mb0050-research methodology.pdf

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

_________

_________

_________

_________

_________

_________

_________

_________

_________

_________

______

_________

_________

_________

_________

_________

_________

_________

_________

_________

_________

______

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



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.



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.



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.



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.



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.



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



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,



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.



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.



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. 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?




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



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



2.1 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.”



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.



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.



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.



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



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.



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.



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.


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.



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



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.


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?


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



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


3.8 Summary



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.



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:




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



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.



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.



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



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.



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


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?



4. What is the classification of research problems?

5. What are the criteria of good research problem?


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



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


4.7 Summary



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,



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



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



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:



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.



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



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.



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



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.



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



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



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.




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



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.


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?


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



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


5.6 Summary



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



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:


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



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.



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



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.



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)



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:



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



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,



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.



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.



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



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.



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.


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.




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?


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



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


6.7 Summary



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.



Objectives:


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.



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



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



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.



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.


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?


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



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



7.1 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



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:


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.



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



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.



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.



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:



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.



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



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



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.



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.



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.



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.


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.



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.



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.


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?



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



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


8.7 Summary

8.8 Terminal questions


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:


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



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.



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.



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.



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.



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 ?



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.




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



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.


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?


SAQs

1. True

2. True

3. True

4. True



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



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


9.9 Summary



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:


General characteristics of observation method

Process of observation

Types of observation

Participant Observation

Non-participant observation



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.



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



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.



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.



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.



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.



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.



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.




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?


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



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


10.6 Summary



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



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:


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.



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.



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.



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.


Fill in the blanks

1. The response rate in mail surveys is generally very –––––––––––.

2. –––––––– can cover extensive geographical areas.



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



is filled by the respondent himself whereas the schedule is filled by the

investigator.


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


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



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




11.1 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:


Types of interviews

Structured Directive interview

Unstructured non-directive interview

Focused interview

Clinical interview

Depth interview

Approaches to the interview



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



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.



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.



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



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.



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.



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



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



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.



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?”



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



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



(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.



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.



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.



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



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.


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,



(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.




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?


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



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


12.14 Summary





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:


Checking for analysis

Editing

Coding

Classification

Transcription of data



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.



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.



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.



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



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



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



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



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



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



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



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



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.



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



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



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



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



(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.



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



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,



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



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



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



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


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


Where, fx = 4700

f = 100

Arithmetic mean = 4700 / 100

= 47



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



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



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



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



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


= (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




= (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



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.



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.



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.



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



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



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



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



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


= (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



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


= (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



= 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


= (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.



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 coefficient 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



Mean = 1473/50

= 20

Mean deviation = Σ f D / N

= 505/50

= 10.1


= 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




= 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



= 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.



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



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



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



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.



(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,



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



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



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.



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.



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



Direct method

yx SDSD


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


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



7.64

25.05

35.08

25.05

= 0.714

Interpretation: There is good positive correlation between x and y variable.



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



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



o Segmental presentations.

o Scatter plots

o Bubble charts

o Stock plots

o Pictographs

o Chesnokov Faces


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?


SAQs

1. True

2. True

3. True

TQs

1. Section 12.1 to Section 12.3.2

2. Section 12.3.1



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



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


13.8 Summary


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.



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



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



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


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



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



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.



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.



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.



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.



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.



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



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.


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.


SAQs

1. True

2. True

3. True

TQs

1. Section 13.1

2. Section 13.2

3. Section 13.1

4. Section 13.3.



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


14.9 Summary



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



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.



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.



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.



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.



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.



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



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.


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



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



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.


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.


SAQs

1. False

2. False

3. True

4. False

5. False

6. True

7. False

8. False



TQs

1. Refer 14.3

2. Refer 14.4

3. Refer 14.5

4. Refer 14.7

5. Refer 14.8.1



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.

––––––––––––––––––

mb0050-research methodology.pdf

Documents