
3. Hypothesis should be limited in scope
4. Hypothesis should be state the variables relationship
5. Hypothesis should be consistent with known facts
Types of Hypotheses

vi
i. Descriptive Hypothesis
ii. Relational Hypothesis
iii. Null Hypothesis
iv. Alternative Hypothesis
v. Research Hypothesis
The Role of the Hypothesis
Characteristics of Testable Hypothesis
Hypothesis must be conceptually clear
Hypothesis should have empirical referents
Hypothesis must be specific
Steps in Hypothesis Testing
Decision Rules
One-Tailed and Two-Tailed Tests

vii
Chapter - V
Tools for Data Collection 87-117
Mailed questionnaire
Rating scale
Checklist
Document schedule/data sheet
Schedule for institutions
Construction of schedules and questionnaires
The process of construction
Data need determination
Preparation of “Dummy” tables
Determination of the respondents’ level
Data gathering method decision
Instrument drafting
Evaluation of the draft instrument
Pre-testing
Specification of procedures/instructions
Designing the format
Question Construction
Question relevance and content
Types of questions to be avoided

viii
Types of surveys
Selecting the survey method
Population Issues
Sampling issues
Question issues
Pilot studies and pre-tests
Pre-test
Meaning
Need for Pre-testing
Purposes of Pre-testing
Advantages and disadvantages of various data collection
techniques

ix
Chapter - VI
Data Processing 119-138
Editing
Field Editing
In-House Editing
Editing for Consistency
Editing for Completeness
Item Non-response
Editing Questions Answered out of Order
Coding
Code Construction
Production Coding
Data Entries
Cleaning Data
Data Transformation
Indexes and Scales
Unidimensionality
Index Construction
Weighting
Scoring and Score Index

x
Chapter-VII
Report Writing 139-164
Types of Report Writing
 Research Report Writing
 Business Report Writing
 Science Report Writing
Different Steps in Report Writing
 Logical analysis of subject matter
 Preparation of final outline
 Preparation of Rough Draft
 Rewriting and Polishing
 Preparation of final Bibliography
 Writing the final draft
Mechanics of Report Writing
Title Page
Dedication
Acknowledgements
Table of Contents
Lists of Illustrations
Elements of research report
Appendix
Multiple Choice Questions 166-203
Selected References 204-216

Fundamentals of Research Methodology 1
Chapter- I
Introduction
Definition of research
Types of research
a. Exploratory/ Formulative Research
d. Basic Research
e. Applied Research
i) Action research
ii) Impact Assessment Research
iii) Evaluation Research
The Time Dimension in Research
a. Cross-Sectional Research
b. Longitudinal Research
i. Time series research
ii. The panel study
iii. A cohort analysis
Empirical research
Quantitative research
Conceptual Research
Conclusion-oriented
Decision oriented Research
One-time Research
Diagnostic Research
Exploratory Research
Historical Research

2
Chapter- I
What is Research
General image of the research is that it has something to do with the
laboratory where scientists are supposedly doing some experiments.
Somebody who is interviewing consumers to find out their opinion
about the new packaging of milk is also doing research. Research is
simply the process of finding solutions to a problem after through
study and analysis of the situational factors. It is gathering
information needed to answer a question, and thereby help in solving
a problem. We do not do study in any haphazard manner. Instead we
try to follow a system or a procedure in an organized manner. It is all
the more necessary in case we want to repeat the study, or somebody
else wants to verify our findings. In the latter case the other person
has to follow the same procedure that we followed. Hence not only
we have to do the study in a systematic manner but also that system
should be known to others.
Research can be defined as “a careful study to discover correct
information” or “a way of collecting information to facilitate
problem solving”. In most simple words, it is “search and search
again”.
Formal definition as given in Encarta Dictionary is: “A methodical
investigation into a subject in order to discover facts, to establish or
revise a theory, or to develop a plan of action based on the facts
discovered.”

Research can be defined as the search for knowledge, or as any
systematic investigation, with an open mind, to establish novel facts,
usually using a scientific method. The primary purpose for applied
research (as opposed to basic research) is discovering, interpreting,
and the development of methods and systems for the advancement of
human knowledge on a wide variety of scientific matters of our
world and the universe.
As per books of Business Research, the definition is: “An organized,
systematic, data-based, critical, scientific inquiry or investigation
into a specific problem, undertaken with the objective of finding
answers or solution to it.”
 Prof. C.A. Moser defined it as “systematized investigation to
give new knowledge about social phenomena and surveys, we
call social research”.
 Rummel defined it as “it is devoted to a study to mankind in his
social environment and is concerned with improving his
understanding of social orders, groups, institutes and ethics”.
 M.H. Gopal defined it as “it is scientific analysis of the nature
and trends of social phenomena of groups or in general of
human behavior so as to formulate broad principles and
scientific concepts”.
Therefore, research may be considered as an organized, systematic,
data based, critical, objective, scientific inquiry or investigation into
a specific problem, undertaken with the purpose of finding answers
or solutions to it. In this way research provides the needed

4
information that guides the planners to make informed decisions to
successfully deal with the problems. The information provided could
be the result of a careful analysis of data gathered firsthand or of the
data that are already available with an organization.
Types of Research
a. Exploratory/Formulative Research
Initial research conducted to clarify the nature of the problem. When
a researcher has a limited amount of experience with or knowledge
about a research issue, exploratory research is useful preliminary step
that helps ensure that a more rigorous, more conclusive future study
will not begin with an inadequate understanding of the nature of the
management problem. Exploring a new topic or issue in order to
learn about it. If the issue was new or the researcher has written little
on it, you began at the beginning. This is called exploratory
research. The researcher’s goal is to formulate more precise
questions that future research can answer. Exploratory research
rarely yields definitive answers. It addresses the “what” question:
“what is this social activity really about?” It is difficult to conduct
because there are few guidelines to follow.
Specifically there could be a number of goals of exploratory
research.
Goals of Exploratory Research
1. Become familiar with the basic facts, setting, and concerns;
2. Develop well grounded picture of the situation;

3. Develop tentative theories; generate new ideas, conjectures,
or hypotheses;
4. Determine the feasibility of conducting the study;
5. Formulate questions and refine issues for more systematic
inquiry; and
6. Develop techniques and a sense of direction for future
research.
For exploratory research, the researcher may use different sources for
getting information like (1) experience surveys, (2) secondary data
analysis, (3) case studies, and (4) pilot studies.
As part of the experience survey the researcher tries to contact
individuals who are knowledgeable about a particular research
problem. This constitutes an informal experience survey.
Another economical and quick source of background information is
secondary data analysis. It is preliminary review of data collected for
another purpose to clarify issues in the early stages of a research
effort.
The purpose of case study is to obtain information from one or a few
situations that are similar to the researcher’s problem situation. A
researcher interested in doing a nationwide survey among union
workers, may first look at a few local unions to identify the nature of
any problems or topics that should be investigated.

6
A pilot study implies that some aspect of the research is done on a
small scale. For this purpose focus group discussions could be
carried out.
Descriptive research presents a picture of the specific details of a
situation, social setting, or relationship. The major purpose of
descriptive research, as the term implies, is to describe characteristics
of a population or phenomenon. Descriptive research seeks to
determine the answers to who, what, when, where, and how
questions. Labor Force Surveys, Population Census, and Educational
Census are examples of such research. Descriptive study offers to the
researcher a profile or description of relevant aspects of the
phenomena of interest. Look at the class in research methods and try
to give its profile – the characteristics of the students. When we start
to look at the relationship of the variables, then it may help in
diagnosis analysis. In social science and business research we quite
often use the term Ex post facto research for descriptive research
studies. The main characteristic of this method is that the researcher
has no control over the variables; he can only report what has
happened or what is happening.
Goals of Descriptive Research
1. Describe the situation in terms of its characteristics i.e.
provide an accurate profile of a group;
2. Give a verbal or numerical picture (%) of the situation;
3. Present background information;

4. Create a set of categories or classify the information;
5. Clarify sequence, set of stages; and
6. Focus on ‘who,’ ‘what,’ ‘when,’ ‘where,’ and ‘how’ but not
why?
A great deal of social research is descriptive. Descriptive
researchers use most data –gathering techniques – surveys,
field research, and content analysis
When we encounter an issue that is already known and have a
description of it, we might begin to wonder why things are the way
they are. The desire to know “why,” to explain, is the purpose of
explanatory research. It builds on exploratory and descriptive
research and goes on to identify the reasons for something that
occurs. Explanatory research looks for causes and reasons. For
example, a descriptive research may discover that 10 percent of the
parents abuse their children, whereas the explanatory researcher is
more interested in learning why parents abuse their children.
Goals of Explanatory Research
1. Explain things not just reporting. Why? Elaborate and enrich
a theory’s explanation.
2. Determine which of several explanations is best.
3. Determine the accuracy of the theory; test a theory’s
predictions or principle.
4. Advance knowledge about underlying process.

8
5. Build and elaborate a theory; elaborate and enrich a theory’s
predictions or principle.
6. Extend a theory or principle to new areas, new issues, new
topics:
7. Provide evidence to support or refute an explanation or
prediction.
8. Test a theory’s predictions or principles
d. Basic Research
Basic research advances fundamental knowledge about the human
world. It focuses on refuting or supporting theories that explain how
this world operates, what makes things happen, why social relations
are a certain way, and why society changes. Basic research is the
source of most new scientific ideas and ways of thinking about the
world. It can be exploratory, descriptive, or explanatory; however,
explanatory research is the most common. Basic research generates
new ideas, principles and theories, which may not be immediately
utilized; though are the foundations of modern progress and
development in different fields. Fundamental research is mainly
concerned with generalisations and with the formulation of a theory.
“Gathering knowledge for knowledge’s sake is termed ‘pure’ or
‘basic’ research.” Research concerning some natural phenomenon or
relating to pure mathematics are examples of fundamental research.
A new idea or fundamental knowledge is not generated only by basic
research. Applied research, too, can build new knowledge.
Nonetheless, basic research is essential for nourishing the expansion

of knowledge. Researchers at the center of the scientific community
conduct most of the basic research.
e. Applied Research
Applied researchers try to solve specific policy problems or help
practitioners accomplish tasks. Theory is less central to them than
seeking a solution on a specific problem for a limited setting.
Applied research is frequently a descriptive research, and its main
strength is its immediate practical use. Applied research is conducted
when decision must be made about a specific real-life problem.
Applied research encompasses those studies undertaken to answer
questions about specific problems or to make decisions about a
particular course of action or policy. For example, an organization
contemplating a paperless office and a networking system for the
company’s personal computers may conduct research to learn the
amount of time its employees spend at personal computers in an
average week. Research to identify social, economic or political
trends that may effect a particular institution or copy research or the
marketing research are examples of applied research. Thus, the
central aim of applied research is to discover a solution for some
pressing practical problems. Thus, the central aim of applied
research is to discover a solution for some pressing practical
problems.
Practitioners use several types of applied research. Some of the major
ones are:

10
i) Action research: The applied research that treats knowledge as a
form of power and abolishes the line between research and social
action. Those who are being studied participate in the research
process; research incorporates ordinary or popular knowledge;
research focuses on power with a goal of empowerment; research
seeks to raise consciousness or increase awareness; and research is
tied directly to political action. The researchers try to advance a
cause or improve conditions by expanding public awareness. They
are explicitly political, not value neutral. Because the goal is to
improve the conditions of research participants, formal reports,
articles, or books become secondary. Action researchers assume that
knowledge develops from experience, particularly the experience of
social-political action. They also assume that ordinary people can
become aware of conditions and learn to take actions that can bring
about improvement.
ii) Impact Assessment Research: Its purpose is to estimate the likely
consequences of a planned change. Such an assessment is used for
planning and making choices among alternative policies to make an
impact assessment of Narmatha Dam on the environment; to
determine changes in housing if a major new highway is built.
iii) Evaluation Research: It addresses the question, “Did it work?”
The process of establishing value judgment based on evidence about
the achievement of the goals of a program. Evaluation research
measures the effectiveness of a program, policy, or way of doing
something. “Did the program work?”

“Did it achieve its objectives?” Evaluation researchers use several
research techniques (survey, field research). Practitioners involved
with a policy or program may conduct evaluation research for their
own information or at the request of outside decision makers, who
sometime place limits on researchers by setting boundaries on what
can be studied and determining the outcome of interest. Two types of
evaluation research are formative and summative. Formative
evaluation is built-in monitoring or continuous feedback on a
program used for program management. Summative evaluation looks
at final program outcomes. Both are usually necessary.
3. The Time Dimension in Research
Another dimension of research is the treatment of time. Some studies
give us a snapshot of a single, fixed time point and allow us to
analyze it in detail. Other studies provide a moving picture that lets
us follow events, people, or sale of products over a period of time. In
this way from the angle of time research could be divided into two
broad types:
a. Cross-Sectional Research. In cross-sectional research, researchers
observe at one point in time. Cross-sectional research is usually the
simplest and least costly alternative. Its disadvantage is that it cannot
capture the change processes. Cross-sectional research can be
exploratory, descriptive, or explanatory, but it is most consistent with
a descriptive approach to research.

12
b. Longitudinal Research. Researchers using longitudinal research
examine features of people or other units at more than one time. It is
usually more complex and costly than cross-sectional research but it
is also more powerful, especially when researchers seek answers to
questions about change. There are three types of longitudinal
research: time series, panel, and cohort.
i. Time series research is longitudinal study in which the same type
of information is collected on a group of people or other units across
multiple time periods. Researcher can observe stability or change in
the features of the units or can track conditions overtime. One could
track the characteristics of students registering in the course on
Research Methods over a period of four years i.e. the characteristics
(Total, age characteristics, gender distribution, subject distribution,
and geographic distribution). Such an analysis could tell us the trends
in the characteristic over the four years.
ii. The panel study is a powerful type of longitudinal research. In
panel study, the researcher observes exactly the same people, group,
or organization across time periods. It is a difficult to carry out such
study. Tracking people over time is often difficult because some
people die or cannot be located. Nevertheless, the results of a well-
designed panel study are very valuable.
iii. A cohort analysis is similar to the panel study, but rather than
observing the exact same people, a category of people who share a
similar life experience in a specified time period is studied. The
focus is on the cohort, or category, not on specific individuals.

Commonly used cohorts include all people born in the same year
(called birth cohorts), all people hired at the same time, all people
retire on one or two year time frame, and all people who graduate in
a given year. Unlike panel studies, researchers do not have to locate
the exact same people for cohort studies. The only need to identify
those who experienced a common life event.
Empirical research
Empirical research relies an experience or observation alone, often
without due regard for system and theory. It is data based research,
coming up with conclusions which are capable of being verified by
observation or experiment. We can also call it as experimental type
of research; in such a research it is necessary to get at facts firsthand,
at their source, and actively to go about doing certain things to
stimulate the production of desired information. In such I research,
die researcher must first provide himself with a working hypothesis
or guess as to the probable results. He then works to get enough facts
(data) to prove or disprove his hypothesis. He then sets up
experimental designs which he thinks will manipulate the persons or
the materials concerned so as to bring forth the desired information.
Such research is thus characterised by the experimenter’s control
over the variables under study and his deliberate manipulation of one
of them to study its effects.

14
Qualitative research, on the other hand, is concerned with qualitative
phenomenon, i.e., phenomena relating to or involving quality or
kind. Qualitative research is especially important in the behavioral
sciences where the aim is to discover the underlying motives of
human behavior. For instance, when we are interested in
investigating the reasons for human behavior, we quite often talk of
‘Motivation Research’, an important type of qualitative research.
Quantitative research Quantitative research is based on the
measurement of quantity or amount. It is applicable to phenomena
that can be expressed in terms of quantity
Conceptual Research Conceptual research is that related to some
abstract idea(s) or theory. It is generally used by philosophers and
thinkers to develop new concepts or to reinterpret existing ones.
conclusion-oriented and decision oriented Research While doing
conclusion oriented research, a researcher is free to pick up a
problem, redesign the enquiry as he proceeds and is prepared to
conceptualize as he wishes. Decision-oriented research is always for
the need of a decision maker and the researcher in this case is not
free to embark upon research according to his own inclination.
Operations research is an example of decision oriented research since
it is a scientific method of providing executive departments with a
quantitative basis for decisions regarding operations under their
control.

One-time Research – Research confined to a single time period.
Diagnostic Research – It is also called clinical research which aims
at identifying the causes of a problem, frequency with which it
occurs and the possible solutions for it.
Exploratory Research – It is the preliminary study of an unfamiliar
problem, about which the researcher has little or no knowledge. It is
aimed to gain familiarity with the problem, to generate new ideas or
to make a precise formulation of the problem. Hence it is also known
as formulative research.
Historical Research – It is the study of past records and other
information sources, with a view to find the origin and development
of a phenomenon and to discover the trends in the past, in order to
understand the present and to anticipate the future.
 Research is directed towards the solution of a problem.
 Research is based upon observable experience or empirical
evidence.
 Research demands accurate observation and description.
 Research involves gathering new data from primary sources or
using existing data for a new purpose.
 Research activities are characterized by carefully designed
procedures.

16
 Research requires expertise i.e., skill necessary to carryout
investigation, search the related literature and to understand and
analyze the data gathered.
 Research is objective and logical – applying every possible test
to validate the data collected and conclusions reached.
 Research involves the quest for answers to unsolved problems.
 Research requires courage.
 Research is characterized by patient and unhurried activity.
 Research is carefully recorded and reported.

Chapter- II
Research design
Definitions of research design
Steps in planning the research design
(1) Determining work involved in the project
Importance / utility of research design
1: Identify the Problem
2: Review the Literature
3: Clarify the Problem
4: Clearly Define Terms and Concepts
5: Define the Population
6: Develop the Instrumentation Plan
7: Collect Data
8: Analyze the Data

18
Chapter- II
Research Design
An analogy might help. When constructing a building there is no
point ordering materials or setting critical dates for completion of
project stages until we know what sort of building is being
constructed. The first decision is whether we need a high rise office
building, a factory for manufacturing machinery, a school, a
residential home or an apartment block. Until this is done we cannot
sketch a plan, obtain permits, work out a work schedule or order
materials.
Similarly, social research needs a design or a structure before data
collection or analysis can commence. A research design is not just a
work plan. A work plan details what has to be done to complete the
project. The function of a research design is to ensure that the
evidence obtained enables us to answer the initial question as
unambiguously as possible. Obtaining relevant evidence entails
specifying the type of evidence needed to answer the research
question, to test a theory, to evaluate a programme or to accurately
describe some phenomenon. In other words, when designing research
we need to ask: given this research question (or theory), what type of
evidence is needed to answer the question (or test the theory) in a
convincing way?
Research design `deals with a logical problem and not a logistical
problem'. . Before a builder or architect can develop a work plan or

order materials they must first establish the type of building required,
its uses and the needs of the occupants. The work plan owes from
this. Similarly, in social research the issues of sampling, method of
data collection (e.g. questionnaire, observation, and document
analysis), and design of questions are all subsidiary to the matter of
`What evidence do I need to collect?'
Too often researchers design questionnaires or begin interviewing far
too early before thinking through what information they require to
answer their research questions. Without attending to these research
design matters at the beginning, the conclusions drawn will normally
be weak and unconvincing and fail to answer the research question.
Adopting a skeptical approach to explanations the need for research
design stems from a skeptical approach to research and a view that
scientific knowledge must always be provisional. The purpose of
research design is to reduce the ambiguity of much research
evidence.
We can always find some evidence consistent with almost any
theory. However, we should be skeptical of the evidence, and rather
than seeking evidence that is consistent with our theory we should
seek evidence that provides a compelling test of the theory. There are
two related strategies for doing this: eliminating rival explanations of
the evidence and deliberately seeking evidence that could disprove
the theory.

20
Definitions of Research Design
(1) According to David J. Luck and Ronald S. Rubin, "A research
design is the determination and statement of the general
research approach or strategy adopted/or the particular project.
It is the heart of planning. If the design adheres to the research
objective, it will ensure that the client's needs will be served."
(2) According to Kerlinger "Research design in the plan, structure
and strategy of investigation conceived so as to obtain answers
to research questions and to control variance."
(3) According to Green and Tull "A research design is the
specification of methods and procedures for acquiring the
information needed. It is the over-all operational pattern or
framework of the project that stipulates what information is to
be collected from which source by what procedures."
Steps in Planning the Research Design
There are four broad steps involved in planning the research design
as explained below:
(1) Determining work involved in the project:
The first step in planning research design is determining the work
involved in the project and designing a workable plan to carry out the
research work within specific time limit.

The work involved includes the following
(a) To formulate the marketing problem
(b) To determine information requirement
(c) To identify information sources
(d) To prepare detailed plan for the execution of research project.
This preliminary step indicates the nature and volume of work
involved in the research work. Various forms require for research
work will be decided and finalised. The sample to be selected for the
survey work will also be decided. Staff requirement will also be
estimated. Details will be worked out about their training and
supervision on field investigators, etc. In addition, the questionnaire
will be prepared and tested. This is how the researcher will prepare a
blue-print of the research project. According to this blueprint the
whole research project will be implemented. The researcher gets
clear idea of the work involved in the project through such initial
planning of the project. Such planning avoids confusion,
misdirection and wastage of time, money and efforts at later stages of
research work. The whole research project moves smoothly due to
initial planning of the research project.
The second step in planning research design is estimating the costs
involved in the research project. Marketing research projects are
costly as the questionnaire is to be prepared in large number of
copies, interviewers are to be appointed for data collection and staff

22
will be required for tabulation and analysis of data collected. Finally,
experts will be required for drawing conclusions and for writing the
research report. The researcher has to estimate the expenditure
required for the execution of the project. The sponsoring organisation
will approve the research project and make suitable budget provision
accordingly. The cost calculation is a complicated job as expenditure
on different heads will have to be estimated accurately. The cost of
the project also needs to be viewed from the viewpoint of its utility in
solving the marketing problem. A comprehensive research study for
solving comparatively minor marketing problem will be
uneconomical.
Time factor is important in the execution of the research project.
Planning of time schedule is essential at the initial stage. Time
calculation relates to the preparation of questionnaire and its pre-
testing, training of interviewers, actual survey work, tabulation and
analysis of data and finally reports writing. Time requirement of each
stage needs to be worked out systematically. Such study will indicate
the time requirement of the whole project. Too long period for the
completion of research work is undesirable as the conclusions and
recommendations may become outdated when actually available.
Similarly, time-consuming research projects are not useful for
solving urgent marketing problems faced by a company. Preparing
time schedule is not adequate in research design. In addition, all
operations involved in the research work should be carried out
strictly as per time schedule already prepared. If necessary remedial

measures should be adopted in order to avoid any deviation in the
time schedule. This brings certainty as regards the completion of the
whole research project in time.
Researcher may create new problems before the sponsoring
organisation if the research work is conducted in a faulty manner.
Such unreliable study is dangerous as it may create new problems. It
is therefore, necessary to keep effective check on the whole research
work during the implementing stage. For this suitable provisions
need to be made in the research design. After deciding the details of
the steps noted above, the background for research design will be
ready. Thereafter, the researcher has to prepare the research design of
the whole project. He has to present the project design to the
sponsoring agency or higher authorities for detailed consideration
and approval. The researcher can start the research project (as per
design) after securing the necessary approval to the research design
prepared.
Importance / Utility of Research Design
Research design is important as it prepares proper framework within
which the research work/activity will be actually carried out
Research design acts as a blue print for the conduct of the whole
research project. It introduces efficiency in investigation and
generates confidence in the final outcome of the study. Research
design gives proper direction and time-table to research activity. It
keeps adequate check on the research work and ensures its

24
completion within certain time limit. It keeps the whole research
project on the right track.
Research design avoids possible errors as regards research problem,
information requirement and so on. It gives practical orientation to
the whole research work and makes it relevant to the marketing
problems faced by the sponsoring organisation. Finally, it makes the
whole research process compact and result-oriented. A researcher
should not go ahead with his research project unless the research
design is planned properly.
1. It specifies the sources and types of information relevant to the
research problem
2. It should give smallest experimental error
3. Reliability of data collected and analyzed
4. It should be economical in cost and time
5. It should be flexible
6. It contain the clear statement of the problem
7. It should be appropriate and efficient
Scientific research involves a systematic process that focuses on being
objective and gathering a multitude of information for analysis so that

the researcher can come to a conclusion. This process is used in all
research and evaluation projects, regardless of the research method
(scientific method of inquiry, evaluation research, or action research).
The process focuses on testing hunches or ideas in a park and
recreation setting through a systematic process. In this process, the
study is documented in such a way that another individual can conduct
the same study again. This is referred to as replicating the study. Any
research done without documenting the study so that others can
review the process and results is not an investigation using the
scientific research process. The scientific research process is a
multiple-step process where the steps are interlinked with the other
steps in the process. If changes are made in one step of the process,
the researcher must review all the other steps to ensure that the
changes are reflected throughout the process. Parks and recreation
professionals are often involved in conducting research or evaluation
projects within the agency. These professionals need to understand the
eight steps of the research process as they apply to conducting a study.
Table 2.4 lists the steps of the research process and provides an
example of each step for a sample research study.
Step 1: Identify the Problem
The first step in the process is to identify a problem or develop a
research question. The research problem may be something the
agency identifies as a problem, some knowledge or information that
is needed by the agency, or the desire to identify a recreation trend
nationally. In the example in table 2.4, the problem that the agency

26
has identified is childhood obesity, which is a local problem and
concern within the community. This serves as the focus of the study
Step 2: Review the Literature
Now that the problem has been identified, the researcher must learn
more about the topic under investigation. To do this, the researcher
must review the literature related to the research problem. This step
provides foundational knowledge about the problem area. The review
of literature also educates the researcher about what studies have been
conducted in the past, how these studies were conducted, and the
conclusions in the problem area. In the obesity study, the review of
literature enables the programmer to discover horrifying statistics
related to the long-term effects of childhood obesity in terms of health
issues, death rates, and projected medical costs. In addition, the
programmer finds several articles and information from the Centers
for Disease Control and Prevention that describe the benefits of
walking 10,000 steps a day. The information discovered during this
step helps the programmer fully understand the magnitude of the
problem, recognize the future consequences of obesity, and identify a
strategy to combat obesity (i.e., walking).
Step 3: Clarify the Problem
Many times the initial problem identified in the first step of the
process is too large or broad in scope. In step 3 of the process, the
researcher clarifies the problem and narrows the scope of the study.
This can only be done after the literature has been reviewed. The
knowledge gained through the review of literature guides the

researcher in clarifying and narrowing the research project. In the
example, the programmer has identified childhood obesity as the
problem and the purpose of the study. This topic is very broad and
could be studied based on genetics, family environment, diet, exercise,
self-confidence, leisure activities, or health issues. All of these areas
cannot be investigated in a single study; therefore, the problem and
purpose of the study must be more clearly defined. The programmer
has decided that the purpose of the study is to determine if walking
10,000 steps a day for three days a week will improve the individual’s
health. This purpose is more narrowly focused and researchable than
the original problem.
Step 4: Clearly Define Terms and Concepts
Terms and concepts are words or phrases used in the purpose
statement of the study or the description of the study. These items
need to be specifically defined as they apply to the study. Terms or
concepts often have different definitions depending on who is reading
the study. To minimize confusion about what the terms and phrases
mean, the researcher must specifically define them for the study. In
the obesity study, the concept of “individual’s health” can be defined
in hundreds of ways, such as physical, mental, emotional, or spiritual
health. For this study, the individual’s health is defined as physical
health. The concept of physical health may also be defined and
measured in many ways. In this case, the programmer decides to more
narrowly define “individual health” to refer to the areas of weight,
percentage of body fat, and cholesterol. By defining the terms or
concepts more narrowly, the scope of the study is more manageable

28
for the programmer, making it easier to collect the necessary data for
the study. This also makes the concepts more understandable to the
reader.
Step 5: Define the Population
Research projects can focus on a specific group of people, facilities,
park development, employee evaluations, programs, financial status,
marketing efforts, or the integration of technology into the operations.
For example, if a researcher wants to examine a specific group of
people in the community, the study could examine a specific age
group, males or females, people living in a specific geographic area,
or a specific ethnic group. Literally thousands of options are available
to the researcher to specifically identify the group to study. The
research problem and the purpose of the study assist the researcher in
identifying the group to involve in the study. In research terms, the
group to involve in the study is always called the population. Defining
the population assists the researcher in several ways. First, it narrows
the scope of the study from a very large population to one that is
manageable. Second, the population identifies the group that the
researcher’s efforts will be focused on within the study. This helps
ensure that the researcher stays on the right path during the study.
Finally, by defining the population, the researcher identifies the group
that the results will apply to at the conclusion of the study. In the
example in table 2.4, the programmer has identified the population of
the study as children ages 10 to 12 years. This narrower population
makes the study more manageable in terms of time and resources.

Step 6: Develop the Instrumentation Plan
The plan for the study is referred to as the instrumentation plan. The
instrumentation plan serves as the road map for the entire study,
specifying who will participate in the study; how, when, and where
data will be collected; and the content of the program. This plan is
composed of numerous decisions and considerations that are
addressed in chapter 8 of this text. In the obesity study, the researcher
has decided to have the children participate in a walking program for
six months. The group of participants is called the sample, which is a
smaller group selected from the population specified for the study.
The study cannot possibly include every 10- to 12-year-old child in
the community, so a smaller group is used to represent the population.
The researcher develops the plan for the walking program, indicating
what data will be collected, when and how the data will be collected,
who will collect the data, and how the data will be analyzed. The
instrumentation plan specifies all the steps that must be completed for
the study. This ensures that the programmer has carefully thought
through all these decisions and that she provides a step-by-step plan to
be followed in the study.
Step 7: Collect Data
Once the instrumentation plan is completed, the actual study begins
with the collection of data. The collection of data is a critical step in
providing the information needed to answer the research question.
Every study includes the collection of some type of data whether it is
from the literature or from subjects to answer the research question.

30
Data can be collected in the form of words on a survey, with a
questionnaire, through observations, or from the literature. In the
obesity study, the programmers will be collecting data on the defined
variables: weight, percentage of body fat, cholesterol levels, and the
number of days the person walked a total of 10,000 steps during the
class.
The researcher collects these data at the first session and at the last
session of the program. These two sets of data are necessary to
determine the effect of the walking program on weight, body fat, and
cholesterol level. Once the data are collected on the variables, the
researcher is ready to move to the final step of the process, which is
the data analysis.
Step 8: Analyze the Data
All the time, effort, and resources dedicated to steps 1 through 7 of the
research process culminate in this final step. The researcher finally has
data to analyze so that the research question can be answered. In the
instrumentation plan, the researcher specified how the data will be
analyzed. The researcher now analyzes the data according to the plan.
The results of this analysis are then reviewed and summarized in a
manner directly related to the research questions. In the obesity study,
the researcher compares the measurements of weight, percentage of
body fat, and cholesterol that were taken at the first meeting of the
subjects to the measurements of the same variables at the final
program session. These two sets of data will be analyzed to determine
if there was a difference between the first measurement and the

second measurement for each individual in the program. Then, the
data will be analyzed to determine if the differences are statistically
significant. If the differences are statistically significant, the study
validates the theory that was the focus of the study. The results of the
study also provide valuable information about one strategy to combat
childhood obesity in the community.
As you have probably concluded, conducting studies using the eight
steps of the scientific research process requires you to dedicate time
and effort to the planning process. You cannot conduct a study using
the scientific research process when time is limited or the study is
done at the last minute. Researchers who do this conduct studies that
result in either false conclusions or conclusions that are not of any
value to the organization.
Descriptive  Describe phenomena as they exist. Descriptive
studies generally take raw data and summarize it
in a useable form.
 Can also be qualitative in nature if the sample
size is small and data are collected from
questionnaires, interviews or observations.
Experimental  The art of planning and implementing an
experiment in which the research has control
over some of the conditions where the study
takes place and control over some aspects of the
independent variable(s) (presumed cause or
variable used to predict another variable)

32
Quasi-
experimental
 A form of experimental research. One in which
the researcher cannot control at least one of the
three elements of an experimental design:
 Environment
 Intervention (program or practice)
 Assignment to experimental and control groups
Historical  Collection and evaluation of data related to past
events that are used to describe causes, effects
and trends that may explain present or future
events. Data are often archival.
 Data includes interviews.
Ethnographic  The collection of extensive narrative data over
an extended period of time in natural settings to
gain insights about other types of research.
 Data are collected through observations at
particular points of time over a sustained period.
 Data include observations, records and
interpretations of what is seen.
Case Studies  An in-depth study of an individual group,
institution, organization or program.
 Data include interviews, field notes of
observations, archival data and biographical
data.

Chapter-III
Types of Variables
Interval - scale Variables
Continuous Ordinal Variables
Ratio - scale Variables

34
Chapter-III
Variables and Types of Variables
Variable is central idea in research. Simply defined, variable is a
concept that varies. There are two types of concepts: those that refer
to a fixed phenomenon and those that vary in quantity, intensity, or
amount (e.g. amount of education). The second type of concept and
measures of the concept are variables. A variable is defined as
anything that varies or changes in value. Variables take on two or
more values. Because variable represents a quality that can exhibit
differences in value, usually magnitude or strength, it may be said
that a variable generally is anything that may assume different
numerical or categorical values. Once you begin to look for them,
you will see variables everywhere.
For example gender is a variable; it can take two values: male or
female. Marital status is a variable; it can take on values of never
married, single, married, divorced, or widowed. Family income is a
variable; it can take on values from zero to billions of Rupees. A
person’s attitude toward women empowerment is variable; it can
range from highly favorable to highly unfavorable. In this way the
variation can be in quantity, intensity, amount, or type; the examples
can be production units, absenteeism, gender, religion, motivation,
grade, and age. A variable may be situation specific; for example
gender is a variable but if in a particular situation like a class of

Research Methods if there are only female students, then in this
situation gender will not be considered as a variable.
An independent variable, sometimes called an experimental or
predictor variable, is a variable that is being manipulated in an
experiment in order to observe the effect on a dependent variable,
sometimes called an outcome variable. The independent variable is
the variable that is manipulated by the researcher. The independent
variable is something that is hypothesized to influence the dependent
variable. The researcher determines for the participant what level or
condition of the independent variable that the participant in the
experiment receives. For example, each participant in the
experiment may be randomly assigned to either an experimental
condition or the control condition.
The dependent variable is simply that, a variable that is dependent on
an independent variable(s).The dependent variable is the variable that
is simply measured by the researcher. It is the variable that reflects
the influence of the independent variable. For example, the
dependent variable would be the variable that is influenced by being
randomly assigned to either an experimental condition or a control
condition.

36
Examples of Independent Variable and Examples of Dependent
Variable
If one were to measure the influence of different quantities of
fertilizer on plant growth, the independent variable would be the
amount of fertilizer used (the changing factor of the experiment). The
dependent variables would be the growth in height and/or mass of the
plant (the factors that are influenced in the experiment) and the
controlled variables would be the type of plant, the type of fertilizer,
the amount of sunlight the plant gets, the size of the pots, etc. (the
factors that would otherwise influence the dependent variable if they
were not controlled).
In a study of how different doses of a drug affect the severity of
symptoms, a researcher could compare the frequency and intensity of
symptoms (the dependent variables) when different doses (the
independent variable) are administered, and attempt to draw a
conclusion.
In order to clarify the concepts of independent variable and
dependent variable, it is important to provide examples. Imagine that
you wished to know whether listening to music would increase
productivity in the workplace. You randomly assign each participant
in this experiment to either an experimental condition or a control
condition. In the experimental condition, participants listen to music
while they work. In the control condition, the participants do not
listen to music while they work. In this example, listening to music

vs. not listening to music is the independent variable. The dependent
variable in this example is productivity.
Variables have different properties and to these properties we assign
numerical values. If the values of a variable can be divided into
fractions then we call it a continuous variable. Such a variable can
take infinite number of values. Income, temperature, age, or a test
score are examples of continuous variables. These variables may take
on values within a given range or, in some cases, an infinite set.
Any variable that has a limited number of distinct values and which
cannot be divided into fractions, is a discontinuous variable. Such a
variable is also called as categorical variable or classificatory
variable, or discrete variable. Some variables have only two values,
reflecting the presence or absence of a property: employed-
unemployed or male-female have two values. These variables are
referred to as dichotomous. There are others that can take added
categories such as the demographic variables of race, religion. All
such variables that produce data that fit into categories are said to be
discrete/categorical/classificatory, since only certain values are
possible. Let we assume a variable related to automobile, let say if
we assigned a value for Honda = 5 and Chevrolet = 6 so no option if
available for 5.5 because we cannot divide the value into fractions.

38
A moderating variable is one that has a strong contingent effect on
the independent variable-dependent variable relationship. That is, the
presence of a third variable (the moderating variable) modifies the
original relationship between the independent and the dependent
variable.
For example, a strong relationship has been observed between the
quality of library facilities (X) and the performance of the students
(Y). Although this relationship is supposed to be true generally, it is
nevertheless contingent on the interest and inclination of the
students. It means that only those students who have the interest and
inclination to use the library will show improved performance in
their studies. In this relationship interest and inclination is
moderating variable i.e. which moderates the strength of the
association between X and Y variables.
Extraneous Variables are undesirable variables that influence the
relationship between the variables that an experimenter is examining.
Another way to think of this, is that these are variables the influence
the outcome of an experiment, though they are not the variables that
are actually of interest. These variables are undesirable because they
add error to an experiment. A major goal in research design is to
decrease or control the influence of extraneous variables as much as
possible.

For example, let’s say that an educational psychologist has
developed a new learning strategy and is interested in examining the
effectiveness of this strategy. The experimenter randomly assigns
students to two groups. All of the students study text materials on a
biology topic for thirty minutes. One group uses the new strategy and
the other uses a strategy of their choice. Then all students complete a
test over the materials. One obvious confounding variable in this case
would be pre-knowledge of the biology topic that was studied. This
variable will most likely influence student scores, regardless of
which strategy they use. Because of this extraneous variable (and
surely others) there will be some spread within each of the groups. It
would be better, of course, if all students came in with the exact same
pre-knowledge. However, the experimenter has taken an important
step to greatly increase the chances that, at least, the extraneous
variable will add error variance equivalently between the two groups.
That is, the experimenter randomly assigned students to the two
groups.
A variable, used in the process of explaining an observed relationship
between an independent and dependent variable(s), A basic causal
relationship requires only independent and dependent variable. A
third type of variable, the intervening variable, appears in more
complex causal relationships. It comes between the independent and
dependent variables and shows the link or mechanism between them.
Advances in knowledge depend not only on documenting cause and
effect relationship but also on specifying the mechanisms that

40
account for the causal relation. In a sense, the intervening variable
acts as a dependent variable with respect to independent variable and
acts as an independent variable toward the dependent variable. For
example X is age and Y is reading ability, the causal relationship
between X and Y might be explained by the intervening variable “Z”,
say education, which explains the X → Y link. Hence X is an indirect
cause of Y through the intervening variable Z: “Z” predicts Y but is
simultaneously predicted by X.
Continuous variables can be classified into three categories:
 Interval - scale Variables
Interval scale data has order and equal intervals. Interval scale
variables are measured on a linear scale, and can take on
positive or negative values. It is assumed that the intervals keep
the same importance throughout the scale. They allow us not
only to rank order the items that are measured but also to
quantify and compare the magnitudes of differences between
them. We can say that the temperature of 40°C is higher than
30°C, and an increase from 20°C to 40°C is twice as much as
the increase from 30°C to 40°C. Counts are interval scale
measurements, such as counts of publications or citations, years
of education, etc.

 Continuous Ordinal Variables
They occur when the measurements are continuous, but one is
not certain whether they are on a linear scale, the only
trustworthy information being the rank order of the
observations. For example, if a scale is transformed by an
exponential, logarithmic or any other nonlinear monotonic
transformation, it loses its interval - scale property. Here, it
would be expedient to replace the observations by their ranks.
 Ratio - scale Variables
These are continuous positive measurements on a nonlinear
scale. A typical example is the growth of bacterial population
(say, with a growth function AeBt
.). In this model, equal time
intervals multiply the population by the same ratio.
Ratio data are also interval data, but they are not measured on a
linear scale. . With interval data, one can perform logical
operations, add, and subtract, but one cannot multiply or divide.
For instance, if a liquid is at 40 degrees and we add 10 degrees,
it will be 50 degrees. However, a liquid at 40 degrees does not
have twice the temperature of a liquid at 20 degrees because 0
degrees does not represent "no temperature" -- to multiply or
divide in this way we would have to use the Kelvin temperature
scale, with a true zero point (0 degrees Kelvin = -273.15
degrees Celsius). In social sciences, the issue of "true zero"
rarely arises, but one should be aware of the statistical issues
involved.

42
There are three different ways to handle the ratio-scaled variables.
 Simply as interval scale variables. However this procedure
should be avoided as it can distort the results.
 As continuous ordinal scale.
 By transforming the data (for example, logarithmic
transformation) and then treating the results as interval scale
variables.
Discrete variables are also called categorical variables. A discrete
variable, X, can take on a finite number of numerical values,
categories or codes. Discrete variables can be classified into the
following categories:
1. Nominal Variables
Nominal variables allow for only qualitative classification. That
is, they can be measured only in terms of whether the
individual items belong to certain distinct categories, but we
cannot quantify or even rank order the categories: Nominal data
has no order, and the assignment of numbers to categories is
purely arbitrary. Because of lack of order or equal intervals,

one cannot perform arithmetic (+, -, /, *) or logical operations
(>, <, =) on the nominal data. Typical examples of such
variables are:
Dichotomous variables are nominal variables which have
only two categories or levels. For example, if we were looking
at gender, we would most probably categorize somebody as
either "male" or "female". This is an example of a dichotomous
variable (and also a nominal variable). Another example might
be if we asked a person if they owned a mobile phone. Here, we
may categorise mobile phone ownership as either "Yes" or
"No". In the real estate agent example, if type of property had
been classified as either residential or commercial then "type of
property" would be a dichotomous variable.
Gender: 1.Male
2. Female
Marital Status: 1.Unmarried
2.Married
3.Divorcee
4. Widower
Educational
qualifications
1.illiterate
2.Primary school level
3.Higher secondary level
4.College level

44
2. Ordinal Variables
A discrete ordinal variable is a nominal variable, but its
different states are ordered in a meaningful sequence. Ordinal
data has order, but the intervals between scale points may be
uneven. Because of lack of equal distances, arithmetic
operations are impossible, but logical operations can be
performed on the ordinal data. A typical example of an ordinal
variable is the socio-economic status of families. We know
'upper middle' is higher than 'middle' but we cannot say 'how
much higher'. Ordinal variables are quite useful for subjective
assessment of 'quality; importance or relevance'. Ordinal scale
data are very frequently used in social and behavioral research.
Almost all opinion surveys today request answers on three-,
five-, or seven- point scales. Such data are not appropriate for
analysis by classical techniques, because the numbers are
comparable only in terms of relative magnitude, not actual
magnitude.
Consider for example a questionnaire item on the time
involvement of scientists in the 'perception and identification of
research problems'. The respondents were asked to indicate
their involvement by selecting one of the following codes:
1 = Very low or nil, 2 = Low, 3 = Medium, 4 =
Great, 5 = Very great
Here, the variable 'Time Involvement' is an ordinal variable
with 5 states.

Ordinal variables often cause confusion in data analysis. Some
statisticians treat them as nominal variables. Other statisticians
treat them as interval scale variables, assuming that the
underlying scale is continuous, but because of the lack of a
sophisticated instrument, they could not be measured on an
interval scale.
2. Dummy Variables from Quantitative Variables
A quantitative variable can be transformed into a categorical
variable, called a dummy variable by recoding the values.
Consider the following example: the quantitative variable Age
can be classified into five intervals. The values of the
associated categorical variable, called dummy variables, are 1,
2,3,4,5:
[Up to 25] 1
[25, 40 ] 2
[40, 50] 3
[50, 60] 4
[Above 60] 5
3. Preference Variables
Preference variables are specific discrete variables, whose
values are either in a decreasing or increasing order. For
example, in a survey, a respondent may be asked to indicate the
importance of the following nine sources of information in his
research and development work, by using the code [1] for the

46
most important source and [9] for the least important source:
give the order of preference.
1. Literature published in the country
2. Literature published abroad
3. Scientific abstracts
4. Unpublished reports, material, etc.
5. Discussions with colleagues within the research unit
6. Discussions with colleagues outside the research unit but
within institution
7. Discussions with colleagues outside the institution
8. Scientific meetings in the country
9. Scientific meetings abroad
Note that preference data are also ordinal. The interval distance from
the first preference to the second preference is not the same as, for
example, from the sixth to the seventh preference.
1. Multiple Response Variables
Multiple response variables are those, which can assume more
than one value. A typical example is a survey questionnaire
about the use of computers in research. The respondents were
asked to indicate the purpose(s) for which they use computers
in their research work. The respondents could score more than
one category.

1. Statistical analysis
2. Lab automation/ process control
3. Data base management, storage and retrieval
4. Modeling and simulation
5. Scientific and engineering calculations
6. Computer aided design (CAD)
7. Communication and networking
8. Graphics
FOUR SCALES COMPARED
Nominal Original Interval Ratio
Classification
but no order,
distance or
origin
Classification
but order but no
distance or
unique origin
Classification,
ordered and distance
but no unique origin
Classification,
order, distance
and unique
origin
Determination
of
equality
Determination of
greater or lesser
value
Determination of
equality of intervals
or differences
Determination
of equality of
ratios
Only Label
Ranks, Rating
and Grade
equal grouping Weight, height
Gender (male,
female)
Doneness of
meat, (well,
medium well,
medium rare,
rare)
temperature in
degrees
Age in years

48
Nominal Original Interval Ratio
Counting
Frequency
Distribution
Addition/subtraction
but no
multiplication or
division
All functions
Black & While
AAA, BBB,
CCC
personality measure
Can say no
measurable
value like zero
sales
Religion
Levels, one-star
& 4-star
Mean, range,
variance, standard
deviation
Annual
Income

Chapter- IV
Sampling
Sample
Purpose of Sampling
Population or Universe
Census
Precision
Bias
Frame
Design
Random
Unit
Attribute
Variable
Statistic
1. Defining the target population
2. Specifying the sampling frame
3. Specifying the sampling unit
4. Selection of the sampling method
5. Determination of sample size
6. Specifying the sampling plan
7. Selecting the sample
Sampling Methods and Techniques

50
1. Simple random sample
2. Systematic random sample
3. Stratified sample
4. Cluster sample
1. Convenient sample
2. Judgment sample
3. Quota sampling
Sample Size
Sampling error
Induced bias
Error
Non-Sampling error
Important factors should be considered while frame hypothesis

Types of Hypotheses
Characteristics of Testable Hypothesis
Hypothesis must be conceptually clear
Hypothesis should have empirical referents
Hypothesis must be specific
Decision Rules

52
Chapter- IV
Sampling
Sample
A sample is a finite part of a statistical population whose properties
are studied to gain information about the whole. When dealing with
people, it can be defined as a set of respondents (people) selected
from a larger population for the purpose of a survey.
A population is a group of individuals, persons, objects, or items
from which samples are taken for measurement for example a
population of presidents or professors, books or students.
Sampling
Sampling is the act, process, or technique of selecting a suitable
sample, or a representative part of a population for the purpose of
determining parameters or characteristics of the whole population.
Good and Hatt defined, “A sample is a smaller representation of a
large whole”. Sampling can be defined as selecting part of the
elements in a population. It results in the fact that, conclusions from
the sample may be extended to that about the entire population.

Purpose of Sampling
To draw conclusions about populations from samples, we must use
inferential statistics which enables us to determine a population`s
characteristics by directly observing only a portion (or sample) of the
population. We obtain a sample rather than a complete enumeration
(a census) of the population for many reasons. Obviously, it is
cheaper to observe a part rather than the whole, but we should
prepare ourselves to cope with the dangers of using samples.
Population OR Universe
The entire aggregation of items from which samples can be drawn is
known as a population. In sampling, the population may refer to the
units, from which the sample is drawn. Population or populations of
interest are interchangeable terms. The term “unit” is used, as in a
business research process; samples are not necessarily people all the
time. A population of interest may be the universe of nations or
cities. This is one of the first things the analyst needs to define
properly while conducting a business research. Therefore,
population, contrary to its general notion as a nation’s entire
population has a much broader meaning in sampling. “N” represents
the size of the population.
Census
A complete study of all the elements present in the population is
known as a census. It is a time consuming and costly process and is,
therefore, seldom a popular with researchers. The general notion that

54
a census generates more accurate data than sampling is not always
true. Limitations include failure in generating a complete and
accurate list of all the members of the population and refusal of the
elements to provide information. The national population census is
an example of census survey.
Precision
Precision is a measure of how close an estimate is expected to be, to
the true value of a parameter. Precision is a measure of similarity.
Precision is usually expressed in terms of imprecision and related to
the standard error of the estimate. Less precision is reflected by a
larger standard error.
Bias
Bias is the term refers to how far the average statistic lies from the
parameter it is estimating, that is, the error, which arises when
estimating a quantity. Errors from chance will cancel each other out
in the long run, those from bias will not. Bias can take different
forms.
Frame
The frame describes the population in terms of sampling units. It
may often be a geographical area, such as a list of city blocks or
counties.

Design
The design describes the method by which the sample is chosen.
Random
A mathematical term “random” means that every element in the total
population has an equal chance or probability of being chosen for the
sample and that each of these elements is independent of the other.
Unit
Any “population” or “universe” should contain some specifications
in terms of content units, extend and time.
Attribute
It is a characteristic possessive trait of an element of a population.
For example, if in a class of 35 students 15 had dark hair, then we
could say that 15 students possess the given attribute.
Variable
A variable can always be transformed into an attribute by a broad
grouping the variable.
Statistic
Statistic refers to the value of a variable calculated from a sample
taken out of a universe or population. The characteristics of a sample
are called a statistic.

56
An operational sampling process can be divided into seven steps as
given below:
1. Defining the target population.
2. Specifying the sampling frame.
3. Specifying the sampling unit.
4. Selection of the sampling method.
5. Determination of sample size.
6. Specifying the sampling plan.
7. Selecting the sample.
1. Defining the Target Population
Defining the population of interest, for business research, is the first
step in sampling process. In general, target population is defined in
terms of element, sampling unit, extent, and time frame. The
definition should be in line with the objectives of the research study.
For example, if a kitchen appliances firm wants to conduct a survey
to ascertain the demand for its micro ovens, it may define the
population as ‘all women above the age of 20 who cook (assuming
that very few men cook)’. However this definition is too broad and
will include every household in the country, in the population that is
to be covered by the survey. Therefore the definition can be further
refined and defined at the sampling unit level, that, all women above
the age 20, who cook and whose monthly household income exceeds
Rs.20,000. This reduces the target population size and makes the
research more focused. The population definition can be refined
further by specifying the area from where the researcher has to draw
his sample, that is, households located in Hyderabad.

A well defined population reduces the probability of including the
respondents who do not fit the research objective of the company.
For ex, if the population is defined as all women above the age of 20,
the researcher may end up taking the opinions of a large number of
women who cannot afford to buy a micro oven.
2. Specifying the Sampling Frame
Once the definition of the population is clear a researcher should
decide on the sampling frame. A sampling frame is the list of
elements from which the sample may be drawn. Continuing with the
micro oven ex, an ideal sampling frame would be a database that
contains all the households that have a monthly income above Rs.20,
000. However, in practice it is difficult to get an exhaustive sampling
frame that exactly fits the requirements of a particular research. In
general, researchers use easily available sampling frames like
telephone directories and lists of credit card and mobile phone users.
Various private players provide databases developed along various
demographic and economic variables. Sometimes, maps and aerial
pictures are also used as sampling frames. Whatever may be the case,
an ideal sampling frame is one that entire population and lists the
names of its elements only once.
A sampling frame error pops up when the sampling frame does not
accurately represent the total population or when some elements of
the population are missing another drawback in the sampling frame
is over –representation. A telephone directory can be over
represented by names/household that has two or more connections.

58
3. Specifying the Sampling Unit
A sampling unit is a basic unit that contains a single element or a
group of elements of the population to be sampled. In this case, a
household becomes a sampling unit and all women above the age of
20 years living in that particular house become the sampling
elements. If it is possible to identify the exact target audience of the
business research, every individual element would be a sampling
unit. This would present a case of primary sampling unit. However, a
convenient and better means of sampling would be to select
households as the sampling unit and interview all females above 20
years, who cook. This would present a case of secondary sampling
unit.
4. Selection of the Sampling Method
The sampling method outlines the way in which the sample units are
to be selected. The choice of the sampling method is influenced by
the objectives of the business research, availability of financial
resources, time constraints, and the nature of the problem to be
investigated. All sampling methods can be grouped under two
distinct heads, that is, probability and non-probability sampling.
5. Determination of Sample Size
The sample size plays a crucial role in the sampling process. There
are various ways of classifying the techniques used in determining
the sample size. A couple those hold primary importance and are
worth mentioning are whether the technique deals with fixed or

sequential sampling and whether its logic is based on traditional or
Bayesian methods. In non-probability sampling procedures, the
allocation of budget, thumb rules and number of sub groups to be
analyzed, importance of the decision, number of variables, nature of
analysis, incidence rates, and completion rates play a major role in
sample size determination. In the case of probability sampling,
however, formulas are used to calculate the sample size after the
levels of acceptable error and level of confidence are specified.
6. Specifying the Sampling Plan
In this step, the specifications and decisions regarding the
implementation of the research process are outlined. Suppose, blocks
in a city are the sampling units and the households are the sampling
elements. This step outlines the modus operandi of the sampling plan
in identifying houses based on specified characteristics. It includes
issues like how is the interviewer going to take a systematic sample
of the houses. What should the interviewer do when a house is
vacant? What is the recontact procedure for respondents who were
unavailable? All these and many other questions need to be answered
for the smooth functioning of the research process. These are guide
lines that would help the researcher in every step of the process. As
the interviewers and their co-workers will be on field duty of most of
the time, a proper specification of the sampling plans would make
their work easy and they would not have to revert to their seniors
when faced with operational problems.

60
7. Selecting the Sample
This is the final step in the sampling process, where the actual
selection of the sample elements is carried out. At this stage, it is
necessary that the interviewers stick to the rules outlined for the
smooth implementation of the business research. This step involves
implementing the sampling plan to select the sampling plan to select
a sample required for the survey.
Sampling methods and techniques
There are many different types of sampling technique. The most
popular sampling techniques are below:
Sampling
Method
Definition Uses Limitations
Cluster
Sampling
Units in the population
can often be found in
certain geographic
groups or "clusters"
(e.g. primary school
children in Derbyshire.
A random sample of
clusters is taken, then
all units within the
cluster are examined)
Quick & easy;
does not
require
complete
population
information;
good for face-
to-face surveys
Expensive if
the clusters are
large; greater
risk of
sampling error
Convenience
Sampling
Uses those who are
willing to volunteer
Readily
available;
large amount
of information
can be
gathered
quickly
Cannot
extrapolate
from sample to
infer about the
population;
prone to
volunteer bias

Judgment
Sampling
A deliberate choice of a
sample - the opposite of
random
Good for
providing
illustrative
examples or
case studies
Very prone to
bias; samples
often small;
cannot
extrapolate
from sample
Quota
Sampling
Aim is to obtain a
sample that is
"representative" of the
overall population; the
population is divided
("stratified") by the
most important
variables (e.g. income,.
age, location) and a
required quota sample
is drawn from each
stratum
Quick & easy
way of
obtaining a
sample
Not random, so
still some risk
of bias; need to
understand the
population to
be able to
identify the
basis of
stratification
Simply
Random
Sampling
Ensures that every
member of the
population has an equal
chance of selection
Simply to
design and
interpret; can
calculate
estimate of the
population and
the sampling
error
Need a
complete and
accurate
population
listing; may not
be practical if
the sample
requires lots of
small visits all
over the
country
Systematic
Sampling
After randomly
selecting a starting
point from the
population, between 1
and "n", every nth unit
is selected, where n
equals the population
size divided by the
sample size
Easier to
extract the
sample than
via simple
random;
ensures sample
is spread
across the
population
Can be costly
and time-
consuming if
the sample is
not
conveniently
located

62
A simple random sample
A simple random sample is obtained by choosing elementary units in
search a way that each unit in the population has an equal chance of
being selected. A simple random sample is free from sampling bias.
However, using a random number table to choose the elementary
units can be cumbersome. If the sample is to be collected by a person
untrained in statistics, then instructions may be misinterpreted and
selections may be made improperly. Instead of using a least of
random numbers, data collection can be simplified by selecting say
every 10th or 100th unit after the first unit has been chosen
randomly. Such a procedure is called systematic random sampling.
A systematic random sample
A systematic random sample is obtained by selecting one unit on a
random basis and choosing additional elementary units at evenly

spaced intervals until the desired number of units is obtained. For
example, there are 100 students in your class. You want a sample of
20 from these 100 and you have their names listed on a piece of
paper may be in an alphabetical order. If you choose to use
systematic random sampling, divide 100 by 20, you will get 5.
Randomly select any number between 1 and five. Suppose the
number you have picked is 4, that will be your starting number. So
student number 4 has been selected. From there you will select every
5th name until you reach the last one, number one hundred. You will
end up with 20 selected students.
A stratified sample
A stratified sample is obtained by independently selecting a separate
simple random sample from each population stratum. A population
can be divided into different groups may be based on some
characteristic or variable like income of education. Like any body
with ten years of education will be in group A, between 10 and 20
group B and between 20 and 30 group C. These groups are referred
to as strata. Researcher can then randomly select from each stratum a
given number of units which may be based on proportion like if
group A has 100 persons while group B has 50, and C has 30
researcher may decide you will take 10% of each. So researcher end
up with 10 from group A, 5 from group B and 3 from group C.

64
A cluster sample
A cluster sample is obtained by selecting clusters from the
population on the basis of simple random sampling. The sample
comprises a census of each random cluster selected. For example, a
cluster may be some thing like a village or a school, a state. So you
decide all the elementary schools in Newyork State are clusters. You
want 20 schools selected. You can use simple or systematic random
sampling to select the schools, and then every school selected
becomes a cluster. If you interest to interview teachers on their
opinion of some new program which has been introduced, then all
the teachers in a cluster must be interviewed. Though it is very
economical cluster sampling is very susceptible to sampling bias.
Like for the above case, you are likely to get similar responses from
teachers in one school due to the fact that they interact with one
another.
The convenient sample
A convenience sample results when the more convenient elementary
units are chosen from a population for observation.
The judgment sample
A judgment sample is obtained according to the discretion of
someone who is familiar with the relevant characteristics of the
population.

Quota sampling
A quota sample is one in which the interviewer is instructed to
collect information from an assigned number, or quota, of individuals
in each of several groups-the groups being specified as to age, sex,
income, or other characteristics much like the strata in stratified
sampling.
Sample Size
Before deciding how large a sample should be, researcher has to
define the study population. For example, all children below age
three in particular city. Then determine sampling frame which could
be a list of all the children below three as recorded by city. Then
struggle with the sample size.
The question of how large a sample should be is a difficult one.
Sample size can be determined by various constraints. For example,
the available funding may prespecify the sample size. When research
costs are fixed, a useful rule of thumb is to spend about one half of
the total amount for data collection and the other half for data
analysis. This constraint influences the sample size as well as sample
design and data collection procedures.
In general, sample size depends on the nature of the analysis to be
performed, the desired precision of the estimates one wishes to
achieve, the kind and number of comparisons that will be made, the
number of variables that have to be examined simultaneously and
how heterogeneous a universe is sampled. For example, if the key
analysis of a randomized experiment consists of computing averages

66
for experimental and controls in a project and comparing differences,
then a sample under 100 might be adequate, assuming that other
statistical assumptions hold.
In non-experimental research, most often, relevant variables have to
be controlled statistically because groups differ by factors other than
chance.
More technical considerations suggest that the required sample size
is a function of the precision of the estimates one wishes to achieve,
the variability or variance, one expects to find in the population and
the statistical level of confidence one wishes to use.
Deciding on a sample size for qualitative inquiry can be even more
difficult than quantitative because there are no definite rules to be
followed. It will depend on what you want to know, the purpose of
the inquiry, what is at stake, what will be useful, what will have
credibility and what can be done with available time and resources.
With fixed a resource which is always the case, researcher can
choose to study one specific phenomenon in depth with a smaller
sample size or a bigger sample size when seeking breadth. In
purposeful sampling, the sample should be judged on the basis of the
purpose and rationale for each study and the sampling strategy used
to achieve the studies purpose. The validity, meaningfulness, and
insights generated from qualitative inquiry have more to do with the
information-richness of the cases selected and the
observational/analytical capabilities of the researcher than with
sample size.

For any sample design deciding upon the appropriate sample size
will depend on several key factors
(1) No estimate taken from a sample is expected to be exact: Any
assumptions about the overall population based on the results
of a sample will have an attached margin of error.
(2) To lower the margin of error usually requires a larger sample
size. The amount of variability in the population (i.e. the range
of values or opinions) will also affect accuracy and therefore
the size of sample.
(3) The confidence level is the likelihood that the results obtained
from the sample lie within a required precision. The higher the
confidence level that is the more certain researcher wishes to be
that the results are not atypical. Statisticians often use a 95 per
cent confidence level to provide strong conclusions.
(4) Population size does not normally affect sample size. In fact the
larger the populations size the lower the proportion of that
population that needs to be sampled to be representative. It is
only when the proposed sample size is more than 5 per cent of
the population that the population size becomes part of the
formulae to calculate the sample size.
A sample is expected to mirror the population from which it comes;
however, there is no guarantee that any sample will be precisely
representative of the population from which it comes. Chance may

68
dictate that a disproportionate number of untypical observations will
be made like for the case of testing fuses, the sample of fuses may
consist of more or less faulty fuses than the real population
proportion of faulty cases. In practice, it is rarely known when a
sample is unrepresentative and should be discarded.
Sampling error
What can make a sample unrepresentative of its population? One of
the most frequent causes is sampling error.
Sampling error comprises the differences between the sample and the
population that are due solely to the particular units that happen to
have been selected.
For example, suppose that a sample of 100 women are measured in a
particular city and are all found to be taller than six feet. It is very
clear even without any statistical prove that this would be a highly
unrepresentative sample leading to invalid conclusions. This is a very
unlikely occurrence because naturally such rare cases are widely
distributed among the population. But it can occur. Luckily, this is a
very obvious error and can be elected very easily.
The more dangerous error is the less obvious sampling error against
which nature offers very little protection. An example would be like
a sample in which the average height is overstated by only one inch
or two rather than one foot which is more obvious. It is the
unobvious error that is of much concern.

There are two basic causes for sampling error. One is chance: That is
the error that occurs just because of bad luck. This may result in
untypical choices. Unusual units in a population do exist and there is
always a possibility that an abnormally large number of them will be
chosen.
Sampling bias is a tendency to favour the selection of units that have
particular characteristics.
Sampling bias is usually the result of a poor sampling plan. The most
notable is the bias of non response when for some reason some units
have no chance of appearing in the sample. For example, take a
hypothetical case where a survey was conducted recently by Cornell
Graduate School to find out the level of stress that graduate students
were going through. A mail questionnaire was sent to 100 randomly
selected graduate students. Only 52 responded and the results were
that students were not under stress at that time when the actual case
was that it was the highest time of stress for all students except those
who were writing their thesis at their own pace.
A means of selecting the units of analysis must be designed to avoid
the more obvious forms of bias. Another example would be where
researcher would like to know the average income of some
community and researcher decide to use the telephone numbers to
select a sample of the total population in a locality where only the
rich and middle class households have telephone lines. Researcher
will end up with high average income which will lead to the wrong
policy decisions.

70
No two interviewers are alike and the same person may provide
different answers to different interviewers. The manner in which a
question is formulated can also result in inaccurate responses.
Individuals tend to provide false answers to particular questions. For
example, some people want to feel younger or older for some reason
known to them. If researcher ask such a person their age in years, it
is easier for the individual just to lie to researcher by over stating
their age by one or more years than it is if researcher asked which
year they were born since it will require a bit of quick arithmetic to
give a false date and a date of birth will definitely be more accurate.
Respondents might also give incorrect answers to impress the
interviewer. This type of error is the most difficult to prevent because
it results from out right deceit on the part of the responded. For
example a research made in 1995, a researcher witnessed in his study
in which he was asked farmers how much maize they harvested in
the year 1995. In most cases, the men tended to lie by saying a figure
which is the recommended expected yield that is 25 bags per acre.
The responses from men looked so uniform that he became
suspicious. I compared with the responses of the wives of these men
and their responses were all different. To decide which one was right,
whenever possible he could in a tactful way verify with an older son
or daughter. It is important to acknowledge that certain psychological

factors induce incorrect responses and great care must be taken to
design a study that minimizes their effect.
Knowing why a study is being conducted may create incorrect
responses. A classic example is the question: What is your income?
If a government agency is asking, a different figure may be provided
than the respondent would give on an application for a home
mortgage. One way to guard against such bias is to camouflage the
study`s goals; Another remedy is to make the questions very specific,
allowing no room for personal interpretation. For example, "Where
are you employed?" could be followed by "What is your salary?" and
"Do you have any extra jobs?" A sequence of such questions may
produce more accurate information.
Induced bias
Finally, it should be noted that the personal prejudices of either the
designer of the study or the data collector may tend to induce bias. In
designing a questionnaire, questions may be slanted in such a way
that a particular response will be obtained even though it is
inaccurate. For example, an agronomist may apply fertilizer to
certain key plots, knowing that they will provide more favorable
yields than others. To protect against induced bias, advice of an
individual trained in statistics should be sought in the design and
someone else aware of search pitfalls should serve in an auditing
capacity.

72
Error
Error is defined as, “an act, assertion, or belief that unintentionally
deviates from what is correct, right, or true”. In a business research
process, there is sure to be some error in the results because there is
the involvement of human intelligence and the use of sampling
methods that may not be always accurate. The absolute value of the
difference between an unbiased point estimate and the corresponding
population parameter is known as a sampling error. It arises because
the data is collected from a part, rather than the whole of the
population. The sampling error can be more reliable by increasing
the sample size. Total survey errors are of two types: Random
sampling error & non-sampling error.
Random sampling error or sampling error is the difference between
the sample results and the results of a census conducted by identical
procedures. Although a representative sample is taken, there is
always a slight deviation between the true population value and the
sample value. This is because the sample selected is not perfectly
representative of the test population. Therefore, a small random
sampling error is evident. As the sampling error is the outcome of
chance, the laws of probabilities are applicable to it. The sampling
error is inversely proportional to the sample size. As the sample size
increases, the sampling error decreases. Although sampling errors
cannot be avoided altogether, they can be controlled through careful
sample designs, large samples, and multiple contacts to assure

representative response. Random sampling error represents how
accurately the sample’s true mean value(x sample), is representative
of the population’s true mean value(X population).
Non-Sampling error: (Measurement errors)
Non- sampling errors also known as systematic errors occur due to
the nature of the study’s design and the correctness of execution.
Non-sampling error includes non-observation errors and
measurement errors.
The other main cause of unrepresentative samples is non sampling
error. This type of error can occur whether a census or a sample is
being used. Like sampling error, non sampling error may either be
produced by participants in the statistical study or be an innocent by
product of the sampling plans and procedures.
Non- observational errors occur when data cannot be collected
from the sampling unit or variable. Measurement errors arise from
various sources like respondents, interviewers, supervisors, and even
data processing systems. Non-observation error is further divided
into non-coverage and non-response error. In probability sampling,
each element of the population has a non-zero chance of selection
into the sample. Non-coverage error occurs when an element in the
target population has no chance of being selected into the sample.
Non-response error occurs when data cannot be collected from the
element actually selected into the sample. This may be due to the
refusal of the element to cooperate because of language barrier,
health limitation, or non availability of the element during the survey

74
period. Selection of faulty sampling frame may also result in a non-
sampling error. Sampling frame error is said to occur when certain
non potential respondents are included in the sampling frame and
certain deserving respondents are rejected.
The simplest example of non sampling error is inaccurate
measurements due to malfunctioning instruments or poor procedures.
For example, consider the observation of human weights. If persons
are asked to state their own weights themselves, no two answers will
be of equal reliability. The people will have weighed themselves on
different scales in various states of poor calibration. An individual`s
weight fluctuates diurnally by several pounds, so that the time of
weighing will affect the answer. The scale reading will also vary
with the person`s state of undress. Responses therefore will not be of
comparable validity unless all persons are weighed under the same
circumstances.
There are two types of statistical inferences: estimation of population
parameters and hypothesis testing. Hypothesis testing is one of the
most important tools of application of statistics to real life problems.
Most often, decisions are required to be made concerning
populations on the basis of sample information. Statistical tests are
used in arriving at these decisions.
Error
When using probability to decide whether a statistical test provides
evidence for or against our predictions, there is always a chance of

driving the wrong conclusions. Even when choosing a probability
level of 95%, there is always a 5% chance that one rejects the null
hypothesis when it was actually correct. This is called Type I error,
represented by the Greek letter .
It is possible to err in the opposite way if one fails to reject the null
hypothesis when it is, in fact, incorrect. This is called Type II error,
represented by the Greek letter . These two errors are represented
in the following chart.
Null Hypothesis (H0)
is true
He truly is not guilty
Alternative Hypothesis
(H1) is true
He truly is guilty
Accept Null
Hypothesis
Acquittal
Right decision
Wrong decision
Type II Error
Reject Null
Hypothesis
Conviction
Wrong decision
Type I Error
Right decision
For example, if we reject Ho when it is false, we have made a correct
decision (upper-right cell.) However, if we reject Ho when it is true,
we have made a “Type I error” (upper left cell.) This error has a
particular name, alpha, noted by the Greek character. In a correctly
designed experiment, we make our decision to reject Ho based on a
probability statement – how rare we would see the results under the
assumption of the null hypothesis. If that probability turns out to be

76
small, say at 0.05, we conclude that this is sufficient evidence to
reject innocence and proclaim guilt.
On the other hand, if Ho is false (the person is indeed guilty) and do
not reject Ho, we commit a Type II error. The probability of
committing a Type II error is called beta. The power of the test is
defined to be one minus beta. When a test has low power it means
that we are likely to make a Type II error, (i.e., fail to reject Ho when
it is actually false.) Looking at it the other way, the higher the
“power” the better chance of rejecting Ho when it is false.
A hypothesis may be defined as a logically conjectured relationship
between two or more variables, expressed in the form of a testable
statement. Relationship is proposed by using a strong logical
argumentation. This logical relationship may be part of theoretical
framework of the study.
The following are some of the important factors should be
considered while frame hypothesis

Let us look at some of the hypotheses
1. Employees in organization have higher than average level of
satisfaction (variable).
2. Level of job satisfaction of the employees is associated with
their level of efficiency.
3. Level of job satisfaction of the employees is positively
associated with their level of efficiency.
4. The higher the level of job satisfaction of the employees the
lower their level of absenteeism.
These are testable propositions. First hypothesis contains only one
variable. The second one has two variables which have been shown
to be associated with each other but the nature of association has not
been specified (non-directional relationship). In the third hypothesis
we have gone a step further where in addition to the relationship
between the two variables, the direction of relationship (positive) has
also been given. In the fourth hypothesis level of efficiency has been
replaced with level of absenteeism, the direction of relationship
between the two variables has been specified (which is negative). In
the following discussion the researcher will find these hypotheses
being quoted as part of the examples.
Types of Hypotheses
Descriptive hypothesis contains only one variable thereby it is also
called as univariate hypothesis. Descriptive hypotheses typically
state the existence, size, form, or distribution of some variable. The

78
first hypothesis contains only one variable. It only shows the
distribution of the level of commitment among the officers of the
organization which is higher than average. Such a hypothesis is an
example of a Descriptive Hypothesis. Researchers usually use
research questions rather than descriptive hypothesis. For example a
question can be: What is the level of commitment of the officers in
your organization?
These are the propositions that describe a relationship between two
variables. The relationship could be non-directional or directional,
positive or negative, causal or simply correlational. While stating the
relationship between the two variables, if the terms of positive,
negative, more than, or less than are used then such hypotheses are
directional because the direction of the relationship between the
variables (positive/negative) has been indicated (see hypotheses 3
and 4). These hypotheses are relational as well as directional. The
directional hypothesis is the one in which the direction of the
relationship has been specified. Non-directional hypothesis is the one
in which the direction of the association has not been specified. The
relationship may be very strong but whether it is positive or negative
has not been postulated (see hypothesis 2).
Correlational hypotheses
State merely that the variables occur together in some specified
manner without implying that one causes the other. Such weak
claims are often made when we believe that there are more basic

causal forces that affect both variables. For example: Level of job
satisfaction of the officers is positively associated with their level of
efficiency. Here we do not make any claim that one variable causes
the other to change. That will be possible only if we have control on
all other factors that could influence our dependent variable.
Explanatory (causal) hypotheses
Imply the existence of, or a change in, one variable causes or leads to
a change in the other variable. This brings in the notions of
independent and the dependent variables. Cause means to “help
make happen.” So the independent variable may not be the sole
reason for the existence of, or change in the dependent variable. The
researcher may have to identify the other possible causes, and control
their effect in case the causal effect of independent variable has to be
determined on the dependent variable. This may be possible in an
experimental design of research.
It is used for testing the hypothesis formulated by the researcher.
Researchers treat evidence that supports a hypothesis differently
from the evidence that opposes it. They give negative evidence more
importance than to the positive one. It is because the negative
evidence tarnishes the hypothesis. It shows that the predictions made
by the hypothesis are wrong. The null hypothesis simply states that
there is no relationship between the variables or the relationship
between the variables is “zero.” That is how symbolically null
hypothesis is denoted as “H0”. For example:

80
H0 = There is no relationship between the level of satisfaction and
the level of efficiency. Or
H0 = The relationship between level of job satisfaction and the level
of efficiency is zero. Or
The two variables are independent of each other.
It does not take into consideration the direction of association (i.e.
H0 is non directional), which may be a second step in testing the
hypothesis. First we look whether or not there is an association then
we go for the direction of association and the strength of association.
Experts recommend that we test our hypothesis indirectly by testing
the null hypothesis. In case we have any credibility in our hypothesis
then the research data should reject the null hypothesis. Rejection of
the null hypothesis leads to the acceptance of the alternative
hypothesis.
The alternative (to the null) hypothesis simply states that there is a
relationship between the variables under study. In our example it
could be: there is a relationship between the level of job satisfaction
and the level of efficiency. Not only there is an association between
the two variables under study but also the relationship is perfect
which is indicated by the number “1”. Thereby the alternative
hypothesis is symbolically denoted as “H1”. It can be written like
this:

H1: There is a relationship between the level of job satisfaction of the
officers and their level of efficiency.
Research hypothesis is the actual hypothesis formulated by the
researcher which may also suggest the nature of relationship i.e. the
direction of relationship. In our example it could be:
Level of job satisfaction of the officers is positively associated with
their level of efficiency.
In research, a hypothesis serves several important functions:
1. It guides the direction of the study: Quite frequently one comes
across a situation when the researcher tries to collect all possible
information on which he could lay his hands on. Later on he may
find that only part of it he could utilize. Hence there was an
unnecessary use of resources on trivial concerns. In such a
situation, hypothesis limits what shall be studied and Hypothesis
should be related to available techniques of research.
Hypothesis may have empirical reality; still we are looking for
tools and techniques that could be used for the collection of data.
If the techniques are not there then the researcher is handicapped.
Therefore, either the techniques are already available or the
researcher is in a position to develop suitable techniques for the
study. Hypothesis should be related to a body of theory.
Hypothesis has to be supported by theoretical argumentation. For

82
this purpose the research may develop his/her theoretical
framework which could help in the generation of relevant
hypothesis. For the development of a framework the researcher
shall depend on the existing body of knowledge. In such an effort
a connection between the study in hand and the existing body of
knowledge can be established. That is how the study could
benefit from the existing knowledge and later on through testing
the hypothesis could contribute to the reservoir of knowledge.
2. It identifies facts that are relevant and those that are not:
Who shall be studied (married couples), in what context they
shall be studied (their consumer decision making), and what shall
be studied (their individual perceptions of their roles).
3. It suggests which form of research design is likely to be the
most appropriate: Depending upon the type of hypothesis a
decision is made about the relative appropriateness of different
research designs for the study under consideration. The design
could be a survey design, experimental design, content analysis,
case study, participation observation study, and/or Focus Group
Discussions.
4. It provides a framework for organizing the conclusions of the
findings:
Characteristics of a Testable Hypothesis
 Hypothesis must be conceptually clear. The concepts used in
the hypothesis should be clearly defined, operationally if

possible. Such definitions should be commonly accepted and
easily communicable among the research scholars.
 Hypothesis should have empirical referents. The variables
contained in the hypothesis should be empirical realities. In
case these are not empirical realities then it will not be possible
to make the observations. Being handicapped by the data
collection, it may not be possible to test the hypothesis. Watch
for words like ought, should, bad.
 Hypothesis must be specific. The hypothesis should not only
be specific to a place and situation but also these should be
narrowed down with respect to its operation. Let there be no
global use of concepts whereby the researcher is using such a
broad concept which may all inclusive and may not be able to
tell anything. For example somebody may try to propose the
relationship between urbanization and family size. Yes
urbanization influences in declining the size of families. But
urbanization is such comprehensive variable which hide the
operation of so many other factor which emerge as part of the
urbanization process. These factors could be the rise in
education levels, women’s levels of education, women
empowerment, emergence of dual earner families, decline in
patriarchy, accessibility to health services, role of mass media,
and could be more. Therefore the global use of the word
‘urbanization’ may not tell much. Hence it is suggested to that
the hypothesis should be specific.

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Statisticians follow a formal process to determine whether to reject a
null hypothesis, based on sample data. This process, called
hypothesis testing, consists of five steps.
Step-1- State the hypotheses. This involves stating the null and
alternative hypotheses. The hypotheses are stated in such
a way that they are mutually exclusive. That is, if one is
true, the other must be false.
Step-2- Formulate an analysis plan. The analysis plan describes
how to use sample data to evaluate the null hypothesis.
The evaluation often focuses around a single test statistic.
Step-3- Analyze sample data. Find the value of the test statistic
(mean score, proportion, t-score, z-score, etc.) described
in the analysis plan.
Step-4- Interpret results. Apply the decision rule described in the
analysis plan.
Step- 5- Make a decision.
If the test statistic falls in the
critical region
If the test statistic does not fall
in the Critical region
Reject H0 in favour of HA. Conclude that there is not enough
evidence to reject H0.

Decision Rules
The analysis plan includes decision rules for rejecting the null
hypothesis. In practice, statisticians describe these decision rules in
two ways - with reference to a P-value or with reference to a region
of acceptance.

P-value. The strength of evidence in support of a null
hypothesis is measured by the P-value. Suppose the test
statistic is equal to S. The P-value is the probability of
observing a test statistic as extreme as S, assuming the null
hypotheis is true. If the P-value is less than the significance
level, we reject the null hypothesis.

Region of acceptance. The region of acceptance is a range of
values. If the test statistic falls within the region of acceptance,
the null hypothesis is not rejected. The region of acceptance is
defined so that the chance of making a Type I error is equal to
the significance level.
The set of values outside the region of acceptance is called the
region of rejection. If the test statistic falls within the region of
rejection, the null hypothesis is rejected. In such cases, we say
that the hypothesis has been rejected at the α level of
significance.
These approaches are equivalent. Some statistics texts use the P-
value approach; others use the region of acceptance approach. In
subsequent lessons, this tutorial will present examples that illustrate
each approach.

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A test of a statistical hypothesis, where the region of rejection is on
only one side of the sampling distribution, is called a one-tailed test.
For example, suppose the null hypothesis states that the mean is less
than or equal to 10. The alternative hypothesis would be that the
mean is greater than 10. The region of rejection would consist of a
range of numbers located on the right side of sampling distribution;
that is, a set of numbers greater than 10.
A test of a statistical hypothesis, where the region of rejection is on
both sides of the sampling distribution, is called a two-tailed test.
For example, suppose the null hypothesis states that the mean is
equal to 10. The alternative hypothesis would be that the mean is less
than 10 or greater than 10. The region of rejection would consist of a
range of numbers located on both sides of sampling distribution; that
is, the region of rejection would consist partly of numbers that were
less than 10 and partly of numbers that were greater than 10.

Chapter - V
Tools for Data Collection
Mailed questionnaire
Rating scale
Checklist
Document schedule/data sheet
Schedule for institutions
Construction of schedules and questionnaires
Data need determination
Preparation of “Dummy” tables
Determination of the respondents’ level
Data gathering method decision
Instrument drafting
Evaluation of the draft instrument
Pre-testing:
Specification of procedures/instructions
Designing the format

88
Types of surveys
Selecting the survey method
Population Issues
Sampling issues
Question issues
Pilot studies and pre-tests
Pre-test
Meaning
techniques

Chapter - V
Tools for Data Collection
The researcher would have to decide which sort of data he would be
using (thus collecting) for his study and accordingly he will have to
select one or the other method of data collection. The methods of
collecting primary and secondary data differ since primary data are
to be originally collected, while in case of secondary data the nature
of data collection work is merely that of compilation. We describe
the different methods of data collection, with the pros and cons of
each method.
The various methods of data gathering involve the use of appropriate
recording forms. These are called tools or instruments of data
collection. They consist of
 Observation schedule
 Interview guide
 Interview schedule
 Mailed questionnaire
 Rating scale
 Checklist
 Document schedule/data sheet
 Schedule for institutions
Each of the above tools is used for a specific method of data
gathering: Observation schedule for observation method, interview

90
schedule and interview guide for interviewing, questionnaire for mail
survey, and so on.
Functions
The tools of data collection translate the research objectives into
specific questions/ items, the responses to which will provide the
data required to achieve the research objectives. In order to achieve
this purpose, each question/item must convey to the respondent the
idea or group of ideas required by the research objectives, and each
item must obtain a response which can be analysed for fulfilling the
research objectives.
Information gathered through the tools provides descriptions of
characteristics of individuals, institutions or other phenomena under
study. It is useful for measuring the various variables pertaining to
the study. The variables and their interrelationships are analysed for
testing the hypothesis or for exploring the content areas set by the
research objectives.
A brief description of the various tools of data collection is given
below.
Observation schedule
This is a form on which observations of an object or a phenomenon
are recorded. The items to be observed are determined with reference
to the nature and objectives of the study. They are grouped into
appropriate categories and listed in the schedule in the order in which
the observer would observe them.

The schedule must be so devised as to provide the required verifiable
and quantifiable data and to avoid selective bias and
misinterpretation of observed items. The units of observation must be
simple, and meticulously worded so as to facilitate precise and
uniform recording.
Interview guide
Interviews
Interviews are a far more personal form of research than
questionnaires. In the personal interview, the interviewer works
directly with the respondent. Unlike with mail surveys, the
interviewer has the opportunity to probe or ask follow-up questions.
And, interviews are generally easier for the respondent, especially if
what is sought is opinions or impressions. Interviews can be very
time consuming and they are resource intensive. The interviewer is
considered a part of the measurement instrument and interviewers
have to be well trained in how to respond to any contingency.
Almost everyone is familiar with the telephone interview.
Telephone interviews enable a researcher to gather information
rapidly. Most of the major public opinion polls that are reported were
based on telephone interviews. Like personal interviews, they allow
for some personal contact between the interviewer and the
respondent. And, they allow the interviewer to ask follow-up
questions. But they also have some major disadvantages. Many
people don't have publicly-listed telephone numbers. Some don't
have telephones. People often don't like the intrusion of a call to their

92
homes. And, telephone interviews have to be relatively short or
people will feel imposed upon.
Interview schedule and mailed Questionnaire both these tools are
widely used in surveys. Both are complete lists of questions on
which information is elicited from the respondents. The basic
difference between them lies in recording responses. While the
interviewer fills out a schedule, the respondent completes a
questionnaire.
Rating Scale
This is a recording form used for measuring individual's attitudes,
aspirations and other psychological and behavioural aspects, and
group behaviour.
Checklist
This is the simplest of all the devices. It consists of a prepared list of
items pertinent to an object or a particular task. The presence or
absence of each item may be indicated by checking 'yes' or 'no' or
multipoint scale. The use of a checklist ensures a more complete
consideration of all aspects of the object, act or task. Checklists
contain terms, which the respondent understands, and which more
briefly and succinctly express his views than answers to open-ended
question. It is a crude device, but careful pre-test can make it less so.
It is at best when used to test specific hypothesis. It may be used as
an independent tool or as a part of a schedule/questionnaire.

Document Schedule/Data Sheet
This is a list of items of information to be obtained from documents,
records and other materials. In order to secure measurable data, the
items included in the schedule are limited to those that can be
uniformly secured from a large number of case histories or other
records.
Schedule for Institutions
This is used for survey of organisations like business enterprises,
educational institutions, social or cultural organisations and the like.
It will include various categories of data relating to their profile,
functions and performance. These data are gathered from their
records, annual reports and financial statements.
Construction of Schedules and Questionnaires
Schedule vs. Questionnaire
Schedules and questionnaires are the most common instruments of
data collection. These two types of tools have much in common.
Both of them contain a set of questions logically related to a problem
under study; both aim at eliciting responses from the respondents; in
both cases the content, response structure, the wordings of questions,
question sequence, etc. are the same for all respondents. Then why
should they be denoted by the different terms: 'schedule' and
'questionnaires'? This is because the methods for which they are used
are different. While a schedule is used as a tool for interviewing, a
questionnaire is used for mailing.

94
This difference in usage gives rise to a subtle difference between
these two recording forms. That is, the interviewer in a face-to-face
interviewing fills a schedule, whereas the respondent himself fills in
a questionnaire. Hence the need for using two different terms.
The tool is referred to as a schedule when it is used for interviewing;
and it is called a questionnaire when it is sent to a respondent for
completion and return.
The process of construction of a schedule and a questionnaire is
almost same, except some minor differences in mechanics. This
process is not a matter of simply listing questions that comes to
researchers mind. It is a rational process involving much time, effort
and thought. It consists of the following major steps:
Data need determination: As an interview schedule or a mailed
questionnaire is an instrument for gathering data for a specific study,
its construction should flow logically from the data required for the
given study.
Preparation of “Dummy” tables: The best way to ensure the
requirements of information is to develop “dummy” tables in which
to display the data to be gathered.
Determination of the respondents’ level: Who are our respondents?
Are they persons with specialized knowledge relating to the problem
under study? Or are they lay people? What is their level of

knowledge and understanding? The choice of words and concepts
depends upon the level of the respondents' knowledge.
Data gathering method decision: Which communication mode is
most appropriate - face-to-face interview or mailing? The choice of
question structure depends largely on the communication mode
chosen.
Instrument drafting: After determining the data required for the
study, first, a broad outline of the instrument may be drafted, listing
the various broad categories of data. Second, the sequence of these
groupings must be decided. Third, the questions to be asked under
each group heading must be listed. All conceivable items relevant to
the 'data need' should be compiled.
Evaluation of the draft instrument: In consultation with other
qualified persons, the researcher must rigorously examine each
question in the draft instrument.
Pre-testing: The revised draft must be pre-tested in order to identify
the weaknesses of the instrument and to make the required further
revisions to rectify them.
Specification of procedures/instructions: After the instruction is
finalised after pre-tests, the procedures or instructions, relating to its
use must be specified.
Designing the format: The format should be suited to the needs of
the research. The instrument should be divided into different sections
relating to the different aspects of the problem.

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A survey instrument - interview schedules or questionnaire - is useful
for collecting various types of information, viz., (a) factual
information - facts about the respondents: sex, age, marital status,
education, religion, caste or social class, income and occupation; and
facts about events and circumstances, (b) psychological information
such as attitudes, opinions, beliefs, and expectations, and (c)
behavioural information, like social participation, and so on.
Once the information need is determined as explained in the previous
topic, we can begin question construction. This involves four major
decision areas. They are: (a) question relevance and content, (b)
question wording, (c) response form, and (d) question order or
sequence.
Question to be included in the instrument should pass certain tests. Is
it relevant to the research objectives? Can it yield significant
information for answering an investigative question? If not, it should
note be included in the instrument.
Question wording
This is a difficult task. The function of a question in a
schedule/questionnaire is to elicit particular information without
distortion. “Questioning people”, says opinion “is more like trying to
catch a particular elusive fish, by hopefully casting different kinds of
bait at different depths, without knowing what goes on beneath the

surface.” As the meaning of words differs from person to person, the
question designer should choose words which have the following
characteristics:
a. Shared vocabulary.
b. Uniformity of meaning.
c. Exactness.
d. Simplicity.
e. Neutrality. The words to be used must be neutral ones, i.e.,
free from the distorting influence of fear, prestige, bias or
emotion.
Certain other problem areas of question wording are
a. Unwarranted assumptions,
b. Personalization,
c. Presumptions,
d. Hypothetical question,
e. Questions in embarrassing matters.
Some of the approaches to deal with this problem are:
i. to express the question in the third person; instead of asking
the respondent for his views, he is asked about the views of
others:
ii. to use a drawing of two persons in a certain setting with
'balloons' containing speech coming from their mouths, as in
a cartoon - leaving one person's balloon empty and asking
the respondent to put himself in the position of that person
and to fill in the missing words; and
iii. to use sentence completion tests.

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Response form or types of Questions
The third major area in question construction is the types of
questions to be included in the instrument. They may be classified
into open questions and closed questions. Closed questions may be
dichotomous, multiple choice or declarative ones.
A good questionnaire forms an integrated whole. The researcher
weaves questions together so they flow smoothly. He or she includes
introductory remarks and instructions for clarification and measures
each variable with one or more survey questions.
What should be asked?
The problem definition will indicate which type of information must
be collected to answer the research question; different types of
questions may be better at obtaining certain type of information than
others.
1. Questionnaire Relevancy
A questionnaire is relevant if no unnecessary information is collected
and if the information that is needed to solve the problem is obtained.
Asking the wrong or an irrelevant question is a pitfall to be avoided.
If the task is to pinpoint compensation problems, for example,
questions asking for general information about morale may be
inappropriate. To ensure information relevancy, the researcher must

be specific about data needs, and there should be a rationale for each
item of information.
2. Questionnaire Accuracy
Once the researcher has decided what should be asked, the criterion
of accuracy becomes of primary concern. Accuracy means that the
information is reliable and valid. While experienced researchers
believe that one should use simple, understandable, unbiased,
unambiguous, and nonirritating words. Obtaining accurate answer
from respondents is strongly influenced by the researcher’s ability to
design a questionnaire that facilitates recall and that will motivate the
respondent to cooperate. Therefore avoid jargon, slang, and
abbreviations. The respondents may not understand some basic
terminology. Respondents can probably tell thee interviewer whether
they are married, single, divorced, separated, or widowed, but
providing their “marital status” may present a problem. Therefore,
asking somebody about his/her marital status while the person may
not understand the meaning of marital status is likely to mess up the
information. Words used in the questionnaire should be readily
understandable to all respondents.
3. Avoid Ambiguity, Confusion, and Vagueness.
Ambiguity and vagueness plague most question writers. A researcher
might make implicit assumptions without thinking of respondents’
perspectives. For example, the question, “what is your income?”
could mean weekly, monthly, or annual: family or personal; before
taxes or after taxes; for this year or last year; from salary or from all

100
sources. The confusion causes inconsistencies in how different
respondents assign meaning to and answer the question.
Another source of ambiguity is the use indefinite words or response
categories. Consider the words such as often, occasionally, usually,
regularly, frequently, many, good, fair, and poor. Each of these
words has many meanings. For one person frequent reading of Time
magazine may be reading six or seven issues a year; for another it
may be two issues a year. The word fair has great variety of
meanings; the same is true for many indefinite words.
4. Avoid Double-Barreled Questions
Make each question about one and only one. A double barreled
question consists of two or more questions joined together. It makes
the respondent’s answer ambiguous. For example, if asked, “Does
this company have pension and health insurance benefits?” a
respondent at the company with health insurance benefits only might
answer either yes or no. The response has an ambiguous meaning
and the researcher cannot be certain of the respondent’s intentions.
When multiple questions are asked in one question, the results may
be exceedingly difficult to interpret.
5. Avoid Leading Questions
Make respondents feel that all responses are legitimate. Do not let
them aware of an answer that the researcher wants. A leading
question is the one that leads the respondent to choose one response
over another by its wording. For example, the question, “you don’t
smoke, do you?” leads respondents to state that they do not smoke.

“Don’t you think that women should be empowered?” In most the
cases the respondent is likely to agree with the statement.
6. Avoid Loaded Questions
Loaded questions suggest a socially desirable answer or are
emotionally charged. “Should the city government repair all the
broken streets?” Most of the people are going to agree with this
question simply because this is highly socially desirable. A question
which may be challenging the traditionally set patterns of behavior
may be considered as emotionally charged i.e. it is loaded with such
material which may hit the emotions of the people. Look at some
behaviors associated with masculinity in Pakistani society. Let us ask
a husband “Have you ever been beaten up by your wife?” Straight
away this question may be considered to be a challenge to the
masculinity of the person. Hence it may be embarrassing for the
person to admit such an experience. Therefore, even if the husband
was beaten up by his wife, he might give a socially desirable answer.
7. Avoid Burdensome Questions that may Tax the Respondent’s
Memory
A simple fact of human life is that people forget. Researchers writing
questions about past behavior or events should recognize that certain
questions may make serious demand on the respondent’s memory.
“How did you feel about your brother when you were 6 years old?” It
may very difficult to recall something from the childhood.

102
8. Arrange Questions in a Proper Sequence
The order of question, or the question sequence, may serve several
functions for the researcher. If the opening questions are interesting,
simple to comprehend, and easy to answer, respondent’s cooperation
and involvement can be maintained throughout the questionnaire. If
respondent’s curiosity is not aroused at the outset, they can become
disinterested and terminate the interview.
Sequencing specific questions before asking about broader issues is a
common cause of question order bias. In some situations it may be
advisable to ask general question before specific question to obtain
the freest opinion of the respondent. This procedure, known as funnel
technique, allows the researcher to understand the respondent’s
frame of reference before asking specific questions about the level of
respondent’s information and intensity of his or her opinions.
9. Use Filter Question, if Needed
Asking a question that doesn’t apply to the respondent or that the
respondent is not qualified to answer may be irritating or may cause
a biased response. Including filter question minimizes the chance of
asking questions that are inapplicable. Filter question is that question
which screens out respondents not qualified to answer a second
question. For example the researcher wants to know about the
bringing up of one’s children. “How much time do you spend
playing games with your oldest child?” What if the respondent is
unmarried? Even if the respondent is married but does not have the
child. In both these situations the question is inapplicable to him/her.

Before this question the person may put a filter question whether or
not the respondent is married.
10. Layout of the questionnaire
There are two format or layout issues: the overall physical layout of
the questionnaire and the format of questions and responses. Good
lay out and physical attractiveness is crucial in mail, Internet, and
other self-administered questionnaires. For different reason it is also
important to have a good layout in questionnaires designed for
personal and telephone interviews. Give each question a number and
put identifying information on questionnaire. Never cramp questions
together or create a confusing appearance. Make a cover sheet or
face sheet for each, for administrative use. Put the time and date of
the interview, the interviewer, the respondent identification number,
and interviewer’s comments and observations on it. Give
interviewers and respondents instructions on the questionnaire. Print
instructions in a different style from question to distinguish them.
Lay out is important for mail questionnaires because there is no
friendly interviewer to interact with the respondent. Instead the
questionnaire’s appearance persuades the respondents. In mail
surveys, include a polite, professional cover letter on letterhead
stationery, identifying the researcher and offering a telephone
number for any questions. Always end with “Thank you for your
participation.”

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Question order or Sequence
The order in which questions are arranged in a schedule/
questionnaire is as important as question wording. It has two major
implications. First, an appropriate sequence can ease the respondent's
task in answering. Second, the sequence can either create or avoid
biases due to context effects, i.e., the effects of preceding questions
on the response to later questions.
Mechanics of the Schedule and Questionnaire
In addition to question wording and question construction, the
mechanics of the form should also be considered in the design of a
schedule/questionnaire. The mechanics of the form has several
aspects: items of the form, instruction, pre-coding, sectionalisation,
spacing, paper, printing, margins, etc.
Items of the form: The following items are mandatory for schedules
and questionnaires.
1. The name of the organization collecting the data should appear
at the top of front -page. The name of the sponsor, of the study,
if any should also be shown.
2. The title of the study should appear in large print next to the
name of the organization on the first page. Below this title, the
title of the tool - e.g., 'Schedule for-consumers; - may be noted. .
3. The confidentialness of the data should be made cleat.
4. A place for writing the date of filling in the form should be
provided.
5. A serial number to each copy of the tool may be assigned.
6. The pages of the instrument should be numbered.

Instructions: In the face sheet below the title of the questionnaire, a
brief statement of the objective of the study, the confidentialness of
the data, and instructions relating to answering the questions may be
provided. .
Pre-coding: Items in the tool should be pre-coded so as to facilitate
transcription of data.
Sectionalisation: There should be a separate section for each topical
area.
Spacing: For each open-ended question, an adequate space should be
provided for answer. There should, indeed more space than seems
necessary, for some interviewers/ respondents may write in a large
script for legibility. Moreover, liberal spacing is a stimulus for the
questionnaire respondent to write more fully. Even short-answer
questions should be spaced, so that the interviewer/respondent will
not easily confuse the line, from which he is reading.
Paper: The paper used for mimeographing/printing should be of
good quality.
Printing: Mailed questionnaire should necessarily be printed in order
to make it attractive and to minimise the postal expenditure.
Margins: One inch margin on the left side of the sheet and one-half
inch margin on other sides may be provided. If the instrument is to
be bound, left-side margin should conform to the type of binding
used.

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Indentation: This is required for 'yes' or 'no' questions. If the
respondent's answer is 'yes', then a series of questions is offered. If
the answer is 'no' a different series of questions is offered.
Note of thanks: A final note or comment of thanks for the
cooperation of the respondent should be included at the end of the
instrument.
Types of Surveys
Surveys can be divided into two broad categories: the questionnaire
and the interview. Questionnaires are usually paper-and-pencil
instruments that the respondent completes. Interviews are completed
by the interviewer based on the respondent says. Sometimes, it's hard
to tell the difference between a questionnaire and an interview. For
instance, some people think that questionnaires always ask short
closed-ended questions while interviews always ask broad open-
ended ones. But you will see questionnaires with open-ended
questions (although they do tend to be shorter than in interviews) and
there will often be a series of closed-ended questions asked in an
interview.
Survey research has changed dramatically in the last ten years. We
have automated telephone surveys that use random dialing methods.
There are computerized kiosks in public places that allow people to
ask for input. A whole new variation of group interview has evolved
as focus group methodology. We'll discuss the relative advantages
and disadvantages of these different survey types in Advantages and
Disadvantages of Survey Methods.

Mail survey
There are many advantages to mail surveys.
1. They are relatively inexpensive to administer.
2. It can send the exact same instrument to a wide number of people.
3. They allow the respondent to fill it out at their own convenience.
But there are some disadvantages as well.
1. Response rates from mail surveys are often very low. And,
2. Mail questionnaires are not the best vehicles for asking for
detailed written responses.
A second type is the group administered questionnaire. A sample
of respondents is brought together and asked to respond to a
structured sequence of questions. Traditionally, questionnaires were
administered in group settings for convenience. The researcher could
give the questionnaire to those who were present and be fairly sure
that there would be a high response rate. If the respondents were
unclear about the meaning of a question they could ask for
clarification. And, there were often organizational settings where it
was relatively easy to assemble the group (in a company or business,
for instance).
What's the difference between a group administered questionnaire
and a group interview or focus group?
In the group administered questionnaire, each respondent is handed
an instrument and asked to complete it while in the room. Each
respondent completes an instrument. In the group interview or focus

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group, the interviewer facilitates the session. People work as a group,
listening to each other's comments and answering the questions.
Someone takes notes for the entire group -- people don't complete an
interview individually.
A less familiar type of questionnaire is the household drop-off
survey. In this approach, a researcher goes to the respondent's home
or business and hands the respondent the instrument. In some cases,
the respondent is asked to mail it back or the interview returns to
pick it up. This approach attempts to blend the advantages of the mail
survey and the group administered questionnaire. Like the mail
survey, the respondent can work on the instrument in private, when
it's convenient. Like the group administered questionnaire, the
interviewer makes personal contact with the respondent they don't
just send an impersonal survey instrument. And, the respondent can
ask questions about the study and get clarification on what is to be
done. Generally, this would be expected to increase the percent of
people who are willing to respond.
Selecting the Survey Method
Selecting the type of survey going to use is one of the most critical
decisions in many social research contexts. The researcher has to use
your judgment to balance the advantages and disadvantages of
different survey types. Here, number of questions might ask that can
help guide decision about selecting type of survey.

Population Issues
The first set of considerations has to do with the population and its
accessibility.
 Can the population be enumerated?
For some populations, a complete listing of the units that will be
sampled. For others, such a list is difficult or impossible to compile.
For instance, there are complete listings of registered voters or
person with active drivers’ licenses. But no one keeps a complete list
of homeless people. If doing a study that requires input from
homeless persons, you are very likely going to need to go and find
the respondents personally. In such contexts, you can pretty much
rule out the idea of mail surveys or telephone interviews.
 Is the population literate?
Questionnaires require that your respondents can read. While this
might seem initially like a reasonable assumption for many adult
populations, we know from recent research that the instance of adult
illiteracy is alarmingly high. And, even if your respondents can read
to some degree, your questionnaire may contain difficult or technical
vocabulary. Clearly, there are some populations that you would
expect to be illiterate. Young children would not be good targets for
questionnaires.
 Are there language issues?
We live in a multilingual world. Virtually every society has members
who speak other than the predominant language. Some countries

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(like Canada) are officially multilingual. And, our increasingly
global economy requires us to do research that spans countries and
language groups. Can you produce multiple versions of your
questionnaire? For mail instruments, can you know in advance the
language your respondent speaks, or do you send multiple
translations of your instrument? Can you be confident that important
connotations in your instrument are not culturally specific? Could
some of the important nuances get lost in the process of translating
your questions?
 Will the population cooperate?
People who do research on immigration issues have a difficult
methodological problem. They often need to speak with
undocumented immigrants or people who may be able to identify
others who are. Why would we expect those respondents to
cooperate? Although the researcher may mean no harm, the
respondents are at considerable risk legally if information they
divulge should get into the hand of the authorities. The same can be
said for any target group that is engaging in illegal or unpopular
activities.
 What are the geographic restrictions?
Is your population of interest dispersed over too broad a geographic
range for you to study feasibly with a personal interview? It may be
possible for you to send a mail instrument to a nationwide sample.
You may be able to conduct phone interviews with them. But it will
almost certainly be less feasible to do research that requires

interviewers to visit directly with respondents if they are widely
dispersed.
Sampling Issues
The sample is the actual group you will have to contact in some way.
There are several important sampling issues you need to consider
when doing survey research.
 What data is available?
What information do you have about your sample? Do you know
their current addresses? Their current phone numbers? Are your
contact lists up to date?
 Can respondents be found?
Can your respondents be located? Some people are very busy. Some
travel a lot. Some work the night shift. Even if you have an accurate
phone or address, you may not be able to locate or make contact with
your sample.
 Who is the respondent?
Who is the respondent in your study? Let's say you draw a sample of
households in a small city. A household is not a respondent. Do you
want to interview a specific individual? Do you want to talk only to
the "head of household" (and how is that person defined)? Are you
willing to talk to any member of the household? Do you state that
you will speak to the first adult member of the household who opens
the door? What if that person is unwilling to be interviewed but

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someone else in the house is willing? How do you deal with multi-
family households? Similar problems arise when you sample groups,
agencies, or companies. Can you survey any member of the
organization? Or, do you only want to speak to the Director of
Human Resources? What if the person you would like to interview is
unwilling or unable to participate? Do you use another member of
the organization?
 Can all members of population be sampled?
If you have an incomplete list of the population (i.e., sampling
frame) you may not be able to sample every member of the
population. Lists of various groups are extremely hard to keep up to
date. People move or change their names. Even though they are on
your sampling frame listing, you may not be able to get to them.
And, it's possible they are not even on the list.
 Are response rates likely to be a problem?
Even if you are able to solve all of the other population and sampling
problems, you still have to deal with the issue of response rates.
Some members of your sample will simply refuse to respond. Others
have the best of intentions, but can't seem to find the time to send in
your questionnaire by the due date. Still others misplace the
instrument or forget about the appointment for an interview. Low
response rates are among the most difficult of problems in survey
research. They can ruin an otherwise well-designed survey effort.

Question Issues
Sometimes the nature of what you want to ask respondents will
determine the type of survey you select.
 What types of questions can be asked?
Are you going to be asking personal questions? Are you going to
need to get lots of detail in the responses? Can you anticipate the
most frequent or important types of responses and develop
reasonable closed-ended questions?
 How complex will the questions be?
Sometimes you are dealing with a complex subject or topic. The
questions you want to ask are going to have multiple parts. You may
need to branch to sub-questions.
 Will screening questions be needed?
A screening question may be needed to determine whether the
respondent is qualified to answer your question of interest. For
instance, you wouldn't want to ask someone their opinions about a
specific computer program without first "screening" them to find out
whether they have any experience using the program. Sometimes you
have to screen on several variables (e.g., age, gender, experience).
The more complicated the screening, the less likely it is that you can
rely on paper-and-pencil instruments without confusing the
respondent.

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 Can question sequence be controlled?
Is your survey one where you can construct in advance a reasonable
sequence of questions? Or, are you doing an initial exploratory study
where you may need to ask lots of follow-up questions that you can't
easily anticipate?
 Will lengthy questions be asked?
If your subject matter is complicated, you may need to give the
respondent some detailed background for a question. Can you
reasonably expect your respondent to sit still long enough in a phone
interview to ask your question?
 Will long response scales be used?
If you are asking people about the different computer equipment they
use, you may have to have a lengthy response list (CD-ROM drive,
floppy drive, mouse, touch pad, modem, network connection,
external speakers, etc.). Clearly, it may be difficult to ask about each
of these in a short phone interview.
Pilot Studies and Pre-Tests
Pilot Study
The need for Pilot Study
It is difficult to plan a major study or project without adequate
knowledge of its subject matter, the population it is to cover, their
level of knowledge and understanding and the like. What are the
issues involved? What are the concepts associated with the subject
matter? How can they be operationalised? What method of study is

appropriate? How long the study will take? How much money it will
cost? These and other related questions call for a good deal of
knowledge of the subject matter of the study and its dimensions. In
order to gain such pre-knowledge of the subject matter of an
extensive study, a preliminary investigation is con-ducted. This is
called a pilot study.
Pre-test
Meaning
While a pilot study is a full-fledged miniature study of a problem,
pre-test is a trial test of a specific aspect of the study such as method
of data collection or data collection instrument - interview schedule,
mailed questionnaire or measurement scale.
An instrument of data collection is designed with reference to the
data requirements of the study. But it cannot be perfected purely on
the basis of a critical scrutiny by the designer and other researchers.
It should he empirically tested. As emphatically pointed by Goode
and Hatt, “no amount of thinking, no matter how logical the mind or
brilliant the insight, is likely to take the place of careful empirical
checking”. Hence pre-testing of a draft instrument is indispensable.
Pre-testing-means trial administration of the instrument to a sample
of respondents before finalising it.

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Pre-testing has several purposes: (1) to test whether the instrument
would elicit responses required to achieve the research objectives, (2)
to test whether the content of the instrument is relevant and adequate,
(3) to test whether wording of questions is clear and suited to the
understanding of the respondents, (4) to test the other qualitative
aspects of the instrument like question structure and question
sequence, and (5) to develop appropriate procedure for administering
the instrument with reference to field conditions.

techniques

Chapter - VI
Data Processing
Editing
Field Editing
In-House Editing
Editing for Consistency
Editing for Completeness
Item Non-response
Editing Questions Answered out of Order
Coding
Code Construction
Production Coding
Data Entries
Cleaning Data
Data Transformation
Indexes and Scales
Unidimensionality
Index Construction
Weighting

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Chapter - VI
Data Processing
Once the data begins to flow in, attention turns to data analysis. If the
project has been done correctly, the analysis planning is already
done. Back at the research design stage or at least by the completion
of the proposal or the pilot test, decisions should have been made
about how to analyze the data. During the analysis stage several
interrelated procedures are performed to summarize and rearrange
the data. The goal of most research is to provide information. There
is a difference between raw data and information. Information refers
to a body of facts that are in a format suitable for decision making,
whereas data are simply recorded measures of certain phenomenon.
The raw data collected in the field must be transformed into
information that will answer the sponsor’s (e.g. manager’s)
questions. The conversion of raw data into information requires that
the data be edited and coded so that the data may be transferred to a
computer or other data storage medium. If the database is large, there
are many advantages to utilizing a computer. Assuming a large
database, entering the data into computer follows the coding
procedure.
Editing
Information may have been noted in haste and now required to be
deciphered. Data should be edited before being presented as

information to ensure that figures or words are accurate. Editing can
be done manually or with computer or both depending upon the
medium, whether paper or electronic.
Editing is the process of checking and adjusting the data for
omissions, legibility, and consistency. Editing may be differentiated
from coding, which is the assignment of numerical scales or
classifying symbols to previously edited data. The purpose of editing
is to ensure the completeness, consistency, and readability of the data
to be transferred to data storage. The editor’s task is to check for
errors and omissions on the questionnaires or other data collection
forms. The editor may have to reconstruct some data. For instance, a
respondent may indicate weekly income rather than monthly income,
as requested on the questionnaire. The editor must convert the
information to monthly data without adding any extraneous
information. The editor “should bring to light all hidden values and
extract all possible information from a questionnaire, while adding
nothing extraneous.”
The editing is done on two levels- micro and macro. In micro-
editing, the basic records are corrected. Usually, all records are
securitized one by one for apparent mistakes. The intent is to
determine consistency of the data. For example, at one place the
distances may be in miles while in another place these may be in km.
Or there may be obvious mistake like showing a distance of 100 km
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On macro level, aggregates are compared with data from other
surveys or files or earlier versions of the same data. This is done to
determine compatibility. For example, one survey has estimated total
number of residents in a sector at 2,000. In another survey of family
size, the total number of residents workout to be 2,500. Obviously,
one of the estimates is wrong. In case, the figure of 2,000 was
considered correct because of the double-check, the second would
have to be reviewed for mistakes in totaling or multiplication.
Several types of data edits are available. In validity edits, it is
ensured that specified units of measures (like kgs, liters or sq.
Meters) are written. In range edit, one would observe that the values
are within pre-established or common sense limits. Similarly, there
are edits for duplications, consistency and history.
On the other hand, there are data errors such as (i) unasked questions,
(ii) unrecorded answers and (iii) inappropriate responses.
Sometimes, a researcher is confronted with a exceptional but true
figure like a very unusual temperature of 90 F (34.4 C). This is
“unrepresentative” or “outlying” observations in a data set. What
should we do about the “outliers” in a sample? “Should such data be
deleted?” is for the researcher to decide.
Occasionally, a fieldworker makes a mistake and records an
improbable answer (e.g., birth year: 1843) or interviews an ineligible

respondent (e.g., someone too young to qualify). Seemingly
contradictory answers, such as “no” to automobile ownership but
“yes” to an expenditure on automobile insurance, may appear on a
questionnaire. There are many problems like these that must be dealt
with before the data can be coded. Editing procedures are conducted
to make the data ready for coding and transfer to data storage.
Field Editing
In large projects, field supervisors are often responsible for
conducting preliminary field edits. The purpose of field editing the
same day as the interview is to catch technical omissions (such as a
blank page), check legibility of the handwriting, and clarify
responses that are logically or conceptually inconsistent. If a daily
field editing is conducted, a supervisor who edits completed
questionnaires will frequently be able to question the interviewers,
who may be able to recall the interview well enough to correct any
problems. The number of “no answers,” or incomplete answers can
be reduced with a rapid follow-up simulated by a field edit. The daily
edit also allows fieldworkers to re-contact the respondent to fill in
omissions before the situation has changed. The field edit may also
indicate the need for further training of interviewers.
In-House Editing
Although almost simultaneous editing in the field is highly desirable,
in many situations (particularly with mail questionnaires), early

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reviewing of the data is not possible. In-house editing rigorously
investigates the results of data collection.
Editing for Consistency:
The in-house editor’s task is to ensure that inconsistent or
contradictory responses are adjusted and that answers will not be a
problem for coders and keyboard punchers. Consider the situation in
which a telephone interviewer has been instructed to interview only
registered voters that requires voters to be 18 years old. If the editor’s
reviews of a questionnaire indicate that the respondent was only 17
years of age, the editor’s task is to eliminate this obviously incorrect
sampling unit. Thus, in this example, the editor’s job is to make sure
that thee sampling unit is consistent with thee objectives of the study.
Editing requires checking for logically consistent responses. The in-
house editor must determine if the answers given by a respondent to
one question are consistent with those given to other, related
questions. Many surveys utilize filter questions or skip questions that
direct the sequence of questions, depending upon respondent’s
answer. In some cases the respondent will have answered a sequence
of questions that should not have been asked. The editor should
adjust these answers, usually to “no answer’ or “inapplicable,” so
that the responses will be consistent.
Editing for Completeness: In some cases the respondent may have
answered only the second portion of a two-part question. An in-
house editor may have to adjust the answers to the following

question for completeness. Does your organization have more than
one Internet Web site? Yes ____ No. _____
If a respondent checked neither “yes” nor “No”, but indicated three
Internet Web sites, the editor may check the “yes” to ensure that this
answer is not missing from the questionnaire.
Item Non-response: It is a technical term for an unanswered
question on an otherwise complete questionnaire. Specific decision
rules for handling this problem should be meticulously outlined in
the editorial instructions. In many situations the decision rule will be
to do nothing with the unanswered question: the editor merely
indicates in item non response by writing a message instructing the
coder to record a “missing value” or blank as the response. However,
in case the response is necessary then the editor uses the plug value.
The decision rule may to “plug in” an average or neutral value in
each case of missing data. A blank response in an interval scale item
with a midpoint would be to assign the midpoint in the scale as the
response to that particular item. Another way is to assign to the item
the mean value of the responses of all those who have responded to
that particular item. Another choice is to give the item the mean of
the responses of this particular respondent to all other questions
measuring the variables. Another decision rule may be to alternate
the choice of the response categories used as plug values (e.g. “yes”
the first time, “no” the second time, “yes” the third time, and so on).
The editor must also decide whether or not an entire questionnaire is

126
“usable.” When a questionnaire has too many (say 25%) answers
missing, it may not be suitable for the planned data analysis. In such
a situation the editor simply records the fact that a particular
incomplete questionnaire has been dropped from the sample.
Editing Questions Answered out of Order: Another situation an
editor may face is thee need to rearrange the answers to an open-
ended response to a question. For example, a respondent may have
provided the answer to a subsequent question in his answer to an
earlier open-ended response question. Because the respondent had
already clearly identified his answer, the interviewer may have
avoided asking the subsequent question. The interviewer may have
wanted to avoid hearing “I have already answered that earlier” and to
maintain rapport with the respondent and therefore skipped the
question. To make the response appear in the same order as on other
questionnaires, the editor may remove the out-of-order answer to the
section related to the skipped question.
Coding
Coding is a “systematic way in which to condense extensive data sets
into smaller analyzable units through the creation of categories and
concepts derived from the data.”
It is the “process by which verbal data are converted into variables
and categories of variables using numbers, so that the data can be
entered into computers for analysis.”

Coding involves assigning numbers or other symbols to answers so
the responses can be grouped into limited number of classes or
categories. The classifying of data into limited categories sacrifices
some data detail but is necessary for efficient analysis. Nevertheless,
it is recommended that try to keep the data in raw form so far it is
possible. When the data have been entered into the computer you can
always ask the computer to group and regroup the categories. In case
the data have been entered in the compute in grouped form, it will
not be possible to disaggregate it. Although codes are generally
considered to be numerical symbols, they are more broadly defined
as the rules for interpreting, classifying, and recording data. Codes
allow data to be processed in a computer. Researchers organize data
into fields, records, and files. A field is a collection of characters (a
character is a single number, letter of the alphabet, or special symbol
such as the question mark) that represent a single type of data. A
record is collection of related fields. A file is a collection of related
records. File, records, and fields are stored on magnetic tapes, floppy
disks, or hard drives. Researchers use a coding procedure and
codebook. A coding procedure is a set of rules stating that certain
numbers are assigned to variable attributes. For example, a
researchers codes males as 1 and females as 2. Each category of
variable and missing information needs a code. A codebook is a
document (i.e. one or more pages) describing the coding procedure
and the location of data for variables in a format that computers can
use. When you code data, it is very important to create a well-

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organized, detailed codebook and make multiple copies of it. If you
do not write down the details of the coding procedure, or if you
misplace thee codebook, you have lost the key to the data and may
have to recode the data again.
Researchers begin thinking about a coding procedure and a codebook
before they collect data. For example a survey researcher pre-codes a
questionnaire before collecting thee data. Pre-coding means placing
the code categories (e.g. 1 for male, 2 for female) on the
questionnaire. Sometimes to reduce dependence on codebooks,
researchers also place the location in the computer format on the
questionnaire. If the researcher does not pre-code, his or her first step
after collecting and editing of data is to create a codebook. He or she
also gives each case an identification number to keep track of the
cases. Next, the researcher transfers the information from each
questionnaire into a format that computers can read.
Code Construction
When the question has a fixed-alternative (closed ended) format, the
number of categories requiring codes is determined during the
questionnaire design stage. The codes 8 and 9 are conventionally
given to “don’t know” (DK) and “no answer” (NA) respectively.
However, many computer program fields recognize a blank field or a
certain character symbol, such as a period (.), as indicating a missing
value (no answer). There are two basic rules for code construction.
First, the coding categories should be exhaustive – that is, coding

categories should be provided for all subjects or objects or responses.
With a categorical variable such as sex, making categories
exhaustive is not a problem. However, when the response represents
a small number of subjects or when the responses might be
categorized in a class not typically found, there may be a problem.
Second, the coding categories should also be mutually exclusive and
independent. This means that there should be no overlap between the
categories, to ensure that a subject or response can be placed in only
one category. This frequently requires that an “other” code category
be included, so that the categories are all inclusive and mutually
exclusive. For example, managerial span of control might be coded
1, 2, 3, 4, and “5 or more.” The “5 or more” category ensures
everyone a place in a category. When a questionnaire is highly
structured, pre-coding of the categories typically occurs before the
data are collected. In many cases, such as when researchers are using
open-ended response questions, a framework for classifying
responses to questions cannot be established before data collection.
This situation requires some careful thought concerning the
determination of categories after editing process has been completed.
This is called post-coding or simply coding.
The purpose of coding open-ended response questions is to reduce
the large number of individual responses to a few general categories
of answers that can be assigned numerical scores. Code construction
in these situations necessarily must reflect the judgment of the

130
researcher. A major objective in code-building process is to
accurately transfer the meaning from written answers to numeric
codes.
Production Coding
Transferring the data from the questionnaire or data collection form
after the data have been collected is called production coding.
Depending upon the nature of the data collection form, codes may be
written directly on the instrument or on a special coding sheet.
Data Entries
Use of scanner sheets for data collection may facilitate the entry of
the responses directly into the computer without manual keying in
the data. In studies involving highly structured paper questionnaires,
an Optical scanning system may be used to read material directly to
the computer’s memory into the computer’s memory. Optical
scanners process the marked-sensed questionnaires and store thee
answers in a file.
Cleaning Data
The final stage in the coding process is the error checking and
verification, or “data cleaning” stage, which is a check to make sure
that all codes are legitimate. Accuracy is extremely important when
coding data. Errors made when coding or entering data into a
computer threaten the validity of measures and cause misleading
results. A researcher who has perfect sample, perfect measures, and

no errors in gathering data, but who makes errors in the coding
process or in entering data into a computer, can ruin a whole research
project.
DATA TRANSFROMATION
Data transformation is the process of changing data from their
original form to a format that is more suitable to perform a data
analysis that will achieve the research objectives. Researchers often
modify the values of a scalar data or create new variables. For
example many researchers believe that response bias will be less if
interviewers ask consumers for their year of birth rather than their
age, even though the objective of the data analysis is to investigate
respondents’ age in years. This does not present a problem for thee
research analyst, because a simple data transformation is possible.
The raw data coded at birth year can be easily transformed to age by
subtracting the birth year from the current year. Collapsing or
combining categories of a variable is a common data transformation
that reduces the number of categories. For example five categories of
Likert scale response categories to a question may be combined like:
the “strongly agree” and the “agree” response categories are
combined. The “strongly disagree” and the “disagree” response
categories are combined into a single category. The result is the
collapsing of the five-category scale down to three. Creating new
variables by re-specifying the data numeric or logical
transformations is another important data transformation. For

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example, Likert summated scale reflect the combination of scores
(raw data) from various attitudinal statements. The summative score
for an attitude scale with three statements is calculated as follows:
Summative Score = Variable 1 + Variable 2 + Variable 3
This calculation can be accomplished by using simple arithmetic or
by programming a computer with a data transformation equation that
creates the new variable “summative score.”
The researchers have created numerous different scales and indexes
to measure social phenomenon. For example scales and indexes have
been developed to measure the degree of formalization in
bureaucratic organization, the prestige of occupations, the adjustment
of people in marriage, the intensity of group interaction, the level of
social activity in a community, and the level of socio-economic
development of a nation. Keep it in mind that every social
phenomenon can be measured. Some constructs can be measured
directly and produce precise numerical values (e.g. family income).
Other constructs require the use of surrogates or proxies that
indirectly measure a variable (e.g. job satisfaction). Second, a lot can
be learned from measures used by other researchers. We are
fortunate to have the work of thousands of researchers to draw on. It
is not always necessary to start from a scratch. We can use a past
scale or index, or we can modify it for our own purposes. The
process of creating measures for a construct evolves over time.
Measurement is an ongoing process with constant change; new

concepts are developed, theoretical definitions are refined, and scales
or indexes that measure old or new constructs are improved.
Technology had made life easy. Data can be collected on scanner
answer sheet which enable a researcher to enter them directly into
computer file. In other cases, raw data would be manually entered
into computer as data file. Here some software like SPSS data editor
can be used to enter, edit and view the contents. It is easy to add,
change or delete values after the data has been entered.
Indexes and Scales
Scales and indexes are often used interchangeably. One researcher’s
scale is another’s index. Both produce ordinal- or interval- level
measures of variable. To add to the confusion, scale and index
techniques can be combined in one measure. Scales and indexes give
a researcher more information about variables and make it possible
to assess the quality of measurement. Scales and indexes increase
reliability and validity, and they aid in data reduction; that is
condense and simplify the information that is collected.
A scale is a measure in which the researcher captures the intensity,
direction, level, or potency of a variable construct. It arranges
responses or observation on a continuum. A scale can use single
indicator or multiple indicators. Most are at thee ordinal level of
measurement.

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An index is a measure in which a researcher adds or combines
several distinct indicators of a construct into a single score. This
composite score is often a simple sum of multiple indicators. It is
used for content or convergent validity. Indexes are often measured
at the interval or ratio level. Researchers sometimes combine the
features of scales and indexes in a single measure. This is common
when a researcher has several indicators that are scales. He or she
then adds these indicators together to yield a single score, thereby an
index.
Unidimensionality: It means that al the items in a scale or index fit
together, or measure a single construct. Unidimensionality says: If
you combine several specific pieces of information into a single
score or measure, have all the pieces measure the same thing. (each
sub dimension is part of the construct’s overall content). For
example, we define the construct “feminist ideology” as a general
ideology about gender. Feminist ideology is a highly abstract and
general construct. It includes a specific beliefs and attitudes towards
social, economic, political, family, sexual relations. The ideology’s
five belief areas parts of a single general construct. The parts are
mutually reinforcing and together form a system of beliefs about
dignity, strength, and power of women.

Index Construction
You may have heard about a consumer price index (CPI). The CPI,
which is a measure of inflation, is created by totaling the cost of
buying a list of goods and services (e.g. food, rent, and utilities) and
comparing the total to the cost of buying the same list in the previous
year. An index is combination of items into a single numerical score.
Various components or subgroups of a construct are each measured,
and then combined into one measure. There are many types of
indexes. For example, if you take an exam with 25 questions, the
total number of questions correct is a kind of index. It is a composite
measure in which each question measures a small piece of
knowledge, and all the questions scored correct or incorrect are
totaled to produce a single measure. One way to demonstrate that
indexes are not a very complicated is to use one. Answer yes or no to
the seven questions that follow on the characteristics of an
occupation. Base your answers on your thoughts regarding the
following four occupations: long-distance truck driver, medical
doctor, accountant, telephone operator. Score each answer 1 for yes
and 0 for no.
1. Does it pay good salary?
2. Is the job secure from layoffs or unemployment?
3. Is the work interesting and challenging?
4. Are its working conditions (e.g. hours, safety, time on the road)
good?

136
5. Are there opportunities for career advancement and promotion?
6. Is it prestigious or looked up to by others?
7. Does it permit self-direction and thee freedom to make decisions?
Total the seven answers for each of the four occupations. Which had
the highest and which had the lowest score? The seven questions are
our operational definition of the construct good occupation. Each
question represents a subpart of our theoretical definition. Creating
indexes is so easy that it is important to be careful that every item in
the index has face validity. Items without face validity should be
excluded. Each part of the construct should be measured with at least
one indicator. Of course, it is better to measure the parts of a
construct with multiple indicators. Another example of an index is
college quality index. Our theoretical definition says that a high
quality college has six distinguished characteristics: (1) fewer
students per faculty member, (2) a highly educated faculty, (3) more
books in the library, (4) fewer students dropping out of college, (5)
more students who go to advanced degrees, and (6) faculty members
who publish books or scholarly articles. We score 100 colleges on
each item, and then add the score for each to create an index score of
college quality that can be used to compare colleges. Indexes can be
combined with one another. For example, in order to strengthen the
college quality index. We add a sub-index on teaching quality. The
index contain eight elements: (1) average size of classes, (2)
percentage of class time devoted to discussion, (3) number of

different classes each faculty member teaches, (4) availability of
faculty to students outside the classroom, (5) currency and amount of
reading assigned, (6) degree to which assignments promote learning,
(7) degree to which faculty get to know each student, and (8) student
ratings of instruction. Similar sub-index measures can be created for
other parts of the college quality index. They can be combined into a
more global measure of college quality. This further elaborates the
definition of a construct “quality of college.”
Weighting
An important issue in index construction is whether to weight items.
Unless it is otherwise stated, assume that an index is un-weighted.
Likewise, unless we have a good reason for assigning different
weights, use equal weights. A weighted index gives each item equal
weight. It involves adding up the items without modification, as if
each were multiplied by 1 (or – 1 for negative items that are
negative).
In one our previous discussions we had tried to measure job
satisfaction. It was operationalized with the help of dimensions and
elements. We had constructed number of statements on each element
with 5 response categories using Likert scale i.e. strongly agree,
agree, undecided, disagree, and strongly disagree. We could score
each of these items from 1 to 5 depending upon the degree of
agreement with the statement. The statements have been both

138
positive as well as negative. For positive statements we can score
straight away from 5 to 1 i.e. strongly agree to strongly disagree. For
the negative statements we have to reverse the score i.e. 1 for
“strongly agree,” 2 for “agree,” 3 for “undecided” to 4 for
“disagree,” and 5 for “strongly disagree.” Reason being that negative
multiplied by a negative becomes positive i.e. a negative statement
and a person strongly disagreeing with it implies that he or she has a
positive responsive so we give a score of 5 in this example. In our
example, let us say there were 23 statements measuring for different
elements and dimensions measuring job satisfaction. When on each
statement the respondent could get a minimum score of 1 and a
maximum score of 5, on 23 statements a respondent could get a
minimum score of (23 X 1) and a maximum score of (23 X 5) 115. In
this way the score index ranges from 23 to 115, the lower end of the
score index showing minimum job satisfaction and upper end as the
highest job satisfaction. In reality we may not find any on the
extremes, rather the respondents could be spread along this
continuum. We could use the raw scores of independent and
dependent variable and apply appropriate statistics for testing the
hypothesis. We could also divide the score index into different
categories like high “job satisfaction” and “low satisfaction” for
presentation in a table. We cross-classify job satisfaction with some
other variable, apply appropriate statistics for testing the hypothesis.

Chapter-VII
Report Writing
Types of Report Writing
Different Steps in Report Writing:
 Logical analysis of subject matter.
 Preparation of final outline.
 Preparation of Rough Draft.
 Rewriting and Polishing.
 Preparation of final Bibliography.
 Writing the final draft.
Title Page
Dedication
Acknowledgements
Table of Contents
Elements of research report

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Chapter-VII
Report Writing
A report is a dreadfully official document that is written to serve the
range of purpose in the engineering and business disciplines;
sciences and social sciences. Therefore, they need to be clear-cut and
accurate. Good report writing call for--- professionalism, profound
knowledge of the subject, attentiveness, and outstanding writing
proficiency.
Types of Report Writing ---
Research Report Writing--- To presents the tangible proof of the
conducted research is the major intention of the academic
assignment. When writing on research report, you must ponder over
clarity, organization, and content. Research reports are all the more
same to technical reports, lab reports, formal reports and scientific
papers which comprise a quite consistent format that will facilitate to
put information noticeably, making it crystal clear.
Business Report Writing--- In business milieu, Business report
writing happens to be an indispensable part of the communication
process. Executive summary is written in a non-technical manner. By
and large, audience for business reports will consist of upper level

manager, for that reason we should take the audience needs in
consideration. Go on with the introduction to articulate the problem
and determine the scope of the research. To attain the desired results,
don't fail to state about the precise quantitative tools.
Science Report Writing--- Parallel to a business report, science
report writing also corresponds with the line of investigation. To
report upon an empirical investigation, these reports make use of
standard scientific report format, portraying technique, fallout and
conclusions. As an assignment in undergraduate papers within the
scientific disciplines, it is required frequently.
The main objective of the Science report is to boast an aim, the
technique which enlightens how the project has been analyzed, the
outcomes which presents the findings and the conclusion. This
embraces advance research suggestions and your own biased opinion
on the topic which has been talked about.
When writing a science report, do not fail to remember to use
heading and subheadings in order to direct a reader through your
work. In the form of tables and graphs, Statistical evidence should be
incorporated in appendices. Than refer to it in the body of scientific
report.
Research Report is the major component of the research study.
Report writing is the important stage in the research activity. The
hypothesis of the study, the objective of the study and the data
collection and data analysis can be well presented in report. This

142
report writing will help others to understand the findings of the
research. Report writing is integral part of research and hence it
cannot be isolated.
Report writing is not a mechanical process but it is an art. It requires
skill. Guidelines on how to prepare a professional-style research
report are not routinely available to the researchers. For this reason,
the following information on report writing with a suggested format
is provided to be helpful to researchers.
Different Steps in Report Writing
It is the critical stage and hence it requires patience. These is no
mechanical formulate to present a report, though there are certain
steps to be followed while writing a research report. The usual steps
in report writing can be indicated in the following manner:
 Logical analysis of subject matter.
 Preparation of final outline.
 Preparation of Rough Draft.
 Rewriting and Polishing.
 Preparation of final Bibliography.
 Writing the final draft.
It is pertinent to follow these steps and hence it is essential to
understand these steps thoroughly.

(a) Logical analysis of subject matter
When a researcher thinks of doing a research, he must select subject
and topic of his research work. The subject must be of his own
interest and there must be scope for further research. Such can be
selected and developed logically or chronologically. He must find
out mental connections and associations by way of analysis to
finalize his subject. Logical treatment often consists in developing
from the simple possible to the most complex strictures. He can use
the deductive method or inductive method in his research work.
Secondly the alternative in selecting research subject is to use
chronological method. In this method, he should concentrate on the
connection or sequence in time or occurrence. The directions for
doing or making something usually follow the chronological method.
(b) Preparation of final outline
Outlines are the framework upon which long written works are
constructed. It is an aid to logical organization of the material and
remainder of the points to be stressed in the report. He should rely on
review of literature. The earlier research works can provide basic
information as well as thinking to the researcher to pursue his
subject.
(c) Preparation of rough draft
This follows the logical analysis of the subject and the preparation of
the final outline. Such a step is of utmost importance for the
researcher now sits to write down what he has done in the context of

144
his research study. Taking into account this purpose of research, the
research report writing has its own significance. The researcher has
already collected primary data and secondary data. He has also set
his objectives of the study. Taking into account the objectives his
study, he should make an attempt to prepare a draft report on the
basis of analysis of the data. He should prepare a procedure to be
followed in report writing. He must mention the limitations of his
study. He may analyze data systematically with the help of statistical
methods to arrive at the conclusions. The research is fact finding
study which may lead the researcher to point out suggestions or
recommendations. All the facts of value are to be brought together.
In addition, accuracy of the facts incorporated into the text becomes
necessary. For writing the rough draft the researcher should have
control over his notes and should think continuously over the
problem. There are three purpose in writing the rough draft, viz., to
weave the material together for making clear connection, to assure
the investigator himself of a satisfactory organisation and fullness of
the facts, and to avoid blank paper fight that may be present in every
researcher. Considerable trimming or editing have to be tone to make
the research precise, concise and brief.
(d) Rewriting and polishing the rough draft
Research is a continuous process. Researcher must consider the data,
write down his findings, reconsider them, and rewrite. This careful
revision makes the difference between mediocre and good lice of
writing. The researcher must concentrate on weakness in the logical
development or presentation. He should check the consistency in his

presentation. He must be aware that his report writing must be of
definite pattern. He must also take utmost care of the language of
writing a report. The purpose of the report is to convey to the
interested persons the whole result of the study in sufficient detail
and so arranged as to enable each reader to comprehend the data an
so determine for himself the validity of conclusions. While drafting
the second draft the researcher should concentrate largely on form of
the research report and language used in the report.
(e) Bibliography
This helps the researcher to collect secondary source of the data. This
is also useful to review the earlier research work. He should prepare
the bibliography from the beginning of his research work. While
selecting a topic or subject of research, he must refer books, journals,
research projects and enlist the important documents in systematic
manner. The bibliography must be in proper form. The researcher
must have separate cards, indicating following details, readily
available with him, so that he can make a note of it while he refers to
a book/journal/research report.
The bibliography must be included in the appendix of his research
report. It must be exhaustive to cover all types of works the
researcher has used. It must be arranged alphabetically. He can
divide it in different sections, such as books in first section, journals
in second, research reports in third etc. Generally the prescribed form
for preparation of bibliography is as given below:

146
The book must be noted in following manner:
1. Name of Author (Surname first).
2. Title of book.
3. Publisher’s name, place and data of publication.
4. Number of volumes.
The article can be mentioned in following manner:
1. Name of author (surname first)
2. Title of article (in quotation mark)
3. Name of periodical (underline it)
4. The volume or volume and number
5. Data of issue
6. The pagination
(f) Final Report
The final report must be written in a concise and objective style and
in simple language. The researcher should avoid expressions in his
report, such as “it seems”, “there may be” and like ones. He should
avoid abstract terminology and technical jargon. He may refer to
usual and common experiences to illustrate his point. The report
writing is an art. No two researchers may have common style of
report writing. But it must be interesting for a common man to add to
his knowledge. A good research report depends not only upon the
amount of the reading or notes taken or upon the form of
presentation but also the accurate and through recording of the
investigation.

Following are some of the important principles for writing a good
research report.
1. Make small sentences
2. Use simple words
3. Use familiar words
4. Avoid unnecessary words
5. Write to express not to impress
6. Use active verbs puts life into report writing
7. Always write research report with a particular reader in mind
8. Make the report short and sweet
9. Remember that every report should be an attempt to solve some
intellectual problem
Lay Out of Research Report
There is scientific method for the layout of the research report. The
layout of the report means as to what the research report should
contain. The contents of the research report are noted below. The
researcher must keep in mind that his research report must contain
following aspects:
(A) Preliminary Page
(B) Main text
1. Introduction
2. Purpose of study
3. Significance of his study or statement of the problem
4. Review of literature

148
5. Methodology
6. Analysis and Interpretation of data
7. Conclusions and suggestions
(C) End mater
1. Bibliography
2. Appendices
These can be discussed in detail as under:
(A) Preliminary Pages
These must be title of the research topic and data. It should be
followed by certificate, declaration. There must be preface of
foreword to the research work followed by table of contents; the list
of Exhibits so that the users of research report can easily locate the
required information in the report.
(B) Main Text
It provides the complete outline of research report along with all
details. The title page is reported in the main text. Details of text are
given continuously as divided in different chapters. Each main
section of the report should begin on an new page.
(1)Introduction
Its purpose is to introduce the research topic to readers. It must cover
statement of the problem, hypotheses, objectives of study, review of
literature, and the methodology to cover primary and secondary data,
limitations of study and chapter scheme. Some may give in brief in

the first chapter the introduction of the research project highlighting
the importance of study. This is followed by research methodology in
separate chapter.
The methodology should point out the method of study, the research
design and method of data collection.
(2) Purpose of study
This is crux of his research. It highlights main theme of his study. It
must be in nontechnical language. It should be in simple manner so
ordinary reader may follow it. The social research must be made
available to common man. The research in agricultural problems
must be easy for farmers to read it. The researcher must use review
of literature or the data from secondary source for explaining the
statement of the problems.
(3) Significance of study
Research is re-search and hence the researcher may highlight the
earlier research in new manner or establish new theory. He must
refer earlier research work and distinguish his own research from
earlier work. He must explain how his research is different and how
his research topic is different and how his research topic is important.
In a statement of his problem, he must be able to explain in brief the
historical account of the topic and way in which he can make and
attempt.

150
(4) Review of Literature
Research is a continuous process. He cannot avoid earlier research
work. He must start with earlier work. He should note down all such
research work, published in books, journals or unpublished thesis. He
will get guidelines for his research from taking a review of literature.
He should collect information in respect of earlier research work. A
literature review is an appraising description of information found in
the literature associated to chosen area of research. The literature
review illustrates, summarize, appraise and clarify the literature for
which are writing literature reviews. It should give a hypothetical
foundation for the research and helps establish the nature of research.
Unrelated works are removed completely while the marginal ones are
considered critically. The importance of literature review cannot be
denied because it is a review of writing on a subject. The under-
mentioned reasons the importance of literature review:
Literature review helps to find new ways to figure out any ambiguity
or flaws in earlier researches.
a. A literature review portrays the link of each work to the
others.
b. Literature review resolves any contradictory findings, or
gaps in previous studies.
c. Most importantly, literature review leads the way forward
for further research.
d. It adds the understanding and knowledge of the particular
field.

Factors should be consider while collect reviews
1. The literature review discovers the areas of controversy
in the literature.
2. The literature review explains how each work is similar
to and how it varies from the others.
3. Literature review should be well-structured around and
directly linked to the research question you are
developing.
4. The literature review should present an overview of the
subject, issue or theory under consideration, along with
the objectives of the literature review.
Stages in Development Of Literature Review
A literature review involves the four stages to advance:
Problem Formulation
First of all, the component issues of topic of literature review to
examine or research are determined.
Literature Search
Finding materials are collected relevant to the subject being explored
to write the literature review.
Data Appraisal
It is determined that which literature makes a worth mentioning
contribution to the understanding of the topic of literature review.

152
Analysis
Finally, the findings of relevant literature are analyzed to conclude
and include in literature review.
Tips to write a good literature review
Need to keep entire and exact records and references of what you
read and find during research.
i. Learn the required citation style.
ii. Make notes or summaries of the articles, books journals,
papers whatever you read.
iii. Researcher must infer and read between the lines when
go through any written work.
iv. Divide the literature review into different thematic parts
which will help to focus.
v. Read the leading published material and search for the
current issues for the latest information.
(5) Methodology
It is related to collection of data. There are two sources for collecting
data; primary and secondary. Primary data is original and collected in
field work, either through questionnaire interviews. The secondary
data relied on library work. Such primary data are collected by
sampling method. The procedure for selecting the sample must be
mentioned. The methodology must give various aspects of the
problem that are studied for valid generalization about the

phenomena. The scales of measurement must be explained along
with different concepts used in the study.
While conducting a research based on field work, the procedural
things like definition of universe, preparation of source list must be
given. We use case study method, historical research etc. He must
make it clear as to which method is used in his research work. When
questionnaire is prepared, a copy of it must be given in appendix.
(6) Analysis and Interpretation of data
Data so collected should be presented in systematic manner and with
its help, conclusions can be drawn. This helps to test the hypothesis.
Data analysis must be made to confirm the objectives of the study.
Mainly the data collected from primary source need to be interpreted
in systematic manner. The tabulation must be completed to draw
conclusions. All the questions are not useful for report writing. One
has to select them or club them according to hypothesis or objectives
of study.
(7) Conclusions/suggestions
Data analysis forms the crux of the problem. The information
collected in field work is useful to draw conclusions of study. In
relation with the objectives of study the analysis of data may lead the
researcher to pin point his suggestions. This is the most important
part of study. The conclusions must be based on logical and
statistical reasoning. The report should contain not only the
generalization of inference but also the basis on which the inferences

154
are drawn. All sorts of proofs, numerical and logical, must be given
in support of any theory that has been advanced. The primary data
may lead to establish the results. He must have separate chapter on
conclusions and recommendations. The conclusions must be based
on data analysis. The conclusions must be such which may lead to
generalization and its applicability in similar circumstances.
(C) End Matter
It covers relevant appendices covering general information,
Questionnaire, Annual reports and the concepts and bibliography.
The index may also be added to the report.
(1) Bibliography
The list of references must be arranged in alphabetical order and be
presented in appendix. The books should be given in first section and
articles are in second section and research projects in the third. The
pattern of bibliography is considered convenient and satisfactory
from the point of view of reader.
Book with one author
Author’s last name, first name. Title of the book. City: Publisher,
Date of Publication.
Example: Jones, Edward. The Toy. New York: Random House,
1987.

Book with two authors
Author’s last name, first name, and second author’s full name. Title
of the book. Place of publication: Publisher, date of publication.
Example: Edward and Amelia Smith. Strangers. New Delhi:
Random House, 1987.
Book without an author
Title of the book. City: Publisher, Date of Publication.
Example: Old Lake. New Delhi: Random House, 1987.
Article in a book without an author
Name of the article. Title of the book. City: Publisher, Date of
Publication.
Example: Agarwal.L.N, Personnel Management, New Delhi: Excel
Books. (1998).
Book with an editor
Editor’s last name, first name, ed. Title of the book. Place of
publication: Publisher, date of publication. Jones, Edward. 100
Recipes for You. New York: Random House, 1987.
Short story or chapter of a book
Author’s last name, first name. “Title.” Title of the book that the
source comes from. Editor (ed.) of the book’s full name. Place of
publication: Publisher, date of publication. Pages of the source.

156
Example: Kundu, Amitabh. “Learning to communicate.” The Toy.
Ed. Helen Stevenson. New York: Random House, 1987.
Encyclopedia article with an author/a signed article
Author’s last name, first name. “Title”. Encyclopedia Title. Volume
Number. Place of publication: Publisher, date of publication.
Example: Charmes, Jacques. “The Wild Swans.” World Book
Encyclopedia. Volume 13. New York: Random House, 1987.
Encyclopedia article without an author/an unsigned article
“Title”. Encyclopedia Title. Volume number. Place of publication:
Publisher, date of publication.
Example: “The Wild Swans.” World Book Encyclopedia. Volume
13. New York: Random House, 1987.
Journal article
Author’s last name, first name “Article Title.” Name of magazine
volume number: issue number (year of publication): page numbers.
Example: Asha C.B “Job satisfaction among women in relation to
their family environment” Journal of Community Guidance And
Research, Vol.11, No.1, (1994) pp.43-50.
Case of multiple authorship
If there are more than two authors or editors, then in the
documentation the name of only the first is given and the multiple
authorship is indicated by “et.al.” or ‘and other”

Magazine article
Author’s last name, first name. “Article title.” Magazine title date of
publication: page numbers.
Example: Banarjee, “Morginalisation”, Social Scientist, 1985,
Vol.13, pp. 48-71.
Newspaper article
Author’s last name, first name. “Article title.” Newspaper title [city
of publication, if not in title] date of publication, edition if necessary:
section if necessary: page numbers.
Example: Gunasekaran, “Low cost houses for Hosiery workers, the
need of the house” The Hindu, 29th
September 2007, : 12
World Wide Web
URL (Uniform Resource Locator or WWW address). Author (or
item’s name, if mentioned), date.
Example: (Boston Globe’s www address) http://www.boston.com.
Today’s News, May 23, 2011.
Interview
Full name (last name first). Occupation. Date of interview.
Example: Balakrishanan . Finance Manager. February 10, 2011.

158
(2) Appendices
The general information in tabular form which is not directly used in
the analysis of data but which is useful to understand the background
of study can be given in appendix.
Title Page
The title page is required. Please see sample page for correct
placement and spacing. The title page should include: 1) a full title
(the use of title case is recommended); 2) identification of document
type(project, dissertation or Thesis); 3) the statement Presented in
Partial Fulfillment of the Requirements for the Degree (insert the
applicable degree such as Doctor of Philosophy, Master of
philosophy, Master of Arts, Master of Science, etc.) in the
University; 4) name of the candidate; 5) initials of previous earned
degrees; 6) Name of the research guide with designation 7)Name of
the college or university and 8) year of submission
Dedication.
A dedication is optional. If used, the dedication must be brief and
centered on the page. See sample pages.
Acknowledgements
Like the dedication, acknowledgements are optional, but it is
strongly suggested that students include them. Either spelling of the
word, acknowledgements or acknowledgments, is acceptable. The
acknowledgement is a record of the author’s indebtedness and

includes notice of permission to use previously copyrighted materials
that appear extensively in the text. The heading Acknowledgement
(title case preferred) is centered without punctuation two inches from
the top of the page.
Table of Contents
A table of contents is required. The heading Table of Contents (title
case preferred) appears without punctuation centered two inches
from the top of the page. The listing of contents begins at the left
margin four spaces below the heading. The titles of all parts,
sections, chapter numbers, and chapters are listed and must be
worded exactly as they appear in the body of the document. The table
of contents must include any appendices and their titles, if
applicable. Use leader dots between the listed items and their page
numbers.
Lists of illustrations are required if the document contains
illustrations. The headings List of Tables, List of Figures, or other
appropriate illustration designations (title case preferred) appears
centered without punctuation two inches from the top of the page.
The listing begins at the left margin four spaces below the heading.
Illustrations should be identified by the same numbers and captions
in their respective lists as they have been assigned in the document
itself.

160
Margins. Top, right, and bottom margins should be set at one inch;
the left margin should be at least 1.5 inches. Any pages with major
headings, such as document title, chapter/major section titles,
preliminary page divisions, abstract, appendices, and references at
the end of the document should be set with a 1.5-inch left margin and
a two-inch top margin.
Font. The selected font should be 10 to 12 point and readable. The
font should be consistent throughout the document. Captions,
endnotes, footnotes, and long quotations may be slightly smaller than
text font, if readable.
Spacing. Double spacing is preferred, but 1.5 inch spacing is
acceptable for long documents. Single spacing is recommended for
bibliography entries, long quotations, long endnotes or footnotes, and
long captions. Double spacing between each bibliography entry is
recommended.
Titles. Each major division of the document, including appendices,
must have a title, Titles must be centered and have a two-inch top
margin. The use of title case is recommended. If chapters are being
used, they should be numbered and titled. For example: Chapter 1:
Introduction.
Page Numbers. Every page must have a page number except the
title page and the copyright page. If a frontispiece (usually an
illustration or quotation relevant to the subject) is included before the
title page, it is neither counted nor numbered. Small Roman numerals

(ii, iii, iv, etc.) are used for the preliminary pages: abstract,
dedication, acknowledgments, vita, table of contents, and the lists of
illustrations, symbols, abbreviations, and/or nomenclature. Page
numbering begins with ii, which is the number assigned to the
abstract. Arabic numerals are used for the remainder of the
document, including the text and the reference material. The pages
are numbered consecutively beginning with 1 and continue through
the end of the document. The page numbers are centered at the
bottom center of the page above the one inch margin. Note: You may
need to set the footer margin to one inch and the body bottom margin
to 1.3 or 1.5 inches to place the page number accurately.
Notation. Notation practices differ widely among publications in the
sciences, the humanities, and the social sciences. Candidates should
confer with their advisors regarding accepted practice in their
individual disciplines. That advice should be coupled with careful
reference to appropriate general style manuals.
1. Arabic numerals should be used to indicate a note in the text.
2. Notes may be numbered in one of two ways: either
consecutively throughout the entire manuscript or
consecutively within each chapter.
3. Notes can be placed at the bottom of the page (footnotes) or at
the end of a chapter or document (endnotes). Once chosen, the
notation style must be consistent throughout the document.

162
4. Notes about information within tables should be placed
directly below the table to which they apply, not at the bottom
of the page along with notes to the text.
Illustrations (tables, figures, charts, graphs, photos, etc.). See
sample pages. Some documents include several types of illustrations.
In such cases, it is necessary that each type of illustration (table,
figure, chart, etc.) be identified with a different numbering series
(Table 1, Table 2, and so on, or Chart 1, Chart 2, and so on). For
each series, include a list with captions and page numbers in the
preliminary pages (for example, List of Tables, List of Charts, etc.).
These lists must be identified with major headings that are centered
and placed at the two-inch margin.
If an illustration is too large to fit on one page, you must indicate
below the illustration on the lower right corner that it is continued.
For example, the phrase continued is placed under the illustration, on
the right hand side. On the following pages, include the illustration
type, number, and the word continued above it at the left margin; for
example, Map 2: Continued. If landscape orientation is used, the
page number goes at the bottom of the page (portrait), not at the
bottom of the illustration. Always stay in the margins.
Most research reports include the following elements
1. Title page
2. Letter of transmittal
3. Table of contents
4. List of tables

5. List of graphs
6. List of appendices
7. List of exhibits
8. Executive summary
a. Major findings
b. Conclusions
c. Recommendations
9. Introduction
i. Background to the problem
ii. Statement of the problem
10. Approach to the problem
11. Research design
a. Type of research design
b. Information needs
c. Data collection from secondary sources
d. Data collection from primary sources
e. Scaling techniques
f. Questionnaire development and pretesting
g. Sampling techniques
h. Field work
12. Data analysis
a. Methodology
b. Plan of data analysis
13. Results

164
14. Limitations and caveats
15. Conclusions and recommendations
16. Appendix
a. Questionnaires and forms
b. Statistical output
c. Lists

Appendix

166
Multiple Choice Questions
1. Personal interviews conducted in shopping malls are known as:
a. Mall interviews
b. Mall intercept interviews
c. Brief interviews
d. None of the given options
2. WATS lines provided by long distance telephone service at
fixed rates. In this regard, WATS is the abbreviation of:
a. West Africa Theological Seminary
b. Washtenaw Area Transportation Study
c. Wide Area Telecommunications Service
d. World Air Transport Statistics
3. A list of questions which is handed over to the respondent,
who reads the questions and records the answers himself is
known as the:
a. Interview schedule
b. Questionnaire
c. Interview guide
d. All of the given options
4. One of the most critical stages in the survey research process
is:
a. Research design
b. Questionnaire design
c. Interview design
d. Survey design

5. Question that consists of two or more questions joined
together is called a:
a. Double barreled question
b. General question
c. Accurate question
d. Confusing question
6. The number of questionnaires returned or completed divided
by the total number of eligible people who were contacted or
asked to participate in the survey is called the:
a. Response rate
b. Participation rate
c. Inflation rate
7. To obtain the freest opinion of the respondent, when we ask
general question before a specific question then this procedure
is called as the:
a. Research technique
b. Qualitative technique
c. Funnel technique
d. Quantitative technique
8. A small scale trial run of a particular component is known as:
a.Pilot testing
b.Pre-testing
c.Lab experiments
d.Both A & B

168
9. Field testing of the questionnaire shows that:
a. Respondents are willing to co-operate
b. Respondents are not willing to co-operate
c. Respondents do not like any participation
d. All of the given options
10. Service evaluation of hotels and restaurants can be done by
the:
a. Self-administered questionnaires
b. Office assistant
c. Manager
11. Which one of the following sets is the measure of central
tendency?
a. Mean, standard deviation, mode
b. Mean, median, standard deviation
c. Arithmetic mean, median, mode
d. Standard deviation, internal validity, mode
12. In lab experiment the effect of Variables is controlled to
evaluate the causal relationship.
a. Extraneous
b. Moderate
c. Intervening
d. All of the above

13. Internal validity refers to .
a.Researcher’s degree of confidence.
b.Generalizability
c.Operationalization
d.All of the above
14. Which of the following is the weakest experimental design?
a. One group pretest-posttest design
b. Quasi- experimental design
c. Two group posttest only design
d. Ex post facto design
15. How many times the students appear in the research class is the
example of _________.
a. Intensity
b. Space
c. Frequency
d. Direction
16. Disadvantage of content analysis is _
a. Researcher can increase the sample size
b. Provides access on the subjects to which researcher does
have physical access.
c. Sometime documents provide incomplete account to the
researcher
d. Spontaneous feelings can be recorded when they occurred

170
17. Time consumed in mall intercept interview is_______
a. High
b. Moderate
c. Low
d. Nil
18. “Teacher should create a friendly environment in the classroom”
this is the type of
a. Leading question
b. Loaded question
c. Double Barreled
d. Burdensome question
19. Departmental stores selected to test a new merchandising
display system is the example of
a. Quota sampling
b. Convenience sampling
c. Judgmental sampling
d. Purposive sampling
20. Discrete variable is also called……….
a. zategorical variable
b. Discontinuous variable
c. Both A & B
d. None of the above

21. Which one of the following is not a characteristic of scientific
method?
a. Deterministic
b. Rationalism
c. Empirical
d. Abstraction
22. The theoretical framework discusses the interrelationships
among the……….
a. Variables
b. Hypothesis
c. Concept
d. Theory
23. Personal interviews conducted in shopping malls are known
as__________
a. Mall interviews
b. Mall intercept interviews
c. Brief interviews
24. _______is used to obtain the freest opinion of the respondent,
by asking general question before a specific question.
a. Research technique
b. Qualitative technique
c. Funnel technique
d. Quantitative technique

172
25. In________ the interviewer and members jointly control the
pace and direction of the interview.
a. Field interview
b. Telephonic interview
c. Both A and B
26. Randomization of test units is a part of _____________
a. Pretest
b. Posttest
c. Matching
d. Experiment
27. Hypothesis refers to
a. The outcome of an experiment
b. A conclusion drawn from an experiment
c. A form of bias in which the subject tries to outguess the
experimenter
d. A tentative statement about the relationship
38. A literature review requires
a. Planning
b. Good & clear writing
c. Lot of rewriting
d. All of the above

29. A literature review is based on the assumption that
a. Copy from the work of others
b. Knowledge accumulates and learns from the work of
others
c. Knowledge disaccumulates
d. None of the above option
30. A theoretical framework
a. Elaborates the r/s among the variables
b. Explains the logic underlying these r/s
c. Describes the nature and direction of the r/s
d. All of the above
31. Which of the following statement is not true?
a. A research proposal is a document that presents a plan for
a project
b. A research proposal shows that the researcher is capable
of successfully conducting the proposed research project
c. A research proposal is an unorganized and unplanned
project
d. A research proposal is just like a research report and
written before the research project
32. Preliminary data collection is a part of the
a. Descriptive research
b. Exploratory research
c. Applied research
d. Explanatory research

174
33. Conducting surveys is the most common method of generating
a. Primary data
b. Secondary data
c. Qualitative data
34. After identifying the important variables and establishing the
logical reasoning in theoretical framework, the next step in the
research process is
a. To conduct surveys
b. To generate the hypothesis
c. To focus group discussions
d. To use experiments in an investigation
35. The appropriate analytical technique is determined by
a. The research design
b. Nature of the data collected
c. Nature of the hypothesis
d. Both A & B
36. The process of marking segments of data with symbols,
descriptive words, or category names is known as
a. Concurring
b. Coding
c. Coloring
d. Segmenting

37. What is the cyclical process of collecting and analyzing data
during a single research study called?
a. Interim analysis
b. Inter analysis
c. Inter-item analysis
d. Constant analysis
38. What is the recording of reflective notes about what you are
learning from your data during data analysis called?
a. Coding
b. Segmenting
c. Memoing
d. Reflecting
39. Which of the following is not one of Spradley’s types of
relationships?
a. Strict inclusion
b. Sequence
c. Cause-effect
d. Correlational
40. Codes that apply to a complete document or case are called
a. Cover codes
b. False sheet codes
c. c. Factual codes
d. Factsheets codes

176
41. A classification system generally used in the social sciences
that breaks something down into different types or levels is
called a
a. Diagram
b. Flow chart
c. Hierarchical category system
d. Category
42. Codes developed before examining the current data being
coded are called_____
a. Co-occuring codes
b. Inductive codes
c. A priori codes
d. Facesheet codes
43. The process of quantifying data is referred to as ________
a. Typology
b. Diagramming
c. Enumeration
d. Coding
44. Which of the following refers to the cyclical process of
collecting and analyzing data during a single research study?
a. Memoing
b. Segmenting c. Coding
c. Interim analysis
d. Interim analysis

45. Which research paradigm is based on the pragmatic view of
reality?
a. quantitative research
b. qualitative research
c. mixed research
d. none of the above
46. Which research paradigm is least concerned about generalizing
its findings?
a.quantitative research
b.qualitative research
c.mixed research
d.none of the above
47. Which of the following best describes quantitative research?
a. the collection of nonnumerical data
b. an attempt to confirm the researcher’s hypotheses
c. research that is exploratory
d. research that attempts to generate a new theory
48. A condition or characteristic that can take on different values or
categories is called
a. a constant
b. a variable
c. a cause-and-effect relationship
d. a descriptive relationship

178
49. A variable that is presumed to cause a change in another
variable is called a(n):
a. categorical variable
b. dependent variable
c. independent variable
d. intervening variable
50. All of the following are common characteristics of experimental
research except:
a. it relies primarily on the collection of numerical data
b. it can produce important knowledge about cause and
effect
c. it uses the deductive scientific method
d. it rarely is conducted in a controlled setting or
environment
51. Which type of research provides the strongest evidence about
the existence of cause- and-effect relationships?
a. nonexperimental Research
b. experimental Research
52. What is the key defining characteristic of experimental research?
a. extraneous variables are never present
b. a positive correlation usually exists
c. a negative correlation usually exists
d. manipulation of the independent variable

53. In, random assignment to groups is never possible and the
researcher cannot manipulate the independent variable.
a. basic research
b. quantitative research
c. experimental research
d. causal-comparative and correlational research
54. A positive correlation is present when
a. Two variables move in opposite directions.
b. Two variables move in the same direction.
c. One variable goes up and one goes down
d. Several variables never change
55. Research in which the researcher uses the qualitative paradigm
for one phase and the quantitative paradigm for another phase
is known as
a. action research
b. basic research
c. quantitative research
d. mixed method research
e. mixed model research
56. Research that is done to understand an event from the past is
known as ?
a. experimental research
b. historical research
c. replication
d. archival research

180
57. Which of the following includes examples of quantitative
variables?
a. age, temperature, income, height
b. grade point average, anxiety level, reading performance
c. gender, religion, ethnic group
d. both a and b
58. What is the opposite of a variable?
a. a constant
b. an extraneous variable
c. a dependent variable
d. a data set
59. Which of the following is the type of non-experimental
research in which the primary independent variable of interest
is categorical?
a. causal-comparative research
b. experimental research
c. qualitative research
d. mixed research
60. Which of the following can best be described as a categorical
variable?
a. age
b. annual income
c. grade point average d. religion

61. In research, something that does not "vary" is called a
a. variable
b. method c. constant
c. control group
62. When interpreting a correlation coefficient expressing the
relationship between two variables, it is very important to avoid
a. checking the strength of relationship
b. jumping to the conclusion of causality
c. checking the direction of the relationship
d. expressing a relationship with a correlation coefficient
63. The strongest evidence for causality comes from which of
the following research methods?
a. Experimental
b. Causal-comparative
c. Correlational
d. Ethnography
64. Which correlation is the strongest?
a. +.10
b. -.95
c. +.90
d. -1.00

182
65. The correlation between intelligence test scores and grades is:
a. Positive
b. Negative
c. Perfect
d. They are not correlated
66. A statement of the quantitative research question should:
a. Extend the statement of purpose by specifying exactly
the question(s) the researcher will address
b. Help the research in selecting appropriate participants,
research methods, measures, and materials
c. Specify the variables of interest
d. All of the above
67. Research hypotheses are ______
a. Formulated prior to a review of the literature
b. Statements of predicted relationships between variables
c. Stated such that they can be confirmed or refuted
d. b and c
68. Hypotheses in qualitative research studies usually _____
a. Are very specific and stated prior to beginning the study
b. Are often generated as the data are collected, interpreted,
and analyzed
c. Are never used
d. Are always stated after the research study has been
completed

69. A research plan _____.
a. Should be detailed
b. Should be given to others for review and comments
c. Sets out the rationale for a research study
d. All of the above
70. The Introduction section of the research plan
a. Gives an overview of prior relevant studies
b. Contains a statement of the purpose of the study
c. Concludes with a statement of the research questions and,
for quantitative research, it includes the research
hypothesis
d. All of the above
71. According to your text, which of the following is not a source
of research ideas?
a. Everyday life
b. Practical issues
c. Past research
d. Theory
e. All of the above
72. A review of the literature prior to formulating research
questions allows the researcher to do which of the following?
a. To become familiar with prior research on the
phenomenon of interest

184
b. To identify potential methodological problems in the
research area
c. To develop a list of pertinent problems relative to the
phenomenon of interest
d. All of the above
73. If a baseball coach calculates batting averages, what scale
would be used?
a. Interval scale
b. Ratio scale
c. Nominal scale
d. Ordinal scale
74. The number of police officers and the number of crimes are
positively related. This relationship is:
a. A causal relationship
b. A direct relationship
c. A probabilistic causal relation
d. A spurious relationship
75. Partial correlation analysis involves:
a. Examining the relationship between two or more
variables controlling for additional variables statistically
b. Including only one group in a correlational analysis
c. Matching participants on potential confounding variables
d. Limiting the sample to individuals at a constant level of
an extraneous variable

76. When research is done to test hypotheses and theories about
how and why phenomena operate as they do, then the primary
purpose of such research is:
a. Descripti ve
b. Predictive
c. Explan at ory
77. The variable the researcher matches to eliminate it as an
alternative explanation is called Variable.
a. Matching
b. Independent
c. Dependent
d. Partial
78. Which of the following is not a longitudinal design?
a. Panel
b. Cross-sectional
c. Trend
d. Both a and c are longitudinal designs
79. The positive correlation between teachers’ salaries and the
price of liquor is
a. Spurious
b. Due to a third-variable
c. Nonspurious
d. Both a and b

186
80. Which of the following is considered a special case of the
general linear model?
a. A variable
b. Partial correlation
c. Analysis of covariance
d. Both b and c
81. When a researcher starts with the dependent variable and
moves backwards, it is called
a. Predictive research
b. Retrospective research
c. Exploratory research
d. Descriptive research
82. The method of working multiple hypotheses refers to a
technique for identifying rival explanations.
a. True
b. False
83. GLM refers to which of the following?
a. General Logit Model
b. General Limited Model
c. General Lab Model
d. General Linear Model

84. The post hoc fallacy is .
a. Making the argument that because A preceded B, A
must have caused B
b. Making the argument that because A preceded B, A
and B must be correlated
c. Making the argument that because A preceded B, they
cannot be correlated
85. Which one of the following is not a step in non-experimental
research?
a. Determine research problem and hypotheses
b. Analyze data
c. Interpret results
d. All are steps
86. If a research finding is statistically significant, then
a. The observed result is probably not due to chance
b. The observed result cannot possibly be due to chance
c. The observed result is probably a chance result
d. The null hypothesis of “no relationship” is probably
true

188
87. Which of the following is/are necessary condition(s) for
causation?
a. The relationship condition
b. The temporal antecedence condition
c. The lack of alternative explanation condition
d. All of the above
88. Which of the following independent variables cannot be
manipulated in a research study?
a. Gender
b. Ethnicity
c. Intelligence and other traits
d. None of ht above can be manipulated in a research
study
89. Non-experimental research in which the primary independent
variable of interest is categorical is sometimes called
a. Causal-comparative research
b. Correlational research
90. Which approach is the strongest for establishing that a
relationship is causal?
a. Causal-comparative
b. Correlational
c. Experimental d. Historical

91. ________ is the most commonly used technique for
controlling for extraneous variables in nonexperimental
research.
a. Matching
b. Holding extraneous variables constant
c. Statistical control
d. Static control
92. It is best to use the method of working multiple hypotheses
when
a. You are finished with your research
b. You are planning your research study
c. You are hoping to publish your already obtained
research results
93. Matching can be done when your independent variable is
categorical or quantitative.
a. True
b. False
94. If a correlation coefficient is .96, we would probably be able to
say that the relationship is
a. Weak
b. Strong
c. Statistically significant
d. b is true and c is probably true

190
95. Which of the following symbols represents a population
parameter?
a.SD
b.σ
c. r
d. 
96. If you drew all possible samples from some population,
calculated the mean for each of the samples, and constructed
a line graph (showing the shape of the distribution) based on
all of those means, what would you have?
a. A population distribution
b. A sample distribution
c. A sampling distribution
d. A parameter distribution
97. What does it mean when you calculate a 95% confidence
interval?
a. The process you used will capture the true parameter
95% of the time in the long run
b. You can be “95% confident” that your interval will
include the population parameter
c. You can be “5% confident” that your interval will not
include the population parameter
d. All of the above statements are true

98. What would happen (other things equal) to a confidence
interval if you calculated a 99 percent confidence interval
rather than a 95 percent confidence interval?
a. It will be narrower
b. It will not change
c. The sample size will increase
d. It will become wider
99. Which of the following statements sounds like a null
hypothesis?
a. The coin is not fair
b. There is a correlation in the population
c. There is no difference between male and female
incomes in the population
d. The defendant is guilty
100. The analysis of variance is a statistical test that is used to
compare how many group means?
a. Three or more
b. Two or more
101. What is the standard deviation of a sampling distribution
called?
a. Sampling error
b. Sample error
c. Standard error
d. Simple error

192
102. Hypothesis testing and estimation are the two key branches of
the field of inferential statistics?
a. True
b. False
103. A ______ is a subset of a _________.
a. Sample, population
b. Population, sample
c. Statistic, parameter
d. Parameter, statistic
104. A _______ is a numerical characteristic of a sample and a
______ is a numerical characteristic of a population.
a. Sample, population
b. Population, sample
c. Statistic, parameter
d. Parameter, statistic
105. A sampling distribution might be based on which of the
following?
a. Sample means
b. Sample correlations
c. Sample proportions
d. All of the above

106. As a general rule, researchers tend to use ____ percent
confidence intervals.
a. 99%
b. 95%
c. c. 50%
d. none of the above
107. Which of the following is the researcher usually interested in
supporting when he or she is engaging in hypothesis testing?
a. The alternative hypothesis
b. The null hypothesis
c. Both the alternative and null hypothesis
d. Neither the alternative or null hypothesis
108. When p<.05 is reported in a journal article that you read for an
observed relationship, it means that the author has rejected the
null hypothesis (assuming that the author is using a significance
or alpha level of .05).
a. True
b. False
109. When p>05 is reported in a journal article that you read for an
observed relationship, it means that the author has rejected the
null hypothesis (assuming that the author is using a significance
or alpha level of .05).
a. True
b. False

194
110. _________ are the values that mark the boundaries of the
confidence interval.
a. Confidence intervals
b. Confidence limits
c. Levels of confidence d. Margin of error
111. _____ results if you fail to reject the null hypothesis when the
null hypothesis is actually false.
a. Type I error
b. Type II error
c. Type III error
d. Type IV error
112. A good way to get a small standard error is to use a ________.
a. Repeated sampling
b. Small sample
c. Large sample
d. Large population
113. Identify which of the following steps would not be included in
hypothesis testing.
a. State the null and alternative hypotheses
b. Set the significance level before the research study
c. Eliminate all outliers
d. Obtain the probability value using a computer program
such as SPSS
e. Compare the probability value to the significance level
and make the statistical decision

114. A ________ is a range of numbers inferred from the sample
that has a certain probability of including the population
parameter over the long run.
a. Hypothesis
b. Lower limit
c. Confidence interval
d. Probability limit
115. ________ is the standard deviation of a sampling distribution.
a. Standard error
b. Sample standard deviation
c. Replication error
d. Meta error
116. An effect size indicator is a statistical measure of the strength of
a relationship.
a. True
b. False
117. Which of the following can be viewed as an effect size
indicator?
a. r-squared
b. r
c. Eta-squared
d. Omega-squared
e. All of the above

196
118. When the researcher rejects a true null hypothesis, a ____ error
occurs.
a. Type I
b. Type A
c. Type II
d. Type B
119. The use of the laws of probability to make inferences and draw
statistical conclusions about populations based on sample data
is referred to as ___________.
a. Descriptive statistics
b. Inferential statistics
c. Sample statistics
d. Population statistics
120. A statistical test used to compare 2 or more group means is
known as _____.
a. One-way analysis of variance
b. Post hoc test
c. t-test for correlation coefficients
d. Simple regression
121. A statistical test used to determine whether a correlation
coefficient is statistically significant is called the ___________.
a. One-way analysis of variance
b. t-test for independent samples
c. Chi-square test for contingency tables
d. t-test for correlation coefficients

122. The cutoff the researcher uses to decide whether to reject the
null hypothesis is called the:
a. Significance level
b. Alpha level
c. Probability value
d. Both a and b are correct
123. __________ is the failure to reject a false null hypothesis.
a. Type I error
b. Type II error
c. Type A error
d. Type B error
124. Which of the following statements is/are true according to the
logic of hypothesis testing?
a. When the null hypothesis is true, it should be rejected
b. When the null hypothesis is true, it should not be
rejected
c. When the null hypothesis is false, it should be rejected
d. When the null hypothesis is false, it should not be
rejected
e. Both b and c are true
125. What is the key question in the field of statistical estimation?
a. Based on my random sample, what is my estimate of
the population parameter?
b.Based on my random sample, what is my estimate of
normal distribution?

198
c. Is the value of my sample statistic unlikely enough for
me to reject the null hypothesis?
d.There is no key question in statistical estimation
126. Assuming innocence until “proven” guilty, a Type I error
occurs when an innocent person is found guilty.
a. True
b. False
127. This is the difference between a sample statistic and the
corresponding population parameter.
a. Standard error
b. Sampling error
c. Difference error
128. The “equals” sign (=) is included in which hypothesis when
conducting hypothesis testing?
a. Null
b. Alternative
c. It can appear in both the null and the alternative
hypothesis
129. A Type I error is also known as a ______.
a. False positive
b. False negative
c. Double negative
d. Positive negative

130. A Type II error is also known as a ______.
a. False positive
b. False negative
c. Double negative
d. Positive negative
131. If a finding is statistically significant one must also interpret the
data, calculate an effect size indicator, and make an assessment
of practical significance.
a. True
b. False
132. The p-value used in statistical significance testing should be
used to assess how strong a relationship is. For example, if
relationship A has a p=.04 and relationship B has a p=.03 then
you can conclude that relationship B is stronger than
relationship A.
a. True
b. False

200
Multiple choice question answer
1. Mall intercept interviews
2. Wide Area
Telecommunications
Service
3. Questionnaire
4. Questionnaire design
5. Double barreled question
6. Response rate
7. Funnel technique
8. Both A & B
9. Respondents are willing
to co-operate
10. Self-administered
questionnaires
11. Arithmetic mean, median,
mode
12. All of the above
13. Researcher’s degree of
confidence
14. Quasi- experimental
design
15. Frequency
16. Sometime documents
provide incomplete
account to the researcher
17. Moderate
18. Loaded question
19. Judgmental sampling
20. Both A & B
21. Abstraction
22. Variables
23. Mall intercept interviews
24. Funnel technique
25. Field interview
26. Experiment
27. A tentative statement
about the relationship
29. Knowledge accumulates
and learns from the work
of others
31. A research proposal is an
unorganized and
unplanned project
32. Exploratory research
33. Primary data
34. To generate the
hypothesis
35. Both A & B

36. Coding
37. Interim analysis
38. Memoing
39. Correlational
40. Factsheets codes
41. Hierarchical category
system
42. A priori codes
43. Enumeration
44. Interim analysis
45. Mixed research
46. Qualitative research
47. An attempt to confirm the
researcher’s hypotheses
48. A variable
49. Independent variable
50. It rarely is conducted in a
controlled setting or
environment
51. Experimental Research
52. Manipulation of the
independent variable
53. Causal-comparative and
correlational research
54. Several variables never
change
55. Mixed method research
56. Experimental research
57. Both a and b
58. An extraneous variable
59. Causal-comparative
research
60. Annual income
61. Control group
62. Jumping to the conclusion
of causality
63. Experimental
64. -1.00
65. Positive
67. b and c
68. Are often generated as the
data are collected,
interpreted, and analyzed
71. Past research
73. Ratio scale
74. A spurious relationship
75. Examining the
relationship between two
or more variables

202
controlling for additional
variables statistically
76. Ex planatory
77. Matching
78. Both a and c are
longitudinal designs
79. Both a and b
80. Both b and c
81. Retrospective research
82. True
83. Linear Model
84. Makin g the argument
that because A preceded
B, A must have caused B
85. All are steps
86. The observed result is
probably not due to
chance
88. None of ht above can be
manipulated in a research
study
89. Causal-comparative
research
90. Experimental d.
Historical
91. Statistical control
92. You are planning your
research study
93. True
94. b is true and c is probably
true
95. σ
96. A sampling distribution
statements are true
98. It will become wider
99. There is no difference
between male and female
incomes in the population
100. Two or more
101. Standard error
102. True
103. Sample, population
104. Statistic, parameter
106. 95%
107. The alternative hypothesis
108. True
109. False
110. Confidence limits
111. Type II error
112. Large sample
113. Eliminate all outliers

114. Confidence interval
115. Standard error
116. True
118. Type I
119. Inferential statistics
120. One-way analysis of
variance
121. T-test for correlation
coefficients
122. Significance level
123. Type II error
124. Both b and c are true
125. Based on my random
sample, what is my
estimate of the population
parameter?
126. True
127. Sampling error
128. Null
129. False positive
130. False negative
131. True
132. False

204
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