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
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
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

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

Fundamentals of Research Methodology

Fundamentals of Research Methodology

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