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MB 0050
Research Methodology
Contents
Unit 1
An Introduction to Research 1
Unit 2
The Importance of Measurement in Research 13
Unit 3
Selection and Formulation of a
Research Problem 23
Unit 4
Hypothesis 32
Unit 5
Research Design 46
Unit 6
Case Study Method 61 69
Unit 7
Sampling 66
Unit 8
Sources of Data 82
Edition: Spring 2010
BKID – B1206 10th
June 2010
Unit 9
Observation 92
Unit 10
Schedule and Questionnaire 101
Unit 11
Interviewing 108
Unit 12
Processing Data 129
Unit 13
Research Report Writing 187
Unit 14
Ethics in Research 198
Acknowledgements, References &
Suggested Readings 209
Dean Directorate of Distance Education Sikkim Manipal University
Board of Studies
Chairman Mr. Pankaj Khanna HOD Management & Commerce Director SMU – DDE HR, Fidelity Mutual Fund
Additional Registrar Mr. Shankar Jagannathan SMU – DDE Former Group Treasurer Wipro Technologies Limited
Controller of Examination Mr. Abraham Mathew SMU – DDE Chief Financial Officer Infosys BPO, Bangalore
Dr. T. V. Narasimha Rao Ms. Sadhna Dash Adjunct Faculty & Advisor Ex-Senior Manager, HR SMU – DDE Microsoft India Corporation (Pvt.) Ltd.
Prof. K. V. Varambally Director, Manipal Institute of Management, Manipal
Content Preparation Team Content Modification & Review Content Writing Vimala Parthasarathy Prof. Xavier V. K. Assistant Professor Christ College, Bangalore SMU DDE
Format Editing Language Editing Ms. Shulagna Sarkar Mr. Radhakrishna Rao Former Lecturer, Dept. of Lecturer in English Management & Commerce UPMC, Udupi SMU DDE, Manipal
Edition : Spring 2010 Printed : June 2010
This book is a distance education module comprising of written and compiled learning material for our students.
All rights reserved. No part of this work may be reproduced in any form by any means without permission in writing from Sikkim Manipal University of Health, Medical and Technological Sciences, Gangtok, Sikkim.
Printed and Published on behalf of Sikkim Manipal University of Health, Medical and Technological Sciences, Gangtok, Sikkim by Mr. Rajkumar Mascreen, GM, Manipal Universal Learning Pvt. Ltd., Manipal – 576 104. Printed at Manipal Press Limited, Manipal.
SUBJECT INTRODUCTION
Research simply means a search for facts – answer to questions and
solutions to problems. It is a purposive investigation. It is an organized
inquiry. It seeks to find explanations to unexplained phenomenon to clarify
the doubtful facts and to correct the misconceived facts.
Research is a scientific endeavour. It involves scientific method. “The
scientific method is a systematic step-by-step procedure following the logical
processes of reasoning”. Scientific method is a means for gaining
knowledge of the universe. It does not belong to any particular body of
knowledge; it is universal. It does not refer to a field of specific subject of
matter, but rather to a procedure or mode of investigation.
Unit 1 : An Introduction to Research
Meaning of research – Purpose of research
Types of research
Significance of research in Social and Business Sciences
Unit 2 : The importance of Measurement in Research
Definition and Purpose of Measurement
Levels of Measurement
Characteristics of Good Measurement
Unit 3 : Selection and Formulation of a Research Problem
Choosing the problem
Review of literature
Formulating the problem
Criteria of a good research problem
Unit 4 : Hypothesis
Hypothesis – Meaning and Examples of hypothesis
Types of hypothesis
Testing of hypothesis
Unit 5 : Research Design
Needs of research design
Components of research design – Different research designs
Research design for studies in commerce and management.
Unit 6 : Case Study Method
Assumptions of case study method
Advantages and disadvantages of case study method – Making case
study effective
Case study as a method of business research
Unit 7 : Sampling
Sampling procedure
Characteristics of good sample
Methods of sampling
Unit 8 : Sources of Data
Primary sources of data
Methods of collecting primary data
Secondary sources of data
Unit 9 : Observation
General characteristics of observation method
Process of observation
Use of observation in business research
Unit 10 : Schedules and Questionnaire
Process of data collection
Importance of questionnaire
Distinction between schedules and questionnaire
Unit 11 : Interview
Types of interviews
Approach to the interview
Qualities of interview
Interview techniques in business research
Unit 12 : Processing Data
Checking – Editing – Coding
Transcriptions and Tabulation
Data analysis
Unit 13 : Report Writing
Types of reports
Contents, styles of reporting
Steps in drafting reports
Editing the final draft
Evaluating the final drafts
Unit 14 : Ethics in Research
Meaning of Research Ethics
Ethical issues in the overall research process
Ethical issues in Gaining Access to Participants
Ethical issues in Data Collection
Ethical issues related to data analysis and reporting
Ethically questionable research situations
Responsibility for ethics in research
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Research Methodology Unit 1
Sikkim Manipal University Page No. 1
Unit 1 An Introduction to Research
Structure:
1.1 Meaning and Definition of Research
Objectives
1.1.1 Research and Scientific Method
1.1.2 Characteristics of Research
1.2 Purpose of Research
1.3 Types of Research
1.3.1 Pure Research
1.3.2 Applied Research
1.3.3 Exploratory Research
1.3.4 Descriptive Research
1.3.5 Diagnostic Study
1.3.6 Evaluation Studies
1.3.7 Action Research
1.4 Research Approaches
1.5 Significance of Research in Social and Business Sciences
Self Assessment Questions I
1.6 Summary
1.7 Terminal Questions
1.8 Answers to SAQs and TQs
1.1 Meaning and Definition of Research
Research simply means a search for facts – answers to questions and
solutions to problems. It is a purposive investigation. It is an organized
inquiry. It seeks to find explanations to unexplained phenomenon to clarify
the doubtful facts and to correct the misconceived facts.
The search for facts may be made through either:
Arbitrary (or unscientific) Method: It’s a method of seeking answers to
question consists of imagination, opinion, blind belief or impression.
E.g. it was believed that the shape of the earth was flat; a big snake
swallows sun or moon causing solar or lunar eclipse. It is subjective; the
finding will vary from person to person depending on his impression or
imagination. It is vague and inaccurate. Or
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Scientific Method: this is a systematic rational approach to seeking
facts. It eliminates the drawbacks of the arbitrary method. It is objective,
precise and arrives at conclusions on the basis of verifiable evidences.
Therefore, search of facts should be made by scientific method rather
than by arbitrary method. Then only we may get verifiable and accurate
facts. Hence research is a systematic and logical study of an issue or
problem or phenomenon through scientific method.
Young defines Research as “a scientific undertaking which, by means of
logical and systematic techniques”, aims to:
(a) Discover of new facts or verify and test old facts,
(b) Analyze their sequences, interrelationships and causal explanations,
(c) Develop new scientific tools, concepts and theories which would
facilitate reliable and valid study of human behaviour.
(d) Kerlinger defines research as a “systematic, controlled, empirical and
critical investigation of hypothetical propositions about the presumed
relations among natural phenomena”.
Objectives:
After studying this lesson the students should be able to understand:
Research and scientific method
Characteristics of Research
Purpose of research
Different types of Research
Research Approaches
Significance of research in Social and Business Sciences
1.1.1 Research and Scientific Method
Research is a scientific endeavour. It involves scientific method. “The
scientific method is a systematic step-by-step procedure following the logical
processes of reasoning”. Scientific method is a means for gaining
knowledge of the universe. It does not belong to any particular body of
knowledge; it is universal. It does not refer to a field of specific subject of
matter, but rather to a procedure or mode of investigation.
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Sikkim Manipal University Page No. 3
The scientific method is based on certain “articles of faith.” These are:
Reliance on Empirical Evidence: Truth is established on the basis of
evidence. Conclusion is admitted, only when it is based on evidence.
The answer to a question is not decided by intuition or imagination.
Relevant data are collected through observation or experimentation. The
validity and the reliability of data are checked carefully and the data are
analyzed thoroughly, using appropriate methods of analysis.
Use of Relevant Concepts: We experience a vast number of facts
through our sense. Facts are things which actually exist. In order to deal
with them, we use concepts with specific meanings. They are symbols
representing the meaning that we hold. We use them in our thinking and
communication. Otherwise, clarity and correct understanding cannot be
achieved.
Commitment of Objectivity: Objectivity is the hallmark of the scientific
method. It means forming judgement upon facts unbiased by personal
impressions. The conclusion should not vary from person to person. It
should be the same for all persons.
Ethical Neutrality: Science does not pass normal judgment on facts. It
does not say that they are good or bad. According to Schrödinger
“Science never imposes anything, science states. Science aims at
nothing but making true and adequate statements about its object.”
Generalization: In formulating a generalization, we should avoid the
danger of committing the particularistic fallacy, which arises through an
inclination to generalize on insufficient or incomplete and unrelated data.
This can be avoided by the accumulation of a large body of data and by
the employment of comparisons and control groups.
Verifiability: The conclusions arrived at by a scientist should be
verifiable. He must make known to others how he arrives at his
conclusions. He should thus expose his own methods and conclusions
to critical scrutiny. When his conclusion is tested by others under the
same conditions, then it is accepted as correct.
Logical reasoning process: The scientific method involves the logical
process of reasoning. This reasoning process is used for drawing
inference from the finding of a study or for arriving at conclusion.
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1.1.2 Characteristics of Research
It is a systematic and critical investigation into a phenomenon.
It is a purposive investigation aiming at describing, interpreting and
explaining a phenomenon.
It adopts scientific method.
It is objective and logical, applying possible test to validate the
measuring tools and the conclusions reached.
It is based upon observable experience or empirical evidence.
Research is directed towards finding answers to pertinent questions and
solutions to problems.
It emphasizes the development of generalization, principles or theories.
The purpose of research is not only to arrive at an answer but also to
stand up the test of criticism.
1.2 Purpose of Research
The objectives or purposes of research are varied. They are:
Research extends knowledge of human beings, social life and
environment. The search is for answers for various types of questions:
What, Where, When, How and Why of various phenomena, and
enlighten us.
Research brings to light information that might never be discovered fully
during the ordinary course of life.
Research establishes generalizations and general laws and contributes
to theory building in various fields of knowledge.
Research verifies and tests existing facts and theory and these help
improving our knowledge and ability to handle situations and events.
General laws developed through research may enable us to make
reliable predictions of events yet to happen.
Research aims to analyze inter-relationships between variables and to
derive causal explanations: and thus enables us to have a better
understanding of the world in which we live.
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Applied research aims at finding solutions to problems… socio-
economic problems, health problems, human relations problems in
organizations and so on.
Research also aims at developing new tools, concepts and theories for a
better study of unknown phenomena.
Research aids planning and thus contributes to national development.
1.3 Types of Research
Although any typology of research is inevitably arbitrary, Research may be
classified crudely according to its major intent or the methods. According to
the intent, research may be classified as:
1.3.1 Pure Research
It is undertaken for the sake of knowledge without any intention to apply it in
practice, e.g., Einstein’s theory of relativity, Newton’s contributions, Galileo’s
contribution, etc. It is also known as basic or fundamental research. It is
undertaken out of intellectual curiosity or inquisitiveness. It is not necessarily
problem-oriented. It aims at extension of knowledge. It may lead to either
discovery of a new theory or refinement of an existing theory. It lays
foundation for applied research. It offers solutions to many practical
problems. It helps to find the critical factors in a practical problem. It
develops many alternative solutions and thus enables us to choose the best
solution.
1.3.2 Applied Research
It is carried on to find solution to a real-life problem requiring an action or
policy decision. It is thus problem-oriented and action-directed. It seeks an
immediate and practical result, e.g., marketing research carried on for
developing a news market or for studying the post-purchase experience of
customers. Though the immediate purpose of an applied research is to find
solutions to a practical problem, it may incidentally contribute to the
development of theoretical knowledge by leading to the discovery of new
facts or testing of theory or o conceptual clarity. It can put theory to the test.
It may aid in conceptual clarification. It may integrate previously existing
theories.
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1.3.3 Exploratory Research
It is also known as formulative research. It is preliminary study of an
unfamiliar problem about which the researcher has little or no knowledge. It
is ill-structured and much less focused on pre-determined objectives. It
usually takes the form of a pilot study. The purpose of this research may be
to generate new ideas, or to increase the researcher’s familiarity with the
problem or to make a precise formulation of the problem or to gather
information for clarifying concepts or to determine whether it is feasible to
attempt the study. Katz conceptualizes two levels of exploratory studies. “At
the first level is the discovery of the significant variable in the situations; at
the second, the discovery of relationships between variables.”
1.3.4 Descriptive Study
It is a fact-finding investigation with adequate interpretation. It is the simplest
type of research. It is more specific than an exploratory research. It aims at
identifying the various characteristics of a community or institution or
problem under study and also aims at a classification of the range of
elements comprising the subject matter of study. It contributes to the
development of a young science and useful in verifying focal concepts
through empirical observation. It can highlight important methodological
aspects of data collection and interpretation. The information obtained may
be useful for prediction about areas of social life outside the boundaries of
the research. They are valuable in providing facts needed for planning social
action program.
1.3.5 Diagnostic Study
It is similar to descriptive study but with a different focus. It is directed
towards discovering what is happening, why it is happening and what can
be done about. It aims at identifying the causes of a problem and the
possible solutions for it. It may also be concerned with discovering and
testing whether certain variables are associated. This type of research
requires prior knowledge of the problem, its thorough formulation, clear-cut
definition of the given population, adequate methods for collecting accurate
information, precise measurement of variables, statistical analysis and test
of significance.
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1.3.6 Evaluation Studies
It is a type of applied research. It is made for assessing the effectiveness of
social or economic programmes implemented or for assessing the impact of
developmental projects on the development of the project area. It is thus
directed to assess or appraise the quality and quantity of an activity and its
performance, and to specify its attributes and conditions required for its
success. It is concerned with causal relationships and is more actively
guided by hypothesis. It is concerned also with change over time.
1.3.7 Action Research
It is a type of evaluation study. It is a concurrent evaluation study of an
action programme launched for solving a problem for improving an exiting
situation. It includes six major steps: diagnosis, sharing of diagnostic
information, planning, developing change programme, initiation of
organizational change, implementation of participation and communication
process, and post experimental evaluation.
According to the methods of study, research may be classified as:
1. Experimental Research: It is designed to asses the effects of particular
variables on a phenomenon by keeping the other variables constant or
controlled. It aims at determining whether and in what manner variables
are related to each other.
2. Analytical Study: It is a system of procedures and techniques of
analysis applied to quantitative data. It may consist of a system of
mathematical models or statistical techniques applicable to numerical
data. Hence it is also known as the Statistical Method. It aims at testing
hypothesis and specifying and interpreting relationships.
3. Historical Research: It is a study of past records and other information
sources with a view to reconstructing the origin and development of an
institution or a movement or a system and discovering the trends in the
past. It is descriptive in nature. It is a difficult task; it must often depend
upon inference and logical analysis or recorded data and indirect
evidences rather than upon direct observation.
4. Survey: It is a fact-finding study. It is a method of research involving
collection of data directly from a population or a sample thereof at
particular time. Its purpose is to provide information, explain
phenomena, to make comparisons and concerned with cause and effect
relationships can be useful for making predications
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1.4 Research Approaches
There are two main approaches to research, namely quantitative approach
and qualitative approach. The quantitative approach involves the collection
of quantitative data, which are put to rigorous quantitative analysis in a
formal and rigid manner. This approach further includes experimental,
inferential, and simulation approaches to research. Meanwhile, the
qualitative approach uses the method of subjective assessment of opinions,
behaviour and attitudes. Research in a situation is a function of the
researcher’s impressions and insights. The results generated by this type of
research are either in non-quantitative form or in the form which cannot be
put to rigorous quantitative analysis. Usually, this approach uses techniques
like depth interviews, focus group interviews, and projective techniques.
1.5 Significance of Research in Social and Business Sciences
According to a famous Hudson Maxim, “All progress is born of inquiry.
Doubt is often better than overconfidence, for it leads to inquiry, and inquiry
leads to invention”. It brings out the significance of research, increased
amounts of which makes progress possible. Research encourages scientific
and inductive thinking, besides promoting the development of logical habits
of thinking and organization.
The role of research in applied economics in the context of an economy or
business is greatly increasing in modern times. The increasingly complex
nature of government and business has raised the use of research in
solving operational problems. Research assumes significant role in
formulation of economic policy, for both the government and business. It
provides the basis for almost all government policies of an economic
system. Government budget formulation, for example, depends particularly
on the analysis of needs and desires of the people, and the availability of
revenues, which requires research. Research helps to formulate alternative
policies, in addition to examining the consequences of these alternatives.
Thus, research also facilitates the decision making of policy-makers,
although in itself it is not a part of research. In the process, research also
helps in the proper allocation of a country’s scare resources. Research is
also necessary for collecting information on the social and economic
structure of an economy to understand the process of change occurring in
the country. Collection of statistical information though not a routine task,
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Sikkim Manipal University Page No. 9
involves various research problems. Therefore, large staff of research
technicians or experts is engaged by the government these days to
undertake this work. Thus, research as a tool of government economic
policy formulation involves three distinct stages of operation which are as
follows:
Investigation of economic structure through continual compilation of
facts
Diagnoses of events that are taking place and the analysis of the forces
underlying them; and
The prognosis, i.e., the prediction of future developments
Research also assumes a significant role in solving various operational and
planning problems associated with business and industry. In several ways,
operations research, market research, and motivational research are vital
and their results assist in taking business decisions. Market research is
refers to the investigation of the structure and development of a market for
the formulation of efficient policies relating to purchases, production and
sales. Operational research relates to the application of logical,
mathematical, and analytical techniques to find solution to business
problems such as cost minimization or profit maximization, or the
optimization problems. Motivational research helps to determine why people
behave in the manner they do with respect to market characteristics. More
specifically, it is concerned with the analyzing the motivations underlying
consumer behaviour. All these researches are very useful for business and
industry, which are responsible for business decision making.
Research is equally important to social scientist for analyzing social
relationships and seeking explanations to various social problems. It gives
intellectual satisfaction of knowing things for the sake of knowledge. It also
possesses practical utility for the social scientist to gain knowledge so as to
be able to do something better or in a more efficient manner. This, research
in social sciences is concerned with both knowledge for its own sake, and
knowledge for what it can contribute to solve practical problems.
Self Assessment Questions
State whether the following are true or false:
1. Research is a repetitive search.
2. Applied research gives a solution to problem.
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3. Scientific method is systematic.
4. Objectivity is not required for all types of research.
5. Pure research is not fundamental research.
1.6 Summary
Research simply means a search for facts. The search for facts may be
made through either arbitrary (or unscientific) method or scientific method.
Young defines Research as “a scientific undertaking which, by means of
logical and systematic techniques”, aims to: Discover of new facts or verify
and test old facts, analyze their sequences, interrelationships and causal
explanations, develop new scientific tools, concepts and theories which
would facilitate reliable and valid study of human behaviour. Kerlinger
defines research as a “systematic, controlled, empirical and critical
investigation of hypothetical propositions about the presumed relations
among natural phenomena”.
The scientific method is based on certain “articles of faith.” These are:
1. Reliance on empirical evidence:
2. Use of relevant concepts
3. Commitment of objectivity
4. Ethical neutrality
5. Generalization
6. Verifiability
7. Logical reasoning process
Research is directed towards finding answers to pertinent questions and
solutions to problems. It emphasizes the development of generalization,
principles or theories. The purpose of research is not only to arrive at an
answer but also to stand up the test of criticism. The purpose of research is
to extend knowledge of human beings Research establishes generalizations
and general laws and contributes to theory building in various fields of
knowledge. Research verifies and tests existing facts and theory and these
help improving our knowledge and ability to handle situations and events.
General laws developed through research may enable us to make reliable
predictions of events yet to happen. Research aims to analyze inter-
relationships between variables and to derive causal explanations: and thus
enables us to have a better understanding of the world in which we live.
Research Methodology Unit 1
Sikkim Manipal University Page No. 11
Applied research aims at finding solutions to problems… socio-economic
problems, health problems, human relations problems in organizations and
so on. Research also aims at developing new tools, concepts and theories
for a better study of unknown phenomena. Research aids planning and
thus contributes to national development. Pure Research is undertaken for
the sake of knowledge without any intention to apply it in practice. Applied
Research is carried on to find solution to a real-life problem requiring an
action or policy decision. It is thus problem-oriented and action-directed.
Exploratory Research is also known as formulative research. It is
preliminary study of an unfamiliar problem about which the researcher has
little or no knowledge. Descriptive Study is a fact-finding investigation with
adequate interpretation. Diagnostic Study is similar to descriptive study but
with a different focus. Evaluation Studies is a type of applied research.
Action Research is a type of evaluation study. The role of research in
applied economics in the context of an economy or business is greatly
increasing in modern times. Research also assumes a significant role in
solving various operational and planning problems associated with business
and industry. Research is equally important to social scientist for analyzing
social relationships and seeking explanations to various social problems.
1.7 Terminal Questions
1. Define the following:
i) Scientific Method ii) Research
iii) Applied Research iv) Exploratory Research
v) Descriptive Study vi) Diagnostic Study
vii) Action Research
2. What is the meaning of research?
3. What are the articles of faith in scientific method?
4. What are the features of research?
5 What are the purposes of research?
6 What are the types of research?
7. What is the significance of research in social and business sciences?
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1.8 Answers to SAQs and TQs
SAQs
1. True
2. True
3. True
4. False
5. False
TQs
1)
i) Section 1.1.1
ii) Section 1.3.3
iii) Section 1.3.2
iv) Section 1.3.3
v) Section 1.3.4
vi) Section 1.3.5
vii) Section 1.3.7
2) Section 1.1
3) Section 1.1.1
4) Section 1.2.2
5) Section 1.2
6) Section 1.3
7) Section 1.5
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Sikkim Manipal University Page No. 13
Unit 2 The Importance of Measurement
in Research
Structure:
2.1 Introduction
Objectives
2.2 Definition and Purpose of Measurement
2.3 Levels of Measurement
2.4 Characteristics of Good Measurement
2.4.1 Validity
2.4.2 Reliability
2.5 Summary
2.6 Terminal Questions
2.7 Answers to SAQs and TQs
2.1 Introduction
Research basically deals with the measurement of various variables. While
the measurement of variables is an important stage in the research process,
it is also a difficult task. This section helps to understand the concept of
measurement, the need for measurement, its nature, functions and
procedure. The different levels of measurement and the validity and
reliability of measuring instruments will also be explained in detail.
Objectives:
After studying this unit, you will be able to:
Explain what is meant by measurement in research
Describe the different levels of measurement
Recognize what makes for good measurement
Distinguish between the various concepts used to describe good
measurement
2.2 Definition and Purpose of Measurement
Different definitions of measurement have been offered by different authors–
1. According to Stevens, measurement is “the assignment of numerals to
objects or events according to rules.”
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A simple example of assignment of numerals according to a rule is
described below –
Suppose a survey is conducted to study the applicants of an MBA
program and one of the objectives of the study is to find out the sex-wise
break-up of applicants. In this case, we may assign the number “0” to
male applicants and the number “1” to female applicants. Thus numbers
may be used to label individuals, events or things.
2. Campbell defines measurement as “the assignment of numbers to
represent properties.”
3. In the words of Torgerson, measurement is “the assignment of numbers
to objects to represent amounts or degrees of a property possessed by
all of the objects.
In research, it is necessary to distinguish between “objects” and “properties’
or characteristics of these objects. For example, a person is an object and
his/her physical characteristics include height, weight, color, etc. while his or
her psychological characteristics include intelligence and attitudes. The
important point to remember is that the researcher is concerned with
measuring properties and not the objects themselves. While physical
properties may be directly observed, psychological properties such as
intelligence are inferred. For example, a child’s score in an IQ test indicates
his or her level of intelligence.
Measurement also has several purposes –
The researcher constructs theories to explain social and psychological
phenomena (e.g. labor unrest, employee satisfaction), which in turn are
used to derive hypotheses or assumptions. These hypotheses can be
verified statistically only by measuring the variables in the hypotheses.
Measurement makes the empirical description of social and
psychological phenomena easier.
Example – When conducting a study of a tribal community, measuring
devices help the researcher in classifying cultural patterns and behaviors.
Measurement also makes it possible to quantify variables and use
statistical techniques to analyze the data gathered.
Measurement enables the researcher to classify individuals or objects
and to compare them in terms of specific properties or characteristics by
measuring the concerned variables.
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Examples
Comparison of male and female students’ performance in college exams or
of length of stay on the job of older and younger employees.
2.3 Levels of Measurement
Measurement may be classified into four different levels, based on the
characteristics of order, distance and origin.
1. Nominal measurement
This level of measurement consists in assigning numerals or symbols to
different categories of a variable. The example of male and female
applicants to an MBA program mentioned earlier is an example of nominal
measurement. The numerals or symbols are just labels and have no
quantitative value. The number of cases under each category are counted.
Nominal measurement is therefore the simplest level of measurement. It
does not have characteristics such as order, distance or arithmetic origin.
2. Ordinal measurement
In this level of measurement, persons or objects are assigned numerals
which indicate ranks with respect to one or more properties, either in
ascending or descending order.
Example
Individuals may be ranked according to their “socio-economic class”, which
is measured by a combination of income, education, occupation and wealth.
The individual with the highest score might be assigned rank 1, the next
highest rank 2, and so on, or vice versa.
The numbers in this level of measurement indicate only rank order and not
equal distance or absolute quantities. This means that the distance between
ranks 1 and 2 is not necessarily equal to the distance between ranks 2
and 3.
Ordinal scales may be constructed using rank order, rating and paired
comparisons. Variables that lend themselves to ordinal measurement
include preferences, ratings of organizations and economic status.
Statistical techniques that are commonly used to analyze ordinal scale data
are the median and rank order correlation coefficients.
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3. Interval measurement
This level of measurement is more powerful than the nominal and ordinal
levels of measurement, since it has one additional characteristic – equality
of distance. However, it does not have an origin or a true zero. This implies
that it is not possible to multiply or divide the numbers on an interval scale.
Example
The Centigrade or Fahrenheit temperature gauge is an example of the
interval level of measurement. A temperature of 50 degrees is exactly 10
degrees hotter than 40 degrees and 10 degrees cooler than 60 degrees.
Since interval scales are more powerful than nominal or ordinal scales, they
also lend themselves to more powerful statistical techniques, such as
standard deviation, product moment correlation and “t” tests and “F” tests of
significance.
4. Ratio measurement
This is the highest level of measurement and is appropriate when measuring
characteristics which have an absolute zero point. This level of
measurement has all the three characteristics – order, distance and origin.
Examples
Height, weight, distance and area.
Since there is a natural zero, it is possible to multiply and divide the
numbers on a ratio scale. Apart from being able to use all the statistical
techniques that are used with the nominal, ordinal and interval scales,
techniques like the geometric mean and coefficient of variation may also be
used.
The main limitation of ratio measurement is that it cannot be used for
characteristics such as leadership quality, happiness, satisfaction and other
properties which do not have natural zero points.
The different levels of measurement and their characteristics may be
summed up.
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In the table below –
Levels of measurement Characteristics
Nominal No order, distance or origin
Ordinal Order, but no distance or origin
Interval Both order and distance, but no origin
Ratio Order, distance and origin
2.4 Characteristics of Good Measurement
A good measurement tool must possess the following characteristics –
1. Unidimensionality – This means that the measurement scale should
not measure more than one characteristic at a time. For example, a
scale should measure only length and not both length and temperature
at the same time.
2. Linearity – A good measurement scale should follow the straight line
model.
3. Validity – This means that a measurement scale should measure what it
is supposed to measure.
4. Reliability – This refers to consistency. The measurement scale should
give consistent results.
5. Accuracy and Precision – The measurement scale should give an
accurate and precise measure of what is being measured.
6. Simplicity – A measurement tool should not be very complicated or
elaborate.
7. Practicability – The measurement tool should be easy to understand
and administer. There should be proper guidelines regarding its purpose
and construction procedure, so that the results of a test can be
interpreted easily.
Of the above characteristics, validity and reliability are the most important
requirements of a measurement scale and will be explained in more detail.
2.4.1 Validity
A measurement scale may be considered to be valid if it effectively
measures a specific property or characteristic that it intends to measure.The
question of validity does not arise in the case of measurement of physical
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characteristics such as length, weight and height. This is because the
measurement is direct and can be done through standard measuring
devices. On the other hand, the measurement of abstract characteristics
such as motivation and attitudes is more indirect and therefore poses the
problem of validity. In such cases, there must be some evidence to prove
that the measurement scale actually measures what it is supposed to
measure. Such evidence is generally gathered through the application of
statistical techniques.
Validity may be classified into different types, as described below. The
degree of validity of each type is determined by applying logic, statistical
procedures or both.
1. Content validity: This type of validity may be of two types – a) Face
validity and b) Sampling validity. Face validity is determined through a
subjective evaluation of a measuring scale. For example, a researcher
may develop a scale to measure consumer attitudes towards a brand
and pre-test the scale among a few experts. If the experts are satisfied
with the scale, the researcher may conclude that the scale has face
validity. However, the limitation of this type of validity is that it is
determined by opinions, rather than through a statistical method.
Sampling validity refers to how representative the content of the
measuring instrument is. In other words, the measuring instrument’s
content must be representative of the content universe of the
characteristic being measured.
For example, if attitude is the characteristic being measured, its content
universe may comprise statements and questions indicating which
aspects of attitude need to be measured. In this case, sampling validity
will be determined by comparing the items in the measuring instrument
with the items in the content universe.
Sampling validity, like face validity, is also based on the judgment and
subjective evaluation of both the researcher and outside experts. The
determination of the content universe and the selection of the relevant
items that are to be included in the measuring scale are both done
based on the knowledge and skill of the investigator and other judges.
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2. Predictive validity: This type of validity refers to the extent to which one
behavior can be predicted based on another, based on the association
between the results yielded by the measuring instrument and the
eventual outcome.
Example – In the case of an admission test designed for prospective
MBA students, the predictive validity of the test would be determined by
the association between the scores on the test and the grade point
average secured by students during the first semester of study. A
statistical measure of this association – the correlation coefficient –
could be computed to determine the predictive validity of the admission
test. Predictive validity would be strong if the coefficient is greater than
.50.
One limitation of determining predictive validity using this statistical
association is that the eventual outcome, in this case, the grade point
average of students during the first semester, may be influenced by
other “extraneous” variables or factors. In other words, the grade point
average may have been influenced by other factors (e.g. extra training
or coaching) and may not necessarily be linked to the score on the
admission test. Therefore, predicting behavior from one situation to
another is not always accurate.
3. Construct validity: A construct is a conceptual equation that is
developed by the researcher based on theoretical reasoning. Various
kinds of relationships may be perceived by the researcher between a
variable under study and other variables. These relationships must be
tested in order to determine the construct validity of a measuring
instrument. The instrument may be considered to have construct validity
only if the expected relationships are found to be true.
When determining the validity of a particular measurement instrument, all
the three types of validity discussed above should be determined.
2.4.2 Reliability
This refers to the ability of a measuring scale to provide consistent and
accurate results. To give a simple example, a weighing machine may be
said to be reliable if the same reading is given every time the same object is
weighed.
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There are two dimensions of reliability – stability and equivalence or non-
variability. Stability refers to consistency of results with repeated
measurements of the same object, as in the weighing machine example.
Non variability refers to consistency at a given point of time among different
investigators and samples of items.
The problem of reliability is more likely to arise with measurements in the
social sciences than with measurements in the physical sciences, due to
factors such as poor memory or recall of respondents, lack of clear
instructions given to respondents and irrelevant contents of the measuring
instrument.
Reliability can be improved in three ways – 1) By reducing the external
sources of variation. This in turn can be achieved by standardizing the
conditions under which measurement is carried out, by employing trained
investigators and by providing standard instructions. 2) By making the
measuring instrument more consistent internally, through an analysis of the
different items 3) By adding more number of items to the measuring
instrument, in order to increase the probability of more accurate
measurement.
The desired level of reliability depends on the research objectives, as well
as the homogeneity of the population under study. If precise estimates are
required, the higher will be the desired level of accuracy. In the case of a
homogeneous population, a lower level of reliability may be sufficient, since
there is not much variation in the data.
Reliability and validity are closely interlinked. A measuring instrument that is
valid is always reliable, but the reverse is not true. That is, an instrument
that is reliable is not always valid. However, an instrument that is not valid
may or may not be reliable and an instrument that is not reliable is never
valid.
Self Assessment Questions
Are the following statements true or false?
1. Research is concerned with the measurement of objects.
2. A person’s emotions may be directly observed.
3. The most powerful level of measurement is ratio measurement.
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4. Linearity means that the measuring scale should not measure more than
one characteristic at a time.
5. The problem of extraneous variables arises in the case of construct
validity.
6. Validity is determined mainly by predictive validity.
7. Validity and reliability do not always go together.
8. Different research situations require different levels of reliability.
2.4 Summary
Measurement is an important concept in research and is a difficult task. It
refers to the assignment of numerals to objects in order to measure the
characteristics or properties of objects. Measurement facilitates the
verification of hypotheses, helps to quantify variables, makes data suitable
for statistical analysis and enables comparison between objects in terms of
specific characteristics.
Measurement may be classified into four different levels, based on three
characteristics – order, distance and origin. The lowest level of
measurement is nominal measurement and involves assigning numerals or
labels to different categories of a variable. The next level is ordinal
measurement in which objects are rank ordered with respect to a specific
characteristic. The interval level of measurement has the characteristics of
order, distance and equality of interval but no origin. The highest level of
measurement is ratio measurement which is suitable for measuring
properties which have an absolute zero point. It permits the use of advanced
statistical techniques to analyze the data.
The characteristics of good measurement are uni-dimensionality, linearity,
validity, reliability, accuracy, precision, simplicity and practicability.
Validity refers to how effective an instrument is in measuring a property
which it intends to measure. There are three types of validity – content
validity, predictive validity and construct validity.
Content validity may be of two types – face validity and sampling validity.
Face validity is determined by a subjective evaluation of a measuring scale.
Sampling validity refers to the extent to which the measuring instrument’s
content is representative of the content universe of the characteristic being
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measured. The main limitation of content validity is that it is determined in a
subjective manner, rather than through a statistical method.
Predictive validity of a measuring instrument refers to the extent to which it
may be used to predict a particular behavior, based on another behavior.
Construct validity of a measuring instrument is determined by testing the
relationships between the variables in the study and other variables.
Reliability of a measuring instrument refers to its ability to provide consistent
and accurate results with repeated measurements.
Reliability and validity are closely associated. An instrument that is valid is
also reliable, but not vice versa.
2.6 Terminal Questions
1. Differentiate between nominal, ordinal, interval and ratio scales, with an
example of each.
2. What is meant by validity? How does it differ from reliability and what are
its types?
3. What are the purposes of measurement in social science research?
2.7 Answers to SAQs and TQs
SAQs
1. F
2. F
3. T
4. F
5. F
6. F
7. T
8. T
TQs
1. Refer 2.3
2. Refer 2.4.1, 2.4.2
3. Refer 2.2
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Unit 3 Selection and Formulation of a
Research Problem
Structure:
3.1 Meaning of Research Problem
Objectives
3.2 Choosing the Problem
3.3 Review of Literature
3.4 Formulating the Problem
3.4.1 Internal Criteria
3.4.2 External Criteria
3.5 Objective of Formulating the Problem
3.6 Techniques involved in Formulating the Problem
3.7 Criteria of Good Research Problem
Self Assessment Questions I
3.8 Summary
3.9 Terminal Questions
3.10 Answers to SAQs and TQs
3.1 Meaning of Research Problem
Research really begins when the researcher experiences some difficulty,
i.e., a problem demanding a solution within the subject-are of his discipline.
This general area of interest, however, defines only the range of subject-
matter within which the researcher would see and pose a specific problem
for research. Personal values play an important role in the selection of a
topic for research. Social conditions do often shape the preference of
investigators in a subtle and imperceptible way.
The formulation of the topic into a research problem is, really speaking the
first step in a scientific enquiry. A problem in simple words is some difficulty
experienced by the researcher in a theoretical or practical situation. Solving
this difficulty is the task of research.
R. L. Ackoffs analysis affords considerable guidance in identifying problem
for research. He visualizes five components of a problem.
1) Research-consumer: There must be an individual or a group which
experiences some difficulty.
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2) Research-consumer’s Objectives: The research-consumer must have
available, alternative means for achieving the objectives he desires.
3) Alternative Means to Meet the Objectives: The research-consumer must
have available, alternative means for achieving the objectives he
desires.
4) Doubt in Regard to Selection of Alternatives: The existence of
alternative courses of action in not enough; in order to experience a
problem, the research consumer must have some doubt as to which
alternative to select.
5) There must be One or More Environments to which the Difficulty or
Problem Pertains: A change in environment may produce or remove a
problem. A research-consumer may have doubts as to which will be the
most efficient means in one environment but would have no such doubt
in another.
Objectives:
After studying this unit you should be able to understand:
The meaning of Research Problem
Choosing the problem
Review of Literature
Criteria for formulating the problem
Objective of Formulating the Problem
Techniques involved in Formulating the Problem
Criteria of Good Research Problem
3.2 Choosing the Problem
The selection of a problem is the first step in research. The term problem
means a question or issue to be examined. The selection of a problem for
research is not an easy task; it self is a problem. It is least amenable to
formal methodological treatment. Vision, an imaginative insight, plays an
important role in this process. One with a critical, curious and imaginative
mind and is sensitive to practical problems could easily identify problems for
study.
The sources from which one may be able to identify research problems or
develop problems awareness are:
Review of literature
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Academic experience
Daily experience
Exposure to field situations
Consultations
Brain storming
Research
Intuition
3.3 Review of Literature
Frequently, an exploratory study is concerned with an area of subject matter
in which explicit hypothesis have not yet been formulated. The researcher’s
task then is to review the available material with an eye on the possibilities
of developing hypothesis from it. In some areas of the subject matter,
hypothesis may have been stated by previous research workers. The
researcher has to take stock of these various hypotheses with a view to
evaluating their usefulness for further research and to consider whether they
suggest any new hypothesis. Sociological journals, economic reviews, the
bulletin of abstracts of current social sciences research, directory of doctoral
dissertation accepted by universities etc afford a rich store of valuable clues.
In addition to these general sources, some governmental agencies and
voluntary organizations publish listings of summaries of research in their
special fields of service. Professional organizations, research groups and
voluntary organizations are a constant source of information about
unpublished works in their special fields.
3.4 Formulating the Problem
The selection of one appropriate researchable problem out of the identified
problems requires evaluation of those alternatives against certain criteria,
which may be grouped into:
3.4.1 Internal Criteria
Internal Criteria consists of:
1) Researcher’s interest: The problem should interest the researcher and
be a challenge to him. Without interest and curiosity, he may not
develop sustained perseverance. Even a small difficulty may become an
excuse for discontinuing the study. Interest in a problem depends upon
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the researcher’s educational background, experience, outlook and
sensitivity.
2) Researcher’s competence: A mere interest in a problem will not do.
The researcher must be competent to plan and carry out a study of the
problem. He must have the ability to grasp and deal with int. he must
possess adequate knowledge of the subject-matter, relevant
methodology and statistical procedures.
3) Researcher’s own resource: In the case of a research to be done by a
researcher on his won, consideration of his own financial resource is
pertinent. If it is beyond his means, he will not be able to complete the
work, unless he gets some external financial support. Time resource is
more important than finance. Research is a time-consuming process;
hence it should be properly utilized.
3.4.2 External Criteria
1) Research-ability of the problem: The problem should be researchable,
i.e., amendable for finding answers to the questions involved in it
through scientific method. To be researchable a question must be one
for which observation or other data collection in the real world can
provide the answer.
2) Importance and urgency: Problems requiring investigation are
unlimited, but available research efforts are very much limited.
Therefore, in selecting problems for research, their relative importance
and significance should be considered. An important and urgent problem
should be given priority over an unimportant one.
3) Novelty of the problem: The problem must have novelty. There is no
use of wasting one’s time and energy on a problem already studied
thoroughly by others. This does not mean that replication is always
needless. In social sciences in some cases, it is appropriate to replicate
(repeat) a study in order to verify the validity of its findings to a different
situation.
4) Feasibility: A problem may be a new one and also important, but if
research on it is not feasible, it cannot be selected. Hence feasibility is a
very important consideration.
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5) Facilities: Research requires certain facilities such as well-equipped
library facility, suitable and competent guidance, data analysis facility,
etc. Hence the availability of the facilities relevant to the problem must
be considered.
6) Usefulness and social relevance: Above all, the study of the problem
should make significant contribution to the concerned body of
knowledge or to the solution of some significant practical problem. It
should be socially relevant. This consideration is particularly important in
the case of higher level academic research and sponsored research.
7) Research personnel: Research undertaken by professors and by
research organizations require the services of investigators and
research officers. But in India and other developing countries, research
has not yet become a prospective profession. Hence talent persons are
not attracted to research projects.
Each identified problem must be evaluated in terms of the above internal
and external criteria and the most appropriate one may be selected by a
research scholar.
3.5 Objective of Formulating the Problem
A problem well put is half-solved. The primary task of research is collection
of relevant data and the analysis of data for finding answers to the research
questions. The proper performance of this task depends upon the
identification of exact data and information required for the study. The
formulation serves this purpose. The clear and accurate statement of the
problem, the development of the conceptual model, the definition of the
objectives of the study, the setting of investigative questions, the formulation
of hypothesis to be tested and the operational definition of concepts and the
delimitation of the study determine the exact data needs of the study. Once
the exact data requirement is known, the researcher can plan and execute
the other steps without any waste of time and energy. Thus formulation
gives a direction and a specific focus to the research effort. It helps to
delimit the field of enquiry by singling out the pertinent facts from a vast
ocean of facts and thus saves the researcher from becoming lost in a welter
of irrelevancies. It prevents a blind search and indiscriminate gathering of
data which may later prove irrelevant to the problem under study. It helps in
determining the methods to be adopted for sampling and collection of data.
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3.6 Techniques involved in Formulating Problem
The problem selected for research may initially be a vague topic. The
question to be studied or the problem to be solved may not be known.
Hence the selected problem should be defined and formulated. This is a
difficult process. It requires intensive reading of a few selected articles or
chapters in books in order to understand the nature of the problem selected.
The process of defining a problem includes:
1. Developing title: The title should be carefully worded. It should indicate
the core of the study, reflect the real intention of the researcher, and
show on what is the focus e.g., “Financing small-scale industries by
commercial banks.” This shows that the focus is on commercial banks
and not on small-scale industries. On the other hand, if the title is “The
Financial Problem of Small-scale industries”, the focus is on small-scale
industries.
2. Building a conceptual model: On the basis of our theoretical
knowledge of the phenomenon under study, the nature of the
phenomenon, its properties / elements and their inter-relations should be
identified and structured into a framework. This conceptual model gives
an exact idea of the research problem and shows its various properties
and variables to be studied. It serves as a basis for the formulation of
the objectives of the study, on the hypothesis to be tested. In order to
workout a conceptual model we must make a careful and critical study of
the available literature on the subject-matter of the selected research
problem. It is for this reason; a researcher is expected to select a
problem for research in his field of specialization. Without adequate
background knowledge, a researcher cannot grasp and comprehend the
nature of the research problem.
3. Define the Objective of the Study: The objectives refer to the
questions to be answered through the study. They indicate what we are
trying to get through the study. The objectives are derived from the
conceptual model. They state which elements in the conceptual model-
which levels of, which kinds of cases, which properties, and which
connections among properties – are to be investigated, but it is the
conceptual model that defines, describes, and states the assumptions
underlying these elements. The objectives may aim at description or
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explanation or analysis of causal relationship between variables, and
indicate the expected results or outcome of the study. The objectives
may be specified in the form of either the statements or the questions.
3.7 Criteria of Good Research Problem
Horton and Hunt have given following characteristics of scientific research:
1. Verifiable evidence: That is factual observations which other observers
can see and check.
2. Accuracy: That is describing what really exists. It means truth or
correctness of a statement or describing things exactly as they are and
avoiding jumping to unwarranted conclusions either by exaggeration or
fantasizing.
3. Precision: That is making it as exact as necessary, or giving exact
number or measurement. This avoids colourful literature and vague
meanings.
4. Systematization: That is attempting to find all the relevant data, or
collecting data in a systematic and organized way so that the
conclusions drawn are reliable. Data based on casual recollections are
generally incomplete and give unreliable judgments and conclusions.
5. Objectivity: That is free being from all biases and vested interests. It
means observation is unaffected by the observer’s values, beliefs and
preferences to the extent possible and he is able to see and accept facts
as they are, not as he might wish them to be.
6. Recording: That is jotting down complete details as quickly as possible.
Since human memory is fallible, all data collected are recorded.
7. Controlling conditions: That is controlling all variables except one and
then attempting to examine what happens when that variable is varied.
This is the basic technique in all scientific experimentation – allowing
one variable to vary while holding all other variables constant.
8. Training investigators: That is imparting necessary knowledge to
investigators to make them understand what to look for, how to interpret
in and avoid inaccurate data collection.
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Self Assessment Question I
1. ––––––––––––––––– is the first step in research.
2. Journals are ––––––––––––– of research problems.
3. Internal criteria of research problem consist of –––––– and –––––– .
3.8 Summary
Research really begins when the researcher experiences some difficulty,
i.e., a problem demanding a solution within the subject-are of his discipline.
The formulation of the topic into a research problem is, really speaking the
first step in a scientific enquiry. The selection of one appropriate
researchable problem out of the identified problems requires evaluation of
those alternatives against certain criteria, which may be grouped into
internal criteria and external criteria. A problem well put is half-solved. The
primary task of research is collection of relevant data and the analysis of
data for finding answers to the research questions. The problem selected for
research may initially be a vague topic. The process of defining a problem
includes:
Developing title
Building a conceptual model
Define the Objective of the Study
Horton and Hunt have given following characteristics of scientific research:
Verifiable evidence
Accuracy
Precision
Systematization
Objectivity
Recording
Controlling conditions
3.9 Terminal Questions
1. How is a research problem formulated?
2. What are the sources from which one may be able to identify research
problems?
3. Why literature survey is important in research?
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4. What is the classification of research problems?
5. What are the criteria of good research problem?
3.10 Answers to SAQs and TQs
SAQs
1. Selection of a problem
2. Sources of problem
3. Researcher’s interest and competence
TQs
1. Section 4
2. Section 3.3
3. Section 3.3
4. Section 3.6
5. Section 3.7
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Unit 4 Hypothesis
Structure:
4.1 Introduction
Objectives
4.2 Meaning and Examples of Hypothesis
4.2.1 Criteria for constructing of hypothesis
4.2.2 Nature of Hypothesis
4.2.3 The need for having Hypothesis
4.2.4 Characteristics of good hypothesis
4.3 Types of hypothesis
4.3.1 Null Hypothesis and alternative hypothesis
4.4 Concepts of Hypothesis
4.4.1 The level of Significance
4.4.2 Decision rule of testing hypothesis
4.4.3 Type I and Type II Errors
4.4.4 Two Tailed and One Tailed Test
4.5 Procedures for testing hypothesis
4.5.1 Making formal statement
4.5.2 Selecting a significant level
4.5.3 Deciding the distribution to use
4.5.4 Selecting a Random Sample and computing am approximate
value
4.5.5 Calculation of Probability
4.5.6 Comparing the Probability
4.6 Testing of Hypothesis
4.6.1 Important Parametric Tests
Self Assessment Questions
4.7 Summary
4.8 Terminal Questions
4.9 Answers to SAQs and TQs
4.1 Introduction
A hypothesis is an assumption about relations between variables. It is a
tentative explanation of the research problem or a guess about the research
outcome. Before starting the research, the researcher has a rather general,
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diffused, even confused notion of the problem. It may take long time for the
researcher to say what questions he had been seeking answers to. Hence,
an adequate statement about the research problem is very important. What
is a good problem statement? It is an interrogative statement that asks: what
relationship exists between two or more variables? It then further asks
questions like: Is A related to B or not? How are A and B related to C? Is A
related to B under conditions X and Y? Proposing a statement pertaining to
relationship between A and B is called a hypothesis.
Objectives:
After studying this lesson you should be able to understand:
Meaning and Examples of Hypothesis
Criteria for constructing of hypothesis
Nature of Hypothesis
the need for having Hypothesis
Characteristics of good hypothesis
Types of hypothesis
Null Hypothesis and alternative hypothesis
Concepts of Hypothesis
The level of Significance
Decision rule of testing hypothesis
Type I and Type II Errors
Two Tailed and One Tailed Test
Procedures for Testing hypothesis
Testing of Hypothesis
4.2 Meaning and Examples of Hypothesis
According to Theodorson and Theodorson, “a hypothesis is a tentative
statement asserting a relationship between certain facts. Kerlinger describes
it as “a conjectural statement of the relationship between two or more
variables”. Black and Champion have described it as “a tentative statement
about something, the validity of which is usually unknown”. This statement is
intended to be tested empirically and is either verified or rejected. It the
statement is not sufficiently established, it is not considered a scientific law.
In other words, a hypothesis carries clear implications for testing the stated
relationship, i.e., it contains variables that are measurable and specifying
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how they are related. A statement that lacks variables or that does not
explain how the variables are related to each other is no hypothesis in
scientific sense.
4.2.1 Criteria for Hypothesis Construction
Hypothesis is never formulated in the form of a question. The standards to
be met in formulating a hypothesis:
It should be empirically testable, whether it is right or wrong.
It should be specific and precise.
The statements in the hypothesis should not be contradictory.
It should specify variables between which the relationship is to be
established.
It should describe one issue only.
4.2.2 Nature of Hypothesis
A scientifically justified hypothesis must meet the following criteria:
It must accurately reflect the relevant sociological fact.
It must not be in contradiction with approved relevant statements of
other scientific disciplines.
It must consider the experience of other researchers.
4.2.3 The Need for having Working Hypothesis
A hypothesis gives a definite point to the investigation, and it guides the
direction on the study.
A hypothesis specifies the sources of data, which shall be studied, and
in what context they shall be studied.
It determines the data needs.
A hypothesis suggests which type of research is likely to be most
appropriate.
It determines the most appropriate technique of analysis.
A hypothesis contributes to the development of theory
4.2.4 Characteristics of Good Hypothesis
1. Conceptual Clarity
2. Specificity
3. Testability
4. Availability of Techniques
5. Theoretical relevance
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6. Consistency
7. Objectivity
8. Simplicity
4.3 Types of Hypothesis
There are many kinds of hypothesis the researcher has to be working with.
One type of hypothesis asserts that something is the case in a given
instance; that a particular object, person or situation has particular
characteristics. Another type of hypothesis deals with the frequency of
occurrence or of association among variables; this type of hypothesis may
state that X is associated with Y. A certain Y proportion of items e.g.
urbanism tends to be accompanied by mental disease or than something
are greater or lesser than some other thing in specific settings. Yet another
type of hypothesis asserts that a particular characteristics is one of the
factors which determine another characteristic, i.e. X is the producer of Y.
hypothesis of this type are called causal hypothesis.
4.3.1 Null Hypothesis and Alternative Hypothesis
In the context of statistical analysis, we often talk null and alternative
hypothesis. If we are to compare method A with method B about its
superiority and if we proceed on the assumption that both methods are
equally good, then this assumption is termed as null hypothesis. As against
this, we may think that the method A is superior, it is alternative hypothesis.
Symbolically presented as:
Null hypothesis = H0 and Alternative hypothesis = Ha
Suppose we want to test the hypothesis that the population mean is equal to
the hypothesis mean (µ H0) = 100. Then we would say that the null
hypotheses are that the population mean is equal to the hypothesized mean
100 and symbolical we can express as: H0: µ= µ H0=100
If our sample results do not support these null hypotheses, we should
conclude that something else is true. What we conclude rejecting the null
hypothesis is known as alternative hypothesis. If we accept H0, then we are
rejecting Ha and if we reject H0, then we are accepting Ha. For H0: µ= µ
H0=100, we may consider three possible alternative hypotheses as follows:
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Alternative Hypothesis To be read as follows
Ha: µ≠µ H0 (The alternative hypothesis is that the
population mean is not equal to 100 i.e., it
may be more or less 100)
Ha: µ>µ H0 (The alternative hypothesis is that the
population mean is greater than 100)
Ha: µ< µ H0 (The alternative hypothesis is that the
population mean is less than 100)
The null hypothesis and the alternative hypothesis are chosen before the
sample is drawn (the researcher must avoid the error of deriving hypothesis
from the data he collects and testing the hypothesis from the same data). In
the choice of null hypothesis, the following considerations are usually kept in
view:
Alternative hypothesis is usually the one which wishes to prove and the
null hypothesis are ones that wish to disprove. Thus a null hypothesis
represents the hypothesis we are trying to reject, the alternative
hypothesis represents all other possibilities.
If the rejection of a certain hypothesis when it is actually true involves
great risk, it is taken as null hypothesis because then the probability of
rejecting it when it is true is α (the level of significance) which is chosen
very small.
Null hypothesis should always be specific hypothesis i.e., it should not
state about or approximately a certain value.
Generally, in hypothesis testing we proceed on the basis of null
hypothesis, keeping the alternative hypothesis in view. Why so? The
answer is that on assumption that null hypothesis is true, one can assign
the probabilities to different possible sample results, but this cannot be
done if we proceed with alternative hypothesis. Hence the use of null
hypothesis (at times also known as statistical hypothesis) is quite
frequent.
4.4 Concepts of Hypothesis Testing
Basic concepts in the context of testing of hypothesis need to be explained.
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4.4.1 The Level of Significance
This is a very important concept in the context of hypothesis testing. It is
always some percentage (usually 5%) which should be chosen with great
care, thought and reason. In case we take the significance level at 5%, then
this implies that H0 will be rejected when the sampling result (i.e., observed
evidence) has a less than 0.05 probability of occurring if H0 is true. In other
words, the 5% level of significance means that researcher is willing to take
as much as 5% risk rejecting the null hypothesis when it (H0) happens to be
true. Thus the significance level is the maximum value of the probability of
rejecting H0 when it is true and is usually determined in advance before
testing the
Decision Rule of Test of Hypothesis:
Given a hypothesis H0 and an alternative hypothesis H0 we make rule which
is known as decision rule according to which we accept H0 (i.e., reject Ha) or
reject H0 (i.e., accept a). For instance, if (H0 is that a certain lot is good (there
are very few defective items in it) against Ha that the lot is not good (there
are many defective items in it), that we must decide the number of items to
be tested and the criterion for accepting or rejecting the hypothesis. We
might test 10 items in the lot and plan our decision saying that if there are
none or only 1 defective item among the 10, we will accept H0 otherwise we
will reject H0 (or accept Ha). This sort of basis is known as decision rule.
Type I & Type II Errors
In the context of testing of hypothesis there are basically two types of errors
that researchers make. We may reject H0 when H0 is true & we may accept
H0 when it is not true. The former is known as Type I & the later is known as
Type II. In other words, Type I error mean rejection of hypothesis which
should have been accepted & Type II error means accepting of hypothesis
which should have been rejected. Type I error is donated by α (alpha), also
called as level of significance of test; and Type II error is donated by β(beta).
Decision
Accept H0 Reject H0
H0 (true) Correct decision Type I error (α error)
Ho (false) Type II error (β error) Correct decision
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The probability of Type I error is usually determined in advance and is
understood as the level of significance of testing the hypothesis. If type I
error is fixed at 5%, it means there are about chances in 100 that we will
reject H0 when H0 is true. We can control type I error just by fixing it at a
lower level. For instance, if we fix it at 1%, we will say that the maximum
probability of committing type I error would only be 0.01.
But with a fixed sample size, n when we try to reduce type I error, the
probability of committing type II error increases. Both types of errors can not
be reduced simultaneously. There is a trade-off in business situations,
decision-makers decide the appropriate level of type I error by examining
the costs of penalties attached to both types of errors. If type I error involves
time & trouble of reworking a batch of chemicals that should have been
accepted, where as type II error means taking a chance that an entire group
of users of this chemicals compound will be poisoned, then in such a
situation one should prefer a type I error to a type II error means taking a
chance that an entire group of users of this chemicals compound will be
poisoned, then in such a situation one should prefer a type II error. As a
result one must set very high level for type I error in one‟s testing techniques
of a given hypothesis. Hence, in testing of hypothesis, one must make all
possible effort to strike an adequate balance between Type I & Type II error.
4.4.2 Two Tailed Test & One Tailed Test
In the context of hypothesis testing these two terms are quite important and
must be clearly understood. A two-tailed test rejects the null hypothesis if,
say, the sample mean is significantly higher or lower than the hypnotized
value of the mean of the population. Such a test inappropriate when we
haveH0: µ= µ H0 and Ha: µ≠µ H0 which may µ>µ H0 or µ<µ H0. If significance
level is % and the two-tailed test to be applied, the probability of the
rejection area will be 0.05 (equally split on both tails of curve as 0.025) and
that of the acceptance region will be 0.95. If we take µ = 100 and if our
sample mean deviates significantly from µ, in that case we shall accept the
null hypothesis. But there are situations when only one-tailed test is
considered appropriate. A one-tailed test would be used when we are to
test, say, whether the population mean in either lower than or higher than
some hypothesized value.
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4.5 Procedure for Testing Hypothesis
To test a hypothesis means to tell (on the basis of the data researcher has
collected) whether or not the hypothesis seems to be valid. In hypothesis
testing the main question is: whether the null hypothesis or not to accept the
null hypothesis? Procedure for hypothesis testing refers to all those steps
that we undertake for making a choice between the two actions i.e.,
rejection and acceptance of a null hypothesis. The various steps involved in
hypothesis testing are stated below:
4.5.1 Making a Formal Statement
The step consists in making a formal statement of the null hypothesis (Ho)
and also of the alternative hypothesis (Ha). This means that hypothesis
should clearly state, considering the nature of the research problem. For
instance, Mr. Mohan of the Civil Engineering Department wants to test the
load bearing capacity of an old bridge which must be more than 10 tons, in
that case he can state his hypothesis as under:
Null hypothesis HO: µ =10 tons
Alternative hypothesis Ha: µ >10 tons
Take another example. The average score in an aptitude test administered
at the national level is 80. To evaluate a state‟s education system, the
average score of 100 of the state‟s students selected on the random basis
was 75. The state wants to know if there is a significance difference
between the local scores and the national scores. In such a situation the
hypothesis may be state as under:
Null hypothesis HO: µ =80
Alternative hypothesis Ha: µ ≠ 80
The formulation of hypothesis is an important step which must be
accomplished with due care in accordance with the object and nature of the
problem under consideration. It also indicates whether we should use a
tailed test or a two tailed test. If Ha is of the type greater than, we use alone
tailed test, but when Ha is of the type “whether greater or smaller” then we
use a two-tailed test.
4.5.2 Selecting a Significant Level
The hypothesis is tested on a pre-determined level of significance and such
the same should have specified. Generally, in practice, either 5% level or
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1% level is adopted for the purpose. The factors that affect the level of
significance are:
The magnitude of the difference between sample ;
The size of the sample;
The variability of measurements within samples;
Whether the hypothesis is directional or non – directional (A directional
hypothesis is one which predicts the direction of the difference between,
say, means). In brief, the level of significance must be adequate in the
context of the purpose and nature of enquiry.
4.5.3 Deciding the Distribution to Use
After deciding the level of significance, the next step in hypothesis testing is
to determine the appropriate sampling distribution. The choice generally
remains between distribution and the t distribution. The rules for selecting
the correct distribution are similar to those which we have stated earlier in
the context of estimation.
4.5.4 Selecting A Random Sample & Computing An Appropriate Value
Another step is to select a random sample(S) and compute an appropriate
value from the sample data concerning the test statistic utilizing the relevant
distribution. In other words, draw a sample to furnish empirical data.
4.5.5 Calculation of the Probability
One has then to calculate the probability that the sample result would
diverge as widely as it has from expectations, if the null hypothesis were in
fact true.
4.5.6 Comparing the Probability
Yet another step consists in comparing the probability thus calculated with
the specified value for α, the significance level. If the calculated probability is
equal to smaller than α value in case of one tailed test (and α/2 in case of
two-tailed test), then reject the null hypothesis (i.e. accept the alternative
hypothesis), but if the probability is greater then accept the null hypothesis.
In case we reject H0 we run a risk of (at most level of significance)
committing an error of type I, but if we accept H0, then we run some risk of
committing error type II.
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Flow Diagram for Testing Hypothesis
Specify the level of significance
Decide the correct sampling distribution
Sample a random sample and workout an appropriate value
Calculate the probability that sample result would diverge as
widely as it has form expectations, if H0 were true
Is this probability equal to or smaller than α value in case of
one-tailed test and α/2 in case of two-tailed test
Run the risk of Run
some risk of
committing type I error committing type II
error
4.6 Testing of Hypothesis
The hypothesis testing determines the validity of the assumption (technically
described as null hypothesis) with a view to choose between the conflicting
hypotheses about the value of the population hypothesis about the value of
the population of a population parameter. Hypothesis testing helps to
secede on the basis of a sample data, whether a hypothesis about the
population is likely to be true or false. Statisticians have developed several
State H0 as well as Ha
Reject H0 Accept H0
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tests of hypothesis (also known as tests of significance) for the purpose of
testing of hypothesis which can be classified as:
Parametric tests or standard tests of hypothesis ;
Non Parametric test or distribution – free test of the hypothesis.
Parametric tests usually assume certain properties of the parent population
from which we draw samples. Assumption like observations come from a
normal population, sample size is large, assumptions about the population
parameters like mean, variants etc must hold good before parametric test
can be used. But there are situation when the researcher cannot or does not
want to make assumptions. In such situations we use statistical methods for
testing hypothesis which are called non parametric tests because such tests
do not depend on any assumption about the parameters of parent
population. Besides, most non-parametric test assumes only nominal or
original data, where as parametric test require measurement equivalent to at
least an interval scale. As a result non-parametric test needs more
observation than a parametric test to achieve the same size of Type I &
Type II error.
4.6.1 Important Parametric Tests
The important parametric tests are:
z-test
t-test
x2-test
f-test
All these tests are based on the assumption of normality i.e., the source of
data is considered to be normally distributed. In some cases the population
may not be normally distributed, yet the test will be applicable on account of
the fact that we mostly deal with samples and the sampling distributions
closely approach normal distributions.
Z-test is based on the normal probability distribution and is used for judging
the significance of several statistical measures, particularly the mean. The
relevant test statistic is worked out and compared with its probable value (to
be read from the table showing area under normal curve) at a specified level
of significance for judging the significance of the measure concerned. This is
a most frequently used test in research studies. This test is used even when
binomial distribution or t-distribution is applicable on the presumption that
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such a distribution tends to approximate normal distribution as „n‟ becomes
larger. Z-test is generally used for comparing the mean of a sample to some
hypothesis mean for the population in case of large sample, or when
population variance is known as z-test is also used for judging the
significance of difference between means to of two independent samples in
case of large samples or when population variance is known z-test is
generally used for comparing the sample proportion to a theoretical value of
population proportion or for judging the difference in proportions of two
independent samples when happens to be large. Besides, this test may be
used for judging the significance of median, mode, co-efficient of correlation
and several other measures
T-test is based on t-distribution and is considered an appropriate test for
judging the significance of sample mean or for judging significance of
difference between the two means of the two samples in case of samples
when population variance is not known (in which case we use variance of
the sample as an estimate the population variance). In case two samples
are related, we use paired t-test (difference test) for judging the significance
of their mean of difference between the two related samples. It can also be
used for judging the significance of co-efficient of simple and partial
correlations. The relevant test statistic, t, is calculated from the sample data
and then compared with its probable value based on t-distribution at a
specified level of significance for concerning degrees of freedom for
accepting or rejecting the null hypothesis it may be noted that t-test applies
only in case of small sample when population variance is unknown.
X2-test is based on chi-square distribution and as a parametric test is used
for comparing a sample variance to a theoretical population variance is
unknown.
F-test is based on f-distribution and is used to compare the variance of the
two-independent samples. This test is also used in the context of variance
(ANOVA) for judging the significance of more than two sample means at
one and the same time. It is also used for judging the significance of multiple
correlation coefficients. Test statistic, f, is calculated and compared with its
probable value for accepting or rejecting the H0.
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Self Assessment Questions
Fill in the Blanks
1. –––––––––- is a negative statement.
2. Type II error is –––––––––––.
3. –––––––––– is tentative statement.
4.7 Summary
A hypothesis is an assumption about relations between variables. It is a
tentative explanation of the research problem or a guess about the research
outcome. Before starting the research, the researcher has a rather general,
diffused, even confused notion of the problem. A hypothesis gives a definite
point to the investigation, and it guides the direction on the study. A
hypothesis specifies the sources of data, which shall be studied, and in what
context they shall be studied. In the context of hypothesis testing these two
terms are quite important and must be clearly understood. A two-tailed test
rejects the null hypothesis if, say, the sample mean is significantly higher or
lower than the hypnotized value of the mean of the population.
The hypothesis is tested on a pre-determined level of significance and such
the same should have specified. Generally, in practice, either 5% level or
1% level is adopted for the purpose. After deciding the level of significance,
the next step in hypothesis testing is to determine the appropriate sampling
distribution. The hypothesis testing determines the validity of the assumption
(technically described as null hypothesis) with a view to choose between the
conflicting hypotheses about the value of the population of a population
parameter. Z-test is based on the normal probability distribution and is used
for judging the significance of several statistical measures, particularly the
mean. The relevant test statistic is worked out and compared with its
probable value (to be read from the table showing area under normal curve)
at a specified level of significance for judging the significance of the
measure concerned. This is a most frequently used test in research studies.
T-test is based on t-distribution and is considered an appropriate test for
judging the significance of sample mean or for judging significance of
difference between the two means of the two samples in case of samples
when population variance is not known (in which case we use variance of
the sample as an estimate of the population variance). X2-test is based on
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chi-square distribution and as a parametric test is used for comparing a
sample variance to a theoretical population variance is unknown. F-test is
based on f-distribution and is used to compare the variance of the two-
independent samples.
4.8 Terminal Questions
1. What is the meaning of Hypothesis?
2 What are the criteria for Hypothesis Construction?
3. What is the need for having Working Hypothesis?
3. What are the characteristics of Good Hypothesis?
4. What are the types of Hypothesis?
5. What is Type I & Type II Errors?
6. What are Two Tailed Test & One Tailed Test?
7. What are the procedure and Flow Diagram for Testing Hypothesis?
8. Which are the important Parametric Tests?
4.9 Answers to SAQs and TQs
SAQs
1. Null hypothesis
2. Accepting a statement that is false
3. Hypothesis
TQs
1. Section 4.1
2. Section 4.2.1
3. Section 4.2.3
4. Section 4.2.4
5. Section 4.3
6. Section 4.4.3
7. Section 4.4.4
8. Section 4.5
9. Section 4.6
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Unit 5 Research Design
Structure:
5.1 Meaning
Objectives
5.2 Needs of Research Design
5.2.1 Characteristics of a Good Research Design
5.3 Components of Research Design
5.3.1 Experimental and Non-experimental Hypothesis Testing
Research
5.4 Different Research Designs
5.5 Research Design for Studies in Commerce and Management
5.5.1 Research Design in Case of Exploratory Research Studies
5.5.2 Research Design in case of Descriptive and Diagnostic
Research Studies
5.5.3 Research Design in case of Hypothesis testing Research
Studies
5.5.4 Principles of Experimental Designs
5.5.5 Important Experimental Designs
5.5.6 Formal Experimental Designs
Self Assessment Questions
5.6 Summary
5.7 Terminal Questions
5.8 Answers to SAQs and TQs
5.1 Meaning of Research Design
The research designer understandably cannot hold all his decisions in his
head. Even if he could, he would have difficulty in understanding how these
are inter-related. Therefore, he records his decisions on paper or record disc
by using relevant symbols or concepts. Such a symbolic construction may
be called the research design or model. A research design is a logical and
systematic plan prepared for directing a research study. It specifies the
objectives of the study, the methodology and techniques to be adopted for
achieving the objectives. It constitutes the blue print for the collection,
measurement and analysis of data. It is the plan, structure and strategy of
investigation conceived so as to obtain answers to research questions. The
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plan is the overall scheme or program of research. A research design is the
program that guides the investigator in the process of collecting, analyzing
and interpreting observations. It provides a systematic plan of procedure for
the researcher to follow elltiz, Jahoda and Destsch and Cook describe, “A
research design is the arrangement of conditions for collection and analysis
of data in a manner that aims to combine relevance to the research purpose
with economy in procedure.”
Objectives:
After studying this lesson you should be able to understand:
Needs of Research Design
Characteristics of a Good Research Design
Components of Research Design
Experimental and Non-experimental Hypothesis Testing Research
Different Research Designs
Research Design for Studies in Commerce and Management
Research Design in Case of Exploratory Research Studies
Research Design in case of Descriptive and Diagnostic Research
Studies
Research Design in case of Hypothesis testing Research Studies
Principles of Experimental Designs
Important Experimental Designs
Formal Experimental Designs
5.2 Needs of Research Design
The need for the methodologically designed research:
a. In many a research inquiry, the researcher has no idea as to how
accurate the results of his study ought to be in order to be useful. Where
such is the case, the researcher has to determine how much inaccuracy
may be tolerated. In a quite few cases he may be in a position to know
how much inaccuracy his method of research will produce. In either
case he should design his research if he wants to assure himself of
useful results.
b. In many research projects, the time consumed in trying to ascertain what
the data mean after they have been collected is much greater than the
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time taken to design a research which yields data whose meaning is
known as they are collected.
c. The idealized design is concerned with specifying the optimum research
procedure that could be followed were there no practical restrictions.
5.2.1 Characteristics of a Good Research Design
1. It is a series of guide posts to keep one going in the right direction.
2. It reduces wastage of time and cost.
3. It encourages co-ordination and effective organization.
4. It is a tentative plan which undergoes modifications, as circumstances
demand, when the study progresses, new aspects, new conditions and
new relationships come to light and insight into the study deepens.
5. It has to be geared to the availability of data and the cooperation of the
informants.
6. It has also to be kept within the manageable limits
5.3 Components of Research Design
It is important to be familiar with the important concepts relating to research
design. They are:
1. Dependent and Independent variables: A magnitude that varies is
known as a variable. The concept may assume different quantitative
values, like height, weight, income, etc. Qualitative variables are not
quantifiable in the strictest sense of objectivity. However, the qualitative
phenomena may also be quantified in terms of the presence or absence
of the attribute considered. Phenomena that assume different values
quantitatively even in decimal points are known as „continuous
variables‟. But, all variables need not be continuous. Values that can be
expressed only in integer values are called „non-continuous variables‟. In
statistical term, they are also known as „discrete variable‟. For example,
age is a continuous variable; where as the number of children is a non-
continuous variable. When changes in one variable depends upon the
changes in one or more other variables, it is known as a dependent or
endogenous variable, and the variables that cause the changes in the
dependent variable are known as the independent or explanatory or
exogenous variables. For example, if demand depends upon price, then
demand is a dependent variable, while price is the independent variable.
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And if, more variables determine demand, like income and prices of
substitute commodity, then demand also depends upon them in addition
to the own price. Then, demand is a dependent variable which is
determined by the independent variables like own price, income and
price of substitute.
2. Extraneous variable: The independent variables which are not directly
related to the purpose of the study but affect the dependent variable are
known as extraneous variables. For instance, assume that a researcher
wants to test the hypothesis that there is relationship between children‟s
school performance and their self-concepts, in which case the latter is
an independent variable and the former, the dependent variable. In this
context, intelligence may also influence the school performance.
However, since it is not directly related to the purpose of the study
undertaken by the researcher, it would be known as an extraneous
variable. The influence caused by the extraneous variable on the
dependent variable is technically called as an „experimental error‟.
Therefore, a research study should always be framed in such a manner
that the dependent variable completely influences the change in the
independent variable and any other extraneous variable or variables.
3. Control: One of the most important features of a good research design
is to minimize the effect of extraneous variable. Technically, the term
control is used when a researcher designs the study in such a manner
that it minimizes the effects of extraneous independent variables. The
term control is used in experimental research to reflect the restrain in
experimental conditions.
4. Confounded relationship: The relationship between dependent and
independent variables is said to be confounded by an extraneous
variable, when the dependent variable is not free from its effects.
Research hypothesis: When a prediction or a hypothesized
relationship is tested by adopting scientific methods, it is known as
research hypothesis. The research hypothesis is a predictive
statement which relates a dependent variable and an independent
variable. Generally, a research hypothesis must consist of at least
one dependent variable and one independent variable. Whereas, the
relationships that are assumed but not be tested are predictive
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statements that are not to be objectively verified are not classified as
research hypothesis.
Experimental and control groups: When a group is exposed to
usual conditions in an experimental hypothesis-testing research, it is
known as „control group‟. On the other hand, when the group is
exposed to certain new or special condition, it is known as an
„experimental group‟. In the afore-mentioned example, the Group A
can be called a control group and the Group B an experimental one.
If both the groups A and B are exposed to some special feature, then
both the groups may be called as „experimental groups‟. A research
design may include only the experimental group or the both
experimental and control groups together.
Treatments: Treatments are referred to the different conditions to
which the experimental and control groups are subject to. In the
example considered, the two treatments are the parents with regular
earnings and those with no regular earnings. Likewise, if a research
study attempts to examine through an experiment regarding the
comparative impacts of three different types of fertilizers on the yield
of rice crop, then the three types of fertilizers would be treated as the
three treatments.
Experiment: An experiment refers to the process of verifying the
truth of a statistical hypothesis relating to a given research problem.
For instance, experiment may be conducted to examine the yield of
a certain new variety of rice crop developed. Further, Experiments
may be categorized into two types namely, absolute experiment and
comparative experiment. If a researcher wishes to determine the
impact of a chemical fertilizer on the yield of a particular variety of
rice crop, then it is known as absolute experiment. Meanwhile, if the
researcher wishes to determine the impact of chemical fertilizer as
compared to the impact of bio-fertilizer, then the experiment is
known as a comparative experiment.
Experiment unit: Experimental units refer to the predetermined
plots, characteristics or the blocks, to which the different treatments
are applied. It is worth mentioning here that such experimental units
must be selected with great caution.
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5.3.1 Experimental and Non-Experimental Hypothesis Testing
Research
When the objective of a research is to test a research hypothesis, it is
known as a hypothesis-testing research. Such research may be in the
nature of experimental design or non-experimental design. A research in
which the independent variable is manipulated is known as „experimental
hypothesis-testing research‟, where as a research in which the independent
variable is not manipulated is termed as „non-experimental hypothesis-
testing research‟. E.g., assume that a researcher wants to examine whether
family income influences the social attendance of a group of students, by
calculating the coefficient of correlation between the two variables. Such an
example is known as a non-experimental hypothesis-testing research,
because the independent variable family income is not manipulated. Again
assume that the researcher randomly selects 150 students from a group of
students who pay their school fees regularly and them classifies them into
tow sub-groups by randomly including 75 in Group A, whose parents have
regular earning, and 75 in group B, whose parents do not have regular
earning. And that at the end of the study, the researcher conducts a test on
each group in order to examine the effects of regular earnings of the parents
on the school attendance of the student. Such a study is an example of
experimental hypothesis-testing research, because in this particular study
the independent variable regular earnings of the parents have been
manipulated
5.4 Different Research Designs
There are a number of crucial research choices, various writers advance
different classification schemes, some of which are:
1. Experimental, historical and inferential designs (American Marketing
Association).
2. Exploratory, descriptive and causal designs (Selltiz, Jahoda, Deutsch
and Cook).
3. Experimental, and expost fact (Kerlinger)
4. Historical method, and case and clinical studies (Goode and Scates)
5. Sample surveys, field studies, experiments in field settings, and
laboratory experiments (Festinger and Katz)
6. Exploratory, descriptive and experimental studies (Body and Westfall)
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7. Exploratory, descriptive and casual (Green and Tull)
8. Experimental, „quasi-experimental designs‟ (Nachmias and Nachmias)
9. True experimental, quasi-experimental and non-experimental designs
(Smith).
10. Experimental, pre-experimental, quasi-experimental designs and
Survey Research (Kidder and Judd).
These different categorizations exist, because „research design‟ is a
complex concept. In fact, there are different perspectives from which any
given study can be viewed. They are:
1. The degree of formulation of the problem (the study may be exploratory
or formalized)
2. The topical scope-breadth and depth-of the study(a case or a statistical
study)
3. The research environment: field setting or laboratory (survey,
laboratory experiment)
4. The time dimension(one-time or longitudinal)
5. The mode of data collection (observational or survey)
6. The manipulation of the variables under study (experimental or expost
facto)
7. The nature of the relationship among variables (descriptive or causal)
5.5 Research Design for Studies in Commerce and Management
The various research designs are:
5.5.1 Research design in case of exploratory research studies
Exploratory research studies are also termed as formulative research
studies. The main purpose of such studies is that of formulating a problem
for more precise investigation or of developing the working hypothesis from
an operational point of view. The major emphasis in such studies is on the
discovery of ideas and insights. As such the research design appropriate for
such studies must be flexible enough to provide opportunity for considering
different aspects of a problem under study. Inbuilt flexibility in research
design is needed because the research problem, broadly defined initially, is
transformed into one with more precise meaning in exploratory studies,
which fact may necessitate changes in the research procedure for gathering
relevant data. Generally, the following three methods in the context of
research design for such studies are talked about:
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1. The survey of concerning literature happens to be the most simple
and fruitful method of formulating precisely the research problem or
developing hypothesis. Hypothesis stated by earlier workers may be
reviewed and their usefulness be evaluated as a basis for further
research. It may also be considered whether the already stated
hypothesis suggests new hypothesis. In this way the researcher should
review and build upon the work already done by others, but in cases
where hypothesis have not yet been formulated, his task is to review the
available material for deriving the relevant hypothesis from it. Besides,
the bibliographical survey of studies, already made in one‟s area of
interest may as well as made by the researcher for precisely formulating
the problem. He should also make an attempt to apply concepts and
theories developed in different research contexts to the area in which he
is himself working. Sometimes the works of creative writers also provide
a fertile ground for hypothesis formulation as such may be looked into by
the researcher.
2. Experience survey means the survey of people who have had practical
experience with the problem to be studied. The object of such a survey
is to obtain insight into the relationships between variables and new
ideas relating to the research problem. For such a survey, people who
are competent and can contribute new ideas may be carefully selected
as respondents to ensure a representation of different types of
experience. The respondents so selected may then be interviewed by
the investigator. The researcher must prepare an interview schedule for
the systematic questioning of informants. But the interview must ensure
flexibility in the sense that the respondents should be allowed to raise
issues and questions which the investigator has not previously
considered. Generally, the experience of collecting interview is likely to
be long and may last for few hours. Hence, it is often considered
desirable to send a copy of the questions to be discussed to the
respondents well in advance. This will also give an opportunity to the
respondents for doing some advance thinking over the various issues
involved so that, at the time of interview, they may be able to contribute
effectively. Thus, an experience survey may enable the researcher to
define the problem more concisely and help in the formulation of the
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research hypothesis. This, survey may as well provide information about
the practical possibilities for doing different types of research.
3. Analyses of ‘insight-stimulating’ examples are also a fruitful method
for suggesting hypothesis for research. It is particularly suitable in areas
where there is little experience to serve as a guide. This method
consists of the intensive study of selected instance of the phenomenon
in which one is interested. For this purpose the existing records, if nay,
may be examined, the unstructured interviewing may take place, or
some other approach may be adopted. Attitude of the investigator, the
intensity of the study and the ability of the researcher to draw together
diverse information into a unified interpretation are the main features
which make this method an appropriate procedure for evoking insights.
Now, what sorts of examples are to be selected and studied? There is
no clear cut answer to it. Experience indicates that for particular
problems certain types of instances are more appropriate than others.
One can mention few examples of „insight-stimulating‟ cases such as the
reactions of strangers, the reactions of marginal individuals, the study of
individuals who are in transition from one stage to another, the reactions
of individuals from different social strata and the like. In general, cases
that provide sharp contrasts or have striking features are considered
relatively more useful while adopting this method of hypothesis
formulation. Thus, in an exploratory of formulative research study which
merely leads to insights or hypothesis, whatever method or research
design outlined above is adopted, the only thing essential is that it must
continue to remain flexible so that many different facets of a problem
may be considered as and when they arise and come to the notice of
the researcher.
5.5.2 Research design in case of descriptive and diagnostic research
studies
Descriptive research studies are those studies which are concerned with
describing the characteristics of a particular individual, or of a group, where
as diagnostic research studies determine the frequency with which
something occurs or its association with something else. The studies
concerning whether certain variables are associated are the example of
diagnostic research studies. As against this, studies concerned with specific
predictions, with narration of facts and characteristics concerning individual,
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group of situation are all examples of descriptive research studies. Most of
the social research comes under this category. From the point of view of the
research design, the descriptive as well as diagnostic studies share
common requirements and as such we may group together these two types
of research studies. In descriptive as well as in diagnostic studies, the
researcher must be able to define clearly, what he wants to measure and
must find adequate methods for measuring it along with a clear cut definition
of population he wants to study. Since the aim is to obtain complete and
accurate information in the said studies, the procedure to be used must be
carefully planned. The research design must make enough provision for
protection against bias and must maximize reliability. With due concern for
the economical completion of the research study, the design in such studies
must be rigid and not flexible and must focus attention on the following:
1. Formulating the objective of the study
2. Designing the methods of data collection
3. Selecting the sample
4. Collecting the data
5. Processing and analyzing the data
6. Reporting the findings.
In a descriptive / diagnostic study the first step is to specify the objectives
with sufficient precision to ensure that the data collected are relevant. If this
is not done carefully, the study may not provide the desired information.
Then comes the question of selecting the methods by which the data are to
be obtained. While designing data-collection procedure, adequate
safeguards against bias and unreliability must be ensured. Which ever
method is selected, questions must be well examined and be made
unambiguous; interviewers must be instructed not to express their own
opinion; observers must be trained so that they uniformly record a given
item of behaviour.
More often than not, sample has to be designed. Usually, one or more forms
of probability sampling or what is often described as random sampling, are
used. To obtain data, free from errors introduced by those responsible for
collecting them, it is necessary to supervise closely the staff of field workers
as they collect and record information. Checks may be set up to ensure that
the data collecting staffs performs their duty honestly and without prejudice.
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The data collected must be processed and analyzed. This includes steps
like coding the interview replies, observations, etc., tabulating the data; and
performing several statistical computations.
Last of all comes the question of reporting the findings. This is the task of
communicating the findings to others and the researcher must do it in an
efficient manner.
5.5.3 Research Design in case of Hypothesis-Testing Research Studies
Hypothesis-testing research studies (generally known as experimental
studies) are those where the researcher tests the hypothesis of causal
relationships between variables. Such studies require procedures that will
not only reduce bias and increase reliability, but will permit drawing
inferences about causality. Usually, experiments meet these requirements.
Hence, when we talk of research design in such studies, we often mean the
design of experiments.
5.5.4 Principles of Experimental Designs
Professor Fisher has enumerated three principles of experimental designs:
1. The principle of replication: The experiment should be reaped more
than once. Thus, each treatment is applied in many experimental units
instead of one. By doing so, the statistical accuracy of the experiments
is increased. For example, suppose we are to examine the effect of two
varieties of rice. For this purpose we may divide the field into two parts
and grow one variety in one part and the other variety in the other part.
We can compare the yield of the two parts and draw conclusion on that
basis. But if we are to apply the principle of replication to this
experiment, then we first divide the field into several parts, grow one
variety in half of these parts and the other variety in the remaining parts.
We can collect the data yield of the two varieties and draw conclusion by
comparing the same. The result so obtained will be more reliable in
comparison to the conclusion we draw without applying the principle of
replication. The entire experiment can even be repeated several times
for better results. Consequently replication does not present any
difficulty, but computationally it does. However, it should be remembered
that replication is introduced in order to increase the precision of a study;
that is to say, to increase the accuracy with which the main effects and
interactions can be estimated.
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2. The principle of randomization: It provides protection, when we
conduct an experiment, against the effect of extraneous factors by
randomization. In other words, this principle indicates that we should
design or plan the „experiment in such a way that the variations caused
by extraneous factors can all be combined under the general heading of
“chance”. For instance if we grow one variety of rice say in the first half
of the parts of a field and the other variety is grown in the other half, then
it is just possible that the soil fertility may be different in the first half in
comparison to the other half. If this is so, our results would not be
realistic. In such a situation, we may assign the variety of rice to be
grown in different parts of the field on the basis of some random
sampling technique i.e., we may apply randomization principle and
protect ourselves against the effects of extraneous factors. As such,
through the application of the principle of randomization, we can have a
better estimate of the experimental error.
3. Principle of local control: It is another important principle of
experimental designs. Under it the extraneous factors, the known source
of variability, is made to vary deliberately over as wide a range as
necessary and this needs to be done in such a way that the variability it
causes can be measured and hence eliminated from the experimental
error. This means that we should plan the experiment in a manner that
we can perform a two-way analysis of variance, in which the total
variability of the data is divided into three components attributed to
treatments, the extraneous factor and experimental error. In other
words, according to the principle of local control, we first divide the field
into several homogeneous parts, known as blocks, and then each such
block is divided into parts equal to the number of treatments. Then the
treatments are randomly assigned to these parts of a block. In general,
blocks are the levels at which we hold an extraneous factors fixed, so
that we can measure its contribution to the variability of the data by
means of a two-way analysis of variance. In brief, through the principle
of local control we can eliminate the variability due to extraneous factors
from the experimental error.
5.5.5 Important Experimental Designs
Experimental design refers to the framework or structure of an experiment
and as such there are several experimental designs. We can classify
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experimental designs into two broad categories, viz., informal experimental
designs and formal experimental designs. Informal experimental designs are
those designs that normally use a less sophisticated form of analysis based
on differences in magnitudes, where as formal experimental designs offer
relatively more control and use precise statistical procedures for analysis.
Informal experimental designs:
Before and after without control design: In such a design, single test
group or area is selected and the dependent variable is measured
before the introduction of the treatment. The treatment is then
introduced and the dependent variable is measured again after the
treatment has been introduced. The effect of the treatment would be
equal to the level of the phenomenon after the treatment minus the level
of the phenomenon before the treatment.
After only with control design: In this design, two groups or areas (test
and control area) are selected and the treatment is introduced into the
test area only. The dependent variable is then measured in both the
areas at the same time. Treatment impact is assessed by subtracting the
value of the dependent variable in the control area from its value in the
test area.
Before and after with control design: In this design two areas are
selected and the dependent variable is measured in both the areas for
an identical time-period before the treatment. The treatment is then
introduced into the test area only, and the dependent variable is
measured in both for an identical time-period after the introduction of the
treatment. The treatment effect is determined by subtracting the change
in the dependent variable in the control area from the change in the
dependent variable in test area.
5.5.6 Formal Experimental Designs
1. Completely randomized design (CR design): It involves only two
principle viz., the principle of replication and randomization. It is
generally used when experimental areas happen to be homogenous.
Technically, when all the variations due to uncontrolled extraneous
factors are included under the heading of chance variation, we refer to
the design of experiment as C R Design.
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2. Randomized block design (RB design): It is an improvement over the
C Research design. In the RB design the principle of local control can be
applied along with the other two principles.
3. Latin square design (LS design): It is used in agricultural research.
The treatments in a LS design are so allocated among the plots that no
treatment occurs more than once in any row or column.
4. Factorial design: It is used in experiments where the effects of varying
more than one factor are to be determined. They are especially
important in several economic and social phenomena where usually a
large number of factors affect a particular problem.
Self Assessment Questions I
State whether the following statements are true or false.
1. A research design is a logical and systematic plan
2. Exploratory research studies are also called formulative research studies
3. Descriptive research is concerned with describing the features of a
particular individual or group.
5.6 Summary
A research design is a logical and systematic plan prepared for directing a
research study. In many research projects, the time consumed in trying to
ascertain what the data mean after they have been collected is much
greater than the time taken to design a research which yields data whose
meaning is known as they are collected. Research design is a series of
guide posts to keep one going in the right direction. It is a tentative plan
which undergoes modifications, as circumstances demand, when the study
progresses, new aspects, new conditions and new relationships come to
light and insight into the study deepens. Exploratory research studies are
also termed as formulative research studies. The main purpose of such
studies is that of formulating a problem for more precise investigation or of
developing the working hypothesis from an operational point of view.
Descriptive research studies are those studies which are concerned with
describing the characteristics of a particular individual, or of a group, where
as diagnostic research studies determine the frequency with which
something occurs or its association with something else.
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5.7 Terminal Questions
1. What is research design?
2. Why research design is needed in research?
3. What are the characteristics of a good research design?
4. What are the components of a research design?
5. What are the different types of research designs?
6. What are the features of an exploratory research design?
7. How is a research design made incase of descriptive and diagnostic
research studies?
8. What are the principles of experimental designs?
5.8 Answers to SAQs and TQs
SAQs I
1. True
2. True
3. True
TQs
1. Section 5.1
2. Section 5.2
3. Section 5.2.1
4. Section 5.3
5. Section 5.4
6. Section 5.5.1
7. Section 5.5.2
8. Section 5.5.4
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Unit 6 Case Study Method
Structure:
6.1 Meaning of Case Study
Objectives
6.2 Assumptions of Case Study Method
6.3 Advantages of Case Study Method
6.4 Disadvantages of Case Study Method
6.5 Making Case Study Effective
6.6 Case Study as a Method of Business Research
Self Assessment Questions
6.7 Summary
6.8 Terminal Questions
6.9 Answers to SAQs and TQs
6.1 Meaning of Case Study
Case study is a method of exploring and analyzing the life of a social unit or
entity, be it a person, a family, an institution or a community. The aim of
case study method is to locate or identify the factors that account for the
behaviour patterns of a given unit, and its relationship with the environment.
The case data are always gathered with a view to attracting the natural
history of the social unit, and its relationship with the social factors and
forces operative and involved in this surrounding milieu. In short, the social
researcher tries, by means of the case study method, to understand the
complex of factors that are working within a social unit as an integrated
totality. Looked at from another angle, the case study serves the purpose
similar to the clue-providing function of expert opinion. It is most appropriate
when one is trying to find clues and ideas for further research.
The major credit for introducing case study method into social investigation
goes to Frederick Leplay. Herbert Spencer was the first social philosopher
who used case study in comparative studies of different cultures. William
Healey used case study in his study of juvenile delinquency. Anthropologists
and ethnologists have liberally utilized cast study in the systematic
description of primitive cultures. Historians have used this method for
portraying some historical character or particular historical period and
describing the developments within a national community.
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Objectives:
After studying this lesson you should be able to understand:
Assumptions of Case Study Method
Advantages of Case Study Method
Disadvantages of Case Study Method
Making Case Study Effective
Case Study as a Method of Business Research
6.2 Assumptions of Case Study Method
Case study would depend upon wit, commonsense and imagination of
the person doing the case study. The investigator makes up his
procedure as he goes along.
If the life history has been written in the first person, it must be as
complete and coherent as possible.
Life histories should have been written for knowledgeable persons.
It is advisable to supplement case data by observational, statistical and
historical data since these provide standards for assessing the reliability
and consistency of the case material.
Efforts should be made to ascertain the reliability of life history data
through examining the internal consistency of the material.
A judicious combination of techniques of data collection is a prerequisite
for securing data that are culturally meaningful and scientifically
significant.
6.3 Advantages of Case Study Method
Case study of particular value when a complex set of variables may be at
work in generating observed results and intensive study is needed to
unravel the complexities. For example, an in-depth study of a firm’s top
sales people and comparison with worst salespeople might reveal
characteristics common to stellar performers. Here again, the exploratory
investigation is best served by an active curiosity and willingness to deviate
from the initial plan when findings suggest new courses of inquiry might
prove more productive. It is easy to see how the exploratory research
objectives of generating insights and hypothesis would be well served by
use of this technique.
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6.4 Disadvantages of Case Study Method
Blummer points out that independently, the case documents hardly fulfil the
criteria of reliability, adequacy and representativeness, but to exclude them
form any scientific study of human life will be blunder in as much as these
documents are necessary and significant both for theory building and
practice.
6.5 Making Case Study Effective
Let us discuss the criteria for evaluating the adequacy of the case history or
life history which is of central importance for case study. John Dollard has
proposed seven criteria for evaluating such adequacy as follows:
i) The subject must be viewed as a specimen in a cultural series. That is,
the case drawn out from its total context for the purposes of study
must be considered a member of the particular cultural group or
community. The scrutiny of the life histories of persons must be done
with a view to identify thee community values, standards and their
shared way of life.
ii) The organic motto of action must be socially relevant. That is, the
action of the individual cases must be viewed as a series of reactions
to social stimuli or situation. In other words, the social meaning of
behaviour must be taken into consideration.
iii) The strategic role of the family group in transmitting the culture must
be recognized. That is, in case of an individual being the member of a
family, the role of family in shaping his behaviour must never be
overlooked.
iv) The specific method of elaboration of organic material onto social
behaviour must be clearly shown. That is case histories that portray in
detail how basically a biological organism, the man, gradually
blossoms forth into a social person, are especially fruitful.
v) The continuous related character of experience for childhood through
adulthood must be stressed. In other words, the life history must be a
configuration depicting the inter-relationships between thee person’s
various experiences.
vi) Social situation must be carefully and continuously specified as a
factor. One of the important criteria for the life history is that a person’s
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life must be shown as unfolding itself in the context of and partly owing
to specific social situations.
vii) The life history material itself must be organised according to some
conceptual framework, this in turn would facilitate generalizations at a
higher level.
6.6 Case Study as a Method of Business Research
In-depth analysis of selected cases is of particular value to business
research when a complex set of variables may be at work in generating
observed results and intensive study is needed to unravel the complexities.
For instance, an in-depth study of a firm’s top sales people and comparison
with the worst sales people might reveal characteristics common to stellar
performers. The exploratory investigator is best served by the active
curiosity and willingness to deviate from the initial plan, when the finding
suggests new courses of enquiry, might prove more productive
Self Assessment Questions
State whether the following statements are true or false.
1. Case study is a method of exploring and analyzing the life of a social
unit.
2. Case study of not particular value when a complex set of variables may
be at work.
3. In-depth analysis of selected cases is not of particular value to business
research
6.7 Summary
Case study is a method of exploring and analyzing the life of a social unit or
entity, be it a person, a family, an institution or a community. Case study
would depend upon wit, commonsense and imagination of the person doing
the case study. The investigator makes up his procedure as he goes along.
Efforts should be made to ascertain the reliability of life history data through
examining the internal consistency of the material. A judicious combination
of techniques of data collection is a prerequisite for securing data that are
culturally meaningful and scientifically significant. Case study of particular
value when a complex set of variables may be at work in generating
observed results and intensive study is needed to unravel the complexities.
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The case documents hardly fulfil the criteria of reliability, adequacy and
representativeness, but to exclude them form any scientific study of human
life will be blunder in as much as these documents are necessary and
significant both for theory building and practice. In-depth analysis of
selected cases is of particular value to business research when a complex
set of variables may be at work in generating observed results and intensive
study is needed to unravel the complexities.
6.8 Terminal Questions
1. What is the Meaning of case study?
2. What are the assumptions of Case Study Method?
3. What are the advantages of Case Study Method?
4. What are the disadvantages of Case Study Method?
5. How can a case study be made effective?
6. How case study method is useful to Business Research?
6.9 Answers to SAQs and TQs
SAQs
1. True
2. False
3. False
TQs
1. Section 6.1
2. Section 6.2
3. Section 6.3
4. Section 6.4
5. Section 6.5
6. Section 6.6
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Unit 7 Sampling
Structure:
7.1 Meaning of Sampling
Objectives
7.2 Advantages of Sampling
7.3 Sampling Procedure
7.4 Characteristics of Good Sample
7.5 Methods of Sampling
7.5.1 Probability or Random Sampling
7.5.2 Simple Random Sampling
7.5.3 Stratified Random Sampling
7.5.4 Systematic Random Sampling
7.5.5 Cluster Sampling
7.5.6 Area sampling
7.5.7 Multi-stage and sub-sampling
7.5.8 Random Sampling with Probability Proportional to Size
7.5.9 Double Sampling and Multiphase Sampling
7.5.10 Replicated or Interpenetrating Sampling
7.5.11 Non-probability or Non Random Sampling
7.5.12 Convenience or Accidental Sampling
7.5.13 Purposive (or judgment) Sampling
7.5.14 Quota sampling
7.5.15 Snow-ball Sampling
Self assessment Questions
7.6 Summary
7.7 Terminal Questions
7.8 Answers to SAQs and TQs
7.1 Meaning of Sampling
A part of the population is known as sample. The method consisting of the
selecting for study, a portion of the ‘universe’ with a view to draw
conclusions about the ‘universe’ or ‘population’ is known as sampling. A
statistical sample ideally purports to be a miniature model or replica of the
collectivity or the population constituted of all the items that the study should
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principally encompass, that is, the items which potentially hold promise of
affording information relevant to the purpose of a given research.
Sampling helps in time and cost saving. It also helps in checking their
accuracy. But on the other hand it demands exercise of great care caution;
otherwise the results obtained may be incorrect or misleading.
Objectives:
After studying this lesson you should be able to understand:
Advantages of sampling
Sampling procedure
Characteristics of good sample
Methods of Sampling
Probability or Random Sampling
Non-probability or Non Random Sampling
7.2 Advantage of Sample Survey
Sampling has the following advantages:
The size of the population: If the population to be studied is quite
large, sampling is warranted. However, the size is a relative matter.
Whether a population is large or small depends upon the nature of the
study, the purpose for which it is undertaken, and the time and other
resources available for it.
Amount of funds budgeted for the study: Sampling is opted when
the amount of money budgeted is smaller than the anticipated cost of
census survey.
Facilities: The extent of facilities available – staff, access to computer
facility and accessibility to population elements - in another factor to be
considered in deciding to sample or not. When the availability of these
facilities is limited, sampling is preferable.
Time: The time limit within the study should be completed in another
important factor to be considered in deciding the question of sample
survey. This, in fact, is a primary reason for using sampling by academic
and marketing researchers.
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7.3 Sampling Procedure
The decision process of sampling is complicated one. The researcher has to
first identify the limiting factor or factors and must judiciously balance the
conflicting factors. The various criteria governing the choice of the sampling
technique:
1. Purpose of the Survey: What does the researcher aim at? If he intends
to generalize the findings based on the sample survey to the population,
then an appropriate probability sampling method must be selected. The
choice of a particular type of probability sampling depends on the
geographical area of the survey and the size and the nature of the
population under study.
2. Measurability: The application of statistical inference theory requires
computation of the sampling error from the sample itself. Probability
samples only allow such computation. Hence, where the research
objective requires statistical inference, the sample should be drawn by
applying simple random sampling method or stratified random sampling
method, depending on whether the population is homogenous or
heterogeneous.
3. Degree of Precision: Should the results of the survey be very precise,
or even rough results could serve the purpose? The desired level of
precision as one of the criteria of sampling method selection. Where a
high degree of precision of results is desired, probability sampling
should be used. Where even crude results would serve the purpose
(E.g., marketing surveys, readership surveys etc) any convenient non-
random sampling like quota sampling would be enough.
4. Information about Population: How much information is available
about the population to be studied? Where no list of population and no
information about its nature are available, it is difficult to apply a
probability sampling method. Then exploratory study with non-probability
sampling may be made to gain a better idea of population. After gaining
sufficient knowledge about the population through the exploratory study,
appropriate probability sampling design may be adopted.
5. The Nature of the Population: In terms of the variables to be studied,
is the population homogenous or heterogeneous? In the case of a
homogenous population, even a simple random sampling will give a
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representative sample. If the population is heterogeneous, stratified
random sampling is appropriate.
6. Geographical Area of the Study and the Size of the Population: If
the area covered by a survey is very large and the size of the population
is quite large, multi-stage cluster sampling would be appropriate. But if
the area and the size of the population are small, single stage probability
sampling methods could be used.
7. Financial resources: If the available finance is limited, it may become
necessary to choose a less costly sampling plan like multistage cluster
sampling or even quota sampling as a compromise. However, if the
objectives of the study and the desired level of precision cannot be
attained within the stipulated budget, there is no alternative than to give
up the proposed survey. Where the finance is not a constraint, a
researcher can choose the most appropriate method of sampling that fits
the research objective and the nature of population.
8. Time Limitation: The time limit within which the research project should
be completed restricts the choice of a sampling method. Then, as a
compromise, it may become necessary to choose less time consuming
methods like simple random sampling instead of stratified
sampling/sampling with probability proportional to size; multi-stage
cluster sampling instead of single-stage sampling of elements. Of
course, the precision has to be sacrificed to some extent.
9. Economy: It should be another criterion in choosing the sampling
method. It means achieving the desired level of precision at minimum
cost. A sample is economical if the precision per unit cost is high or the
cost per unit of variance is low.
The above criteria frequently conflict and the researcher must balance and
blend them to obtain to obtain a good sampling plan. The chosen plan thus
represents an adaptation of the sampling theory to the available facilities
and resources. That is, it represents a compromise between idealism and
feasibility. One should use simple workable methods instead of unduly
elaborate and complicated techniques.
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7.4 Characteristics of a Good Sample
The characteristics of a good sample are described below:
Representativeness: a sample must be representative of the
population. Probability sampling technique yield representative sample.
Accuracy: accuracy is defined as the degree to which bias is absent
from the sample. An accurate sample is the one which exactly
represents the population.
Precision: the sample must yield precise estimate. Precision is
measured by standard error.
Size: a good sample must be adequate in size in order to be reliable.
7.5 Methods of Sampling
Sampling techniques or methods may be classified into two generic types:
7.5.1 Probability or Random Sampling
Probability sampling is based on the theory of probability. It is also known as
random sampling. It provides a known nonzero chance of selection for each
population element. It is used when generalization is the objective of study,
and a greater degree of accuracy of estimation of population parameters is
required. The cost and time required is high hence the benefit derived from
it should justify the costs.
The following are the types of probability sampling:
i) Simple Random Sampling: This sampling technique gives each
element an equal and independent chance of being selected. An equal
chance means equal probability of selection. An independent chance
means that the draw of one element will not affect the chances of other
elements being selected. The procedure of drawing a simple random
sample consists of enumeration of all elements in the population.
1. Preparation of a List of all elements, giving them numbers in serial
order 1, 2, B, and so on, and
2. Drawing sample numbers by using (a) lottery method, (b) a table of
random numbers or (c) a computer.
Suitability: This type of sampling is suited for a small homogeneous
population.
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Advantages: The advantage of this is that it is one of the easiest
methods, all the elements in the population have an equal chance of
being selected, simple to understand, does not require prior
knowledge of the true composition of the population.
Disadvantages: It is often impractical because of non-availability of
population list or of difficulty in enumerating the population, does not
ensure proportionate representation and it may be expensive in time
and money. The amount of sampling error associated with any sample
drawn can easily be computed. But it is greater than that in other
probability samples of the same size, because it is less precise than
other methods.
ii) Stratified Random Sampling: This is an improved type of random or
probability sampling. In this method, the population is sub-divided into
homogenous groups or strata, and from each stratum, random sample
is drawn. E.g., university students may be divided on the basis of
discipline, and each discipline group may again be divided into juniors
and seniors. Stratification is necessary for increasing a sample’s
statistical efficiency, providing adequate data for analyzing the various
sub-populations and applying different methods to different strata. The
stratified random sampling is appropriate for a large heterogeneous
population. Stratification process involves three major decisions. They
are stratification base or bases, number of strata and strata sample
sizes.
Stratified random sampling may be classified into:
a) Proportionate stratified sampling: This sampling involves
drawing a sample from each stratum in proportion to the latter’s
share in the total population. It gives proper representation to each
stratum and its statistical efficiency is generally higher. This
method is therefore very popular. E.g., if the Management Faculty
of a University consists of the following specialization groups:
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Specialization stream
No. of students Proportion of each stream
Production
Finance
Marketing
Rural development
40
20
30
10
0.4
0.2
0.3
0.1
100 1.0
The research wants to draw an overall sample of 30. Then the
strata sample sizes would be:
Strata Sample size
Production
Finance
Marketing
Rural development
30 x 0.4
30 x 0.2
30 x 0.3
30 x 0.1
12
6
9
3
30
Advantages: Stratified random sampling enhances the
representativeness to each sample, gives higher statistical
efficiency, easy to carry out, and gives a self-weighing sample.
Disadvantages: A prior knowledge of the composition of the
population and the distribution of the population, it is very
expensive in time and money and identification of the strata may
lead to classification of errors.
b) Disproportionate stratified random sampling: This method
does not give proportionate representation to strata. It necessarily
involves giving over-representation to some strata and under-
representation to others. The desirability of disproportionate
sampling is usually determined by three factors, viz, (a) the sizes
of strata, (b) internal variances among strata, and (c) sampling
costs.
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Suitability: This method is used when the population contains
some small but important subgroups, when certain groups are
quite heterogeneous, while others are homogeneous and when it
is expected that there will be appreciable differences in the
response rates of the subgroups in the population.
Advantages: The advantages of this type is it is less time
consuming and facilitates giving appropriate weighing to particular
groups which are small but more important.
Disadvantages: The disadvantage is that it does not give each
stratum proportionate representation, requires prior knowledge of
composition of the population, is subject to classification errors and
its practical feasibility is doubtful.
iii) Systematic Random Sampling: This method of sampling is an
alternative to random selection. It consists of taking kth item in the
population after a random start with an item form 1 to k. It is also
known as fixed interval method. E.g., 1st, 11th, 21st ……… Strictly
speaking, this method of sampling is not a probability sampling. It
possesses characteristics of randomness and some non-probability
traits.
Suitability: Systematic selection can be applied to various populations
such as students in a class, houses in a street, telephone directory etc.
Advantages: The advantages are it is simpler than random sampling,
easy to use, easy to instruct, requires less time, it’s cheaper, easier to
check, sample is spread evenly over the population, and it is
statistically more efficient.
Disadvantages: The disadvantages are it ignores all elements
between two kth elements selected, each element does not have equal
chance of being selected, and this method sometimes gives a biased
sample.
7.5.5 Cluster Sampling
It means random selection of sampling units consisting of population
elements. Each such sampling unit is a cluster of population elements. Then
from each selected sampling unit, a sample of population elements is drawn
by either simple random selection or stratified random selection. Where the
population elements are scattered over a wide area and a list of population
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elements is not readily available, the use of simple or stratified random
sampling method would be too expensive and time-consuming. In such
cases cluster sampling is usually adopted. The cluster sampling process
involves: identify clusters, examine the nature of clusters, and determine the
number of stages.
Suitability: The application of cluster sampling is extensive in farm
management surveys, socio-economic surveys, rural credit surveys,
demographic studies, ecological studies, public opinion polls, and large
scale surveys of political and social behaviour, attitude surveys and so on.
Advantages: The advantages of this method is it is easier and more
convenient, cost of this is much less, promotes the convenience of field
work as it could be done in compact places, it does not require more time,
units of study can be readily substituted for other units and it is more
flexible.
Disadvantages: The cluster sizes may vary and this variation could
increase the bias of the resulting sample. The sampling error in this method
of sampling is greater and the adjacent units of study tend to have more
similar characteristics than do units distantly apart.
7.5.6 Area sampling
This is an important form of cluster sampling. In larger field surveys cluster
consisting of specific geographical areas like districts, talluks, villages or
blocks in a city are randomly drawn. As the geographical areas are selected
as sampling units in such cases, their sampling is called area sampling. It is
not a separate method of sampling, but forms part of cluster sampling.
7.5.7 Multi-stage and sub-sampling
In multi-stage sampling method, sampling is carried out in two or more
stages. The population is regarded as being composed of a number of
second stage units and so forth. That is, at each stage, a sampling unit is a
cluster of the sampling units of the subsequent stage. First, a sample of the
first stage sampling units is drawn, then from each of the selected first stage
sampling unit, a sample of the second stage sampling units is drawn. The
procedure continues down to the final sampling units or population
elements. Appropriate random sampling method is adopted at each stage. It
is appropriate where the population is scattered over a wider geographical
area and no frame or list is available for sampling. It is also useful when a
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survey has to be made within a limited time and cost budget. The major
disadvantage is that the procedure of estimating sampling error and cost
advantage is complicated.
Sub-sampling is a part of multi-stage sampling process. In a multi-stage
sampling, the sampling in second and subsequent stage frames is called
sub-sampling. Sub-sampling balances the two conflicting effects of
clustering i.e., cost and sampling errors.
7.5.8 Random Sampling with Probability Proportional to Size
The procedure of selecting clusters with probability Proportional to size
(PPS) is widely used. If one primary cluster has twice as large a population
as another, it is give twice the chance of being selected. If the same number
of persons is then selected from each of the selected clusters, the overall
probability of any person will be the same. Thus PPS is a better method for
securing a representative sample of population elements in multi-stage
cluster sampling.
Advantages: The advantages are clusters of various sizes get
proportionate representation, PPS leads to greater precision than would a
simple random sample of clusters and a constant sampling fraction at the
second stage, equal-sized samples from each selected primary cluster are
convenient for field work.
Disadvantages: PPS cannot be used if the sizes of the primary sampling
clusters are not known.
7.5.9 Double Sampling and Multiphase Sampling
Double sampling refers to the subsection of the final sample form a pre-
selected larger sample that provided information for improving the final
selection. When the procedure is extended to more than two phases of
selection, it is then, called multi-phase sampling. This is also known as
sequential sampling, as sub-sampling is done from a main sample in
phases. Double sampling or multiphase sampling is a compromise solution
for a dilemma posed by undesirable extremes. “The statistics based on the
sample of ‘n’ can be improved by using ancillary information from a wide
base: but this is too costly to obtain from the entire population of N
elements. Instead, information is obtained from a larger preliminary sample
nL which includes the final sample n.
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7.5.10 Replicated or Interpenetrating Sampling
It involves selection of a certain number of sub-samples rather than one full
sample from a population. All the sub-samples should be drawn using the
same sampling technique and each is a self-contained and adequate
sample of the population. Replicated sampling can be used with any basic
sampling technique: simple or stratified, single or multi-stage or single or
multiphase sampling. It provides a simple means of calculating the sampling
error. It is practical. The replicated samples can throw light on variable non-
sampling errors. But disadvantage is that it limits the amount of stratification
that can be employed.
7.5.11 Non-probability or Non Random Sampling
Non-probability sampling or non-random sampling is not based on the
theory of probability. This sampling does not provide a chance of selection
to each population element.
Advantages: The only merits of this type of sampling are simplicity,
convenience and low cost.
Disadvantages: The demerits are it does not ensure a selection chance to
each population unit. The selection probability sample may not be a
representative one. The selection probability is unknown. It suffers from
sampling bias which will distort results.
The reasons for usage of this sampling are when there is no other feasible
alternative due to non-availability of a list of population, when the study does
not aim at generalizing the findings to the population, when the costs
required for probability sampling may be too large, when probability
sampling required more time, but the time constraints and the time limit for
completing the study do not permit it. It may be classified into:
7.5.12 Convenience or Accidental Sampling
It means selecting sample units in a just ‘hit and miss’ fashion E.g.,
interviewing people whom we happen to meet. This sampling also means
selecting whatever sampling units are conveniently available, e.g., a teacher
may select students in his class. This method is also known as accidental
sampling because the respondents whom the researcher meets accidentally
are included in the sample.
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Suitability: Though this type of sampling has no status, it may be used for
simple purposes such as testing ideas or gaining ideas or rough impression
about a subject of interest.
Advantage: It is the cheapest and simplest, it does not require a list of
population and it does not require any statistical expertise.
Disadvantage: The disadvantage is that it is highly biased because of
researcher’s subjectivity, it is the least reliable sampling method and the
findings cannot be generalized.
7.5.13 Purposive (or judgment) sampling
This method means deliberate selection of sample units that conform to
some pre-determined criteria. This is also known as judgment sampling.
This involves selection of cases which we judge as the most appropriate
ones for the given study. It is based on the judgement of the researcher or
some expert. It does not aim at securing a cross section of a population.
The chance that a particular case be selected for the sample depends on
the subjective judgement of the researcher.
Suitability: This is used when what is important is the typicality and specific
relevance of the sampling units to the study and not their overall
representativeness to the population.
Advantage: It is less costly and more convenient and guarantees inclusion
of relevant elements in the sample.
Disadvantage: It is less efficient for generalizing, does not ensure the
representativeness, requires more prior extensive information and does not
lend itself for using inferential statistics.
7.5.14 Quota sampling
This is a form of convenient sampling involving selection of quota groups of
accessible sampling units by traits such as sex, age, social class, etc. it is a
method of stratified sampling in which the selection within strata is non-
random. It is this Non-random element that constitutes its greatest
weakness.
Suitability: It is used in studies like marketing surveys, opinion polls, and
readership surveys which do not aim at precision, but to get quickly some
crude results.
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Advantage: It is less costly, takes less time, non need for a list of
population, and field work can easily be organized.
Disadvantage: It is impossible to estimate sampling error, strict control if
field work is difficult, and subject to a higher degree of classification.
7.5.15 Snow-ball sampling
This is the colourful name for a technique of Building up a list or a sample of
a special population by using an initial set of its members as informants.
This sampling technique may also be used in socio-metric studies.
Suitability: It is very useful in studying social groups, informal groups in a
formal organization, and diffusion of information among professional of
various kinds.
Advantage: It is useful for smaller populations for which no frames are
readily available.
Disadvantage: The disadvantage is that it does not allow the use of
probability statistical methods. It is difficult to apply when the population is
large. It does not ensure the inclusion of all the elements in the list.
Self Assessment Questions
1. A sample must be ––––––––––––– representative of the population.
2. –––––––––– Probability sampling technique yield representative sample.
3. ––––––––– accuracy is defined as the degree to which bias is absent
from the sample. An accurate sample is the one which exactly
represents the population.
4. Precision is measured by –––––––––––– standard error.
5. A good sample must be adequate in ––––––––size in order to be
reliable.
7.6 Summary
A statistical sample ideally purports to be a miniature model or replica of the
collectivity or the population. Sampling helps in time and cost saving. If the
population to be studied is quite large, sampling is warranted. However, the
size is a relative matter. The decision regarding census or sampling
depends upon the budget of the study. Sampling is opted when the amount
of money budgeted is smaller than the anticipated cost of census survey.
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The extent of facilities available – staff, access to computer facility and
accessibility to population elements – is another factor to be considered in
deciding to sample or not. In the case of a homogenous population, even a
simple random sampling will give a representative sample. If the population
is heterogeneous, stratified random sampling is appropriate. Probability
sampling is based on the theory of probability. It is also known as random
sampling. It provides a known non-zero chance of selection for each
population element.
Simple random sampling technique gives each element an equal and
independent chance of being selected. An equal chance means equal
probability of selection.
Stratified random sampling is an improved type of random or probability
sampling. In this method, the population is sub-divided into homogenous
groups or strata, and from each stratum, random sample is drawn.
Proportionate stratified sampling involves drawing a sample from each
stratum in proportion to the latter’s share in the total population.
Disproportionate stratified random sampling does not give proportionate
representation to strata.
Systematic random sampling method is an alternative to random
selection. It consists of taking kth item in the population after a random start
with an item form 1 to k. It is also known as fixed interval method.
Cluster sampling means random selection of sampling units consisting of
population elements.
In Area sampling larger field surveys cluster consisting of specific
geographical areas like districts, taluks, villages or blocks in a city are
randomly drawn.
Multi-stage sampling is carried out in two or more stages. The population
is regarded as being composed of a number of second stage units and so
forth. That is, at each stage, a sampling unit is a cluster of the sampling
units of the subsequent stage.
Double sampling and multiphase sampling refers to the subsection of the
final sample form a pre-selected larger sample that provided information for
improving the final selection.
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Replicated or interpenetrating sampling involves selection of a certain
number of sub-samples rather than one full sample from a population.
Non-probability or non random sampling is not based on the theory of
probability. This sampling does not provide a chance of selection to each
population element.
Purposive (or judgment) sampling method means deliberate selection of
sample units that conform to some pre-determined criteria. This is also
known as judgment sampling.
Quota sampling is a form of convenient sampling involving selection of
quota groups of accessible sampling units by traits such as sex, age, social
class, etc. it is a method of stratified sampling in which the selection within
strata is non-random.
Snow-ball sampling is the colourful name for a technique of Building up a
list or a sample of a special population by using an initial set of its members
as informants.
7.7 Terminal Questions
1. What is the significance of Sampling in research?
2. Distinguish between Census and sample survey
3. Explain the Sampling process
4. How is Sample size determined?
5. What are the types of Probability or random sampling?
6. Explain Multi-stage and sub-sampling?
7. What is Random sampling with probability proportional to size?
8. Distinguish between Double sampling and multiphase sampling
9. What is replicated or interpenetrating sampling?
10. What is Non-probability or non random sampling?
11. What is Purposive (or judgment) sampling?
12. What is Quota sampling?
13. What is Snow-ball sampling?
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7.8 Answers SAQs and TQs
SAQs
1. Representative
2. Probability sampling
3. Accuracy
4. Standard error
5. Size
TQs
1. Section 7.1
2. Section 7.1
3. Section 7.3
4. Section 7.5.3
5. Section 7.5.1 to Section 7.5.10
6. Section 7.5.7
7. Section 7.5.8
8. Section 7.5.9
9. Section 7.5.10
10. Section 7.5.11
11. Section 7.5.13
12. Section 7.5.14
13. Section 7.15
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Unit 8 Sources of Data
Structure:
8.1 Meaning and Importance of Data
Objectives
8.2 Primary Sources of Data
8.2.1 Advantages and Disadvantages of Primary Data
8.2.2 Disadvantages of Primary Data
8.2.3 Methods of Collecting Primary Data
8.3 Secondary Sources of Data
8.3.1 Features of Secondary Data
8.3.2 Use of Secondary Data
8.4 Advantages of Secondary Data
8.5 Disadvantages of Secondary Data
8.6 Evaluation and of Secondary Data
Self Assessment Questions
8.7 Summary
8.8 Terminal questions
8.9 Answers to SAQs and TQs
8.1 Meaning and Importance of Data
The search for answers to research questions is called collection of data.
Data are facts, and other relevant materials, past and present, serving as
bases for study and analyses. The data needed for a social science
research may be broadly classified into (a) Data pertaining to human beings,
(b) Data relating to organization and (c) Data pertaining to territorial areas.
Objectives:
After studying this lesson you should be able to understand:
Primary sources of data
Advantages and disadvantages of primary data
Disadvantages of primary data
Methods of collecting primary data
Secondary sources of data
Features of secondary data
Use of Secondary data
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Advantages of secondary data
Disadvantages of secondary data
Evaluation and of secondary data
Personal data or data related to human beings consist of:
1. Demographic and socio-economic characteristics of individuals: Age,
sex, race, social class, religion, marital status, education, occupation
income, family size, location of the household life style etc.
2. Behavioral variables: Attitudes, opinions, awareness, knowledge,
practice, intentions, etc.
3. Organizational data consist of data relating to an organizations origin,
ownership, objectives, resources, functions, performance and growth.
4. Territorial data are related to geo-physical characteristics, resource
endowment, population, occupational pattern infrastructure degree of
development, etc. of spatial divisions like villages, cities, talluks, districts,
state and the nation.
The data serve as the bases or raw materials for analysis. Without an
analysis of factual data, no specific inferences can be drawn on the
questions under study. Inferences based on imagination or guess work
cannot provide correct answers to research questions. The relevance,
adequacy and reliability of data determine the quality of the findings of a
study.
Data form the basis for testing the hypothesis formulated in a study. Data
also provide the facts and figures required for constructing measurement
scales and tables, which are analyzed with statistical techniques. Inferences
on the results of statistical analysis and tests of significance provide the
answers to research questions. Thus, the scientific process of
measurements, analysis, testing and inferences depends on the availability
of relevant data and their accuracy. Hence, the importance of data for any
research studies.
The sources of data may be classified into (a) primary sources and
(b) secondary sources.
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8.2 Primary Sources of Data
Primary sources are original sources from which the researcher directly
collects data that have not been previously collected e.g., collection of data
directly by the researcher on brand awareness, brand preference, brand
loyalty and other aspects of consumer behaviour from a sample of
consumers by interviewing them. Primary data are first hand information
collected through various methods such as observation, interviewing,
mailing etc.
8.2.1 Advantage of Primary Data
It is original source of data
It is possible to capture the changes occurring in the course of time.
It flexible to the advantage of researcher.
Extensive research study is based of primary data
8.2.2 Disadvantage of Primary Data
1. Primary data is expensive to obtain
2. It is time consuming
3. It requires extensive research personnel who are skilled.
4. It is difficult to administer.
8.2.3 Methods of Collecting Primary Data
Primary data are directly collected by the researcher from their original
sources. In this case, the researcher can collect the required date precisely
according to his research needs, he can collect them when he wants them
and in the form he needs them. But the collection of primary data is costly
and time consuming. Yet, for several types of social science research
required data are not available from secondary sources and they have to be
directly gathered from the primary sources.
In such cases where the available data are inappropriate, inadequate or
obsolete, primary data have to be gathered. They include: socio economic
surveys, social anthropological studies of rural communities and tribal
communities, sociological studies of social problems and social institutions.
Marketing research, leadership studies, opinion polls, attitudinal surveys,
readership, radio listening and T.V. viewing surveys, knowledge-awareness
practice (KAP) studies, farm managements studies, business management
studies etc.
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There are various methods of data collection. A ‘Method’ is different from a
‘Tool’ while a method refers to the way or mode of gathering data, a tool is
an instruments used for the method. For example, a schedule is used for
interviewing. The important methods are
(a) observation, (b) interviewing, (c) mail survey, (d) experimentation,
(e) simulation and (f) projective technique. Each of these methods is
discussed in detail in the subsequent sections in the later chapters.
8.3 Secondary Sources of Data
These are sources containing data which have been collected and compiled
for another purpose. The secondary sources consists of readily compendia
and already compiled statistical statements and reports whose data may be
used by researchers for their studies e.g., census reports , annual reports
and financial statements of companies, Statistical statement, Reports of
Government Departments, Annual reports of currency and finance published
by the Reserve Bank of India, Statistical statements relating to Co-
operatives and Regional Banks, published by the NABARD, Reports of the
National sample survey Organization, Reports of trade associations,
publications of international organizations such as UNO, IMF, World Bank,
ILO, WHO, etc., Trade and Financial journals newspapers etc.
Secondary sources consist of not only published records and reports, but
also unpublished records. The latter category includes various records and
registers maintained by the firms and organizations, e.g., accounting and
financial records, personnel records, register of members, minutes of
meetings, inventory records etc.
8.3.1 Features of Secondary Sources
Though secondary sources are diverse and consist of all sorts of materials,
they have certain common characteristics.
First, they are readymade and readily available, and do not require the
trouble of constructing tools and administering them.
Second, they consist of data which a researcher has no original control over
collection and classification. Both the form and the content of secondary
sources are shaped by others. Clearly, this is a feature which can limit the
research value of secondary sources.
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Finally, secondary sources are not limited in time and space. That is, the
researcher using them need not have been present when and where they
were gathered.
8.3.2 Use of Secondary Data
The second data may be used in three ways by a researcher. First, some
specific information from secondary sources may be used for reference
purpose. For example, the general statistical information in the number of
co-operative credit societies in the country, their coverage of villages, their
capital structure, volume of business etc., may be taken from published
reports and quoted as background information in a study on the evaluation
of performance of cooperative credit societies in a selected district/state.
Second, secondary data may be used as bench marks against which the
findings of research may be tested, e.g., the findings of a local or regional
survey may be compared with the national averages; the performance
indicators of a particular bank may be tested against the corresponding
indicators of the banking industry as a whole; and so on.
Finally, secondary data may be used as the sole source of information for a
research project. Such studies as securities Market Behaviour, Financial
Analysis of companies, Trade in credit allocation in commercial banks,
sociological studies on crimes, historical studies, and the like, depend
primarily on secondary data. Year books, statistical reports of government
departments, report of public organizations of Bureau of Public Enterprises,
Censes Reports etc, serve as major data sources for such research studies.
8.4 Advantages of Secondary Data
Secondary sources have some advantages:
1. Secondary data, if available can be secured quickly and cheaply. Once
their source of documents and reports are located, collection of data is
just matter of desk work. Even the tediousness of copying the data from
the source can now be avoided, thanks to Xeroxing facilities.
2. Wider geographical area and longer reference period may be covered
without much cost. Thus, the use of secondary data extends the
researcher’s space and time reach.
3. The use of secondary data broadens the data base from which scientific
generalizations can be made.
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4. Environmental and cultural settings are required for the study.
5. The use of secondary data enables a researcher to verify the findings
bases on primary data. It readily meets the need for additional empirical
support. The researcher need not wait the time when additional primary
data can be collected.
8.5 Disadvantages of Secondary Data
The use of a secondary data has its own limitations.
1. The most important limitation is the available data may not meet our
specific needs. The definitions adopted by those who collected those
data may be different; units of measure may not match; and time periods
may also be different.
2. The available data may not be as accurate as desired. To assess their
accuracy we need to know how the data were collected.
3. The secondary data are not up-to-date and become obsolete when they
appear in print, because of time lag in producing them. For example,
population census data are published tow or three years later after
compilation, and no new figures will be available for another ten years.
4. Finally, information about the whereabouts of sources may not be
available to all social scientists. Even if the location of the source is
known, the accessibility depends primarily on proximity. For example,
most of the unpublished official records and compilations are located in
the capital city, and they are not within the easy reach of researchers
based in far off places.
8.6 Evaluation of Secondary Data
When a researcher wants to use secondary data for his research, he should
evaluate them before deciding to use them.
1. Data Pertinence
The first consideration in evaluation is to examine the pertinence of the
available secondary data to the research problem under study. The
following questions should be considered.
What are the definitions and classifications employed? Are they
consistent ?
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What are the measurements of variables used? What is the degree to
which they conform to the requirements of our research?
What is the coverage of the secondary data in terms of topic and time?
Does this coverage fit the needs of our research?
On the basis of above consideration, the pertinence of the secondary data
to the research on hand should be determined, as a researcher who is
imaginative and flexible may be able to redefine his research problem so as
to make use of otherwise unusable available data.
2. Data Quality
If the researcher is convinced about the available secondary data for his
needs, the next step is to examine the quality of the data. The quality of data
refers to their accuracy, reliability and completeness. The assurance and
reliability of the available secondary data depends on the organization which
collected them and the purpose for which they were collected. What is the
authority and prestige of the organization? Is it well recognized? Is it noted
for reliability? It is capable of collecting reliable data? Does it use trained
and well qualified investigators? The answers to these questions determine
the degree of confidence we can have in the data and their accuracy. It is
important to go to the original source of the secondary data rather than to
use an immediate source which has quoted from the original. Then only, the
researcher can review the cautionary ands other comments that were made
in the original source.
3. Data Completeness
The completeness refers to the actual coverage of the published data. This
depends on the methodology and sampling design adopted by the original
organization. Is the methodology sound? Is the sample size small or large?
Is the sampling method appropriate? Answers to these questions may
indicate the appropriateness and adequacy of the data for the problem
under study. The question of possible bias should also be examined.
Whether the purpose for which the original organization collected the data
had a particular orientation? Has the study been made to promote the
organization’s own interest? How the study was conducted? These are
important clues. The researcher must be on guard when the source does
not report the methodology and sampling design. Then it is not possible to
determine the adequacy of the secondary data for the researcher’s study.
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Self Assessment Questions
State whether following statements are true or false.
1. The sources of data may be classified into (a) primary sources and
(b) secondary sources.
2. Primary data are first hand information collected through various
methods such as observation, interviewing, mailing etc.
3. The secondary sources consist of readily compendia and already
complied statistical statements and reports.
4. The important methods are observation, (b) interviewing, (c) mail survey,
(d) experimentation, (e) simulation and projective technique.
8.7 Summary
Data are facts and other relevant materials, past and present, serving as
bases for study and analyses. The data needed for a social science
research may be broadly classified into (a) Data pertaining to human beings,
(b) Data relating to organization and (c) Data pertaining to territorial areas.
Personal data or data related to human beings consists of: Demographic
and socio-economic characteristics of individuals: Age, sex, race, social
class, religion, martial status, education, occupation income, family size,
location of the household life style etc.
Behavioural variables: Attitudes, opinions, awareness, knowledge, practice,
intentions, etc. Organizational data consist of data relating to an
organizations origin, ownership, objectives, resources, functions,
performance and growth. Territorial data are related to geophysical
characteristics, resource endowment, population, occupational pattern
infrastructure degree of development, etc. of spatial divisions like villages,
cities, taluks, districts, state and the nation. Data form the basis for testing
the hypothesis formulated in a study. Data also provide the facts and figures
required for constructing measurement scales and tables. The sources of
data may be classified into (a) primary sources and (b) secondary sources.
Primary data are first hand information collected through various methods
such as observation, interviewing, mailing etc. The secondary sources
consist of readily compendia and already complied statistical statements
and reports. Finally secondary sources are not limited in time and space.
That is, the researcher using them need not have been present when and
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where they were gathered. Secondary data, if available can be secured
quickly and cheaply.
Wider geographical area and longer reference period may be covered
without much cost. Thus, the use of secondary data extends the
researcher’s space and time reach. The use of secondary data broadens
the data base from which scientific generalizations can be made. The use
of a secondary data has its own limitations. The most important limitation is
the available data may not meet our specific needs. The secondary data are
not up-to-date and become obsolete when they appear in print, because of
time lag in producing them. Primary data are directly collected by the
researcher from their original sources. There are various methods of data
collection. A ‘Method’ is different from a ‘Tool’ while a method refers to the
way or mode of gathering data, a tool is an instruments used for the method.
For example, a schedule is used for interviewing. The important methods
are (a) observation, (b) interviewing, (c) mail survey, (d) experimentation,
(e) simulation and projective technique.
8.8 Terminal Questions
1. What are the types of data?
2. What are the primary sources of data?
3. What are the sources of secondary sources?
4. How is secondary data useful to researcher?
5. What are the advantages of secondary data?
6. Describe the disadvantages of secondary data.
7. What are the criteria used for evaluation of secondary data?
8.9 Answers to SAQs and TQs
SAQs
1. True
2. True
3. True
4. True
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TQs
1. Section 8.0
2. Section 8.1
3. Section 8.4
4. Section 8.4.2
5. Section 8.5
6. Section 8.6
7. Section 8.6
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Unit 9 Observation
Structure:
9.1 Meaning of Observation
Objectives
9.2 General Characteristics of Observation Method
9.3 Process of Observation
9.4 Types of Observation
9.4.1 Participant Observation
9.4.2 Non-participant Observation
9.4.3 Direct Observation
9.4.4 Indirect Observation
9.4.5 Controlled Observation
9.4.6 Uncontrolled Observation
9.5 Prerequisites of Observation
9.6 Advantages of Observation
9.7 Limitations of Observation
9.8 Use of Observation in Business Research
Self Assessment Questions
9.9 Summary
9.10 Terminal Questions
9.11 Answers to SAQs and TQs
9.1 Meaning of Observation
Observation means viewing or seeing. Observation may be defined as a
systematic viewing of a specific phenomenon in its proper setting for the
specific purpose of gathering data for a particular study. Observation is
classical method of scientific study.
Objectives:
After studying this lesson you should be able to understand:
General characteristics of observation method
Process of observation
Types of observation
Participant Observation
Non-participant observation
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Direct observation
Indirect observation
Controlled observation
Uncontrolled observation
Prerequisites of observation
Advantages of observation
Limitations of observation
Use of observation in business research
9.2 General Characteristics of Observation Method
Observation as a method of data collection has certain characteristics.
1. It is both a physical and a mental activity: The observing eye catches
many things that are present. But attention is focused on data that are
pertinent to the given study.
2. Observation is selective: A researcher does not observe anything and
everything, but selects the range of things to be observed on the basis
of the nature, scope and objectives of his study. For example, suppose a
researcher desires to study the causes of city road accidents and also
formulated a tentative hypothesis that accidents are caused by violation
of traffic rules and over speeding. When he observed the movements of
vehicles on the road, many things are before his eyes; the type, make,
size and colour of the vehicles, the persons sitting in them, their hair
style, etc. All such things which are not relevant to his study are ignored
and only over speeding and traffic violations are keenly observed by
him.
3. Observation is purposive and not casual: It is made for the specific
purpose of noting things relevant to the study. It captures the natural
social context in which persons behaviour occur. It grasps the significant
events and occurrences that affect social relations of the participants.
4. Observation should be exact and be based on standardized tools of
research and such as observation schedule, social metric scale etc., and
precision instruments, if any.
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9.3 Process of Observations
The use of observation method requires proper planning.
First, the researcher should carefully examine the relevance of
observation method to the data needs of the selected study.
Second, he must identify the specific investigative questions which call
for use of observation method. These determine the data to be
collected.
Third, he must decide the observation content, viz., specific conditions,
events and activities that have to be observed for the required data. The
observation content should include the relevant variables.
Fourth, for each variable chosen, the operational definition should be
specified.
Fifth, the observation setting, the subjects to be observed, the timing
and mode of observation, recording, procedure, recording instruments to
be used, and other details of the task should be determined.
Last, observers should be selected and trained. The persons to be
selected must have sufficient concentration powers, strong memory
power and unobtrusive nature. Selected persons should be imparted
both theoretical and practical training.
9.4 Types of Observations
Observations may be classified in different ways. With reference to
investigator’s role, it may be classified into (a) participant observation and
(b) non-participant observation. In terms of mode of observation, it may be
classified into (c) direct observation. With reference to the rigor of the
system adopted. Observation is classified into (e) controlled observation,
and (f) uncontrolled observation
9.4.1 Participant Observation
In this observation, the observer is a part of the phenomenon or group which
is observed and he acts as both an observer and a participant. For example,
a study of tribal customs by an anthropologist by taking part in tribal
activities like folk dance. The persons who are observed should not be
aware of the researcher’s purpose. Then only their behaviour will be
‘natural’. The concealment of research objective and researcher’s identity is
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justified on the ground that it makes it possible to study certain aspects of
the group’s culture which are not revealed to outsiders.
Advantages: The advantages of participant observation are:
The observer can understand the emotional reactions of the observed
group, and get a deeper insight of their experiences.
The observer will be able to record context which gives meaning to the
observed behaviour and heard statements.
Disadvantages: Participant observation suffers from some demerits.
1. The participant observer narrows his range of observation. For example,
if there is a hierarchy of power in the group/community under study, he
comes to occupy one position within in, and thus other avenues of
information are closed to him.
2. To the extent that the participant observer participates emotionally, the
objectivity is lost.
3. Another limitation of this method is the dual demand made on the
observer. Recording can interfere with participation, and participation
can interfere with observation. Recording on the spot is not possible and
it has to be postponed until the observer is alone. Such time lag results
in some inaccuracy in recording
9.4.2 Non-participant observations
In this method, the observer stands apart and does not participate in the
phenomenon observed. Naturally, there is no emotional involvement on the
part of the observer. This method calls for skill in recording observations in
an unnoticed manner.
9.4.3 Direct observation
This means observation of an event personally by the observer when it
takes place. This method is flexible and allows the observer to see and
record subtle aspects of events and behaviour as they occur. He is also free
to shift places, change the focus of the observation. A limitation of this
method is that the observer’s perception circuit may not be able to cover all
relevant events when the latter move quickly, resulting in the
incompleteness of the observation.
9.4.4 Indirect observation
This does not involve the physical presence of the observer, and the
recording is done by mechanical, photographic or electronic devices, e.g.
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recording customer and employee movements by a special motion picture
camera mounted in a department of a large store. This method is less
flexible than direct observations, but it is less biasing and less erratic in
recording accuracy. It is also provides a permanent record for an analysis of
different aspects of the event.
9.4.5 Controlled observation
This involves standardization of observational techniques and exercises of
maximum control over extrinsic and intrinsic variables by adopting
experimental design and systematically recording observations. Controlled
observation is carried out either in the laboratory or in the field. It is typified
by clear and explicit decisions on what, how and when to observe.
9.4.6 Uncontrolled observation
This does not involve control over extrinsic and intrinsic variables. It is
primary used for descriptive research. Participant observation is a typical
uncontrolled one
9.5 Prerequisites of Effective Observation
The prerequisites of observation consist of:
Observations must be done under conditions which will permit accurate
results. The observer must be in vantage point to see clearly the objects
to be observed. The distance and the light must be satisfactory. The
mechanical devices used must be in good working conditions and
operated by skilled persons.
Observation must cover a sufficient number of representative samples of
the cases.
Recording should be accurate and complete.
The accuracy and completeness of recorded results must be checked. A
certain number of cases can be observed again by another
observer/another set of mechanical devices, as the case may be. If it is
feasible, two separate observers and sets of instruments may be used in
all or some of the original observations. The results could then be
compared to determine their accuracy and completeness.
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9.6 Advantages of observation
Observation has certain advantages:
1. The main virtue of observation is its directness: it makes it possible to
study behaviour as it occurs. The researcher need not ask people about
their behaviour and interactions; he can simply watch what they do and
say.
2. Data collected by observation may describe the observed phenomena
as they occur in their natural settings. Other methods introduce
elements or artificiality into the researched situation for instance, in
interview; the respondent may not behave in a natural way. There is no
such artificiality in observational studies, especially when the observed
persons are not aware of their being observed.
3. Observations is more suitable for studying subjects who are unable to
articulate meaningfully, e.g. studies of children, tribal, animals, birds etc.
4. Observations improve the opportunities for analyzing the contextual
back ground of behaviour. Further more verbal resorts can be validated
and compared with behaviour through observation. The validity of what
men of position and authority say can be verified by observing what they
actually do.
5. Observations make it possible to capture the whole event as it occurs.
For example only observation can provide an insight into all the aspects
of the process of negotiation between union and management
representatives.
6. Observation is less demanding of the subjects and has less biasing
effect on their conduct than questioning.
7. It is easier to conduct disguised observation studies than disguised
questioning.
8. Mechanical devices may be used for recording data in order to secure
more accurate data and also of making continuous observations over
longer periods.
9.7 Limitations of Observation
Observation cannot be used indiscriminately for all purposes. It has its own
limitations:
1. Observation is of no use, studying past events or activities. One has to
depend upon documents or narrations people for studying such things.
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2. Observation is not suitable for studying and attitudes. However, an
observation of related behaviour affords a good clue to the attitudes.
E.g. and observations of the seating pattern of high caste and class
persons in a general meeting in a village may be useful for forming an
index of attitude.
3. Observation poses difficulties in obtaining a representative sample. For
interviewing and mailing methods, the selection of a random sampling
can be rapidly ensured. But observing people of all types does not make
the sample a random one.
4. Observation cannot be used as and when the researcher finds a
convenient to use it. He has to wait for the eve n to occur. For example,
an observation of folk dance of a tribal community is possible, only when
it is performed.
5. A major limitation of this method is that the observer normally must be at
the scene of the event when it takes place. Yet it may not be possible to
predict where and when the even will occur, e.g., road accident,
communal clash.
6. Observation is slow and expensive process, requiring human observers
and/or costly surveillance equipments.
9.8 Use of Observation in Business Research
Observation is suitable for a variety of research purposes. It may be used
for studying (a) The behaviour of human beings in purchasing goods and
services.: life style, customs, and manner, interpersonal relations, group
dynamics, crowd behaviour, leadership styles, managerial style, other
behaviours and actions; (b) The behaviour of other living creatures like
birds, animals etc. (c) Physical characteristics of inanimate things like
stores, factories, residences etc. (d) Flow of traffic and parking problems
(e) movement of materials and products through a plant.
Self Assessment Questions
State whether the following statements are true or false.
1. Observations may be classified into (a) participant observation and
(b) non-participant observation.
2. In terms of mode of observation, it may be classified into (c) direct
observation.
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3. With reference to the rigor of the system adopted. Observation is
classified into (e) controlled observation, and (f) uncontrolled
observation.
4. Observation involves standardization of observational techniques and
exercises of maximum control over extrinsic and intrinsic variables.
9.9 Summary
Observation means viewing or seeing. Observation may be defined as a
systematic viewing of a specific phenomenon in its proper setting for the
specific purpose of gathering data for a particular study. Observation is
classical method of scientific study. Observation as a method of data
collection has certain characteristics. Observations may be classified in
different ways. With reference to investigator’s role, it may be classified into
(a) participant observation and (b) non-participant observation. In terms of
mode of observation, it may be classified into (c) direct observation. With
reference to the rigor of the system adopted. Observation is classified into
(e) controlled observation, and (f) uncontrolled observation. This does not
involve the physical presence of the observer, and the recording is done by
mechanical, photographic or electronic devices, e.g. recording customer and
employee movements by a special motion picture camera mounted in a
department of a large store. This involves standardization of observational
techniques and exercises of maximum control over extrinsic and intrinsic
variables by adopting experimental design and systematically recording
observations. This does not involve control over extrinsic and intrinsic
variables. It is primary used for descriptive research. Participant observation
is a typical uncontrolled one.
Observation has certain advantages: Observation cannot be used
indiscriminately for all purposes. It has its own limitations. Observation is
suitable for a variety of research purposes. (a) The behaviour of human
beings in purchasing goods and services: life style, customs, and manner,
interpersonal relations, group dynamics, crowd behaviour, leadership styles,
managerial style, other behaviours and actions.
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9.10 Terminal Questions
1. What is Observation?
2. Explain the General Characteristics of Observation.
3. What are the types of Observations?
4. What are non-participant observations?
5. Distinguish between Direct and Indirect observation:
6. What is Controlled observation?
7. Describe the features of uncontrolled observation:
8. What are the advantages of observation?
9. What are the Limitations of Observation?
10. What is the utility of Observation in Business Research?
9.11 Answers to SAQs and TQs
SAQs
1. True
2. True
3. True
4. True
TQs
1. Section 9.1
2. Section 9.2
3. Section 9.4
4. Section 9.4.2
5. Section 9.4.3 and 9.4.3
6. Section 9.4.5
7. Section 9.4.6
8. Section 9.5
9. Section 9.6
10. Section 9.7
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Unit 10 Schedule and Questionnaire
Structure:
10.1 Meaning of Schedules and Questionnaire
Objectives
10.2 Types of Questionnaire
10.2.1 Structured or Standard Questionnaire
10.2.2 Unstructured Questionnaire
10.3 Processes of Data Collection
10.3.1 Alternate Method of Sending Questionnaires
10.3.2 Personal Delivery
10.3.3 Attaching Questionnaire to Products
10.3.4 Advertising Questionnaire
10.3.5 News Stat Insert
10.3.6 Improving the response in a Mail Survey
10.4 Importance of Questionnaire
10.4.1 Advantages of Questionnaire
10.4.2 Disadvantages of Questionnaire
10.5 Distinction between Schedule and Questionnaire
Self Assessment Questions
10.6 Summary
10.7 Terminal Questions
10.8 Answers to SAQs and TQs
10.1 Meaning of Schedule and Questionnaire
The mail survey is another method of collecting primary data. This method
involves sending questionnaires to the respondents with a request to
complete them and return them by post. This can be used in the case of
educated respondents only. The mail questionnaires should be simple so
that the respondents can easily understand the questions and answer them.
It should preferably contain mostly closed-end and multiple choice questions
so that it could be completed within a few minutes.
The distinctive feature of the mail survey is that the questionnaire is self-
administered by the respondents themselves and the responses are
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recorded by them, and not by the investigator as in the case of personal
interview method. It does not involve face-to-face conversation between the
investigator and the respondent. Communication is carried out only in
writing and this required more cooperation from the respondents than in
verbal communication
Objectives:
After studying this lesson you should be able to understand:
Types of questionnaire
Structured or standard questionnaire
Unstructured questionnaire
Processes of data collection
Alternate method of sending questionnaires
Importance of questionnaire
Advantages of questionnaire
Disadvantages of Questionnaire
Distinction between schedule and questionnaire
10.2 Types of Questionnaires
Questionnaires may be classified as:
10.2.1 Structured/ Standardized Questionnaire
Structured questionnaires are those in which there are definite, concrete
and preordained questions with additional questions limited to those
necessary to clarify inadequate answers or to elicit more detailed
responses. The questions are presented with exactly the same wording and
in the same order to all the respondents.
10.2.2 Unstructured Questionnaire
In unstructured questionnaires the respondent is given the opportunity to
answer in his own terms and in his own frame of reference.
10.3 Process of Data Collection
The researcher should prepare a mailing list of the selected respondents by
collecting the addresses from the telephone directory of the association or
organization to which they belong.
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A covering letter should accompany a copy of the questionnaire. Exhibit 7.1
is a copy of a covering letter used by the author in a research study on
‘corporate planning’. It must explain to the respondent the purpose of the
study and the importance of his cooperation to the success of the project.
Anonymity may be assured.
10.3.1 Alternative Modes of Sending Questionnaires
There are some alternative methods of distributing questionnaires to the
respondents. They are: (1) personal delivery, (2) attaching questionnaire to
a product (3) advertising questionnaire in a newspaper of magazine, and
(4) news stand insets.
10.3.2 Personal Delivery
The researcher or his assistant may deliver the questionnaires to the
potential respondents with a request to complete them at their convenience.
After a day or two he can collect the completed questionnaires from them.
Often referred to as the self-administered questionnaire method, it combines
the advantages of the personal interview and the mail survey. Alternatively,
the questionnaires may be delivered in person and the completed
questionnaires may be returned by mail by the respondents.
10.3.3 Attaching Questionnaire to a Product
A firm test marketing a product may attach a questionnaire to a product and
request the buyer to complete it and mail it back to the firm. The respondent
is usually rewarded by a gift or a discount coupon.
10.3.4 Advertising the Questionnaires
The questionnaire with the instructions for completion may be advertised on
a page of magazine or in section of newspapers. The potential respondent
completes it tears it out and mails it to the advertiser. For example, the
committee of Banks customer services used this method. Management
studies for collecting information from the customers of commercial banks in
India. This method may be useful for large-scale on topics of common
interest.
10.3.5 News-Stand Inserts
This method involves inserting the covering letter, questionnaire and self
addressed reply-paid envelope into a random sample of news-stand copies
of a newspaper or magazine.
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10.3.6 Improving the Response Rate in a Mail survey
The response rate in mail surveys is generally very low more so in
developing countries like India. Certain techniques have to be adopted to
increase the response rate. They are:
1. Quality Printing: The questionnaire may be neatly printed in quality
light coloured paper, so as to attract the attention of the respondent.
2. Covering Letter: The covering letter should be couched in a pleasant
style so as to attract and hold the interest of the respondent. It must
anticipate objections and answer them briefly. It is a desirable to
address the respondent by name.
3. Advance Information: Advance information can be provided to
potential respondents by a telephone call or advance notice in the
newsletter of the concerned organization or by a letter. Such preliminary
contact with potential respondents is more successful than follow up
efforts.
4. Incentives: Money, stamps for collection and other incentives are also
used to induce respondents to complete and return mail questionnaire.
5. Follow-up-contacts: In the case of respondents belonging to an
organization, they may be approached through some one in that
organization known as the researcher.
6. Larger sample size: A larger sample may be drawn than the estimated
sample size. For example, if the required sample size is 1000, a sample
of 1500 may be drawn. This may help the researcher to secure an
effective sample size closer to the required size.
10.4 Importance of Questionnaire
The significance of questionnaire method is that it affords great facilities in
collecting data from large, diverse, and widely scattered groups of people. It
is used in gathering objective, quantitative data as well as for securing
information of a qualitative nature. In some studies, questionnaire is the
sole research tool utilised but it is more often used in conjunction with other
methods of investigations. In questionnaire technique, great reliance is
placed on the respondent’s verbal report for data on the stimuli or
experiences which is exposed as also for data on his behaviour.
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10.4.1 Advantages of Questionnaires
The advantages of mail surveys are:
They are less costly than personal interviews, as cost of mailing is the
same through out the country, irrespective of distance.
They can cover extensive geographical areas.
Mailing is useful in contacting persons such as senior business
executives who are difficult to reach in any other way.
The respondents can complete the questionnaires at their convenience.
Mail surveys, being more impersonal, provide more anonymity than
personal interviews.
Mail surveys are totally free from the interviewer’s bias, as there is no
personal contact between the respondents and the investigator.
Certain personal and economic data may be given accurately in an
unsigned mail questionnaire.
10.4.2 Disadvantages of Questionnaires
The disadvantages of mail surveys are:
1. The scope for mail surveys is very limited in a country like India where
the percentage of literacy is very low.
2. The response rate of mail surveys is low. Hence, the resulting sample
will not be a representative one.
10.5 Distinction between Schedules and Questionnaires
Questionnaires are mailed to the respondent whereas schedules are carried
by the investigator himself. Questionnaires can be filled by the respondent
only if he is able to understand the language in which it is written and he is
supposed to be a literate. This problem can be overcome in case of
schedule since the investigator himself carries the schedules and the
respondent’s response is accordingly taken. A questionnaire is filled by the
respondent himself whereas the schedule is filled by the investigator.
Self Assessment Questions
Fill in the blanks
1. The response rate in mail surveys is generally very –––––––––––.
2. –––––––– can cover extensive geographical areas.
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3. Mail surveys, being more impersonal, provide more –––––––– than
personal interviews.
4. Mail surveys are totally free from –––––––– as there is no personal
contact between the respondents and the investigator
10.6 Summary
The mail survey is another method of collecting primary data. This method
involves sending questionnaires to the respondents with a request to
complete them and return them by post. The distinctive feature of the mail
survey is that the questionnaire is self-administered by the respondents
themselves and the responses are recorded by them, and not by the
investigator as in the case of personal interview method. There are some
alternative methods of distributing questionnaires to the respondents. They
are: (1) personal delivery, (2) attaching questionnaire to a product
(3) advertising questionnaire in a newspaper or a magazine, and (4) news
stand insets. The response rate in mail surveys is generally very low, more
so in developing countries like India. Certain techniques have to be adopted
to increase the response rate. They are less costly than personal interviews,
as cost of mailing is the same through out the country, irrespective of
distances. They can cover extensive geographical areas. Mailing is useful in
contacting persons such as senior business executives who are difficult to
reach in any other way. The respondents can complete the questionnaires
at their conveniences
Mail surveys, being more impersonal, provide more anonymity than
personal interviews. Mail surveys are totally free from the interviewer’s bias,
as there is no personal contact between the respondents and the
investigator. Certain personal and economic data may be given accurately
in an unsigned mail questionnaire. The scope for mail surveys is very limited
in a country like India where the percentage of literacy is very low. The
response rate of mail surveys is low. Hence, the resulting sample will not be
a representative one. The significance of questionnaire method is that it
affords great facilities in collecting data from large, diverse, and widely
scattered groups of people. Questionnaires are mailed to the respondent
whereas schedules are carried by the investigator himself. A questionnaire
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is filled by the respondent himself whereas the schedule is filled by the
investigator.
10.7 Terminal Questions
1. What are a Schedule and a Questionnaire?
2. What are the alternative modes of sending Questionnaires?
3. What are the ways to improve the Response Rate in a Mail survey?
4. What are the advantages of Questionnaires?
5. Discuss the disadvantages of Questionnaires
6. What is the importance of Questionnaire?
7. Distinguish between schedules and questionnaires
10.8 Answers to SAQs and TQs
SAQs
1. Low
2. Mail surveys
3. Anonymity
4. The interviewer’s bias
TQs
1. Section 10.1
2. Section 10.3.3
3. Section 10.3.6
4. Section 10.4.1
5. Section 10.4.2
6. Section 10.4
7. Section 10.5
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Unit 11 Interviewing
Structure:
11.1 Meaning of interview
Objectives
11.2 Types of interviews
11.2.1 Structured Directive interview
11.2.2 Unstructured non-directive interview
11.2.3 Focused interview
11.2.4 Clinical interview
11.2.5 Depth interview
11.3 Approaches to the interview
11.4 Qualities of interview
11.5 Merits of interview method
11.6 Demerits of interview method
11.7 Interview techniques in business research
11.7.1 Preparation
11.7.2 Introduction
11.7.3 Developing Report
11.7.4 Carrying the interview forward
11.7.5 Additional sittings
11.7.6 Recording the interview
11.7.7 Closing the interview
11.7.8 Editing
11.8 Interview Problems
11.8.1 Inadequate response
11.8.2 Interviewer‟s bias
11.8.3 Non-response
11.8.4 Non-availability
11.8.5 Refusal
11.8.6 Inaccessibility
11.8.7 Methods and Aims of controlling non-response
11.9 Telephone Interviewing
11.10 Group Interviews
Self assessment Questions
11.11 Summary
11.12 Terminal questions
11.13 Answers to SAQs and TQs
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11.1 Meaning of Interview
Interviewing is one of the prominent methods of data collection. It may be
defined as a two way systematic conversation between an investigator and
an informant, initiated for obtaining information relevant to a specific study. It
involves not only conversation, but also learning from the respondent‟s
gesture, facial expressions and pauses, and his environment. Interviewing
requires face to face contact or contact over telephone and calls for
interviewing skills. It is done by using a structured schedule or an
unstructured guide.
Interviewing may be used either as a main method or as a supplementary
one in studies of persons. Interviewing is the only suitable method for
gathering information from illiterate or less educated respondents. It is
useful for collecting a wide range of data from factual demographic data to
highly personal and intimate information relating to a person‟s opinions,
attitudes, values, beliefs past experience and future intentions. When
qualitative information is required or probing is necessary to draw out fully,
and then interviewing is required. Where the area covered for the survey is
a compact, or when a sufficient number of qualified interviewers are
available, personal interview is feasible.
Interview is often superior to other data-gathering methods. People are
usually more willing to talk than to write. Once report is established, even
confidential information may be obtained. It permits probing into the context
and reasons for answers to questions.
Interview can add flesh to statistical information. It enables the investigator
to grasp the behavioural context of the data furnished by the respondents.
Objectives:
After studying this lesson you should be able to understand:
Types of interviews
Structured Directive interview
Unstructured non-directive interview
Focused interview
Clinical interview
Depth interview
Approaches to the interview
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Qualities of interview
Merits of interview method
Demerits of interview method
Interview techniques in business research
Interview Problems
Methods and Aims of controlling non-response
Telephone Interviewing
Group Interviews
11.2 Types of Interviews
The interview may be classified into: (a) structured or directive interview,
(b) unstructured or non-directive interview, (c) focused interview, (d) clinical
interview and (e) depth interview.
11.2.1 Structured Directive Interview
This is an interview made with a detailed standardized schedule. The same
questions are put to all the respondents and in the same order. Each
question is asked in the same way in each interview, promoting
measurement reliability. This type of interview is used for large-scale
formalized surveys.
Advantages: This interview has certain advantages. First, data from one
interview to the next one are easily comparable. Second, recording and
coding data do not pose any problem, and greater precision is achieved.
Lastly, attention is not diverted to extraneous, irrelevant and time consuming
conversation.
Limitation: However, this type of interview suffers from some limitations.
First, it tends to lose the spontaneity of natural conversation. Second, the
way in which the interview is structured may be such that the respondent‟s
views are minimized and the investigator‟s own biases regarding the
problem under study are inadvertent introduced. Lastly, the scope for
exploration is limited.
11.2.2 Unstructured or Non-Directive Interview
This is the least structured one. The interviewer encourages the respondent
to talk freely about a give topic with a minimum of prompting or guidance. In
this type of interview, a detailed pre-planned schedule is not used. Only a
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broad interview guide is used. The interviewer avoids channelling the
interview directions. Instead he develops a very permissive atmosphere.
Questions are not standardized and ordered in a particular way.
This interviewing is more useful in case studies rather than in surveys. It is
particularly useful in exploratory research where the lines of investigations
are not clearly defined. It is also useful for gathering information on sensitive
topics such as divorce, social discrimination, class conflict, generation gap,
drug-addiction etc. It provides opportunity to explore the various aspects of
the problem in an unrestricted manner.
Advantages: This type of interview has certain special advantages. It can
closely approximate the spontaneity of a natural conversation. It is less
prone to interviewer‟s bias. It provides greater opportunity to explore the
problem in an unrestricted manner.
Limitations: Though the unstructured interview is a potent research
instrument, it is not free from limitations. One of its major limitations is that
the data obtained from one interview is not comparable to the data from the
next. Hence, it is not suitable for surveys. Time may be wasted in
unproductive conversations. By not focusing on one or another facet of a
problem, the investigator may run the risk of being led up blind ally. As there
is no particular order or sequence in this interview, the classification of
responses and coding may required more time. This type of informal
interviewing calls for greater skill than the formal survey interview.
11.2.3 Focused Interview
This is a semi-structured interview where the investigator attempts to focus
the discussion on the actual effects of a given experience to which the
respondents have been exposed. It takes place with the respondents known
to have involved in a particular experience, e.g, seeing a particular film,
viewing a particular program on TV., involved in a train/bus accident, etc.
The situation is analysed prior to the interview. An interview guide specifying
topics relating to the research hypothesis used. The interview is focused on
the subjective experiences of the respondent, i.e., his attitudes and
emotional responses regarding the situation under study. The focused
interview permits the interviewer to obtain details of personal reactions,
specific emotions and the like.
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Merits: This type of interview is free from the inflexibility of formal methods,
yet gives the interview a set form and insured adequate coverage of all the
relevant topics. The respondent is asked for certain information, yet he has
plenty of opportunity to present his views. The interviewer is also free to
choose the sequence of questions and determine the extent of probing,
11.2.4 Clinical Interview
This is similar to the focused interview but with a subtle difference. While the
focused interview is concerned with the effects of specific experience,
clinical interview is concerned with broad underlying feelings or motivations
or with the course of the individual‟s life experiences.
The „personal history‟ interview used in social case work, prison
administration, psychiatric clinics and in individual life history research is the
most common type of clinical interview. The specific aspects of the
individual‟s life history to be covered by the interview are determined with
reference to the purpose of the study and the respondent is encouraged to
talk freely about them.
11.2.5 Depth Interview
This is an intensive and searching interview aiming at studying the
respondent‟s opinion, emotions or convictions on the basis of an interview
guide. This requires much more training on inter-personal skills than
structured interview. This deliberately aims to elicit unconscious as well as
extremely personal feelings and emotions.
This is generally a lengthy procedure designed to encourage free
expression of affectively charged information. It requires probing. The
interviewer should totally avoid advising or showing disagreement. Of
course, he should use encouraging expressions like “uh-huh” or “I see” to
motivate the respondent to continue narration. Some times the interviewer
has to face the problem of affections, i.e. the respondent may hide
expressing affective feelings. The interviewer should handle such situation
with great care.
11.3 Approaches to Interview
Interviewing as a method of data collection has certain features. They are:
The Participants: The interviewer and the respondent – are strangers.
Hence, the investigator has to get him introduced to the respondent in an
appropriate manner.
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The Relationship between the Participants is a Transitory one: It has a
fixed beginning and termination points. The interview proper is a fleeting,
momentary experience for them.
Interview is not a mere casual conversational exchange: Interview is a
conversation with a specific purpose, viz., obtaining information relevant to a
study.
Interview is a mode of obtaining verbal answers to questions put
verbally: The interaction between the interviewer and the respondent need
not necessarily be on a face-to-face basis, because interview can be
conducted over the telephone also. Although interview is usually a
conversation between two persons, it need not be limited to a single
respondent. It can also be conducted with a group of persons, such as
family members, or a group of children or a group of customers, depending
on the requirements of the study.
Interview is an inter-actionable process: The interaction between the
interviewer and the respondent depends upon how they perceive each
other.
The respondent reacts to the interviewer‟s appearance, behaviour, gestures,
facial expression and intonation, his perception of the thrust of the questions
and his own personal needs. As far as possible, the interviewer should try to
be closer to the social-economic level of the respondents. Moreover, he
should realize that his respondents are under no obligations to extend
response.
One should, therefore, be tactful and be alert to such reactions of the
respondents as lame-excuse, suspicion, reluctance or indifference, and deal
with them suitably. One should not also argue or dispute. One should rather
maintain an impartial and objective attitude. Information furnished by the
respondent in the interview is recorded by the investigator. This poses a
problem of seeing that recording does not interfere with the tempo of
conversation.
Interviewing is not a standardized process: Like that of a chemical
technician; it is rather a flexible psychological process. The implication of
this feature is that the interviewer cannot apply unvarying standardized
technique, because he is dealing with respondents with varying motives and
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diverse perceptions. The extent of his success as an interviewer is very
largely dependent upon his insight and skill in dealing with varying socio-
physiological situations.
11.4 Qualities of Interviews
The requirements or conditions necessary for a successful interview are:
Data availability: The needed information should be available with the
respondent. He should be able to conceptualize it in terms to the study, and
be capable of communicating it.
Role perception: The respondent should understand his role and know
what is required of him. He should know what is a relevant and how
complete it should be. He can learn much of this from the interviewer‟s
introduction, explanations and questioning procedure.
The interviewer should also know his role: He should establish a
permissive atmosphere and encourage frank and free conversation. He
should not affect the interview situation through subjective attitude and
argumentation.
Respondent’s motivation: The respondent should be willing to respond
and give accurate answer. This depends partly on the interviewer‟s
approach and skill. The interview has interest in it for the purpose of his
research, but the respondent has no personal interest in it. Therefore, the
interviewer should establish a friendly relationship with the respondent, and
create in him an interest in the subject-matter of the study. The interviewer
should try to reduce the effect of demotivating factors like desire to get on
with other activities, embarrassment at ignorance, dislike of the interview
content, suspicious about the interviewer, and fear of consequence, He
should also try to build up the effect of motivating actors like curiosity,
loneliness, politeness, sense of duty, respect of the research agency and
liking for the interviewer.
The above requirement reminds that the interview is an interaction process.
The investigator should keep this in mind and take care to see that his
appearance and behaviour do not distort the interview situation.
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11.5 Merits of Interview Method
There are several real advantages to personal interviewing.
First the greatest value of this method is the depth and detail of
information that can be secured. When used with well conceived
schedules, an interview can obtain a great deal of information. It far
exceeds mail survey in amount and quality of data that can be secured.
Second, the interviewer can do more to improve the percentage of
responses and the quality of information received than other method. He
can note the conditions of the interview situation, and adopt appropriate
approaches to overcome such problems as the respondent‟s
unwillingness, incorrect understanding of question, suspicion, etc.
Third, the interviewer can gather other supplemental information like
economic level, living conditions etc. through observation of the
respondent‟s environment.
Fourth, the interviewer can use special scoring devices, visual materials
and the like in order to improve the quality of interviewing.
Fifth, the accuracy and dependability of the answers given by the
respondent can be checked by observation and probing.
Last, interview is flexible and adaptable to individual situations. Even
more, control can be exercised over the interview situation.
11.6 Demerits of Interview Method
Interviewing is not free limitations.
Its greatest drawback is that it is costly both in money and time.
Second, the interview results are often adversely affected by
interviewer‟s mode of asking questions and interactions, and incorrect
recording and also by the respondent‟s faulty perception, faulty memory,
inability to articulate etc.
Third, certain types of personal and financial information may be refused
in face-to face interviews. Such information might be supplied more
willingly on mail questionnaires, especially if they are to be unsigned.
Fourth, interview poses the problem of recording information obtained
from the respondents. No full proof system is available. Note taking is
invariably distracting to both the respondent and the interviewer and
affects the thread of the conversation.
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Last, interview calls for highly interviewers. The availability of such
persons is limited and the training of interviewers is often a long and
costly process.
11.7 Interviewing techniques in Business Research
The interview process consists of the following stages:
Preparation
Introduction
Developing rapport
Carrying the interview forward
Recording the interview
Closing the interview
11.7.1 Preparation
The interviewing requires some preplanning and preparation. The
interviewer should keep the copies of interview schedule/guide (as the case
may be) ready to use. He should have the list of names and addresses of
respondents, he should regroup them into contiguous groups in terms of
location in order to save time and cost in traveling. The interviewer should
find out the general daily routine of the respondents in order to determine
the suitable timings for interview. Above all, he should mentally prepare
himself for the interview. He should think about how he should approach a
respondent, what mode of introduction he could adopt, what situations he
may have to face and how he could deal with them. The interviewer may
come across such situations as respondents; avoidance, reluctance,
suspicion, diffidence, inadequate responses, distortion, etc. The investigator
should plan the strategies for dealing with them. If such preplanning is not
done, he will be caught unaware and fail to deal appropriately when he
actually faces any such situation. It is possible to plan in advance and keep
the plan and mind flexible and expectant of new development.
11.7.2 Introduction
The investigator is a stranger to the respondents. Therefore, he should be
properly introduced to each of the respondents. What is the proper mode of
introduction? There is no one appropriate universal mode of introduction.
Mode varies according to the type of respondents. When making a study of
an organization or institution, the head of the organization should be
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approached first and his cooperation secured before contacting the sample
inmates/employees. When studying a community or a cultural group, it is
essential to approach the leader first and to enlist cooperation. For a survey
or urban households, the research organization‟s letter of introduction and
the interviewer‟s identity card can be shown. In these days of fear of
opening the door for a stranger, residents cooperation can be easily
secured, if the interviewer attempts to get him introduced through a person
known to them, say a popular person in the area e.g., a social worker. For
interviewing rural respondents, the interviewer should never attempt to
approach them along with someone from the revenue department, for they
would immediately hide themselves, presuming that they are being
contacted for collection of land revenue or subscription to some government
bond. He should not also approach them through a local political leader,
because persons who do not belong to his party will not cooperate with the
interviewer. It is rather desirable to approach the rural respondents through
the local teacher or social worker.
After getting himself introduced to the respondent in the most appropriate
manner, the interviewer can follow a sequence of procedures as under, in
order to motivate the respondent to permit the interview:
1. With a smile, greet the respondent in accordance with his cultural
pattern.
2. Identify the respondent by name.
3. Describe the method by which the respondent was selected.
4. Mention the name of the organization conducting the research.
5. Assure the anonymity or confidential nature of the interview.
6. Explain their usefulness of the study.
7. Emphasize the value of respondent‟s cooperation, making such
statements as “You are among the few in a position to supply the
information”. “Your response is invaluable.” “I have come to learn from
your experience and knowledge”.
11.7.3 Developing Rapport
Before starting the research interview, the interviewer should establish a
friendly relationship with the respondent. This is described as “rapport”. It
means establishing a relationship of confidence and understanding between
the interviewer and the respondent. It is a skill which depends primarily on
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the interviewer‟s commonsense, experience, sensitivity, and keen
observation.
Start the conversation with a general topic of interest such as weather,
current news, sports event, or the like perceiving the probable of the
respondent from his context. Such initial conversation may create a friendly
atmosphere and a warm interpersonal relationship and mutual
understanding. However, the interviewer should “guard against the over
rapport” as cautioned by Herbert Hyman. Too much identification and too
much courtesy result in tailoring replied to the image of a “nice interviewer.”
The interviewer should use his discretion in striking a happy medium.
11.7.4 Carrying the Interview Forward
After establishing rapport, the technical task of asking questions from the
interview schedule starts. This task requires care, self-restraint, alertness
and ability to listen with understanding, respect and curiosity. In carrying on
this task of gathering information from the respondent by putting questions
to him, the following guidelines may be followed:
1. Start the interview. Carry it on in an informal and natural
conversational style.
2. Ask all the applicable questions in the same order as they appear on
the schedule without any elucidation and change in the wording. Ask
all the applicable questions listed in the schedule. Do not take answers
for granted.
3. If interview guide is used, the interviewer may tailor his questions to
each respondent, covering of course, the areas to be investigated.
4. Know the objectives of each question so as to make sure that the
answers adequately satisfy the question objectives.
5. If a question is not understood, repeat it slowly with proper emphasis
and appropriate explanation, when necessary.
6. Talk all answers naturally, never showing disapproval or surprise.
When the respondent does not meet the interruptions, denial,
contradiction and other harassment, he may feel free and may not try
to withhold information. He will be motivated to communicate when the
atmosphere is permissive and the listener‟s attitude is non judgmental
and is genuinely absorbed in the revelations.
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7. Listen quietly with patience and humility. Give not only undivided
attention, but also personal warmth. At the same time, be alert and
analytic to incomplete, non specific and inconsistent answers, but
avoid interrupting the flow of information. If necessary, jot down
unobtrusively the points which need elaboration or verification for later
and timelier probing. The appropriate technique for this probing is to
ask for further clarification in such a polite manner as “I am not sure, I
understood fully, is this….what you meant?”
8. Neither argue nor dispute.
9. Show genuine concern and interest in the ideas expressed by the
respondent; at the same time, maintain an impartial and objective
attitude.
10. Should not reveal your own opinion or reaction. Even when you are
asked of your views, laugh off the request, saying “Well, your opinions
are more important than mine.”
11. At times the interview “runs dry” and needs re-stimulation. Then use
such expressions as “Uh-huh” or “That interesting” or “I see” “can you
tell me more about that?” and the like.
12. When the interviewee fails to supply his reactions to related past
experiences, represent the stimulus situation, introducing appropriate
questions which will aid in revealing the past. “Under what
circumstances did such and such a phenomenon occur?” or “How did
you feel about it and the like.
13. At times, the conversation may go off the track. Be alert to discover
drifting, steer the conversation back to the track by some such remark
as, “you know, I was very much interested in what you said a moment
ago. Could you tell me more about it?”
14. When the conversation turns to some intimate subjects, and
particularly when it deals with crises in the life of the individual,
emotional blockage may occur. Then drop the subject for the time
being and pursue another line of conversation for a while so that a less
direct approach to the subject can be made later.
15. When there is a pause in the flow of information, do not hurry the
interview. Take it as a matter of course with an interested look or a
sympathetic half-smile. If the silence is too prolonged, introduce a
stimulus saying “You mentioned that… What happened then?”
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11.7.5 Additional Sittings
In the case of qualitative interviews involving longer duration, one single
sitting will not do, as it would cause interview weariness. Hence, it is
desirable to have two or more sittings with the consent of the respondent.
11.7.6 Recording the Interview
It is essential to record responses as they take place. If the note taking is
done after the interview, a good deal of relevant information may be lost.
Nothing should be made in the schedule under respective question. It
should be complete and verbatim. The responses should not be
summarized or paraphrased. How can complete recording be made without
interrupting the free flow of conversation? Electronic transcription through
devices like tape recorder can achieve this. It has obvious advantages over
note-taking during the interview. But it also has certain disadvantages.
Some respondents may object to or fear “going on record”. Consequently
the risk of lower response rate will rise especially for sensitive topics.
If the interviewer knows short-hand, he can use it with advantage.
Otherwise, he can write rapidly by abbreviating word and using only key
words and the like. However, even the fast writer may fail to record all that is
said at conversational speed. At such times, it is useful to interrupt by some
such comment as “that seems to be a very important point, would you mind
repeating it, so that I can get your words exactly.” The respondent is usually
flattered by this attention and the rapport is not disturbed.
The interviewer should also record all his probes and other comments on
the schedule, in brackets to set them off from responses. With the pre-
coded structured questions, the interviewer‟s task is easy. He has to simply
ring the appropriate code or tick the appropriate box, as the case may be.
He should not make mistakes by carelessly ringing or ticketing a wrong
item.
11.7.7 Closing the Interview
After the interview is over, take leave off the respondent thanking him with a
friendly smile. In the case of a qualitative interview of longer duration, select
the occasion for departure more carefully. Assembling the papers for putting
them in the folder at the time of asking the final question sets the stage for a
final handshake, a thank-you and a good-bye. If the respondent desires to
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know the result of the survey, note down his name and address so that a
summary of the result could be posted to him when ready.
11.7.8 Editing
At the close of the interview, the interviewer must edit the schedule to check
that he has asked all the questions and recorded all the answers and that
there is no inconsistency between answers. Abbreviations in recording must
be replaced by full words. He must ensure that everything is legible. It is
desirable to record a brief sketch of his impressions of the interview and
observational notes on the respondent‟s living environment, his attitude to
the survey, difficulties, if any, faced in securing his cooperation and the
interviewer‟s assessment of the validity of the respondent‟s answers.
11.8 Interview Problems
In personal interviewing, the researcher must deal with two major problems,
inadequate response, non-response and interviewer‟s bias.
11.8.1 Inadequate response
Kahn and Cannel distinguish five principal symptoms of inadequate
response. They are:
o partial response, in which the respondent gives a relevant but
incomplete answer
o non-response, when the respondent remains silent or refuses to answer
the question
o irrelevant response, in which the respondent‟s answer is not relevant to
the question asked
o inaccurate response, when the reply is biased or distorted and
o verbalized response problem, which arises on account of respondent‟s
failure to understand a question or lack of information necessary for
answering it.
11.8.2 Interviewer’s Bias
The interviewer is an important cause of response bias. He may resort to
cheating by „cooking up‟ data without actually interviewing. The interviewers
can influence the responses by inappropriate suggestions, word emphasis,
tone of voice and question rephrasing. His own attitudes and expectations
about what a particular category of respondents may say or think may bias
the data. Another source of response of the interviewer‟s characteristics
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(education, apparent social status, etc) may also bias his answers. Another
source of response bias arises from interviewer‟s perception of the situation,
if he regards the assignment as impossible or sees the results of the survey
as possible threats to personal interests or beliefs he is likely to introduce
bias.
As interviewers are human beings, such biasing factors can never be
overcome completely, but their effects can be reduced by careful selection
and training of interviewers, proper motivation and supervision,
standardization or interview procedures (use of standard wording in survey
questions, standard instructions on probing procedure and so on) and
standardization of interviewer behaviour. There is need for more research
on ways to minimize bias in the interview.
11.8.3 Non-response
Non-response refers to failure to obtain responses from some sample
respondents. There are many sources of non-response; non-availability,
refusal, incapacity and inaccessibility.
11.8.4 Non-availability
Some respondents may not be available at home at the time of call. This
depends upon the nature of the respondent and the time of calls. For
example, employed persons may not be available during working hours.
Farmers may not be available at home during cultivation season. Selection
of appropriate timing for calls could solve this problem. Evenings and
weekends may be favourable interviewing hours for such respondents. If
someone is available, then, line respondent‟s hours of availability can be
ascertained and the next visit can be planned accordingly.
11.8.5 Refusal
Some persons may refuse to furnish information because they are ill-
disposed, or approached at the wrong hour and so on. Although, a hardcore
of refusals remains, another try or perhaps another approach may find some
of them cooperative. Incapacity or inability may refer to illness which
prevents a response during the entire survey period. This may also arise on
account of language barrier.
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11.8.6 Inaccessibility
Some respondents may be inaccessible. Some may not be found due to
migration and other reasons. Non-responses reduce the effective sample
size and its representativeness.
11.8.7 Methods and Aims of control of non-response
Kish suggests the following methods to reduce either the percentage of non-
response or its effects:
1. Improved procedures for collecting data are the most obvious remedy
for non-response. Improvements advocated are (a) guarantees of
anonymity, (b) motivation of the respondent to co-operate (c) arousing
the respondents‟ interest with clever opening remarks and questions,
(d) advance notice to the respondents.
2. Call-backs are most effective way of reducing not-at-homes in personal
interviews, as are repeated mailings to no-returns in mail surveys.
3. Substitution for the non-response is often suggested as a remedy.
Usually this is a mistake because the substitutes resemble the
responses rather than the non-responses. Nevertheless, beneficial
substitution methods can sometimes be designed with reference to
important characteristics of the population. For example, in a farm
management study, the farm size is an important variable and if the
sampling is based on farm size, substitution for a respondent with a
particular size holding by another with the holding of the same size is
possible.
Attempts to reduce the percentage or effects on non-responses aim at
reducing the bias caused by differences on non-respondents from
respondents. The non-response bias should not be confused with the
reduction of sampled size due to non-response. The latter effect can be
easily overcome, either by anticipating the size of non-response in designing
the sample size or by compensating for it with a supplement. These
adjustments increase the size of the response and the sampling precision,
but they do not reduce the non-response percentage or bias.
11.9 Telephone Interviewing
Telephone interviewing is a non-personal method of data collection. It may
be used as a major method or supplementary method.
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It will be useful in the following situations:
1. When the universe is composed of those persons whose names are
listed in telephone directories, e.g. business houses, business
executives, doctors, other professionals.
2. When the study required responses to five or six simple questions. E.g.
Radio or Television program survey.
3. When the survey must be conducted in a very short period of time,
provided the units of study are listed in telephone directory.
4. When the subject is interesting or important to respondents, e.g. a
survey relating to trade conducted by a trade association or a chamber
of commerce, a survey relating to a profession conducted by the
concerned professional association.
5. When the respondents are widely scattered.
Advantages: The advantages of telephone interview are:
1. The survey can be completed at very low cost, because telephone
survey does not involve travel time and cost and all calls can be made
from a single location.
2. Information can be collected in a short period of time. 5 to 10 interviews
can be conducted per hours.
3. Quality of response is good, because interviewer bias is reduced as
there is no face-to-face contact between the interviewer and the
respondent.
4. This method of interviewing is less demanding upon the interviewer.
5. It does not involve field work.
6. Individuals who could not be reached or who might not care to be
interviewed personally can be contacted easily.
Disadvantages: Telephone interview has several limitations:
1. It is limited to persons with listed telephones. The sample will be
distorted. If the universe includes persons not on phone in several
counties like India only a few persons have phone facility and that too in
urban areas only. Telephone facility is very rare in rural areas. Hence,
the method is not useful for studying the general population.
2. There is a limit to the length of interview. Usually, a call cannot last over
five minutes. Only five or six simple questions can be asked. Hence,
telephone cannot be used for a longer questionnaire.
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3. The type of information to be collected is limited to what can be given in
simple, short answers of a few words. Hence, telephone is not suitable
for complex surveys, and there is no possibility of obtaining detailed
information.
4. If the questions cover personal matters, most respondents will not
cooperate with the interviewer.
5. The respondent‟s characteristics and environment cannot be observed.
6. It is not possible to use visual aids like charts, maps, illustrations or
complex scales.
7. It is rather difficult to establish rapport between the respondent and the
interviewer.
8. There is no possibility to ensure the identity of the interviewer and to
overcome suspicions.
11.10 Group Interviews
A group interview may be defined as a method of collecting primary data in
which a number of individuals with a common interest interact with each
other. In a personal interview, the flow of information is multi dimensional.
The group may consist of about six to eight individuals with a common
interest. The interviewer acts as the discussion leader. Free discussion is
encouraged on some aspect of the subject under study. The discussion
leader stimulates the group members to interact with each other.
The desired information may be obtained through self-administered
questionnaire or interview, with the discussion serving as a guide to ensure
consideration of the areas of concern. In particular, the interviewers look for
evidence of common elements of attitudes, beliefs, intentions and opinions
among individuals in the group. At the same time, he must be aware that a
single comment by a member can provide important insight.
Samples for group interview can be obtained through schools, clubs and
other organized groups. The group interview technique can be employed by
researchers in studying people‟s reactions on public amenities, public health
projects, welfare schemes etc. It is a popular method in marketing research
to evaluate new product or service concepts, brands names, packages,
promotional strategies and attitudes. When an organization needs a great
variety of information in as much detail as possible at a relatively low cost
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and in a short period of time, the group interview technique is more useful. It
can be used to generate primary data in the exploratory phase of a project.
Advantages: The advantages of this technique are:
1. The respondents comment freely and in detail.
2. The method is highly flexible. The flexibility helps the research work with
new concepts or topics which have not been previously investigated.
3. Visual aids can be used.
4. A group can be interviewed in the time required for one personal
interview.
5. The client can watch the interview unobserved.
6. Respondents are more articulated in a group than in the individual
interviews.
7. The technique eliminates the physical limitations inherent in individual
interviews.
Disadvantages: This method is not free from draw backs.
1. It is difficult to get a representative sample.
2. There is the possibility of the group being dominated by one individual.
3. The respondents may answer to please the interviewer or the other
members in the group.
4. Nevertheless, the advantage of this technique outweighs the
disadvantages and the technique is found to be useful for surveys on
topics of common interest.
Self Assessment Questions
State whether the following statements are true or false:
1. This is an interview made with a details standardized schedule.
2. A semi-structured interview where the investigator attempts to focus the
discussion on the actual effects of a given experience to which the
respondents have been exposed.
3. The focused interview is concerned with the effects of specific
experience; clinical interview is concerned with broad underlying feelings
or motivations or with the course of the individual‟s life experiences.
11.11 Summary
Interviewing is one of the prominent methods of data collection. The
interview may be classified into: (a) structured or directive interview,
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(b) unstructured or non-directive interview, (c) focused interview, and
(d) clinical interview and (e) depth interview. Structured interview is made
with a details standardized schedule. The same questions are put to all the
respondents and in the same order. Non-directive method is the least
structured one. The interviewer encourages the respondent to talk freely
about a given topic with a minimum of prompting or guidance. In focused
type of interview, a detailed pre-planned schedule is not used. Clinical
interview is a semi-structured interview where the investigator attempts to
focus the discussion on the actual effects of a given experience to which the
respondents have been exposed. This is similar to the focused interview but
with a subtle difference. While the focused interview is concerned with the
effects of specific experience, clinical interview is concerned with broad
underlying feelings or motivations or with the course of the individual‟s life
experiences. This is an intensive and searching interview aiming at studying
the respondent‟s opinion, emotions or convictions on the basis of an
interview guide. Detailed interview requires much more training on inter-
personal skills than structured interview. This deliberately aims to elicit
unconscious as well as extremely personal feelings and emotions.
Interviewing as a method of data collection has certain features. They are:
1. The requirements or conditions necessary for a successful interview are:
2. There are several real advantages to personal interviewing.
3. Interviewing is not free limitations.
In personal interviewing, the researcher must deal with two major problems,
inadequate response, non-response and interviewer‟s bias. Telephone
interviewing is a non-personal method of data collection. It may be used as
a major method or supplementary method. It will be useful in the following
situations. A group interview may be defined as a method of collecting
primary data in which a number of individuals with a common interest
interact with each other. In a personal interview the flow of information is
multi dimensional. The group may consist of about six to eight individuals
with a common interest. The interviewer acts as the discussion. The quality
of data collected depends ultimately upon the capabilities of interviewers.
Hence, careful selection and proper training of interviewers is essential.
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11.12 Terminal Questions
1. What is the meaning of Interview method?
2. Briefly explain the types of Interviews
3. What is Structured Directive Interview?
4. What is Unstructured or Non-Directive Interview?
5. What is Focused Interview?
6. What is Clinical Interview?
7. What is Depth Interview?
8. Explain the approaches to Interview.
9. What are the qualities of Interviews?
10. What are the advantages of Interviews?
11. What are the limitations of Interviews?
12. Briefly explain Interviewing techniques in Business Research
13. What are the Problems encountered in interview?
11.13 Answers to SAQs and TQs
SAQs
1. True
2. True
3. True
TQs
1. Section 11.1
2. Section 11.2
3. Section 11.2.1
4. Section 11.2.2
5. Section 11.2.3
6. Section 11.2.4
7. Section 11.2.5
8. Section 11.3
9. Section 11.4
10. Section 11.5
11. Section 11.6
12. Section 11.7
13. Section 11.8
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Unit 12 Processing Data
Structure:
12.1 Meaning of Data Processing
Objective
12.2 Checking for Analysis
12.3 Editing
12.3.1 Data Editing at the Time of Recording the Data
12.3.2 Data Editing at the Time of Analysis of Data
12.4 Coding
12.5 Classification
12.6 Transcription of Data
12.6.1 Methods of Transcription
12.6.2 Manual Transcription
12.6.3 Long Work Sheets
12.7 Tabulation
12.7.1 Manual Tabulation
12.8 Construction of Frequency Table
12.9 Components of a Table
12.10 Principles of Table Construction
12.11 Frequency Distribution and Class intervals
12.12 Graphs, Charts and Diagrams
12.12.1 Types of Graphs and General Rules
12.12.2 Line Graphs
12.13 Quantitative and Qualitative Analysis
12.13.1 Measures of Central Tendency
12.13.2 Dispersion
12.13.3 Correlation Analysis
12.13.4 Coefficient of Determination
Self Assessment Questions
12.14 Summary
12.15 Terminal Questions
12.16 Answers to SAQs and TQs
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12.1 Meaning of Data Processing
Data in the real world often comes with a large quantum and in a variety of
formats that any meaningful interpretation of data cannot be achieved
straightaway. Social science researches, to be very specific, draw
conclusions using both primary and secondary data. To arrive at a
meaningful interpretation on the research hypothesis, the researcher has to
prepare his data for this purpose. This preparation involves the identification
of data structures, the coding of data and the grouping of data for
preliminary research interpretation. This data preparation for research
analysis is teamed as processing of data. Further selections of tools for
analysis would to a large extent depend on the results of this data
processing.
Data processing is an intermediary stage of work between data collections
and data interpretation. The data gathered in the form of
questionnaires/interview schedules/field notes/data sheets is mostly in the
form of a large volume of research variables. The research variables
recognized is the result of the preliminary research plan, which also sets out
the data processing methods beforehand. Processing of data requires
advanced planning and this planning may cover such aspects as
identification of variables, hypothetical relationship among the variables and
the tentative research hypothesis.
The various steps in processing of data may be stated as:
o Identifying the data structures
o Editing the data
o Coding and classifying the data
o Transcription of data
o Tabulation of data.
Objectives:
After studying this lesson you should be able to understand:
Checking for analysis
Editing
Coding
Classification
Transcription of data
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Tabulation
Construction of Frequency Table
Components of a table
Principles of table construction
Frequency distribution and class intervals
Graphs, charts and diagrams
Types of graphs and general rules
Quantitative and qualitative analysis
Measures of central tendency
Dispersion
Correlation analysis
Coefficient of determination
12.2 Checking for Analysis
In the data preparation step, the data are prepared in a data format, which
allows the analyst to use modern analysis software such as SAS or SPSS.
The major criterion in this is to define the data structure. A data structure is
a dynamic collection of related variables and can be conveniently
represented as a graph where nodes are labelled by variables. The data
structure also defines and stages of the preliminary relationship between
variables/groups that have been pre-planned by the researcher. Most data
structures can be graphically presented to give clarity as to the frames
researched hypothesis. A sample structure could be a linear structure, in
which one variable leads to the other and finally, to the resultant end
variable.
The identification of the nodal points and the relationships among the nodes
could sometimes be a complex task than estimated. When the task is
complex, which involves several types of instruments being collected for the
same research question, the procedures for drawing the data structure
would involve a series of steps. In several intermediate steps, the
heterogeneous data structure of the individual data sets can be harmonized
to a common standard and the separate data sets are then integrated into a
single data set. However, the clear definition of such data structures would
help in the further processing of data.
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12.3 Editing
The next step in the processing of data is editing of the data instruments.
Editing is a process of checking to detect and correct errors and omissions.
Data editing happens at two stages, one at the time of recording of the data
and second at the time of analysis of data.
12.3.1 Data Editing at the Time of Recording of Data
Document editing and testing of the data at the time of data recording is
done considering the following questions in mind.
Do the filters agree or are the data inconsistent?
Have „missing values‟ been set to values, which are the same for all
research questions?
Have variable descriptions been specified?
Have labels for variable names and value labels been defined and
written?
All editing and cleaning steps are documented, so that, the redefinition of
variables or later analytical modification requirements could be easily
incorporated into the data sets.
12.3.2 Data Editing at the Time of Analysis of Data
Data editing is also a requisite before the analysis of data is carried out. This
ensures that the data is complete in all respect for subjecting them to further
analysis. Some of the usual check list questions that can be had by a
researcher for editing data sets before analysis would be:
1. Is the coding frame complete?
2. Is the documentary material sufficient for the methodological description
of the study?
3. Is the storage medium readable and reliable.
4. Has the correct data set been framed?
5. Is the number of cases correct?
6. Are there differences between questionnaire, coding frame and data?
7. Are there undefined and so-called “wild codes”?
8. Comparison of the first counting of the data with the original documents
of the researcher.
The editing step checks for the completeness, accuracy and uniformity of
the data as created by the researcher.
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Completeness: The first step of editing is to check whether there is an
answer to all the questions/variables set out in the data set. If there were
any omission, the researcher sometimes would be able to deduce the
correct answer from other related data on the same instrument. If this is
possible, the data set has to rewritten on the basis of the new information.
For example, the approximate family income can be inferred from other
answers to probes such as occupation of family members, sources of
income, approximate spending and saving and borrowing habits of family
members‟ etc. If the information is vital and has been found to be
incomplete, then the researcher can take the step of contacting the
respondent personally again and solicit the requisite data again. If none of
these steps could be resorted to the marking of the data as “missing” must
be resorted to.
Accuracy: Apart from checking for omissions, the accuracy of each
recorded answer should be checked. A random check process can be
applied to trace the errors at this step. Consistency in response can also be
checked at this step. The cross verification to a few related responses would
help in checking for consistency in responses. The reliability of the data set
would heavily depend on this step of error correction. While clear
inconsistencies should be rectified in the data sets, fact responses should
be dropped from the data sets.
Uniformity: In editing data sets, another keen lookout should be for any
lack of uniformity, in interpretation of questions and instructions by the data
recorders. For instance, the responses towards a specific feeling could have
been queried from a positive as well as a negative angle. While interpreting
the answers, care should be taken as a record the answer as a “positive
question” response or as “negative question” response in all uniformity
checks for consistency in coding throughout the questionnaire/interview
schedule response/data set.
The final point in the editing of data set is to maintain a log of all corrections
that have been carried out at this stage. The documentation of these
corrections helps the researcher to retain the original data set.
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12.4 Coding
The edited data are then subject to codification and classification. Coding
process assigns numerals or other symbols to the several responses of the
data set. It is therefore a pre-requisite to prepare a coding scheme for the
data set. The recording of the data is done on the basis of this coding
scheme.
The responses collected in a data sheet varies, sometimes the responses
could be the choice among a multiple response, sometimes the response
could be in terms of values and sometimes the response could be
alphanumeric. At the recording stage itself, if some codification were done to
the responses collected, it would be useful in the data analysis. When
codification is done, it is imperative to keep a log of the codes allotted to the
observations. This code sheet will help in the identification of
variables/observations and the basis for such codification.
The first coding done to primary data sets are the individual observation
themselves. This responses sheet coding gives a benefit to the research, in
that, the verification and editing of recordings and further contact with
respondents can be achieved without any difficulty. The codification can be
made at the time of distribution of the primary data sheets itself. The codes
can be alphanumeric to keep track of where and to whom it had been sent.
For instance, if the data consists of several public at different localities, the
sheets that are distributed in a specific locality may carry a unique part code
which is alphabetic. To this alphabetic code, a numeric code can be
attached to distinguish the person to whom the primary instrument was
distributed. This also helps the researcher to keep track of who the
respondents are and who are the probable respondents from whom primary
data sheets are yet to be collected. Even at a latter stage, any specific
queries on a specific responses sheet can be clarified.
The variables or observations in the primary instrument would also need
codification, especially when they are categorized. The categorization could
be on a scale i.e., most preferable to not preferable, or it could be very
specific such as Gender classified as Male and Female. Certain
classifications can lead to open ended classification such as education
classification, Illiterate, Graduate, Professional, Others. Please specify. In
such instances, the codification needs to be carefully done to include all
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possible responses under “Others, please specify”. If the preparation of the
exhaustive list is not feasible, then it will be better to create a separate
variable for the “Others please specify” category and records all responses
as such.
Numeric Coding: Coding need not necessarily be numeric. It can also be
alphabetic. Coding has to be compulsorily numeric, when the variable is
subject to further parametric analysis.
Alphabetic Coding: A mere tabulation or frequency count or graphical
representation of the variable may be given in an alphabetic coding.
Zero Coding: A coding of zero has to be assigned carefully to a variable. In
many instances, when manual analysis is done, a code of 0 would imply a
“no response” from the respondents. Hence, if a value of 0 is to be given to
specific responses in the data sheet, it should not lead to the same
interpretation of „non response‟. For instance, there will be a tendency to
give a code of 0 to a „no‟, then a different coding than 0 should be given in
the data sheet. An illustration of the coding process of some of the
demographic variables is given in the following table.
Question Variable Response categories Code
Number observation
1.1 Organisation Private Pt
Public Pb
Government Go
3.4 Owner of Vehicle Yes 2
No 1
4.2 Vehicle performs Excellent 5
Good 4
Adequate 3
Bad 2
Worst 1
5.1 Age Up to 20 years 1
21-40 years 2
40-60 years 3
5.2 Occupation Salaried S
Professional P
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Technical T
Business B
Retired R
Housewife H
Others =
= Could be treated as a separate variable/observation and the actual
response could be recorded. The new variable could be termed as “other
occupation”
The coding sheet needs to be prepared carefully, if the data recording is not
done by the researcher, but is outsourced to a data entry firm or individual.
In order to enter the data in the same perspective, as the researcher would
like to view it, the data coding sheet is to be prepared first and a copy of the
data coding sheet should be given to the outsourcer to help in the data entry
procedure. Sometimes, the researcher might not be able to code the data
from the primary instrument itself. He may need to classify the responses
and then code them. For this purpose, classification of data is also
necessary at the data entry stage.
12.5 Classification
When open ended responses have been received, classification is
necessary to code the responses. For instance, the income of the
respondent could be an open-ended question. From all responses, a
suitable classification can be arrived at. A classification method should meet
certain requirements or should be guided by certain rules.
First, classification should be linked to the theory and the aim of the
particular study. The objectives of the study will determine the dimensions
chosen for coding. The categorization should meet the information required
to test the hypothesis or investigate the questions.
Second, the scheme of classification should be exhaustive. That is, there
must be a category for every response. For example, the classification of
martial status into three category viz., “married” “Single” and “divorced” is
not exhaustive, because responses like “widower” or “separated” cannot be
fitted into the scheme. Here, an open ended question will be the best mode
of getting the responses. From the responses collected, the researcher can
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fit a meaningful and theoretically supportive classification. The inclusion of
the classification “Others” tends to fill the cluttered, but few responses from
the data sheets. But “others” categorization has to carefully used by the
researcher. However, the other categorization tends to defeat the very
purpose of classification, which is designed to distinguish between
observations in terms of the properties under study. The classification
“others” will be very useful when a minority of respondents in the data set
give varying answers. For instance, the reading habits of newspaper may be
surveyed. The 95 respondents out of 100 could be easily classified into 5
large reading groups while 5 respondents could have given a unique
answer. These given answer rather than being separately considered could
be clubbed under the “others” heading for meaningful interpretation of
respondents and reading habits.
Third, the categories must also be mutually exhaustive, so that each case is
classified only once. This requirement is violated when some of the
categories overlap or different dimensions are mixed up.
The number of categorization for a specific question/observation at the
coding stage should be maximum permissible since, reducing the
categorization at the analysis level would be easier than splitting an already
classified group of responses. However the number of categories is limited
by the number of cases and the anticipated statistical analysis that are to be
used on the observation.
12.6 Transcription of Data
When the observations collected by the researcher are not very large, the
simple inferences, which can be drawn from the observations, can be
transferred to a data sheet, which is a summary of all responses on all
observations from a research instrument. The main aim of transition is to
minimize the shuffling proceeds between several responses and several
observations. Suppose a research instrument contains 120 responses and
the observations has been collected from 200 respondents, a simple
summary of one response from all 200 observations would require shuffling
of 200 pages. The process is quite tedious if several summary tables are to
be prepared from the instrument. The transcription process helps in the
presentation of all responses and observations on data sheets which can
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help the researcher to arrive at preliminary conclusions as to the nature of
the sample collected etc. Transcription is hence, an intermediary process
between data coding and data tabulation.
12.6.1 Methods of Transcription
The researcher may adopt a manual or computerized transcription. Long
work sheets, sorting cards or sorting strips could be used by the researcher
to manually transcript the responses. The computerized transcription could
be done using a data base package such as spreadsheets, text files or other
databases.
The main requisite for a transcription process is the preparation of the data
sheets where observations are the row of the database and the
responses/variables are the columns of the data sheet. Each variable
should be given a label so that long questions can be covered under the
label names. The label names are thus the links to specific questions in the
research instrument. For instance, opinion on consumer satisfaction could
be identified through a number of statements (say 10); the data sheet does
not contain the details of the statement, but gives a link to the question in
the research instrument though variable labels. In this instance the variable
names could be given as CS1, CS2, CS3, CS4, CS5, CS6, CS7, CS8, CS9
and CS10. The label CS indicating Consumer satisfaction and the number 1
to 10 indicate the statement measuring consumer satisfaction. Once the
labelling process has been done for all the responses in the research
instrument, the transcription of the response is done.
12.6.2 Manual Transcription
When the sample size is manageable, the researcher need not use any
computerization process to analyze the data. The researcher could prefer a
manual transcription and analysis of responses. The choice of manual
transcription would be when the number of responses in a research
instrument is very less, say 10 responses, and the numbers of observations
collected are within 100. A transcription sheet with 100x50 (assuming each
response has 5 options) row/column can be easily managed by a
researcher manually. If, on the other hand the variables in the research
instrument are more than 40 and each variable has 5 options, it leads to a
worksheet of 100x200 sizes which might not be easily managed by the
researcher manually. In the second instance, if the number of responses is
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less than 30, then the manual worksheet could be attempted manually. In all
other instances, it is advisable to use a computerized transcription process.
12.6.3 Long Worksheets
Long worksheets require quality paper; preferably chart sheets, thick
enough to last several usages. These worksheets normally are ruled both
horizontally and vertically, allowing responses to be written in the boxes. If
one sheet is not sufficient, the researcher may use multiple rules sheets to
accommodate all the observations. Heading of responses which are variable
names and their coding (options) are filled in the first two rows. The first
column contains the code of observations. For each variable, now the
responses from the research instrument are then transferred to the
worksheet by ticking the specific option that the observer has chosen. If the
variable cannot be coded into categories, requisite length for recording the
actual response of the observer should be provided for in the work sheet.
The worksheet can then be used for preparing the summary tables or can
be subjected to further analysis of data. The original research instrument
can be now kept aside as safe documents. Copies of the data sheets can
also be kept for future references. As has been discussed under the editing
section, the transcript data has to be subjected to a testing to ensure error
free transcription of data.
A sample worksheet is given below for reference.
Sl vehicle Occupation Vehicle
No Owner performance
Age Age
Y N S P T B R R Other occ 1 2 3 4 5 1 2 3 4
1 x x x x
2 x x x x
3 x x x x
4 x x x x
5 x x x x
6 x x x x
7 x Student x x
8 x Artist x x
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Transcription can be made as and when the edited instrument is ready for
processing. Once all schedules/questionnaires have been transcribed, the
frequency tables can be constructed straight from worksheet. Other
methods of manual transcription include adoption of sorting strips or cards.
In olden days, data entry and processing were made through mechanical
and semi auto-metric devices such as key punch using punch cards. The
arrival of computers has changed the data processing methodology
altogether.
12.7 Tabulation
The transcription of data can be used to summarize and arrange the data in
compact form for further analysis. The process is called tabulation. Thus,
tabulation is a process of summarizing raw data displaying them on compact
statistical tables for further analysis. It involves counting the number of
cases falling into each of the categories identified by the researcher.
Tabulation can be done manually or through the computer. The choice
depends upon the size and type of study, cost considerations, time
pressures and the availability of software packages. Manual tabulation is
suitable for small and simple studies.
12.7.1 Manual Tabulation
When data are transcribed in a classified form as per the planned scheme of
classification, category-wise totals can be extracted from the respective
columns of the work sheets. A simple frequency table counting the number
of “Yes” and “No” responses can be made easily by counting the “Y”
response column and “N” response column in the manual worksheet table
prepared earlier. This is a one-way frequency table and they are readily
inferred from the totals of each column in the work sheet. Sometimes the
researcher has to cross tabulate two variables, for instance, the age group
of vehicle owners. This requires a two-way classification and cannot be
inferred straight from any technical knowledge or skill. If one wants to
prepare a table showing the distribution of respondents by age, a tally sheet
showing the age groups horizontally is prepared. Tally marks are then made
for the respective group i.e., „vehicle owners‟, from each line of response in
the worksheet. After every four tally, the fifth tally is cut across the previous
four tallies. This represents a group of five items. This arrangement
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facilitates easy counting of each one of the class groups. Illustration of this
tally sheet is present below.
Age groups Tally marks No. of Responses
Below II 2
20 – 39 IIII IIII IIII IIII III 23
40 – 59 IIII IIII IIII 15
Above 59 IIII IIII 10
Total 50
Although manual tabulation is simple and easy to construct, it can be
tedious, slow and error-prone as responses increase.
Computerized tabulation is easy with the help of software packages. The
input requirement will be the column and row variables. The software
package then computes the number of records in each cell of three row
column categories. The most popular package is the Statistical package for
Social Science (SPSS). It is an integrated set of programs suitable for
analysis of social science data. This package contains programs for a wide
range of operations and analysis such as handling missing data, recording
variable information, simple descriptive analysis, cross tabulation,
multivariate analysis and non-parametric analysis.
12.8 Construction of Frequency Table
Frequency tables provide a “shorthand” summary of data. The importance of
presenting statistical data in tabular form needs no emphasis. Tables
facilitate comprehending masses of data at a glance; they conserve space
and reduce explanations and descriptions to a minimum. They give a visual
picture of relationships between variables and categories. They facilitate
summation of item and the detection of errors and omissions and provide a
basis for computations.
It is important to make a distinction between the general purpose tables and
specific tables. The general purpose tables are primary or reference tables
designed to include large amount of source data in convenient and
accessible form. The special purpose tables are analytical or derivate ones
that demonstrate significant relationships in the data or the results of
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statistical analysis. Tables in reports of government on population, vital
statistics, agriculture, industries etc., are of general purpose type. They
represent extensive repositories and statistical information. Special purpose
tables are found in monographs, research reports and articles and reused
as instruments of analysis. In research, we are primarily concerned with
special purpose.
12.9 Components of a Table
The major components of a table are:
A Heading:
(a) Table Number
(b) Title of the Table
(c) Designation of units
B Body
1 Sub-head, Heading of all rows or blocks of stub items
2 Body-head: Headings of all columns or main captions and their sub-
captions.
3 Field/body: The cells in rows and columns.
C Notations:
Footnotes, wherever applicable.
Source, wherever applicable.
12.10 Principles of Table Construction
There are certain generally accepted principles of rules relating to
construction of tables. They are:
1. Every table should have a title. The tile should represent a succinct
description of the contents of the table. It should be clear and concise.
It should be placed above the body of the table.
2. A number facilitating easy reference should identify every table. The
number can be centred above the title. The table numbers should run
in consecutive serial order. Alternatively tables in chapter 1 be
numbered as 1.1, 1.2, 1….., in chapter 2 as 2.1, 2.2, 2.3…. and so on.
3. The captions (or column headings) should be clear and brief.
4. The units of measurement under each heading must always be
indicated.
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5. Any explanatory footnotes concerning the table itself are placed
directly beneath the table and in order to obviate any possible
confusion with the textual footnotes such reference symbols as the
asterisk (*) DAGGER (+) and the like may be used.
6. If the data in a series of tables have been obtained from different
sources, it is ordinarily advisable to indicate the specific sources in a
place just below the table.
7. Usually lines separate columns from one another. Lines are always
drawn at the top and bottom of the table and below the captions.
8. The columns may be numbered to facilitate reference.
9. All column figures should be properly aligned. Decimal points and
“plus” or “minus” signs should be in perfect alignment.
10. Columns and rows that are to be compared with one another should
be brought closed together.
11. Totals of rows should be placed at the extreme right column and totals
of columns at the bottom.
12. In order to emphasize the relative significance of certain categories,
different kinds of type, spacing and identifications can be used.
13. The arrangement of the categories in a table may be chronological,
geographical, alphabetical or according to magnitude. Numerical
categories are usually arranged in descending order of magnitude.
14. Miscellaneous and exceptions items are generally placed in the last
row of the table.
15. Usually the larger number of items is listed vertically. This means that
a table‟s length is more than its width.
16. Abbreviations should be avoided whenever possible and ditto marks
should not be used in a table.
17. The table should be made as logical, clear, accurate and simple as
possible.
Text references should identify tables by number, rather than by such
expressions as “the table above” or “the following table”. Tables should not
exceed the page size by photo stating. Tables those are too wide for the
page may be turned sidewise, with the top facing the left margin or binding
of the script. Where tables should be placed in research report or thesis?
Some writers place both special purpose and general purpose tables in an
appendix and refer to them in the text by numbers. This practice has the
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disadvantages of inconveniencing the reader who wants to study the
tabulated data as the text is read. A more appropriate procedure is to place
special purpose tables in the text and primary tables, if needed at all, in an
appendix.
12.11 Frequency Distribution and Class Intervals
Variables that are classified according to magnitude or size are often
arranged in the form of a frequency table. In constructing this table, it is
necessary to determine the number of class intervals to be used and the
size of the class intervals.
A distinction is usually made between continuous and discrete variables. A
continuous variable has an unlimited number of possible values between the
lowest and highest with no gaps or breaks. Examples of continuous variable
are age, weight, temperature etc. A discrete variable can have a series of
specified values with no possibility of values between these points. Each
value of a discrete variable is distinct and separate. Examples of discrete
variables are gender of persons (male/female) occupation (salaried,
business, profession) car size (800cc, 1000cc, 1200cc)
In practice, all variables are treated as discrete units, the continuous
variables being stated in some discrete unit size according to the needs of a
particular situation. For example, length is described in discrete units of
millimetres or a tenth of an inch.
Class Intervals: Ordinarily, the number of class intervals may not be less
than 5 not more than 15, depending on the nature of the data and the
number of cases being studied. After noting the highest and lower values
and the feature of the data, the number of intervals can be easily
determined.
For many types of data, it is desirable to have class intervals of uniform size.
The intervals should neither be too small nor too large. Whenever possible,
the intervals should represent common and convenient numerical divisions
such as 5 or 10, rather than odd division such as 3 to 7. Class intervals must
be clearly designated in a frequency table in such a way as to obviate any
possibility of misinterpretation of confusion. For example, to present the age
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group of a population, the use of intervals of 1-20, 20-50, and 50 and above
would be confusing. This may be presented as 1-20, 21-50, and above 50.
Every class interval has a mid point. For example, the midpoint of an interval
1-20 is 10.5 and the midpoint of class interval 1-25 would be 13. Once class
intervals are determined, it is routine work to count the number of cases that
fall in each interval.
One-Way Tables: One-way frequency tables present the distribution of
cases on only a single dimension or variable. For example, the distribution
of respondents of gender, by religion, socio economic status and the like are
shown in one way tables (Table 10.1) lustrates one-way tables. One way
tables are rarely used since the result of frequency distributions can be
described in simple sentences. For instance, the gender distribution of a
sample study may be described as “The sample data represents 58% by
males and 42% of the sample are females.”
Tow-Way Table: Distributions in terms of two or more variables and the
relationship between the two variables are show in two-way table. The
categories of one variable are presented one below another, on the left
margin of the table those of another variable at the upper part of the table,
one by the side of another. The cells represent particular combination of
both variables. To compare the distributions of cases, raw numbers are
converted into percentages based on the number of cases in each category.
(Table 10.2) illustrate two-way tables.
TABLE 10.2
Category
Members
Extent of participation
Low
No. of
Respon-
dents
%
Medium
No. of
Respon-
dents
%
High
No. of
Respon-
dents
%
Total
Ordinary
Committee
65
4
41.9
10.3
83
33
56.8
84.6
2
2
1.3
5.1
115
39
Another method of constructing a two-way table is to state the percent of
representation as a within brackets term rather than as a separate column.
Here, special care has been taken as to how the percentages are
calculated, either on a horizontal representation of data or as vertical
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representation of data. Sometimes, the table heading itself provides a
meaning as to the method of representation in the two-way table.
12.12 Graphs, Charts & Diagrams
In presenting the data of frequency distributions and statistical
computations, it is often desirable to use appropriate forms of graphic
presentations. In additions to tabular forms, graphic presentation involves
use of graphics, charts and other pictorial devices such as diagrams. These
forms and devices reduce large masses of statistical data to a form that can
be quickly understood at the glance. The meaning of figures in tabular form
may be difficult for the mind to grasp or retain. “Properly constructed graphs
and charts relieve the mind of burdensome details by portraying facts
concisely, logically and simply.” They, by emphasizing new and significant
relationship, are also useful in discovering new facts and in developing
hypothesis.
The device of graphic presentation is particularly useful when the
prospective readers are non-technical people or general public. It is useful
to even technical people for dramatizing certain points about data; for
important points can be more effectively captured in pictures than in tables.
However, graphic forms are not substitutes for tables, but are additional
tools for the researcher to emphasize the research findings.
Graphic presentation must be planned with utmost care and diligence.
Graphic forms used should be simple, clear and accurate and also be
appropriate to the data. In planning this work, the following questions must
be considered.
(a) What is the purpose of the diagram?
(b) What facts are to be emphasized?
Economic
Status
Democratic Participation
Low Medium High Total
Low
Medium
High
Very High
6(35.3)
13(38.2)
6(62.5)
2(33.3)
11(64.7)
18(53.0)
10(62.5)
3(50.0)
0(0.0)
3(8.8)
0(0.0)
1(16.7)
17
34
16
6
Total 27 42 4 73
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(c) What is the educational level of the audience?
(d) How much time is available for the preparation of the diagram?
(e) What kind of chart will portray the data most clearly and accurately?
12.12.1 Types of Graphs and General Rules
The most commonly used graphic forms may be grouped into the following
categories:
a) Line Graphs or Charts
b) Bar Charts
c) Segmental presentations.
d) Scatter plots
e) Bubble charts
f) Stock plots
g) Pictographs
h) Chesnokov Faces
The general rules to be followed in graphic representations are:
1. The chart should have a title placed directly above the chart.
2. The title should be clear, concise and simple and should describe the
nature of the data presented.
3. Numerical data upon which the chart is based should be presented in
an accompanying table.
4. The horizontal line measures time or independent variable and the
vertical line the measured variable.
5. Measurements proceed from left to right on the horizontal line and
from bottom to top on the vertical.
6. Each curve or bar on the chart should be labelled.
7. If there are more than one curves or bar, they should be clearly
differentiated from one another by distinct patterns or colours.
8. The zero point should always be represented and the scale intervals
should be equal.
9. Graphic forms should be used sparingly. Too many forms detract
rather than illuminating the presentation.
10. Graphic forms should follow and not precede the related textual
discussion.
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12.12.2 Line Graphs
The line graph is useful for showing changes in data relationship over a
period of time. In this graph, figures are plotted in relation to two intersecting
lines or axes. The horizontal line is called the abscissa or X-axis and the
vertical, the ordinal or Y-axis. The point at which the two axes intersect is
zero for both X and Y axis. The „O‟ is the origin of coordinates. The two lines
divide the region of the plane into four sections known as quadrants that are
numbered anti-clockwise. Measurements to the right and above „O‟ are
positive (plus) and measurements to the left and below „O‟ are negative
(minus). is an illustration of the features of a rectangular coordinate type of
graph. Any point of plane of the two axes is plotted in terms of the two axes
reading from the origin „O‟. Scale intervals in both the axes should be equal.
If a part of the scale is omitted, a set of parallel jagged lines should be used
to indicate the break in the scale. The time dimension or independent
variable is represented by the X-axis and the other variable by Y-axis.
12.13 Quantitative and Qualitative Analysis
12.13.1 Measures of Central Tendency
Analysis of data involves understanding of the characteristics of the data.
The following are the important characteristics of a statistical data: -
Central tendency
Dispersion
Skew ness
Kurtosis
In a data distribution, the individual items may have a tendency to come to a
central position or an average value. For instance, in a mark distribution,
the individual students may score marks between zero and hundred. In this
distribution, many students may score marks, which are near to the average
marks, i.e. 50. Such a tendency of the data to concentrate to the central
position of the distribution is called central tendency. Central tendency of
the data is measured by statistical averages. Averages are classified into
two groups.
1. Mathematical averages
2. Positional averages
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Statistical Averages
Mathematical averages Positional averages
Arithmetic mean Median
Geometric mean Mode
Harmonic mean
Arithmetic mean, geometric mean and harmonic mean are mathematical
averages. Median and mode are positional averages. These statistical
measures try to understand how individual values in a distribution
concentrate to a central value like average. If the values of distribution
approximately come near to the average value, we conclude that the
distribution has central tendency.
Arithmetic Mean
Arithmetic mean is the most commonly used statistical average. It is the
value obtained by dividing the sum of the item by the number of items in a
series. Symbolically we say
Arithmetic mean = X/n
Where X = the sum of the item
N = the number of items in the series.
If x1 x2 x3… xn are the values of a series, then arithmetic mean of the series
obtained by
(x1 + x2 + x3… +xn) / n. If put (x1 + x2 + x3… +xn) = X,
then arithmetic mean = X/n
When frequencies are also given with the values, to calculate arithmetic
mean, the values are first multiplied with the corresponding frequency. Then
their sum is divided by the number of frequency. Thus in a discrete series,
arithmetic mean is calculated by the following formula.
Arithmetic mean = fx/ f
Where, fx = sum the values multiplied by the corresponding
frequency.
f = sum of the frequency
If x1 x2 x3… xn are the values of a series, and f1 f2 f3… fn are their
corresponding frequencies,
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Arithmetic mean is calculated by (f1 x1 + f2 x2 + f3x3… + fn xn) / (f1 + f2 + f3… +
fn) or
Arithmetic mean = fx / f
Individual series
1. Find arithmetic mean of the following data.
58 67 60 84 93 98 100
Arithmetic mean = X/n
Where X = the sum of the item
n = the number of items in the series.
X = 58 + 67+ 60 + 84 + 93 + 98 + 100 = 560
n = 7
X = 560/7 = 80
2. Find arithmetic mean for the following distribution
2.0 1.8 2.0 2.0 1.9 2.0 1.8 2.3 2.5 2.3
1.9 2.2 2.0 2.3
Arithmetic mean = X/n
Where X = the sum of the item
n = the number of items in the series.
X = 2.0 + 1.8 + 2.0 + 2.0+ 1.9 + 2.0 + 1.8 + 2.3 + 2.5 + 2.3 + 1.9 +
2.2 + 2.0 + 2.3 = 29
n = 14
X = 29/14 = 2.07
Discrete series
3. Calculate arithmetic mean of the following 50 workers according to their
daily wages.
Daily wage : 15 18 20 25 30 35 40 42
Numbers of workers : 2 3 5 10 12 10 5 2
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Arithmetic mean using direct formula
Wages (x) Frequency ( F ) fx
15 2 30
18 3 54
20 5 100
25 10 250
30 12 360
35 10 350
40 5 200
42 2 84
45 1 45
f =50 fx =473
Arithmetic mean = fx/ f
Where, fx = 473
f = 0
Arithmetic mean = 1473 /50
29.46
Continuous Series
4. Find arithmetic mean for the following distribution.
Marks : 10-20 20-30 30-40 40-50 50-60 60-70 70-80 80-90
No. of students : 6 12 18 20 20 14 8 2
Arithmetic mean using direct method
Marks Frequency (f) Mid Value (x) fx
10-20 6 15 90
20-30 12 25 300
30-40 18 35 630
40-50 20 45 900
50-60 20 55 1100
60-70 14 65 910
70-80 8 75 600
80-90 2 85 170
f =100 fx = 4700
Arithmetic mean = fx/ f
Where, fx = 4700
f = 100
Arithmetic mean = 4700 / 100
= 47
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Geometric Mean
Geometric mean is defined as the nth root of the product of N items of a
series. If there are two items in the data, we take the square root; if there
are three items we take the cube root, and so on.
Symbolically,
GM = n21 ...x.xxn
Where x1, x2. ..xn are the items of the given series. To simplify calculations,
logarithms are used.
Accordingly,
GM = Anti log of (log x /n)
In discrete series
GM = Anti log of f . log x / f
Illustration
1. Find Geometric mean for the following data.
25 279 112 3675 84 9 18 54 73 648
Values (x) Log x
25 1.3979
279 2.4456
112 2.0492
3675 3.5652
84 1.9242
9 0.9542
18 1.2552
54 1.7323
73 1.8633
648 2.8116
19.9986
GM = Anti log of (log x /n)
= Anti log of (19.9986 / 10)
= Anti log of 1.9986
= 99.967
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Geometric mean for discrete series
Calculate geometric mean of the following data given below:-
Class No. of families Income
Landlords 1 100
Cultivators 50 80
Landless labourers 25 40
Money lenders 2 750
Scholl teachers 3 100
Shop keepers 4 150
Carpenters 3 120
Weavers 5 60
Income Frequency Log x f. Log x
1000 1 3.0000 3.0000
80 50 1.9031 95.1550
40 25 1.6021 40.0525
750 2 2.8751 5.7502
100 3 2.0000 6.0000
150 4 2.1761 8.7044
120 3 2.0792 6.2376
60 5 1.7782 8.8910
93 173.7907
GM = Anti log of f. log x / f
= Anti log of 173.7907 / 93
= Anti log 1. 86871
= 73.91
Harmonic Mean
In individual series
HM = N / (1/x)
In discrete series
HM = N / f (1/m)
N = Total frequency
M = Mi values of the class
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Illustration
For individual series
1. Find harmonic mean of the following data
5 10 3 7 125 58 47 80 45 26
Values x Factorial 1/x
5 .2
10 .1
3 .33
7 .14
125 .008
58 .017
47 .021
80 .014
45 .022
26 .038
( 1/x) =.89
HM = N / (1/x)
HM = 10 / .89
= 11.235
Harmonic mean for discrete series
Compute harmonic mean for the following data
Marks : 10 20 25 30 40 50
Frequency : 20 10 15 25 10 20
Marks Frequency 1/x f. 1/x
10 20 .1 2.0
20 10 .05 .5
25 15 .04 .6
30 25 .033 .83
40 10 .025 .25
50 20 .02 .4
f = 100 f (1/x) = 4.58
HM = N / f (1/x)
HM = 100/4.58
= 21.834
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Harmonic mean for continuous series
1. Calculate harmonic mean for the given data.
Class : 10-20 20-30 30-40 40-50 50-60 60-70
Frequency : 5 7 3 15 12 8
Class Frequency Mid x 1/x F . 1/x
10-20 5 15 .0661 .33
20-30 7 25 .04 .28
30-40 3 35 .0285 .085
40-50 15 45 .0222 .333
50-60 12 55 .0181 .218
50-60 8 65 .0153 .123
f =50 f ( 1/x) =1.369
HM = N / (1/x)
HM = 50 / 1.369 = 37.8689
Median
Median is the middlemost item of a given series. In individual series, we
arrange the given data according to ascending or descending order and
take the middlemost item as the median. When two values occur in the
middle, we take the average of these two values as median. Since median
is the central value of an ordered distribution, there occur equal number of
values to the left and right of the median.
Individual series
Median = (N+ 1 / 2) th item
Illustration
1. Find the median of the following scores.
97 50 95 51 90 60 85 64 81
65 80 70 75
First we arrange the series according to ascending order.
50 51 60 64 65 70 75 80 81
85 90 95 97
Median = (N+ 1) / 2 th item
= (13+ 1) / 2 th item
= (14 / 2) th item
= (7) th item
= 75
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Median for distribution with even number of items
2. Find the median of the following data.
95 51 91 60 90 64 85 69 80
70 78 75
First we arrange the series according to ascending order.
51 60 64 69 70 75 78 80 85
90 91 95
Median = (N+ 1) / 2 th item
= (12+ 1) / 2 th item
= (13 / 2) th item
= (6.5) th item
= (6th item + 7th item) / 2
= (75 + 78) / 2
= 153/2
= 76.5
Median for Discrete Series
To find the median of a grouped series, we first of all, cumulate the
frequencies. Locate median at the size of (N+ 1) / 2 th cumulative frequency.
N is the cumulative frequency taken.
Steps
1. Arrange the values of the data in ascending order of magnitude.
2. Find out cumulative frequencies
3. Apply the formula (N+ 1) / 2 th item
4. Look at the cumulative frequency column and find the value of the
variable corresponding to the above.
Find median for the following data.
Income : 100 150 80 200 250 180
Number of persons : 24 26 16 20 6 30
First of all arrange the data according to ascending order.
Income Frequency Cum. Frequency
80 16 16
100 24 40
150 26 (N+ 1) / 2 66
180 30 96
200 20 116
250 6 122
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Median = (N+ 1) / 2 th item
= (122+ 1) / 2 th item
= (123) / 2 th item
= (61.5) th item
= Value at the 61.5 cumulative frequency is taken as median
Therefore Median = 150
Median for Continuous Series
To find the median of a grouped series, with class interval, we first of all,
cumulate the frequencies. Locate median at the size of (N) / 2 th cumulative
frequency. Apply the interpolation formula to obtain the median
Median = L1 + (N/2 – m) / f X C
L1 = Lower limit of the median Class
N/2 = Cumulative frequency/ 2
m = Cumulative frequency of the class preceding the median class
f = frequency of the median class
C = Class interval
Find median of the following data.
Class : 12-14 15-17 18-20 21-23 24-26
Frequency : 1 3 8 2 6
Class Frequency CF
12-14 1 1
15-17 3 4
18-20 8 12 (N/2 = 10)
21-23 2 14
24-26 6 20
Median = L1 + (N/2 – m) / f X C
L1 = 18
N/2 = 10
m = 4
f = 8
C = 2
= 18+ (10 – 4) / 8 X 2
= 18 + 6/8 X 2
= 18 + (12/8)
= 18 + 1.5
= 19.5
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Merits of Median
1. Median is easy to calculate and simple to understand.
2. When the data is very large median is the most convenient measure of
central tendency.
3. Median is useful finding average for data with open-ended classes.
4. The median distributes the values of the data equally to either side of
the median.
5. Median is not influenced by the extreme values present in the data.
6. Value of the median can be graphically determined.
Demerits of Median
To calculate median, data should be arranged according to ascending
order. This is tedious when the number of items in a series is numerous.
Since the value of median is determined by observation, it is not a true
representative of all the values.
Median is not amenable to further algebraic treatment.
The value of median is affected by sampling fluctuation.
Mode
Mode is the most repeating value of a distribution. When one item repeats
more number of times than other or when two items repeat equal number of
times, mode is ill defined. Under such case, mode is calculated by the
formula (3 median – 2 mean).
Mode is a widely used measure of central tendency in business. We speak
of model wage which is the wage earned by most of the workers. Model
shoe size is the mostly demanded shoe.
Merits of Mode
Mode is the most typical and frequented value of the distribution.
It is not affected by extreme values.
Mode can be determined even for series with open-ended classes.
Mode can be graphically determined.
Demerits of Mode
1. It is difficult to calculate mode when one item repeats more number of
times than others.
2. Mode is not capable of further algebraic treatment.
3. Mode is not based on all the items of the series.
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4. Mode is not rigidly defined. There are several formulae for calculating
mode.
Mode for Individual Series
1. Calculation of mode for the following data.
7 10 8 5 8 6 8 9
Since item 8 repeats more number of times. Therefore mode = 8
Calculation of mode when mode is ill defined.
2. Calculation of mode for the following data.
15 25 14 18 21 16 19 20
Since no item repeats more number of times mode is ill defined.
Mode = (3 median – 2 mean)
Mean = 18.5
Median = (18 +19)/2
= 18.5
Mode = (3 X 18.5) – (2 X 18.5)
= 55.5 – 36.5 = 19
Mode for Discrete data Series
In discrete series the item with highest frequency is taken as mode.
3. Find mode for the following data.
Size of shirt No. of persons
28 10
29 20
30 40
31 65
32 50
33 15
34 5
Since 65 is the highest frequency its size is taken as mode
Mode = 31
Calculation of Mode Using Grouping Table and Analysis Table
To make Grouping Table
1. Group the frequency in two
2. Frequencies are grouped in two leaving the first frequency.
3. Group the frequency in three
4. Frequencies are grouped in three leaving the first frequency.
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5. Frequencies are grouped in three leaving the first and second
frequency.
To make Analysis Table
1. Analysis table is made based on grouping table.
2. Circle the highest value of each column.
3. Assign marks to classes, which constitute the highest value of the
column.
4. Count the number of marks.
5. The class with the highest marks is selected as the model class.
6. Apply the interpolation formula and find the mode.
Mode = L1 + (f1 – f0 / 2f1-f0-f2) X C
L1 = Lower limit of the model class
f1 = frequency of the model class
f0 = frequency of the class preceding the model class
f2 = frequency of the class succeeding the model class
C = class interval
Illustration
Find mode for the following data using grouping table and analysis table.
Expenditure 0-20 20-40 40-60 60-80 80-100 100-120 120-140
No. of families 14 15 27 13 12 17 2
Grouping Table
Class Frequency I II III IV V
0-20 14
20-40 15 29
40-60 27 42 56
60-80 13 40 55
80-100 12 25 52
100-120 17 29 42
120-140 2 29 31
Steps
1. In column I, the frequencies are grouped in two
2. In column II, frequencies are grouped in two, leaving the first frequency.
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3. In column III, frequencies are grouped in three
4. In column IV frequencies are grouped in three, leaving the first
frequency.
5. In column V frequencies are grouped in three, leaving the first and
second frequency.
Analysis Table
Class Frequency I II III IV V Total
0-20 14 I 1
20-40 15 I I I 3
40-60 27 I I I I I 5
60-80 13 I I I 4
80-100 12 I 1
100-120 17 0
120-140 2 0
Since highest mark is 5 and is obtained by the class 40-60.
Therefore model class = 40-60
Mode is calculated by the formula
Mode = L1 + (f1 – f0) / (2f1-f0-f2) X C
L1 = Lower limit of the model class = 40
f1 = frequency of the model class = 27
f0 = frequency of the class preceding the model class = 15
f2 = frequency of the class succeeding the model class = 13
C = class interval = 20
Mode = 40 + (27 – 15) / (2 X 27 –15-13) X 20
= 40 + (12/ 54-28) 20
= 40 + (12/ 26) 20
= 40 + (.4615) 20
= 40 + 9.23
= 49.23
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Dispersion
Dispersion is the tendency of the individual values in a distribution to spread
away from the average. Many economic variables like income, wage etc.,
are widely varied from the mean. Dispersion is a statistical measure, which
understands the degree of variation of items from the average.
Objectives of Measuring Dispersion
Study of dispersion is needed to:
1. To test the reliability of the average
2. To control variability of the data
3. To enable comparison with two or more distribution with regard to their
variability
4. To facilitate the use of other statistical measures.
Measures of dispersion points out as to how far the average value is
representative of the individual items. If the dispersion value is small, the
average tends to closely represent the individual values and it is reliable.
When dispersion is large, the average is not a typical representative value.
Measures of dispersion are useful to control the cause of variation. In
industrial production, efficient operation requires control of quality variation.
Measures of variation enable comparison of two or more series with regard
to their variability. A high degree of variation would mean little consistency
and low degree of variation would mean high consistency.
Properties of a Good Measure of Dispersion
A good measure of dispersion should be simple to understand.
1. It should be easy to calculate
2. It should be rigidly defined
3. It should be based on all the values of a distribution
4. It should be amenable to further statistical and algebraic treatment.
5. It should have sampling stability
6. It should not be unduly affected by extreme values.
Measures of Dispersion
1. Range
2. Quartile deviation
3. Mean deviation
4. Standard deviation
5. Lorenz curve
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Range, Quartile deviation, Mean deviation and Standard deviation are
mathematical measures of dispersion. Lorenz curve is a graphical measure
of dispersion.
Measures of dispersion can be absolute or relative. An absolute measure of
dispersion is expressed in the same unit of the original data. When two sets
of data are expressed in different units, relative measures of dispersion are
used for comparison. A relative measure of dispersion is the ratio of
absolute measure to an appropriate average.
The following are the important relative measures of dispersion.
1. Coefficient of range
2. Coefficient of Quartile deviation
3. Coefficient of Mean deviation
4. Coefficient of Standard deviation
Range
Range is the difference between the lowest and the highest value.
Symbolically, range = highest value – lowest value
Range = H – L
H = highest value
L = lowest value
Relative measure of dispersion is co-efficient of range. It is obtained by the
following formula.
Coefficient of range = (H – L) / (H + L)
1. Calculate of range of the following distribution, giving income of 10
workers. Also calculate the co-efficient of range.
25 37 40 23 58 75 89 20 81 95
Range = H – L
H = highest value = 95
L = lowest value = 20
Range = 95 –20 = 75
Coefficient of range = (H – L) / (H + L)
= (95 –20) / (95 +20)
= 75/ 115
= .6521
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Range is simple to understand and easy to calculate. But it is not based on
all items of the distribution. It is subject to fluctuations from sample to
sample. Range cannot be calculated for open-ended series.
Quartile Deviation
Quartile deviation is defined as inter quartile range. It is based on the first
and the third quartile of a distribution. When a distribution is divided into four
equal parts, we obtain four quartiles, Q1, Q2, Q3 and Q4.
First quartile Q1 is point of the distribution where 25% of the items of the
distribution lie below Q1, and 75% of the items of the distribution lie above
the Q1. Q2 is the median of the distribution, where 50% of the items of the
distribution lie below Q2, and 50% of the items of the distribution lie above
the Q2. Third quartile Q3 is point of the distribution where 75% of the items of
the distribution lie below Q3, and 25% of the items of the distribution lie
above the Q3.
Quartile deviation is based on the difference between the third and first
quartiles. So quartile deviation is defined as the inter-quartile range.
Symbolically, inter-quartile range = Q3- Q1
Quartile Deviation = (Q3- Q1) / 2
Co-efficient of Quartile Deviation = (Q3- Q1) / (Q3 + Q1)
Merits of Quartile Deviation
1. Quartile Deviation is superior to range as a rough measure of
dispersion.
2. It has a special merit in measuring dispersion in open-ended series.
3. Quartile Deviation is not affected by extreme values.
Demerits of Quartile Deviation
1. Quartile Deviation ignores the first 25% of the distribution below Q1 and
25% of the distribution above the Q3.
2. Quartile Deviation is not amenable to further mathematical treatment.
3. Quartile Deviation is very much affected by sampling fluctuations.
Problems
Individual Series
1. Find the Quartile Deviation and its co-efficient.
20 58 40 12 30 15 50
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First of all arrange the data according to ascending order.
12 15 20 28 30 40 50
Q1 = Size of (N+1) / 4 th item
= Size of (7+1) / 4 th item
= Size of (8 / 4) th item
= 2nd item
= 15
Q3 = Size of 3(N+1) / 4 th item
= Size of 3 X (7+1) / 4 th item
= Size of 3 X 8 / 4 th item
= (3 X 2) nd item
= 6th item
= 40
Co-efficient of Quartile Deviation = (Q3- Q1) / (Q3 + Q1)
= (40- 15) / (40+ 15)
= 25/55
= .4545
Discrete Series
2. Find quartile Deviation and its co-efficient for the following data.
Income : 110 120 130 140 150 160 170 180 190 200
Frequency: 50 45 40 35 30 25 20 15 10 5
Income Frequency CF
110 50 50
120 45 95 (N+1) / 4 th item = 69 = 120
130 40 135
140 35 170
150 30 200
160 25 225 3(N+1) / 4 th item = 207 =160
170 20 245
180 15 260
190 10 270
200 5 275
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Q1 = Size of (N+1) / 4 th item
= Size of (275+1) / 4 th item
= Size of (276 / 4) th item
= size of 69th cumulative frequency
= 120
Q3 = Size of 3(N+1) / 4 th item
= Size of 3 X (275 +1) / 4 th item
= Size of 3 X69 th item
= Size of 207th cumulative frequency
= 160
Quartile Deviation = (160 –120) /2
= 40/2
= 20
Co-efficient of Quartile Deviation = (Q3- Q1) / (Q3 + Q1)
= (160- 120 / (160+ 120)
= 20/280
= .0714
Continuous Series
Find quartile deviation for the following series
Marks : 0-20 20-40 40-60 60-80 80-100
Frequency : 10 30 36 30 14
Income Frequency CF
0-20 10 10
20-40 30 40 (N) / 4 th class = 20- 40
40-60 36 76
60-80 30 106 3(N) / 4 th class = 60-80
80-100 14 120
Q1 = lies in (N) / 4 th class
= lies in (120) / 4 th class
= lies in (30) th cumulative frequency class
= lies in 20- 40
Q1 can be obtained by applying the interpolation formula
= L1 + (N/4) – m / f X C
= 20 + (30 – 10) / 30 X 20
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= 20 + 20/ 30 X 20
= 20 + 400/30
= 20 + 13.33
= 33.33
Q3 = lies in 3(30)th cumulative frequency class
= lies in 60-80 class
Q3 can be obtained by applying the interpolation formula
= L1 + 3 (N/4) – m / f X C
= 60 + (90 – 76) / 30 X 20
= 60 + (14/ 30) X 20
= 60 + 280/30
= 60 + 9.33
= 69.33
Quartile Deviation = (Q3- Q1) /2
= (69.33 –33.33) 2
= 36/2
= 18
Co-efficient of Quartile Deviation = (Q3- Q1) / (Q3 + Q1)
= (69.33 –33.33) / (69.33 + 33.33)
= 36/ 102.66
= .3505
Mean Deviation
Range and quartile deviation do not show any scatter ness from the
average. However, mean deviation and standard deviation help us to
achieve the dispersion.
Mean deviation is the average of the deviations of the items in a distribution
from an appropriate average. Thus, we calculate mean deviation from
mean, median or mode. Theoretically, mean deviation from median has an
advantage because sum of deviations of items from median is the minimum
when signs are ignored. However, in practice, mean deviation from mean is
frequently used. That is why it is commonly called as mean deviation.
Formula for calculating mean deviation = ΣD/N
Where
ΣD = sum of the deviation of the items from mean, median or mode
N = number of items
D is mode less meaning values or deviation is taken without signs.
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Steps
1. Calculate mean, median or mode of the series
2. Find the deviation of items from the mean, median or mode
3. Sum the deviations and obtain ΣD
4. Take the average of the deviations ΣD/N, which is the mean deviation.
The co- efficient of mean deviation is the relative measure of mean
deviation. It is obtained by dividing the mean deviation by a particular
measure of average used for measuring mean deviation.
If mean deviation is obtained from median, the co-efficient of mean deviation
is obtained by dividing mean deviation by median.
The co-efficient of mean deviation = mean deviation / median
If mean deviation is obtained from mean, the co-efficient of mean deviation
is obtained by dividing mean deviation by mean.
The co-efficient of mean deviation = mean deviation / mean
If mean deviation is obtained from mode, the co-efficient of mean deviation
is obtained by dividing mean deviation by mode.
The co-efficient of mean deviation = mean deviation / mode
Problems
Calculate mean deviation for the following data from mean
Daily wages : 15 18 20 25 30 35 40 42 45
Frequency : 2 3 5 10 12 10 5 2 1
Daily wages
Frequency f. x D =x-20 Fd
15 2 30 5 10
18 3 54 2 6
20 5 100 0 0
25 10 250 5 50
30 12 360 10 120
35 10 350 15 150
40 5 200 20 100
42 2 84 22 44
45 1 45 25 25
50 1473 505
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Mean = 1473/50
= 20
Mean deviation = Σ f D / N
= 505/50
= 10.1
The co-efficient of mean deviation = mean deviation / mean
= 10.1 /20
= .505
Continuous series
The procedure remains the same. The only difference is that we have to
obtain the midpoints of the various classes and take deviations of these
midpoints. The deviations are multiplied by their corresponding frequencies.
The value so obtained is added and its average is the mean deviation.
Calculate mean deviation for the following data.
Class : 5-10 10-15 15-20 20-25 25-30 30-35 35-40 40-45
Frequency : 6 5 15 10 5 4 3 2
Class Frequency Mid x d fd D = x-28.8 FD
5-10 6 7.5 – 15 – 90 21.5 127.8
10-15 5 12.5 – 10 – 50 16.3 81.5
15-20 15 17.5 – 5 – 75 11.3 169.5
20-25 10 (22.5) 0 0 6.3 63
25-30 5 27.5 5 25 1.3 6.5
30-35 4 32.5 10 40 3.7 14.8
35-40 3 37.5 15 45 8.7 26.1
40-45 2 42.5 20 40 13.7 27.4
50 -65 516.6
Arithmetic mean = A + Σ fx / ΣF
= 22.5 + 65/50
= 22.5 +1.3
= 28.8
Mean deviation from mean = Σ f D / N
= 516.6/50
= 10.332
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The co-efficient of mean deviation = mean deviation / mean
= 10.332 / 28.8
= .3762
Mean deviation from median
To find median
Class Frequency CF Midx D = X- 17
5-10 6 6 7.5 9.5 57
10-15 5 11 12.5 4.5 22.5
15-20 15 26 (N/2) = 25 17.5 .5 7.5
20-25 10 36 22.5 5.5 55
25-30 5 41 27.5 10.5 52.5
30-35 4 45 32.5 15.5 62
35-40 3 48 37.5 20.5 61.5
40-45 2 50 42.5 25.5 51
50 369
Median = L1 + (n/2 – m/f) C
= 15 + 25 – 11/ 15 X 5
= 15 + 6/15 X 5
= 15 + 30/15
= 15 + 2
= 17
Mean deviation from median = Σ f D / N
= 369/50
= 7.38
The co-efficient of mean deviation = mean deviation / median
= 7.38/17
= .434
Mean deviation from mode = model class 15-20
= L1 + (f1-f0 / 2 f1-f0-f2) C
= 15 + (15-5 / 2X15-5-10) X 5
= 15 + (10 / 30-5-10) X 5
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= 15 + (10 / 15) X 5
= 15 + 3.33
= 18.33
Class Frequency Mid x D = X – 18.33 fD
5-10 6 7.5 10.83 64.98
10-15 5 12.5 5.83 29.15
15-20 15 17.5 .83 12.45
20-25 10 22.5 4.17 41.7
25-30 5 27.5 9.17 45.85
30-35 4 32.5 14.17 56.68
35-40 3 37.5 19.17 57.57
40-45 2 42.5 24.17 48.34
50 356.72
Mean deviation from mode = Σ f D / N
= 356.72/50
= 7.13
The co-efficient of mean deviation = mean deviation / mode
= 7.16/18.3
= .3912
Merits of Mean Deviation
1. Mean deviation is simple to understand and easy to calculate
2. It is based on each and every item of the distribution
3. It is less affected by the values of extreme items compared to standard
deviation.
4. Since deviations are taken from a central value, comparison about
formation of different distribution can be easily made.
Demerits of Mean Deviation
1. Algebraic signs are ignored while taking the deviations of the items.
2. Mean deviation gives the best result when it is calculated from median.
But median is not a satisfactory measure when variability is very high.
3. Various methods give different results.
4. It is not capable of further mathematical treatment.
5. It is rarely used for sociological studies.
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Standard deviation
Standard deviation is the most important measure of dispersion. It satisfies
most of the properties of a good measure of dispersion. It was introduced by
Karl Pearson in 1893. Standard deviation is defined as the mean of the
squared deviations from the arithmetic mean. Standard deviation is denoted
by the Greek letter
Mean deviation and standard deviation are calculated from deviation of each
and every item. Standard deviation is different from mean deviation in two
respects. First of all, algebraic signs are ignored in calculating mean
deviation. Secondly, signs are taken into account in calculating standard
deviation whereas, mean deviation can be found from mean, median or
mode. Whereas, standard deviation is found only from mean.
Standard deviation can be computed in two methods
1. Taking deviation from actual mean
2. Taking deviation from assumed mean.
Formula for finding standard deviation is (x-x)2 / N
Steps
1. Calculate the actual mean of the series x / N
2. Take deviation of the items from the mean ( x-x)
3. Find the square of the deviation from actual mean -x)2 / N
4. Sum the squares of the deviations ( x-x)2
5. Find the average of the squares of the deviations ( x-x)2 / N
6. Take the square root of the average of the sum of the deviation
Problems
1. Calculate the standard deviation of the following data
49 50 65 58 42 60 51 48 68 59
Standard deviation from actual mean
Arithmetic mean = x / N
= 550 /10
= 55
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Values (x-55) (x-55)2
49 -6 36
50 -5 25
65 10 100
58 3 9
42 -13 169
60 5 25
51 -4 16
48 -7 49
68 13 169
59 4 16
550 (x-x)2 614
S.D = (x-x) 2 / N
= 614 /10
= 61.4
= 7.836
Standard deviation from assumed mean
Assumed mean = 50
Values (x-50) (x-55)2
49 -1 1
50 0 0
65 15 225
58 8 64
42 -8 64
60 10 100
51 1 1
48 -2 4
68 18 324
59 9 81
550 ( x-x) = 50 (x-x)2 =864
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S.D = (x-x) 2 / N - {(x-x) / N} 2
= 864 /10 – 50/10
= 86.4 - 52
= 81.4 - 25
= 61.4
= 7.836
Discrete Series
Standard deviation can be obtained by three methods.
1. Direct method
2. Short cut method
3. Step deviation
Direct method
Under this method formula is
S.D = (fx) 2 / N - {(fx) / N}2
Calculate standard deviation for the following frequency distribution.
Marks : 20 30 40 50 60 70
Frequency : 8 12 20 10 6 4
Marks Frequency X2 fx Fx2
20 8 400 160 3200
30 12 900 360 10800
40 20 1600 800 32000
50 10 2500 500 25000
60 6 3600 360 21600
70 4 4900 280 19600
60 2460 112200
S.D = (FX) 2 / N – {(FX) / N} 2
= 112200/60 – {2460 / 60}2
= 1870 – 2
= 1870 – 1681
= 189
= 13.747
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12.13.3 Correlation Analysis
Economic and business variables are related. For instance, demand and
supply of a commodity is related to its price. Demand for a commodity
increases as price falls. Demand for a commodity decreases as its price
rises. We say demand and price are inversely related or negatively
correlated. But sellers supply more of a commodity when its price rises.
Supply of the commodity decreases when its price falls. We say supply and
price are directly related or positively co-related. Thus, correlation indicates
the relationship between two such variables in which changes in the value of
one variable is accompanies with a change in the value of other variable.
According to L.R. Connor, “if two or more quantities vary in sympathy so that
movements in the one tend to be accompanied by corresponding
movements in the other(s) they are said to be correlated”.
W.I. King defined “Correlation means that between two series or groups of
data, there exists some casual connection”.
The definitions make it clear that the term correlation refers to the study of
relationship between two or more variables. Correlation is a statistical
device, which studies the relationship between two variables. If two
variables are said to be correlated, change in the value of one variable
result in a corresponding change in the value of other variable. Heights and
weights of a group of people, age of husbands and wives etc., are examples
of bi-variant data that change together.
Correlation and Causation
Although, the term correlation is used in the sense of mutual dependence of
two or more variable, it is not always necessary that they have cause and
effect relation. Even a high degree of correlation between two variables
does not necessarily indicate a cause and effect relationship between them.
Correlation between two variables can be due to following reasons:-
(a) Cause and effect relationship: Heat and temperature are cause and
effect variable. Heat is the cause of temperature. Higher the heat, higher
will be the temperature.
(b) Both the correlated variables are being affected by a third variable. For
instance, price of rice and price of sugar are affected by rainfall. Here
there may not be any cause and effect relation between price of rice and
price of sugar.
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(c) Related variable may be mutually affecting each other so that none of
them is either a cause or an effect. Demand may be the result of price.
There are cases when price rise due to increased demand.
(d) The correlation may be due to chance. For instance, a small sample
may show correlation between wages and productivity. That is, higher
wage leading to lower productivity. In real life it need not be true. Such
correlation is due to chance.
(e) There might be a situation of nonsense or spurious correlation between
two variables. For instance, relationship between number of divorces
and television exports may be correlated. There cannot be any
relationship between divorce and exports of television.
The above points make it clear that correlation is only a statistical
relationship and it does not necessarily signify a cause and effect
relationship between the variables.
Types of Correlation Analysis
Correlation can be:
Positive or negative
Linear or non-linear
Simple, multiple or partial
Positive and Negative Correlation
When values of two variables move in the same direction, correlation is said
to be positive. When prices rise, supply increases and when prices fall
supply decreases. In this case, an increase in the value of one variable on
an average, results in an increase in the value of other variable or decrease
in the value on one variable on an average results in the decrease in the
value of other variable.
If on the other hand, values of two variables move in the opposite direction,
correlation is said to be negative. When prices rise, demand decreases and
when prices fall demand increases. In this case, an increase in the value of
one variable on an average results in a decrease in the value of other
variable.
Linear and Non-Linear Correlation
When the change in one variable leads to a constant ratio of change in the
other variable, correlation is said to be linear. In case on linear correlation,
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points of correlation plotted on a graph will give a straight line. Correlation is
said to be non-linear when the change in one variable is not accompanied
by a constant ratio of change in the other variable. In case of non-linear
correlation, points of correlation plotted on a graph do not give a straight
line. It is called curvilinear correlation because graph of such correlation
results in a curve.
Simple, Partial and Multiple Correlations
Simple correlation studies relationship between two variables only. For
instance, correlation between price and demand is simple as only two
variables are studied in this case. Multiple correlation studies relationship of
one variable with many variables. For instance, correlation of agricultural
production with rainfall, fertilizer use and seed quality is a multiple
correlation. Partial correlation studies the relationship of a variable with one
of the many variables with which it is related. For instance, seed quality,
temperature and rainfall are three variables, which determine yield of a crop.
In this case, yield and rainfall is a partial correlation.
Utility of Correlation
Study of correlation is of immense practical use in business and economics.
Correlation analysis enables us to measure the magnitude of
relationship existing between variables under study.
Once we establish correlation, we can estimate the value of one variable
on the basis of the other. This is done with the help of regression
equations.
The correlation study is useful for formulation of economic policies. In
economics, we are interested in finding the important dependant
variables on the basis of independent variable.
Correlation study helps us to make relatively more dependable forecasts
Methods of Studying Correlation
Following methods are used in the study of correlation:
Scatter diagram
Karl Pearson method of Correlation
Spearman‟s Rank correlation method
Concurrent Deviation method
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Scatter Diagram
This is a graphical method of studying correlation between two variables. In
scatter diagram, one variable is measured on the x-axis and the other is
measured on the y-axis of the graph. Each pair of values is plotted on the
graph by means of dot marks. If plotted points do not show any trend, two
variables are not correlated. If the trend shows upward rising movement,
correlation is positive. If the trend is downward sloping, correlation is
negative.
Karl Pearson’s Co-Efficient of Correlation
Karl Pearson‟s Co-Efficient of Correlation is a mathematical method for
measuring correlation. Karl Pearson developed the correlation from the
covariance between two sets of variables. Karl Pearson‟s Co-Efficient of
Correlation is denoted by symbol r. The formula for obtaining Karl Pearson‟s
Co-Efficient of Correlation is:
Direct method
SDyx,SD
yandxbetw eenCovariancer
Covariance between x and y = xy / N – (x/N X y/N)
SDx = standard deviation of x series = (x2 / N) – (x/N) 2
SDy = standard deviation of y series = (y2 / N) – (y/N) 2
Shortcut Method using Assumed Mean
If short cut method is used using assumed mean, the formula for obtaining
Karl Pearson‟s Co-Efficient of Correlation is:
Covariance between x and y = dxdy / N – (dx/N X dy/N)
SDx = (dx2 / N) – (dx /N) 2
SDy = (dy2 / N) – (dy /N) 2
N)/dy( - N)/dy( N)/dx( - N)/dx(
Ndy / x Ndx / (Ndxdy / r
2222
Steps in calculating Karl Pearson‟s Correlation Coefficient using Shortcut
Method
Assume means of x and y series
Take deviations of x and y series from assumed mean and get dx and
dy
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Square the dx and dy and find the sum of squares and get dx2 and
dy2.
Multiply the corresponding deviations of x and y series and total the
products to get dxdy.
If the deviations are taken from the arithmetic mean dx = 0 and dy =0
and the formula becomes
22 dydx
dxdyr
Shortcut Method using Arithmetic Mean
If short cut method is used using actual mean, the formula for obtaining Karl
Pearson‟s Co-Efficient of Correlation is:
22 dydx
dydxr
Interpreting Co-Efficient of Correlation
The Co-Efficient of Correlation measures the correlation between two
variables. The value of Co-Efficient of Correlation always lies between +1
and –1. It can be interpreted in the following ways.
If the value of Co-Efficient of Correlation r is 1 it is interpreted as perfect
positive correlation.
If the value of Co-Efficient of Correlation r is –1, it is interpreted as perfect
negative correlation.
If the value of Co-Efficient of Correlation r is 0 < r < 0.5, it is interpreted as
poor positive correlation.
If the value of Co-Efficient of Correlation r is 0.5 < r < 1, it is interpreted as
good positive correlation.
If the value of Co-Efficient of Correlation r is 0 > r > -0.5, it is interpreted as
poor negative correlation.
If the value of Co-Efficient of Correlation r is –0.5 > r > -1, it is interpreted as
good negative correlation.
If the value of Co-Efficient of Correlation r is 0, it is interpreted as zero
correlation.
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Probable Error
Probable Error of Correlation coefficient is estimated to find out the extent to
which the value of r is dependable. If Probable Error is added to or
subtracted from the correlation coefficient, it would give such limits within
which we can reasonably expect the value of correlation to vary.
If the coefficient of correlation is less than Probable Error it will not be
significant. If the coefficient of correlation r is more than six times the
Probable Error, correlation is definitely significant. If Probable Error is 0.5 or
more, it is generally considered as significant. Probable Error is estimated
by the following formula
PE = 0.6745 (1- r2/ N)
12.13.4 Coefficient of Determination
Besides probable error, another important method of interpreting coefficient
of correlation is the Coefficient of Determination. Coefficient of
Determination is the square of correlation or r2. For instance, suppose the
coefficient of correlation between price and supply is 0.8. We calculate the
coefficient of determination as r2, which is .82 or .64. It means that 64% of
the variation in supply is on account of changes in price.
Spearman’s Rank Correlation Method
Charles Edward Spearman, a British psychologist devised a method for
measuring correlation between two variables based on ranks given to the
observations. This method is adopted when the variables are not capable of
quantitative measurements like intelligence, beauty etc. in such cases, it is
impossible to assign numerical values for change taking place in such
variables. It is in such cases rank correlation is useful.
Spearman‟s rank correlation coefficient is given by
rk = 1- 6 D2 / n (n2-1)
Where D is the difference between ranks and n, number of pairs correlated.
Concurrent Deviation Method
In this method, correlation is calculated between direction of deviations and
not their magnitudes. As such only the direction of deviations is taken into
account in the calculation of this coefficient and their magnitude is ignored.
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The formula for the calculation of coefficient of concurrent deviations is
given below:
rc = +- 2C-n / n
Steps in the Calculation of Concurrent Deviation
Find out the direction of change of x-variable. When a successive figure
in the series increase direction is marked as + and when a successive
figure in the series decrease direction of change is marked as -. It is
denoted as dx.
Find out the change in direction of y-variable. It is denoted as dy.
Multiply dx and dy and determine the value of C. C is the number of
positive products of dxdy
(- X - or + X +).
Use the formula rc = +- 2C-n / nto obtain the value of coefficient of
rc.
Problems
1. Calculate Karl Pearson‟s co-efficient of correlation for the following data.
X : 43 44 46 40 44 42 45 42 38 40 42 57
Y : 29 31 19 18 19 27 27 29 41 30 26 10
X Y dx dy dx2 Dy2 dxdy
43 29 3 - 1 9 1 3
44 31 4 1 16 1 4
46 19 6 -11 36 121 -66
A(40) 18 0 -12 0 144 0
44 19 4 -11 16 121 -44
42 27 2 -3 4 9 -6
45 27 5 -3 25 9 -15
42 29 2 -1 4 1
38 41 -2 11 4 121 -22
40 A(30) 0 0 0 0 0
42 26 2 -4 4 16 -8
57 10 17 -20 289 400 -340
43 54 407 944 494
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Direct method
yx SDSD
yandxbetw eenCovariancer
Covariance between x and y = xy / N - (x/N X y/N)
Dx = standard deviation of x series = (x2 / N) - (x/N) 2
Dy = standard deviation of y series = (y2 / N) - (y/N) 2
Shortcut Method using Assumed Mean
If short cut method is used using assumed mean, the formula for obtaining
Karl Pearson‟s Co-Efficient of Correlation is:
yx DD
yandxbetw eenCovariancer
Covariance between x and y = dxdy / N - (dx/N X dy/N)
Dx = (dx2 / N) - (dx /N) 2
Dy = (dy2 / N) - (dy /N) 2
N)/dy( - N)/dy( N)/dx( - N)/dx(
N)dy / x Ndx / (Ndxdy / r
2222
dxdy = 494
N = 12
dx = 43
dy = 54
dx2 = 407
dy2 = 944
22 )12/54(12/944)12/43(12/407
54/12) X (43/12 494/12
20.2578.6612.91 - 33.96
4.5) (3.58 - 41.17
58.4121.09
16.1141.16
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7.64
25.05
35.08
25.05
= 0.714
Interpretation: There is good positive correlation between x and y variable.
Self Assessment Questions
State whether the following statements are true or false:
1. Coding need not necessarily be numeric
2. A mere tabulation or frequency count or graphical representation of the
variable may be given an alphabetic coding.
3. A coding of zero has to be assigned carefully to a variable.
12.14 Summary
Data processing is an intermediary stage of work between data collections
and data interpretation. The various steps in processing of data may be
stated as:
o Identifying the data structures
o Editing the data
o Coding and classifying the data
o Transcription of data
o Tabulation of data.
The identification of the nodal points and the relationships among the nodes
could sometimes be a complex task than estimated. When the task is
complex, which involves several types of instruments being collected for the
same research question, the procedures for drawing the data structure
would involve a series of steps. Data editing happens at two stages, one at
the time of recording the data and second at the time of analysis of data. All
editing and cleaning steps are documented, so that the redefinition of
variables or later analytical modification requirements could be easily
incorporated into the data sets. The editing step checks for the
completeness, accuracy and uniformity of the data set created by the
researcher. The edited data are then subject to codification and
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classification. Coding process assigns numerals or other symbols to the
several responses of the data set. It is therefore a pre-requisite to prepare a
coding scheme for the data set. The recording of the data is done on the
basis of this coding scheme.
Numeric Coding: Coding need not necessarily be numeric. It can also
be alphabetic. Coding has to be compulsorily numeric, when the variable
is subject to further parametric analysis.
Alphabetic Coding: A mere tabulation or frequency count or graphical
representation of the variable may be given an alphabetic coding.
Zero Coding: A coding of zero has to be assigned carefully to a
variable.
The transcription of data can be used to summarize and arrange the data in
compact form for further analysis. Computerized tabulation is easy with the
help of software packages. Frequency tables provide a “shorthand”
summary of data. The importance of presenting statistical data in tabular
form needs no emphasis. The major components of a table are:
o A Heading:
o Table Number
o Title of the Table
o Designation of units
o B Body
o Stub-head, Heading of all rows or blocks of sub items
o Body-head: Headings of all columns or main captions and their sub-
captions.
o Field/body: The cells in rows and columns.
o C Notations:
o Footnotes, wherever applicable.
o Source, wherever applicable.
Variables that are classified according to magnitude or size are often
arranged in the form of a frequency table. In constructing this table, it is
necessary to determine the number of class intervals to be used and the
size of the class intervals. The most commonly used graphic forms may be
grouped into the following categories:
o Line Graphs or Charts
o Bar Charts
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o Segmental presentations.
o Scatter plots
o Bubble charts
o Stock plots
o Pictographs
o Chesnokov Faces
12.15 Terminal Questions
1. What are the various steps in processing of data?
2. How is Data Editing is done at the Time of Recording of Data
3. What are types of Coding?
4. What is data Classification?
5. What is Transcription of Data?
6. Explain the methods of Transcription:
7. Explain the Construction of Frequency Table
8. What are the Components of a Table?
9. What are the principles of Table Construction?
10. What are the fundamentals of Frequency Distribution?
11. Explain the role of Graphs and diagrams
12. What are the Types and General Rules for graphical representation of
data?
13. What are Line Graphs?
12.16 Answers to SAQs and TQs
SAQs
1. True
2. True
3. True
TQs
1. Section 12.1 to Section 12.3.2
2. Section 12.3.1
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3. Section 12.4
4. section 12.5
5. Section 12.6
6. Section 12.6.1 to Section 12.6.2
7. Section 12.11
8. Section 12.9
9. Section 12.10
10. Section 12.11
11. Section 12.12
12. Section 12.12.1
13. Section 12.12.2
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Unit 13 Research Report Writing
Structure:
13.1 Meaning of Research Reports
Objectives
13.1.1 Purpose of Research Report
13.1.2 Characteristics of Research Report
13.1.3 Functions of Research Report
13.2 Types of Research Report
13.2.1 Technical Report
13.2.2 Popular Report
13.2.3 Interim Report
13.2.4 Summary Reports
13.2.5 Research Abstract
13.2.6 Research Articles
13.3 Contents of Reports
13.4 Styles of Reporting
13.4.1 Communicate To Specific Audience
13.4.2 Structure the Presentation
13.4.3 Create Audience Interest
13.4.4 Be Specific and Visual
13.4.5 Address Validity and Reliability Issues
13.5 Steps in Drafting Reports
13.6 Editing the Final Draft
13.7 Evaluating the Final Drafts
Self Assessment Questions
13.8 Summary
13.9 Terminal Questions
13.10 Answers To SAQ’s And TQ’s
13.1 Meaning of Research Report
Research report is a means for communicating research experience to
others. A research report is a formal statement of the research process and
it results. It narrates the problem studied, methods used for studying it and
the findings and conclusions of the study.
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Objectives:
After learning this lesson you should be able to understand:
Purpose of Research Report
Characteristics of Research Report
Functions of Research Report
Types of Research Report
Contents of Reports
Styles of Reporting
Steps in Drafting Reports
Editing the Final Draft
Evaluating the Final Drafts
13.1.1 Purpose of Research Report
The purpose of the research report is to communicate to interested persons
the methodology and the results of the study in such a manner as to enable
them to understand the research process and to determine its validity. The
aim is not to convince but to convey what was done, why and what was its
outcome.
13.1.2 Characteristics of Research Report
Research report is a narrative and authoritative document on the outcome of
a research effort. It represents highly specific information for a clearly
designated audience. It is simple, readable and accurate form of
communication.
13.1.3 Functions of Research Report
It serves as a means for presenting the problem studied, methods and
techniques used for collecting and analyzing data, findings and conclusions
and recommendations. It serves as a basic reference material for future use.
It is a means for judging the quality of research project.
It is a means for evaluating researcher’s competency.
It provides a systematic knowledge on problems and issues analyzed.
13.2 Types of Research Report
Research reports can be classified as:
Technical reports
Popular reports
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Summary reports
Research abstract
Research article
These differ in terms of the degree of formality, physical form, scope, style
and size.
13.2.1 Technical Reports
In a technical report a comprehensive full report of the research process and
its outcome are included. It covers all the aspects of the research process. A
description of the problem studied, the objectives of the study, method and
techniques used, a detailed account of sampling filed and other research
procedures, sources of data, tools for data collection, methods of data
processing and analysis, detailed findings and conclusions and suggestion.
13.2.2 Popular Reports
In popular report the reader is less interested in the methodological details,
but more interested in the findings of the study. Complicated statistics are
avoided and pictorial devices are used. After a brief introduction to the
problem and the objectives of the study, an abstract of the findings of the
study, conclusion and recommendations are presented. More headline,
underlining pictures and graphs may be used. Sentences and paragraphs
should be short.
13.2.3 Interim Report
When there is a time lag between data collection and presentation of the
result, the study may lose significance and usefulness. An interim report in
such case can narrate what has been done so far and what was its
outcome. It presents a summary of the findings of that part of analysis which
has been completed.
13.2.4 Summary Reports
Summary report is meant for lay audience i.e., the general pubic. It is written
in non-technical, simple language with pictorial charts that just contains
objectives, findings and its implications. It is a short report of two to three
pages.
13.2.5 Research Abstract
Research abstract is a short summary of technical report. It is prepared by a
doctoral student on the eve of submitting his thesis. It contains a brief
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presentation of the statement of the problem, the objectives of the study,
methods and techniques used and an overview of the report. A brief
summary of the results of the study may also be used.
13.2.6 Research Article
Research article is designed for publication in a professional journal. A
research article must be clearly written in concise unambiguous language. It
must be logically organized. Progression from a statement of a problem and
purpose of the study, through analysis of evidence to the conclusions and
implications are given in the report.
13.3 Contents of the Research Report
The outline of a research report is given below:
I. Prefatory Items
Title page
Declaration
Certificates
Preface/acknowledgements
Table of contents
List of tables
List of graphs/figures/charts
Abstract or synopsis
II. Body of the Report
Introduction
Theoretical background of the topic
Statement of the problem
Review of literature
The scope of the study
The objectives of the study
Hypothesis to be tested
Definition of the concepts
Models if any
Design of the study
Methodology
Method of data collection
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Sources of data
Sampling plan
Data collection instruments
Field work
Data processing and analysis plan
Overview of the report
Limitation of the study
Results: findings and discussions
Summary, conclusions and recommendations
III. Reference Material
Bibliography
Appendix
Copies of data collection instruments
Technical details on sampling plan
Complex tables
Glossary of new terms used.
13.4 Styles of Reporting
13.4.1 Communicate to a Specific Audience
The first step is to know the audience, its background, and its objectives.
Most effective presentations seem live conversations or memos to a
particular person as opposed to an amorphous group. Audience
identification affects presentation decisions such as selecting the material to
be included and the level of presentation. Excessive detail or material
presented at too low a level can be boring. The audience can become
irritated when material perceived as relevant is excluded or the material is
presented at too high level. In an oral presentation, the presenter can ask
audience whether they already know some of the material.
Frequently, a presentation must be addressed to two or more different
audiences. There are ways to deal with such a problem. In a written
presentation, an executive summary at the outset can provide an overview
of the conclusions for the benefit of those in the audience who are not
interested in details. The presentation must respect the audience’s time
constraints. An appendix can be used to reach some people selectively,
without distracting the others. Sometimes introduction to a chapter or a
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section can convey the nature of the contents, which certain audiences may
bypass. In an oral presentation, the presence of multiple audiences should
be recognized.
13.4.2 Structure the Presentation
Each piece of presentation should fit into the whole, just as individual pieces
fit into a jigsaw puzzle. The audience should not be muttering. The solution
to this is to provide a well-defined structure. The structure should include an
introduction, a body, and a summary. Further, each of the major sections
should be structured similarly. The precept is to tell the audience what you
are going to say, say it and then tell them what you said. Sometimes you
want to withhold the conclusion to create interest.
Introduction should play several roles. First, it should provide audience
interest. A second function is to identify the presentation’s central idea or
objective. Third, it should provide a road map to the rest of the presentation
so that the audience can picture its organisation and flow.
It is better to divide the body of the presentation into two to five parts. The
audience will be able to absorb only so much information. If that information
can be aggregated into chunks, it will be easier to assimilate. Sometimes
the points to be made cannot be combined easily or naturally. In that case, it
is necessary to use a longer list. One way to structure the presentation is by
the research questions. Another method that is often useful when presenting
the research proposal is to base it on the research process. The most useful
presentations will include a statement of implications and recommendations
relevant to the research purpose. However, when researcher lacks
information about the total situation because the research study addresses
only a limited aspect of it, the ability to generate recommendations may be
limited.
The purpose of the presentation summary is to identify and underline the
important points of the presentations and to provide some repetition of their
content. The summary should support the presentation communication
objectives by helping the audience to retain the key parts of the content. The
audience should feel that there is a natural flow from one section to another.
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13.4.3 Create Audience Interest
The audience should be motivated to read or listen to the presentation’s
major parts and to the individual elements of each section the audience
should know why the presentation is relevant to them and why each section
was included. A section that cannot hold interest should be excluded or
relegated to appendix.
The research purpose and objectives are good vehicles to provide
motivation. The research purpose should specify decisions to be made and
should relate to the research questions. A presentation that focuses on
those research questions and their associated hypothesis will naturally be
tied to relevant decisions and hold audience interest. In contrast, a
presentation that attempts to report on all the questions that were included
in the survey and in the cross-tabulations often will be long, uninteresting
and of little value.
As the analysis proceeds and presentation is being prepared, the
researcher should be on the lookout for results that are exceptionally
persuasive, relevant, interesting, and unusual. Sometimes, the deviant
respondent with strange answers can provide the most insight in his or her
responses that are pursued and not discarded.
13.4.4 Be Specific and Visual
Avoid taking or writing in the abstract. If different members of the audience
have different or vague understandings of important concepts, there is a
potential problem. Terms that are ambiguous or not well known should be
defined and illustrated or else omitted. The most interesting presentations
usually use specific stories, anecdotes, studies, or incidents to make points.
13.4.5 Address Validity and Reliability Issues
The presentation should help the audience avoid misinterpreting the results.
The wording of the questions, the order in which they are asked, and the
sampling design are among the design dimensions that can lead to biased
results and misinterpretations. The presentation should not include an
exhaustive description of all the design considerations. Nobody is interested
in a textbook discussion of the advantages of telephone over mail surveys,
or how you locate homes in an area sampling design.
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The presentation should include some indication of the reliability of the
results. At the minimum, it always should be clear what sample size was
involved. The key results should be supported by more precise information
in the form of interval estimates or a hypothesis test. The hypothesis test
basically indicates, given the sample size, what probability exists that the
results were merely an accident of sampling. If the probability of the latter is
not low, then the results probably would not be repeated. Do not imply more
precision than is warranted.
13.5 Steps in Drafting the Research Report
Along with the related skill of working with and motivating people, the ability
to communicate effectively is undoubtedly the most important attribute a
manager can have. Effective communication between research users and
research professional is extremely important to the research process. The
formal presentation usually plays a key role in the communication effort.
Generally, presentations are made twice during the research process. First,
there is the research proposal presentation. Second, there is the
presentation of the research results.
Guidelines for successful presentations
In general a presenter should:
Communicate to a specific audience.
Structure the presentation.
Create audience interest
Be specific and visual
Address validity and reliability issues
13.6 Editing the Final Draft
A research report requires clear organisation. Each chapter may be divided
into two or more sections with appropriate headings and in each section
margin headings and paragraph headings may be used to indicate subject
shifts. Physical presentation is another aspect of organisation. A page
should not be fully filled in from top to bottom. Wider margins should be
provided on both sides and on top and bottom as well.
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Centred section heading is provided in the centre of the page and is usually
in solid font size. It is separated from other textual material by two or three
line space.
Marginal heading is used for a subdivision in each section. It starts from the
left side margin without leaving any space.
Paragraph heading is used to head an important aspect of the subject
matter discussed in a subdivision. There is some space between the margin
and this heading.
Presentation should be free form spelling and grammar errors. If the writer is
not strong in grammar, get the manuscript corrected by a language expert.
Use the rules of punctuations.
Use present tense for presenting the findings of the study and for stating
generalizations.
Do not use masculine nouns and pronouns when the content refers to both
the genders. Do not abbreviate words in the text; spell out them in full.
Footnote citation is indicated by placing an index number, i.e., a superscript
or numeral, at the point of reference. Reference style should have a clear
format and used consistently.
13.7 Evaluating the Final Draft
The general guidelines discussed so far are applicable to both written and
oral presentations. However, it is important to generate a research report
that will be interesting to read. Most researchers are not trained in effective
report writing. In their enthusiasm for research, they often overlook the need
for a good writing style. In writing a report, long sentences should be
reconsidered and the critical main points should stand out.
Here are some hints for effective report writing.
Use main heading and subheadings to communicate the content of the
material discussed.
Use the present tense as much as possible to communicate information.
Whether the presentation is written or oral, use active voice construction
to make it lively and interesting, passive voice is wordy and dull.
Use computer-generated tables and graphs for effective presentations.
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Use informative headings.
Use double-sided presentation if possible. For example, tables or graphs
could be presented on the left side of an open report and their
descriptions on the right side.
Self Assessment Questions I
State whether the following statements are true or false:
1. Research report is a means for communicating research experience to
others.
2. The purpose of the research report is to communicate to interested
persons the methodology and the results of the study.
3. Research report is a narrative and authoritative document.
13.8 Summary
Research report is a means for communicating research experience to
others. The purpose of the research report is to communicate to interested
persons the methodology and the results of the study in such a manner as
to enable them to understand the research process and to determine its
validity. Research report is a narrative and authoritative document on the
outcome of a research effort. It represents highly specific information for a
clearly designated audience. It serves as a means for presenting the
problem studied, methods and techniques used for collecting and analyzing
data, findings and conclusions and recommendations. It serves as a basic
reference material for future use. It is a means for judging the quality of
research project. It is a means for evaluating researcher’s competency. It
provides a systematic knowledge on problems and issues analyzed. In a
technical report a comprehensive full report of the research process and its
outcome. It covers all the aspects of the research process. In popular report
the reader is less interested in the methodological details, but more
interested in the findings of the study. An interim report in such case can
narrate what has been done so far and what was its outcome. It presents a
summary of the findings of that part of analysis which has been completed.
Summary report is meant for lay audience i.e., the general pubic. It is written
in non-technical, simple language with pictorial charts it just contains
objectives, findings and its implications. It is a short report of two to three
pages. Research abstract is a short summary of technical report. It is
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prepared by a doctoral student on the eve of submitting his thesis. Research
article is designed for publication in a professional journal. A research article
must be clearly written in concise and unambiguous language.
13.9 Terminal Questions
1. What is a research report?
2. What are the contents of research report?
3. What are the types of research reports?
4. Draw an outline of research report.
13.10 Answers to SAQs and TQs
SAQs
1. True
2. True
3. True
TQs
1. Section 13.1
2. Section 13.2
3. Section 13.1
4. Section 13.3.
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Unit 14 Ethics in Research
Structure:
14.1 Introduction
Objectives
14.2 Meaning of Research Ethics
14.3 Ethical Issues in the Overall Research Process
14.4 Ethical Issues in Gaining Access to Participants
14.5 Ethical Issues in Data Collection
14.6 Ethical Issues related to Data Analysis & Reporting
14.7 Ethically Questionable Research Situations
14.8 Responsibility for Ethics in Research
Responsibilities of Clients
Responsibilities of Suppliers of Research
Self Assessment Questions
14.9 Summary
14.10 Terminal Questions
14.11 Answers to SAQs and TQs
14.1 Introduction
Apart from being well designed and accurate, one of the most important
characteristics of good research is that it should be conducted in an
appropriate manner that does not encroach upon the rights of the various
parties involved in the process. In other words, research should not have an
adverse impact – either on clients, respondents or on those conducting the
actual research. This final unit will begin by defining “ethics” in research and
will go on to emphasize that ethical research is the responsibility of both
clients and suppliers of research. The various types of ethical issues that
could arise during the different stages of the research process will also be
examined in detail.
Objectives:
After studying this unit, you should be able to :
Explain what is meant by ethical research
Describe the main ethical issues that could arise in the research process
Prepare a code of ethics for the conduct of research
Recognize how ethical research contributes to better quality research
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14.2 Meaning of Research Ethics
According to Mark Saunders, Philip Lewis and Adrian Thornhill (2003),
ethics in a research context refers to “the appropriateness of your behavior
in relation to the rights of those who become the subject of your work, or are
affected by it.” Wells (1994) defines ethics as “a code of behavior
appropriate to academics and the conduct of research.”
In simple words, ethics in research refers to whether a particular practice or
behavior is right or wrong. The appropriateness of behavior means that your
behavior as a researcher should be acceptable to those who are involved in
the research process. This in turn will depend on broad social norms, or the
type of behavior that is expected in a particular situation. A code of ethics is
essentially a set of guidelines and procedures to be followed when
conducting research. Every industry and profession has its own code of
ethics.
14.3 Ethical Issues in the Overall Research Process
Ethical issues in research may be broadly classified into 1) general issues
that may arise during any stage of the research process and 2) issues that
arise during a specific stage of the research process.
The most important ethical concern that may crop up across the various
stages of research is the invasion of privacy of participants or respondents
of a research study. Invasion of privacy is essentially a violation of any of
the following rights of respondents –
The right of respondents not to participate in a research study
The right to refuse to participate beyond a certain limit
Example – A respondent may refuse to participate in an interview beyond
an agreed duration or time limit.
The right to refuse to be contacted during unreasonable times of the day
Example – Respondents would not like to be contacted at their workplace
during working hours or late at night.
The right to refuse to answer any questions that are perceived to be
sensitive or of a confidential nature
Examples – A respondent may not want to reveal his/her monthly income or
expenditure.
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Some respondents may find questions related to religion or political ideology
to be too sensitive.
The right to retain their anonymity and the confidentiality of information
provided, especially when reporting the findings of the study
.
14.4 Ethical Issues in Gaining Access to Participants
The initial stage of trying to approach respondents to participate in a study is
the stage when ethical issues are bound to be most frequent.
Getting people to participate in a research project without their knowledge or
consent is clearly unethical. For example, a researcher may study rural
communities without their knowledge, in the fear that their awareness of the
study may affect their responses and behavior. However, getting the
consent of the participant to take part in a research study alone is not
sufficient. You may still deceive the participant by hiding the real purpose of
the study, or by not revealing that the information gathered from them will be
used for commercial purposes. This is where the concept of “informed
consent” comes in. Informed consent means that the participant gives
his/her consent freely, based on complete and accurate information
regarding the purpose of the study, the use of information gathered and
other aspects. Some of the other aspects of the research about which
participants need to be informed before getting their consent are –
The purpose of the research
The name of the person/organization that will be undertaking the
research
The size of the sample and the type of participants
The type of information that will be required to be gathered
The method of data collection (e.g., face to face interview, online
questionnaire, etc.)
The time required for gathering the data
The time frame for participation in the research
The rights of the participant, as listed in section 15.4
The use of data that will be gathered
The manner in which the findings of the research will be reported
The manner in which the anonymity and confidentiality of participants
will be guaranteed.
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14.5 Ethical Issues in Data Collection
A number of ethical issues may also arise during the data collection stage,
irrespective of the method used to gather data. A key issue during this
stage is to maintain objectivity. Objectivity means that you have to record
information without being selective or influencing the responses with your
own opinions and judgments. Lack of objectivity will lead to “interviewer
bias” and affect the accuracy of data.
Each method of data collection also gives rise to different ethical concerns.
For example, during face to face interviews, you should not force
participants to provide answers. The questions asked should also be
tactfully worded and should not come across as sensitive. The time should
be fixed depending on the convenience of the participant. In the case of
telephone interviews, the respondents should not be contacted at
“unreasonable times” of the day, as mentioned earlier.
When using observation as a method of data collection, care should be
taken not to invade the privacy of those being observed. For example, you
should not observe any behavior related to the private life of the
participants.
Similarly, when using qualitative research methods such as in-depth
interviews and projective techniques, researchers should take care not to
probe into the private lives of respondents or try to get information on
personal matters such as religion or political ideology.
Another ethical concern of a general nature includes the use of the Internet
to collect both primary and secondary data. A separate code of ethical use
of the Internet, popularly known as “netiquette” needs to be developed and
strictly followed for this purpose. While the internet may make it easier to
contact respondents more easily and repeatedly, it may also lead to greater
invasion of privacy.
One example of observing “netiquette” is to administer online surveys or
questionnaires via a website, rather than via email. The questionnaire may
be advertised on email and the respondents invited to fill in an online
questionnaire by accessing a website. This method ensures that
respondents retain their anonymity.
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14.6 Ethical Issues during the Data Analysis & Reporting stages
Being objective is a major ethical issue during the data analysis and
reporting stages as well and is a reflection of the honesty and integrity of the
researcher. This means that the statistical accuracy of the data gathered
should not be misrepresented. The researcher should also not be selective
in reporting only some of the data at the cost of other data. Such subjectivity
will distort the conclusions and recommendations made after the research
has been completed.
The issue of confidentiality and anonymity that was discussed earlier is also
equally important during this stage. Sometimes you may have to seek
permission from an organization before revealing their name while reporting
your findings.
This may involve explaining to them the context in which their name would
be used. The same caution needs to be exercised when naming particular
individuals is true of individuals
14.7 Ethically Questionable Research Situations
Research situations and practices that have a hidden or ulterior purpose
may be considered to be clearly unethical, since they are either
manipulated, involve invasion of privacy or deception of respondents or
clients. Some examples of such situations and practices are described
below –
Undertaking research dictated by top management, in order to arrive at
findings that have already been identified as desirable.
Deliberately using jargon or technical terms more than is needed to give
the reader the impression of being competent.
Pretending to do a survey when you are actually making a door to door
or telephone sales pitch.
Trying to extract information from someone by falsely stating that his or
her superior has authorized this.
Continuing a research study without revealing to the client that major
mistakes have been identified and costly corrections may be needed.
Obtaining information to compile mailing lists in the name of doing a
survey.
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Seeking the co-operation of respondents by promising to give feedback
on the research results and then not keeping up the promise.
Specifying certain techniques to be used in a study and then failing to
apply these techniques.
Using hidden tape recorders and other devices when conducting depth
interviews and other qualitative techniques to probe into respondents’
motivations.
Conducting research under a false or fictitious name, in order to obtain
information that would be difficult to get otherwise.
Accepting to undertake a research study, fully knowing that it cannot be
completed on time.
Including questions developed for one client’s questionnaire for another
client, without getting the permission of the first client.
14.8 Responsibility for Ethics in Research
The situations described above imply that the responsibility for ethical
research lies with three parties that are directly involved in the research
process – the client or manager, the supplier of research and the
respondents or the participants. Of these, the respondents’ respondents are
minimal, since they are only expected to be honest in their behavior and
responses. The responsibilities of the clients and suppliers of research are
described in detail below.
14.8.1 Responsibilities of Clients
The primary responsibility of clients or managers is to be honest with the
researcher, as well as with those to whom the findings of research are being
reported. Being honest with the researcher means - 1) not disguising the
real purpose of the study and 2) encouraging the researcher to be objective
in the process of gathering information. Objectivity in turn implies that the
researcher should refrain from expressing his or her own judgments while
recording responses or from interpreting the findings of the research in a
manner that suits his or her own interests.
Regarding honesty towards those to whom the findings of the study are
being disseminated, the client or manager should not deliberately distort the
results to his own advantage.
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Example
A study may reveal that 97% of respondents did not express their
preference for any particular brand, 2% preferred brand A and the remaining
1% preferred brand B. Based on these results, a marketer of a particular
brand of detergent A should not claim that a majority of respondents favored
brand A as compared to another brand B.
14.8.2 Responsibilities of Suppliers of Research
The bulk of the responsibility for ethical research lies with the researcher.
The researcher may be an individual or an organization, such as an
independent research firm that supplies research studies to client
companies. This is because it is the researcher who deals directly with
participants of a study as well as with clients. The researcher has
responsibilities towards all the parties involved in the research process,
including clients, respondents, competitors and society at large.
The primary responsibilities of the researcher towards clients are honesty,
integrity and confidentiality. For example, if the research can be done with
less money than what the client has available, it would be dishonest to
inflate the cost just to match the client’s budget. The same is true of time
constraints. Confidentiality means not revealing the findings of the research
to the client’s competitors.
The researcher’s responsibility towards respondents includes respecting
their time and their privacy. Researchers doing telephone surveys in
particular call respondents at odd hours of the day to obtain various kinds of
information. Some researchers even conduct fake surveys that deceive
respondents by delivering a sales pitch. This is tantamount to abuse of
respondents.
As emphasized earlier in this chapter, respondents have various rights,
including the right to choose not to participate in a study, the right to safety,
including the desire to remain anonymous and free from physical or
psychological harm and the right to be informed about the sponsor of the
study, its purpose and its impact on them as participants.
Regarding the researcher’s responsibilities towards competitors, the
researcher has to work within ethical limits. For example, ”espionage” or
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stealing product information from competitors is clearly unethical. Other
examples of situations that are unethical include –
Developing a strategy to distort the results of a competitor’s test
marketing experiment.
Hiring a competitor’s employee in order to gain access to competitor
information
Finally, researchers have an ethical responsibility to society at large. This
implies that it is up to researchers to build a positive image of research as a
profession in the eyes of the general public. This can best be achieved by
being honest and objective, both while conducting the research and when
disseminating the results to society at large. Failure to observe these ethical
principles will only lead to a negative attitude towards research by the
public.
Self Assessment Questions
Are the following statements true or false?
1. One of the rights of respondents is to refuse to be contacted over the
telephone.
2. Participants of a study should be informed about the sampling procedure
before getting their consent.
3. Filling in incomplete answers in a questionnaire is an example of lack of
objectivity.
4. Observation is not an ethical method of data collection.
5. Confidentiality implies that you may have to change the name of the
organization that was researched when reporting the findings.
6. Using cameras to observe respondent’s reactions to advertisements
is unethical.
7. The bulk of responsibility for ethical research lies with clients or
managers.
8. It is ethical for top management to modify the findings of a study to
highlight the strengths of the organization.
14.9 Summary
Ethics in the context of research refers to whether a researcher’s behavior is
appropriate and acceptable to all the parties that are involved in the
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research process. These include clients, participants of a study, competitors
and society at large.
Ethical issues in research may crop up during the overall research process
or at a specific stage of the research. Some of the ethical concerns that
arise during the overall process are with regard to the rights of respondents.
It is unethical to violate the rights of respondents such as the right to
privacy, the right to confidentiality and anonymity and the right to refuse to
participate in a study.
While trying to gain initial access to participants of a study, it is important to
get their informed consent. This means getting their consent to participate
based on complete information on various aspects of the research, including
the purpose of the study, the type of information that will be gathered, how it
will be gathered, how it will be reported and used, etc.
Regarding the data collection stage, each method of data collection gives
rise to different ethical concerns. While administering questions face to face,
care must be taken to avoid sensitive questions and to word questions
tactfully. Telephone interviewers must refrain from calling participants at odd
times of the day. While using observation and qualitative research
techniques, researchers should avoid probing into the private lives of
participants. Similarly, when using the internet to collect primary data,
researchers should not invade the privacy of respondents.
During the data analysis and reporting stages, the primary ethical concerns
are objectivity, confidentiality and anonymity. Objectivity means reporting
the statistical accuracy of the data and the findings of the study without
distorting them. Confidentiality and anonymity imply that the permission of
organizations or individuals would have to be sought before revealing their
names and identities.
Research situations and practices that are manipulated, have ulterior
motives, or try to deceive respondents are clearly unethical. The
responsibility for ethical research lies with respondents, clients and
researchers. However, respondents have minimal responsibilities for ethical
research, while researchers have the maximum number of responsibilities.
The responsibilities of respondents and clients include honesty –
respondents are expected to be honest while providing information, while
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clients need to be honest regarding the purpose of the research. The
researcher has ethical responsibilities towards clients, including honesty
regarding the cost and time involved in conducting the study and objectivity
in collecting, analyzing and reporting the data, Responsibilities toward
respondents include being honest and respecting their various rights.
Responsibilities towards competitors include avoiding practices such as
stealing confidential information. Finally, researchers also have
responsibilities towards society at large in terms of building a positive image
of the research profession.
14.10 Terminal Questions
1. Think of three different research questions that might be perceived by
respondents as sensitive or of a confidential nature and are therefore
unethical.
2. Explain with an example how a researcher can deceive participants
even after getting their consent to participate in a study.
3. Develop a code of ethics for use of the internet to conduct online
surveys, listing out the “do’s” and “don’t’ s”.
4. Give examples of two ethically questionable research situations, in
addition to what is mentioned in this unit.
5. Briefly describe three different ways in which a researcher can introduce
subjectivity into a study.
14.11 Answers to SAQs and TQs
SAQs
1. False
2. False
3. True
4. False
5. False
6. True
7. False
8. False
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TQs
1. Refer 14.3
2. Refer 14.4
3. Refer 14.5
4. Refer 14.7
5. Refer 14.8.1
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References:
1. Krishnaswamy O.R., Methodology of Research in Social Sciences,
Himalaya Publishing House, 1993
2. Saunders M., Lewis P. and Thornhill A., Research Methods for Business
Students, Pearson Education (Singapore), 2003.
3. R. Pannershelvam, Research Methodology, Prentice-Hall of India,
New Delhi, 2004.
4. P. L. Bhandarkar and T. S. Wilkinson, Methodology and Techniques of
Social Research, Himalaya Publishing House, Delhi.
5. Ackoff R. L., The Design of Social Research, Chicago, 1953.
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