CHAPTER I RESEARCH METHODOLOGY RESEARCH:...

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1 CHAPTER I RESEARCH METHODOLOGY RESEARCH: MEANING The word research is derived from the French word 'researcher' which means to seek again. Research is an activity directed at 'the systematic search for pertinent information on a topic'. Most of the early researches were directed at revealing the mysteries of nature. This process is still going on with greater vigor and emphasis. Thus, the urge to re-examine and re-understand the world around us rightly is called as research. Research is also defined as 'the systematic and objective analysis and recording of controlled observations that may lead to the development of generalizations, principles or theories, resulting in prediction and possible ultimate control of events'. DEFINITION 1. "Scientific research is systematic, controlled, empirical and critical investigation of hypothetical propositions about the presumed relations among natural phenomena" Kerlinger, Fred. N. 2. "A social research is the systematic method of discovering new facts or verifying old facts, their sequences, interrelationships, casual explanations, and the natural laws which govern them". P. V. Young.

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CHAPTER – I

RESEARCH METHODOLOGY

RESEARCH: MEANING

The word research is derived from the French word 'researcher' which means

to seek again. Research is an activity directed at 'the systematic search for pertinent

information on a topic'. Most of the early researches were directed at revealing the

mysteries of nature. This process is still going on with greater vigor and emphasis.

Thus, the urge to re-examine and re-understand the world around us rightly is

called as research. Research is also defined as 'the systematic and objective

analysis and recording of controlled observations that may lead to the development

of generalizations, principles or theories, resulting in prediction and possible

ultimate control of events'.

DEFINITION

1. "Scientific research is systematic, controlled, empirical and critical

investigation of hypothetical propositions about the presumed relations among

natural phenomena" — Kerlinger, Fred. N.

2. "A social research is the systematic method of discovering new facts or

verifying old facts, their sequences, interrelationships, casual explanations, and the

natural laws which govern them". — P. V. Young.

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CLASSIFICATION

There are two broad classifications of research viz. research m physical or

natural sciences and research in social sciences.

Social research attempts to discover facts concealed in a social phenomenon

or the law governing it. Social research is a tool used by social scientists to

understand the social processes, social values, ideas, reality, their interrelationship

and linkage with natural laws. As the social behaviour is affected by a large

number of environmental and biological factors besides the value system of the

society, social research, therefore, attempts to reveal the cause and effect

relationship existing in various social phenomena and various factors contributing

to change in the society.

CHARACTERISTICS OF SOCIAL RESEARCH

Following are the characteristic features of social research

1) Social research deals with the social phenomena.

2) Social research is carried on both for discovering new facts and verification

of the old ones.

3) Social research tries to establish causal connections between various

human activities.

4) Social research aims at discovering new facts.

5) Social research assists in the understanding of evolution of new theories.

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6) Social research is dynamic in nature.

7) Social research in any field is interrelated.

8) In social research interrelationships between variables under study is must.

9) Research in social science is complementary to research in physical sciences

and actually both branches of knowledge help each other and is the way to

progress.

SCOPE OF RESEARCH

The goal of research is to improve the level of living in society. The word

research carries an aura of respect.

Some of the questions asked most frequently by students in introductory

courses on social research are,

a) What do social researchers concern themselves with?

b) On what general assumptions do they base their researches?

c) Research for what?

If a question is added as to what method and techniques researchers use the

whole field of social research has to be unfolded. The field is broad and complex

the unfolding is slow, gradual and at times uncertain.

The scope of social research may be confined to any of the following fields, viz.

a) Explorations which provide new insights into organized society and social

structures.

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b) Researches which develop new horizons in scientific exploration, advances

and tests new principles or procedures and suggests new concepts.

c) Studies which attempt to test or challenge existing theories, and revise them

in the light of new evidence.

d) Studies which aim at collection and analysis of data more or less within the

existing frame of scientific theory and established techniques of exploration.

e) Studies of an experimental nature in which the systematic study of social life

is carried on under conditions of control and experiment.

Thus, the field of social research is virtually unlimited, and the materials of

research endless. Every group of social phenomena, every phase of social life,

every stage of past and present development is material for a social scientist.

SIGNIFICANCE OF RESEARCH

1) Research has an important role in guiding social planning. Knowledge of the

society and the cultural behaviour of people require proper planning for and

their well development because both knowledge and the cultural behaviour of

human beings are interdependent. A reliable as well as factual knowledge may

be most needed to take decisions for planning. This is possible by means of

research.

2) Knowledge is a kind of power with which one can foresee the implications of

a particular phenomenon. It also dispels the "rust" of old settings, superstitions

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etc. and light are thrown on them for welfare development Thus, social research

may have the effect of promoting better understanding and social cohesion.

3) Research is charged with the responsibility for effective functioning of facts.

Thus, it affords a considerably sound basis for prediction. Otherwise it leads to

failure bound programmes which may have a serious impact on society. For e.g.

Bhopal gas tragedy. Thus, prediction serves as a better control over the

phenomena and helps successful planning. This leads to cherished goals.

4) It is the role of the researcher to effect constant improvement in the

techniques of his trade i.e. Research. He works in spatial-temporal contexts.

Each challenge he faces forces him to perfect his techniques. In other words, the

technique of research has to attain greater perfection and so, big strides in

research leads to big technological changes. Examples are electronics,

industrialization etc.

TYPES OF RESEARCH

Social research has a wide area of application. Important types of social

research are: fundamental research, applied research, quasi-social research, action

research/arid policy research etc, Each of these types is briefly described here.

a) Fundamental Research

It aims at generating theoretical knowledge and understanding of the subject

matter. It pursues knowledge for the sake of knowledge. More often than not it is a

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unique intellectual exercise aimed at developing theoretical knowledge or

providing correct explanation of social phenomena. Its object may also be to verify

an existing theory or to establish a new theory or social law. It may also aim at

throwing additional light on an existing theory; reveal some of the missing links in

existing knowledge about some social aspect or experience.

b) Applied Research

Whereas fundamental research is theoretical, applied research is empirical

and practice based. It incorporates some value judgment and is based on utilitarian

approach. In applied research, the research findings are utilized for the solution of

some felt need or some immediate problem or to form a basis for laying down

certain guidelines for future operations. Applied research is based on fundamental

research. It uses the theoretical concepts, and tests them in real life situation.

Fundamental research forms, the building block for applied research. However, the

two cannot, be put into tight divisions, as they are complementary and one forms

the basis of the other.

c) Policy Research

Research having policy implications and application value is known as

policy research. Policy research is directed at identification of factors having

implications in organizational policy or for making changes in an existing policy.

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d) Action Research

The purpose of action research is to acquire new skills or new approaches to

solve certain problem. For e.g.: action research may he conducted to develop a

demonstration programme for testing a management technique. This type of

research is practice based and directly relevant to real life situations. This provides

a framework and a basis for problem solving and new developments. Action

research is concerned with results, and innovations through controlled

experimentation.

e) Quasi-social Research

Some problems in social sciences cannot be classified as belonging to an

exclusive area of knowledge. Rather, these are interdisciplinary in nature and

require close collaboration among researchers from more than one discipline. This

may include sometimes even physical sciences. This type of research is known as

quasi-social research. The research methodology in such cases is designed to suit

its multidisciplinary nature. Theories and law of different disciplines are used for

interactive reasoning and drawing conclusions.

RESEARCH METHODS

Research methods are basically concerned with observation of reality,

defining the problem and its dimensions, a planned approach towards analysis of

the problem, interpretation of information and drawing conclusions. There are so

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many systems to classify the methods of research and the following are some

important methods followed in research.

1) Historical Method

It describes what was in the past "The process involves investigating,

recording, analyzing and interpreting the events of the past for the purpose of

discovering generalizations and is helpful in understanding generalizations and is

helpful in understanding the past and the present and, to a limited extent, in

anticipating the future”.

Because of the difficulty in obtaining dependable data, historical research is

the most difficult type of research. The main purpose of the historical research is to

arrive at an accurate account of the past so as to gain a clearer perspective of the

present. Historical research is not based upon experimentation, but upon reports of

observation which cannot be repeated. The data is collected from past events in the

form of various types of documents, relics, records and artifacts having a direct or

indirect impact on the event under study.

2) Descriptive Method

It describes 'what is'. It is concerned with describing, recording, analyzing

and interpreting the existing conditions. In involves some type of comparisons or

contrast and attempts to discover relationships between existing non manipulated

normative survey, the 'status' or 'trend' research. Survey studies are designed to

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determine the present status of a given phenomenon. They are often of

considerable immediate value. They are, however, of relatively limited scientific

sophistication.

Descriptive studies are more than just a collection of data; they involve

measurement, classification, analysis, comparison and interpretation.

Descriptive studies investigate phenomena in their natural setting. Their

purpose is both immediate arid long range.

3)Experimental Research Method

It describes "what will be", when certain variables are carefully controlled or

manipulated. Here the focus is on variable relationships'. In the experimental

research there is deliberate manipulation. Experimentation as a scientifically

sophisticated technique is capable of providing precise answers to precise

problems, ft is the most exacting and difficult of all methods and also most

important from the scientific point of view. Experimental method provides much

control and, therefore, establishes a systematic and Jogical association between

manipulated factors and. observed effects. Control, manipulation, observation and

replication are the four essential characteristics of experimental research.

4) Field Study Method

This method of research aims at discovering the relationships existing

among social institutions. A field study attempts to reveal attitudes, values,

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perception and behaviour of individuals and groups under different situations.

There are two broad types in field study method viz. Exploratory and Hypothesis

testing. While an exploratory study attempts to observe what is existing; whereas,

through Hypothesis testing certain beliefs are examined for truth.

These may have any one of the following three purposes.

a) To discover significant variables in a field situation

b) To discover relationships among variables

c) To lay down a basis for more systematic and rigorous testing of the hypothesis.

Field study method, also known as survey method, is popular in social science. It

provides a scientific approach for obtaining reliable results, maintaining objectivity

to a large extent.

5) Case Study Method

In this method, the investigator makes intensive investigation of a social

unit, which may be a person, a family, a group, an institution, a community or even

an entire culture.

The investigator gathers pertinent information about present status, past

experiences and environmental forces which contribute to the infidelity and

behaviour of the unit. The case study is an intensive, integrated and insightful

method of studying a social phenomenon. It can also be used to illustrate the theory

by providing an example. However, it has certain limitations such as,

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• It is more expensive, being exploratory in nature.

• The generalizations based on a single case cannot be applied to the entire

population.

• There is strong possibility for subjectivity in results.

However, with quantitative data, analytical framework, in depth evaluation of the

situation, it may provide valuable insight in the subject of study.

RESEARCH PROCESS

In social research a deductive approach is commonly used.

In a research project there are various scientific activities in which a

researcher engages himself in order to produce knowledge. Although each research

project is unique in some ways, all projects involve, by and large, some common

activities. Each of these activities is interdependent. The research process is thus

the system of these interrelated activities. The various activities are conveniently

grouped as follows.

a) Identifying a problem

Identifying a problem is the most difficult step in the research process. The

researcher must discover and define not only a problem area but also a specific

problem within that area that he chooses to study, from the field of his interest. The

problem should be such that can be clearly and precisely stated. The statement of

the problem must be complete.

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b) Formulation of a Hypothesis

Hypothesis is a hunch, an assumption or an intelligent guess. Once a

problem has been identified, the researcher often employs the logical process of

education and induction to formulate an expectation of the outcome of the study. In

other words he conjectures or hypothesizes about the relationship between the

concepts identified in the problem. There should be careful formulation of

hypothesis.

c) Identifying and labeling variables

After formulating a hypothesis, the researcher must identify and label the

variables both in the hypothesis and elsewhere in the study. There may be different

types of variables such as dependent, independent, moderator, control and

intervening.

d) Constructing operational definitions

The abstract or conceptual forms of variables are to be converted into

operational forms. Here the variables are stated in observable and measurable

forms, they are made available for manipulation, control and examination.

e) Manipulating and controlling variables

To study the relationship between variables, the researchers undertake both

manipulation and control. The concept of internal and external validity is basic to

this undertaking.

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g) Identifying and constructing devices for observation and measurement

Once the researcher has operationally defined the variables in a study and

chosen the design, he must adopt or construct devices for measuring selected

variables.

h) The method to be used

The researcher has to select the research methods to be used. They are

generally classified in to three categories I) Historical, ii) Descriptive, iii)

Experimental. The method to be used is determined by the nature of the problem

and the type of data required for the purpose.

i) Data collection

This step is concerned with the procedures and techniques to be adopted for

data collection. It depends on the nature of the sample chosen for the study. It

refers to selection and development of data gathering devices such as tests,

questionnaires, rating scales, interviews, observation, checklists and the like. Since

many studies in education and in allied fields rely on questionnaires and interviews

as their main source of data it includes the techniques for constructing and using

these measurement devices.

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j) Analysis and Interpretation of data

At this step the data is to be analysed and interpreted very carefully keeping

in mind the objectives of the study. For this purpose appropriate statistical and

other techniques are to be applied for the processing of the data.

Another characteristic feature of the research process is 'self correction'. In this

situation when the results do not support or only partially supported the hypothesis

and the researchers has sufficient reasons to believe that the hypothesis is adequate,

then he may decide that the failure to confirm the hypothesis is due to error in

selecting a sample design or in measurement of the key concepts or in analysis of

data. In these situations, the researchers may decide to repeat the study beginning

with the faulty stage after re citifying the faults. Finally all the stages of the

research process make the study potentially replicable. The researcher designs his

study in such a way that either the researcher or somebody else can replicate it.

The replication of study substantiates the fact further that the findings are not due

to mere coincidence.

PROBLEMS IN RESEARCH

Research requires following an unknown path. The entire process is full of

problems and challenges. Success cannot be taken for granted, inspired by higher

objectives a researcher, therefore, has to pursue research. Obviously, he faces a

number of problems. Some of these problems are briefly discussed here.

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1) A balance between theory and application

Theory and application are two important aspects of social sciences research.

It is observed that while in some cases there is greater emphasis upon theory

without much regard to its utility, in some others there is greater emphasis upon

application without building the theory. In both cases, the quality of research

suffers. The research in social sciences should, therefore, make an optimum

combination of theory and application. As a practical course if the research starts

with theory, it should end with application and if it starts with application, it should

conclude with the formulation of theory.

2) Overemphasis upon quantitative and statistical analysis

A cursory glance at research papers produced by scholars in social sciences

shows the tendency to fill pages with statistical tables and analysis of data.

Quantitative analysis of data is undoubtedly useful for reliable and valid result

However, in the process of generalization based on quantitative analysis

sometimes, certain glaring differences and extraditions are ignored or are attributed

to sampling variations. The researchers should account for and explain as many

variations as possible. Non-quantifiable variables should be also given weight age

while drawing conclusions.

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3) Methodology of Research

Quantification, statistical analysis and use of computers cannot be a

substitute for a strong methodological base. Conceptualization of the problem,

formulation of research objectives, methodology and research design should not be

sacrificed. All these aspects should be given due weight age as per the

requirements of the situation.

4) Inter disciplinary approach

Social phenomena, particularly those related to human behaviour can be

explained better on a broader theoretical basis evolved by integrating the

approaches of different disciplines. In social sciences research, therefore, the

researcher should develop not only a sound understanding of the subject of inquiry

but also of the methods, techniques and approaches in other disciplines and decide

his research approach considering all these aspects.

5) Scientific objectivity

Objectivity is willingness and ability to examine the evidence

dispassionately. Unlike natural sciences, perception, experience, socio-economic

roots, approach to life etc. tend to influence the research pursuit. However, through

scientific objectivity, influence of these external factors could be minimized.

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SIGNIFICANCE OF RESEARCH IN SOCIAL SCIENCE

Social research attempts to discover facts concealed in a social phenomenon

or the law governing it. Social research is a tool used by social scientists to

understand the social processes, social values, ideas, reality, their interrelationship

and linkage with natural laws.

Social research focuses attention on social problems and social events. It

reveals mysteries of social life, by discarding superstitions, orthodox beliefs and

ignorance, it brings people face to face with reality. This helps in the scientific

judgment of social phenomena. Social research thus has great relevance and

practical utility and contributes towards social welfare and development.

Significance of social research may be judged in terms of

1) CONTROL OVER SOCIAL PHENOMENA

Knowledge is power. Social research generates knowledge about society and

its institutions. This can be used for exercising control over social phenomena.

Social research leads to growth of social systems on sound lines.

2) HELPS IN SOCIAL PLANNING

Planning is based on knowledge of the problem, the objectives and the

resources available. Social research, by generating essential information, provides

a basis for formulation of radical social plans.

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3) USEFUL IN SOCIAL PREDICTION

Social research affords a sound basis for predictions in a large number of

situations. Although social predictions cannot be perfect in all cases due to a

variety of constraints and assumptions, yet they prove useful in social planning

and control over social institutions.

4) CREATES SOCIAL UNDERSTANDING

Social research by revealing the truth, dispelling the superstitions and

ignorance, projects interdependence among different social groups. By bringing

about unity in diversity, giving weightage to independent and reasoned opinion, it

promotes good will and understanding and strengthens social cohesion.

5) LEADS TO SOCIAL GROWTH

Social institutions determine the direction of social change. Social research,

by promoting better understanding about these institutions, better social planning

and control leads to wholesome social growth.

6) CONTRIBUTE TO HUMAN WELFARE

The ultimate objective of all social activities is human welfare. Social

research, by identifying social evils, their magnitude, causes and consequences,

provides a basis for appropriate measures and reforms leading to social welfare.

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7) REFINEMENT IN SOCIAL RESEARCH METHODOLOGY

Social research helps in the requirement of research methodology including

the tools and techniques of research. This leads to improvement in the quality of

research.

8) SATISFACTION OF INTELLECTUAL CURIOSITY

A strong desire to seek the truth is always at the root of research activity.

Social research, through better understanding of the environment, satisfies the urge

to know and understand human beings and the society.

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CHAPTER – II

RESEARCH PROBLEM AND HYPOTHESIS

IDENTIFICATION, SELECTION AND FORMULATION OF A PROBLEM

Some unresolved problem can be a topic of research. A researcher Is

expected to identify a problem suitable for research, define it, submit a plan of

research to the sponsoring agency/higher authority and obtain its approval before

proceeding further in this direction.

A research problem in general refers to some theoretical or practical

situation requiring solution. However, every situation may not be suitable for

research. A research problem should offer one or more possible courses of action

and possibly one or more outcomes, which may be pursued to achieve the desired

objective. The researcher is expected to evaluate the results of different courses of

action and identify the most suitable one, which may ensure optimum use of

resources in the given environment and achieve the desired objective.

A RESEARCH PROBLEM Meaning

A research problem should be selected very carefully. The subject matter of

research should be of interest to the researcher. His educational background and

work experience being the main considerations. In the search for a suitable topic,

the literature available on the subject may be surveyed; suggestions and guidance

may be sought from scholars, scientists, co-workers and research supervisor.

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However, ultimately the researcher is expected to make a final decision in this

matter.

The topic selected should be neither too narrow not too broad. It should be

proper, considering the utility as well as the resources. Controversial and irrelevant

topics should be avoided. Personal considerations like researchers' capability and

resources required to complete the project and social considerations like its

contribution to wealth and welfare of the society should also be examined. In brief,

the topic selected should be useful and feasible. In case any difficulty is visualized

a preliminary feasibility study may be undertaken to understand the nature and

dimensions of the challenge involved.

DEFINING THE ‘RESEARCH PROBLEM’

A research problem should be defined in clear and unambiguous terms. This

is essential to delimit the scope of researchers and also to discriminate between the

relevant and irrelevant. Proper care for details is required at this stage, so that a

suitable research design may be prepared and the work may be carried out without

much difficulty.

Defining a research problem involves laying down the limits within which

the research work will be carried out keeping in view the determined objectives.

Defining the research problem requires knowledge of the field of

investigation and experience. A young researcher may find some difficulty in

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defining the research problem. He should seek the guidance of seniors and the

research supervisor in this respect. It should be done in an unhurried and

systematic, manner, first stating the problem in a general way, surveying the

relevant literature, developing proper understanding through discussions and

consultations and finally defining the problem in an appropriate manner.

The following points are to be kept in mind while defining a research problem

1. The right question must be addressed if research is to aid decision makers. A

correct answer to the wrong question leads either poor advice or to no advice.

2. Very often in defining research problems we have a tendency to rationalize and

defend our actions once we have embarked upon a particular research plan. The

best time to review and consider alternative approaches is in the planning stage, if

this is done the needless cost of making a false start and redoing work could be

avoided.

3. A good starting point in problem definition is to ask what the decision maker

would like to know if the requested information could be obtained without error

and without cost.

4. Another good rule to follow is "never settle on a particular approach without

developing and considering at least one alternative."

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5. The problem definition step of research is the determination and structuring of

the decision maker's question. It must be the decision maker's question and the

researcher's question.

6. What decision do you face? If you do not have a decision to make, there is no

research problem.

7. What are your alternatives? If there are no alternatives to choose, again there is

no research problem.

8. What are your criteria to choose the best alternative? If you do not have criteria

for evaluation, again there is no research problem.

9. The researcher must avoid the acceptance of the superficial and the obvious.

MODE OF SELECTION OF A PROBLEM

The selection of a problem is the first step in research. The nature of the

problem to be selected depends upon the level at which the research is done. A

problem appropriate for the students of graduation level will be a modest one. Here

the emphasis is on the learning process. A problem to be selected at M.Phil / PhD

level is a major problem requiring comprehensive treatment. In this case, the

emphasis is upon both skill development and contribution to knowledge. On the

other hand, a problem to be selected by an experienced researcher must be a

complex probiem meant for making a significant contribution or refinement of

theory or to policy making.

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What should be the mode of selection in the case of academic research?

Should a problem be suggested by the guide or be selected by the researcher

himself?

A beginner in research may, of course, prefer the problem to be suggested by

his guide.

However, such suggestion by the guide means an imposition. It destroys

spontaneity and the personal interest of the researcher. Therefore, it is better to

choose the problem oneself. Of course, the guide can help the candidate to help

him self.

A researcher with a critical, curious and imaginative mind and who is

sensitive to practical problems can easily identify problems for study.

SOURCES OF ‘PROBLEMS’

The sources from which one may be able to identify research problems or

develop problem awareness are

1) Reading of study books, articles, research reports etc. Academic

experiences viz. lectures, seminars, discussions, experience

sharing etc.

2) Day to day life experiences of the researcher.

3) Exposure to the field through field visits, internship training, extension work etc.

4) Consultation with experts, researchers, administrators, business executives etc

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5) Research in one area may suggest problems for further research.

6) As the reflective mind is a spring of knowledge, intuition of the researcher may

give new ideas for research.

CRITERIA OF SELECTION

The selection of one appropriate researchable problem out of the identified

problems requires evaluation of those alternatives against certain criteria. These

may be grouped into a) internal criteria or factors, b) external criteria or factors.

Internal criteria consist of Researcher's interest, competence, resource la

finance and time etc.

External criteria include researchability of the problem, its importance and

urgency, novelty of the problem, feasibility, facilities, its social relevance and

usefulness and research personnel.

FORMULATION OF A SELECTED PROBLEM

Formulation means translating and transforming the selected research

problem into a scientifically researchable question. It is concerned with specifying

exactly what the research problem is and why it is studied. The formulation should

include both the „what‟ and the 'why' aspects. Following are the three principal

components In the progressive formulation of a problem for research.

1. the originating question (what does one want to know?)

2. the rationale (why aspect)

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3. the specifying question (possible answer to the originating question)

IMPORTANCE OF FORMULATION

'A problem well put is half-solved.' This saying highlights the importance of

proper formulation of the selected problem. The primary task of the 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. 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. The determination of exact Information needs through the formulation

process prevents a blind search and indiscriminate gathering of data. The data

(information) needs, and their sources determine the methods to be adopted for

sampling and collection of data. The hypotheses to be tested determine the

appropriate statistical techniques to be adopted for analysis. Thus all the major

tasks — sampling, appropriate method of collection of data, construction of tools

for data collection, designing plan of analysis -can be planned exactly without any

waste of efforts. Hence the saying, 'a problem well put is half- solved' is true.

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REVIEW OF LITERATURE

A researcher who is not truly conversant with what has gone on before has

little chance of making a worthwhile contribution. Therefore a researcher has to

'survey1 or 'review' the available literature relating to his field of study. He must

keep himself 'updated' in his field and related areas.

LITERATURE IN RESEARCH CONTEXT CONSISTS OF

A) Books

1. Encyclopedias

- General, e.g.: Encyclopedia Britannica

- Specific, e.g.: Encyclopedia on social sciences

2. Year Books, e.g.: published and supplements to Encyclopedias

3. Text books

4. Reference Books

B) Journals: published monthl;y,quarterly or half yearly or anually.

C) Reports: 1)Reports of committee/commissions appointed by Government and

public instituitions.

2) Seminar reports and conference proceedings.

D) Research Dissertation and theses.

E) Newspapers and Magazines

F) Micro Forms: Audio and video tapes; Microfilm and Micro cards.

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WHAT TO REVIEW AND FOR WHAT PURPOSE?

The review of literature is not mere reading for reading sake. It is also not a

casual reading like reading of a story or novel. It 'focuses' and is 'directed' towards

a specific purpose. In is also 'selective'. A researcher has to select the kinds of

literature to be reviewed and 'determine' the 'purpose' for which he has to study

them. The literature review starts with the selection of a problem for research,

continuous through the various stages of the research process and ends with report

writing. Following are the purpose of review of related literature.

1) To gain a background knowledge of the research topic.

2) To identify the concepts relating to it, potential relationships between

them and to formulate researchable hypotheses.

3) To identify appropriate methodology, research design, methods of

measuring concepts and techniques of analysis.

4) To identify data sources used by other researchers, and

5) To learn how others structured their reports.

LITERATURE SEARCH PROCEEDURE

How can the researcher 'identify' the 'related' materials? This search

procedure involves a series of steps. The exact sequence will vary depending upon

the subject and the knowledge of the researchers. A general approach is suggested

below.

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1) Request learned professors, librarians or others familiar with the field to

suggest relevant references.

2) Find out whether any bibliography already prepared on the subject is

available in libraries. See the Bibliographic index, if any, maintained in the library.

3) Consult bibliographies in the theses on the topic and related topics.

4) See the card catalogues, both general based and subject based.

5) Examine periodicals, monographs, reports and conference proceedings and

other materials including microfilms available in the library.

6) Consult references cited in the books and articles already located. Each book

or article will be a means to locate additional references.

7) Consult the abstract journal on the subject or 'Abstracts' section in the

journals relating to the subject.

8) See the 'Book Review' pages in the leading daily newspapers and in the

journals.

The important point to be noted here is to prepare a source or bibliography

card for each item of reference located. This helps to incorporate them in the

appropriate part of the report in the future.

Thus one can identify many references, relating to the selected topic.

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RESEARCH HYPOTHESES

The formulation of hypothesis or propositions as to the possible answers to

the research questions is an important step in the process of research. Keen

observation, creative thinking, hunch, wit imagination, vision, insight and sound

judgment are of great importance in setting up reasonable hypotheses. A through

knowledge about the phenomenon and related fields is of great value in the

process. The formulation of hypotheses plays an important part in the growth of

knowledge in every science.

MEANING OF HYPOTHESIS

Hypothesis is a tentative proposition formulated for empirical testing. It is a

declarative statement combining concepts. It is a tentative answer to a research

question. It is tentative, because its veracity can be evaluated only after it has been

tested empirically.

Lundberg defines hypothesis as, "a tentative generalization, the validity of

which remains to be tested". Goode and Hatt define it as "a proposition which can

be put to a test to determine its validity".

Hypothesis-necessary or not

Is the formulation of useful hypotheses always necessary and possible? It is

true that hypotheses are useful and they guide the research process in the proper

direction. But can hypothesis be set up in all cases? In mere fact finding

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investigations, no problems may be raised and the need for formulating hypotheses

may not arise. Similarly, in exploratory studies, initially it may not be possible to

set up any worthwhile hypotheses. In fact, the very purpose of such exploratory

studies may be to formulate meaningful hypotheses for further formal studies. But

strictly speaking, the mere fact-finding and the exploratory studies cannot be

considered to be typical research studies. In all analytical and experimental studies,

hypotheses should be set up in order to give a proper direction to them.

SOURCES OF HYPOTHESES

Hypotheses can be derived from various sources

1. Theory: this is one of the main sources of hypotheses. It gives direction to

research by stating what is known. Logical deduction from theory leads to new

hypotheses. For examples, profit/ wealth maximization is considered as the goal of

private enterprises. From this assumption, various hypotheses are derived - "the

rate of return on capital employed is an index of business success"; "the optimum

capital structure is that combination of debt an equity which leads to the maximum

value of the firm"; "Higher the earning per share, more favorable is the financial

leverage".

2. Observation: Hypotheses can be derived from observation. From the

observation of price behaviour in a market, for e.g. the) relationship between the

price and demand for an article, a hypothesis can be framed.

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3. Analogies: These are other sources of useful hypotheses. Julian Huxley has

pointed out that causal observations in nature or in the framework of another

science may be a fertile source of hypotheses .For example, the hypothesis that

"similar human types or activities may be found in similar geophysical regions"

came from plant ecology.

4. Intuition and personal experiences: They may also contribute to the

formulation of a hypothesis. Personal life and experiences of persons determine

their perceptions and conception. These may, In turn, direct a person to a certain

hypothesis more quickly. The story of Newton and the falling apple, the flash of

wisdom to Buddha under the banyan tree illustrate this individual experiential

process.

5. Findings of studies: Hypotheses may be developed out of the findings of

other studies in order to replicate and test.

6. State of knowledge: An important source of hypotheses is the state of

knowledge in any particular science. Where formal theories exist, hypotheses can

be deduced. If the hypothesis is rejected, theories would be modified. Where

theories are scarce, hypothesis are generated from conceptual frameworks. In

either case, the hypotheses are related to the conceptual-theoretical level.

7. Culture Another source of hypotheses is the Culture on which the researcher

was nurtured. Western culture has induced the emergence of sociology as an

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academic discipline. Over the past decade, a large part of the hypotheses on

American society examined by researchers were connected with violence. This

interest is related to the considerable increase in the level of violence in America.

In India, socio-economic and leadership studies, hypotheses based on caste and

economic status is common, because Indian society is caste-ridden, hierarchical

and segmental, and the Indian economic system is riddled with inequalities and

undue privileges.

8. Continuity of research: The continuity of research in a field itself constitutes

an important source of hypotheses. The rejection of some hypotheses leads to the

formulation of new ones capable of explaining the phenomena in subsequent

researches on the same subject.

TYPES OF HYPOTHESES

Hypotheses are classified in several ways. With reference to their function,

hypotheses are of two types: (a) descriptive hypotheses and (b) relational

hypotheses. Another approach is to classify them into: (c) working hypotheses, (d)

null hypotheses and (e) statistical hypotheses (f) common-sense hypotheses, (g)

complex hypotheses and (h) analytical hypotheses.

a) Descriptive hypotheses: these are propositions that describe the characteristics

(such as size, form or distribution) of a variable. The variable may be an object,

person, organization, situation or event. Some examples are:

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"The rate of unemployment among arts graduates is higher than that of commerce

graduates".

“Public enterprises are more amenable to centralized planning.”

The educational system is not oriented to human resource needs of a country".

b) Relational hypotheses: these are propositions which desscribe the relationship

between two variables. The relationship suggested may be positive or negative

correlation or causal relationship. Some examples are

“Families with higher incomes spend more for recreation”.

“Participative management promotes motivation among executives”.

“The lower the rate of job turnover in a work group, the higher the work

productivity”.

“Upper-class people have fewer children than lower-class people”.

“Labour productivity decreases as working duration increases”.

Causal hypotheses state that the existence of, or a change in, one variable causes or

leads to an effect on another variable. The first variable is called the independent

variable, and the latter the dependent variable. When dealing with causal

relationships between variables the researcher must consider the direction in which

such relationships flow, i.e., which is the cause and which is the effect.

c) Working hypotheses: while planning the study of a problem, hypotheses are

formed. Initially they may not be very specific. In such cases, they are referred to

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as 'working hypotheses' which are subject to modification as the investigation

proceeds.

d) Null hypotheses: these are hypothetical statements denying what are explicitly

indicated in working hypotheses. They do not, nor were ever intended to exist in

reality. They state that no difference exists between the parameter and the statistic

being compared to it. For example, even though there is relationship between a

family's income and expenditure on recreation, a null hypothesis may state: "there

is no relationship between families' income level and expenditure on recreation".

Null hypotheses are formulated for testing statistical significance, since this form is

a convenient approach to statistical analysis. As the test would nullify the null

hypotheses, they are so called.

There is some justification for using null hypotheses. They conform to the qualities

of detachment and objectivity to be possessed by a researcher. If he attempts to test

hypothesis which he assumes to be true, it would appear as if he is not behaving

objectively. This problem does not arise when he uses null hypotheses.

Moreover, null hypotheses are more exact. It is easier to reject the contrary of a

hypothesis than to confirm it with complete certainty. Hence the concept of null

hypotheses is found to be very useful.

e) Statistical hypotheses: these are statements about a statistical population. They

specify in a better way the relations between variables. This association is

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measured by the co-efficient of correlation, e.g., if the co-efficient of correlation

between bonus and productivity is +1.0, then there is a perfect positive correlation

between the bonus and productivity.

As Abraham Klan has pointed out, "all inductive inference is based on

samples.....all hypotheses might be said to be statistical hypotheses in a broad

sense; statistics has the task of assessing the weight of evidence for a particular

hypothesis contained in a given set data".

f) Common sense hypotheses: these represent the common sense ideas. They state

the existence of empirical uniformities perceived through day to day observations.

Much empirical uniformity may be observed in business establishments, the social

background of workers, and the behaviour patterns specific group like students,

e.g., "shop-assistants in small shops lack motivation".

"Soldiers from upper-class are less adjusted in the army than lower class men";

"fresh students conform to the conventions set by seniors".

Common sense statements are often a confused mixture of cliches and moral

judgments. Scientists have a large-scale job in transforming and testing them. This

requires three tasks: "first, the removal of value judgments; second, the

clarification of terms; and third, the application of validity tests". 'What everybody

knows' is not known until it has been tested'. The common sense hypotheses that

seek empirical generalization play an important role in the growth of a science.

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g) Complex hypotheses: these aims at testing the existence of logically derived

relationships between empirical uniformities. For example, in the early stages

human ecology described empirical uniformities in the distribution of land values,

industrial concentration, types of business and other phenomena. Further study and

logical analysis of these and other related findings led to the formulation of

complex hypotheses such as "the concentric growth circles characterize a city",

"members of minority groups suffer from oppression psychosis", etc. Such

hypotheses are purposeful distortions of empirical exactness. Because of their

removal from empirical reality, these constructs are termed 'ideal types'. The

function of such hypotheses is "to create tools and problems for further research in

otherwise very complex areas of investigation".

h) Analytical hypotheses: these are concerned with the relationship of analytic

variables. These occur at the highest level of abstraction. These specify

relationship between changes in one property and empirical regularities by wealth,

education, region, and religions. If these were raised to the level of ideal type

formulation, one result might be the hypotheses: "there are two high-fertility

population segments in India, viz., low - income urban Muslims and low- income

rural low caste Hindus".

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At a still higher level of abstraction, the effects of region, education and religion on

fertility might be held constant. This would allow a better measurement of the

relation between the variables of wealth and fertility.

This level of hypothesizing is the most sophisticated mode of formulation and

contributes to the development of 'brilliant' abstract theories.

CHARACTERISTICS OF A GOOD HYPOTHESIS

What is a good hypothesis? What are the criteria for judging? An acceptable

hypothesis should fulfill certain conditions.

1. Conceptual clarity: a hypothesis should be conceptually clear. It should consist

of clearly defined and understandable concepts. Clarity is obtained by means of

defining operationally the concepts in the hypothesis.

2. Specificity: A hypothesis should be specific and explain the expected relations

between the variables and the conditions under which these relations will hold, e.g.

“when there is dissatisfaction and no care is taken, deprivation will engender

violence”.

3. Testability: A hypothesis should be testable and should not be a moral

judgement. It should be possible to collect empirical evidences to test the

hypothesis. Statements like "capitalists exploit their workers", "bad parents

produce bad children" are common place generalizations and cannot be tested, as

they merely express sentiments and their concepts are vague.

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4. Availability of techniques: Hypothesis should be related to available techniques.

Otherwise they will not be researchable; therefore the researcher must make sure

that methods are available for testing the proposed hypothesis.

5. Theoretical relevance: A hypothesis should be related to a body of theory. A

science can be cumulative only by building on an existing body of facts and

theories. It cannot develop if each study is an isolated investigation. When research

is systematically based upon a body of existing theory, a genuine contribution to

knowledge is more likely to result. Therefore a hypothesis should possess a

theoretical relevance.

6. Consistency: A hypothesis should be logically consistent. Two or more

propositions logically derived from the same theory must not be mutually

contradictory.

7. Objectivity: Scientific hypothesis should be a value-judgement. In principle, the

researchers system of values has no place in scientific method. However, a social

phenomenon is affected by the milieu in which it takes place. Hence the researcher

must be aware of these values and state them explicitly.

8. Simplicity. A hypothesis should be a simple one requiring fewer conditions or

assumptions. But 'simple' doesn't mean obvious. Simplicity demands insight. The

more insight the researcher has into a problem, the simpler will be the hypothesis.

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CHAPTER – III

RESEARCH DESIGN

RESEARCH DESIGN MEANING

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 blueprint

for the collection, measurement and analysis of data". "It is the plan, structure and

strategy of investigation conceived 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.

NATURE OF RESEARCH DESIGN

A Research design is indispensable for a research project. However it is not

a precise and specific plan like a building plan to be followed without deviations

but like 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, and when new aspects, new conditions and new relationships

come to light and insight into the study deepens.

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Besides, a research study cannot be as extensive and intensive as the

researchers may like it to be. It has to be geared to the availability of data and the

co-operation of the informants. It has also to be kept within the manageable limits

i.e. within the researcher's mental ability to grasp the implications, his competence

and the amount of time and other resources available for the purpose. Thus a

research design "represents a compromise dictated by many practical

considerations".

CLASSIFICATIONS OF RESEARCH DESIGN

There are a number of crucial research choices. Various writers have advanced

different classifications, some of which are.

1. Exploratory, descriptive and causal designs (Selltiz, Jonada, Deustch and

Cook)

2. Experimental, historical and inferential designs (American Marketing

Association)

3. Historical method, case and clinical studies (Goods and Slates)

4. Sample survey, field studies, experiments in field settings and laboratory

experiments (Festinger and Katz)

5. Exploratory, descriptive and experimental studies (Body and Westfall)

6. Exploratory, descriptive and causal (Green and Tull)

7. Experimental quasi experimental designs (Nachmias and Nachmias)

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8. True experimental, quasi-experimental and non-experimental (Smith)

9. Experimental, pre-experimental, quasi-experimental designs and survey

research (Kidder and Judd)

FORMULATION OF THE RESEARCH DESIGN

Planning involves deciding things in advance. Accordingly, the preparation of

a research plan involves a careful consideration of the following questions and

making appropriate decisions on them.

1. What the study is about?

2. Why is the study made?

3. What is its scope?

4. What are the objectives of the study?

5. What are the propositions to be tested?

6. What are the major concepts to be defined operationally?

7. What criteria or measurements are to be used?

8.When or in what place the study will be conducted?

9.What is the reference period of the study?

10. What is the typology of the design?

11. What kinds of data are needed?

12. What are the sources of data?

13. What is the universe from which the sample has to be drawn?

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14. What is the sample size?

15. What sampling technique can be used?

16. What methods are to be used for collecting data?

17. What tools are to be used for collecting data?

18. How is the data to be processed?

19. What techniques of analysis are to be adopted?

20. What is the significance of the study?

21. To what target audience is the finding meant?

22. What is the type of knowledge to be updated?

23. What is the time limit within which the whole work should be comleted?

24. What is the cost involved?

These questions should be considered with reference to the researcher's

interest competence, time and other resources, and the requirements of the

sponsoring agency, if any. Thus the considerations which enter into making

decisions regarding what, where, when, how much, by what means constitute a

plan of study or a study design.

FEATURES OF A GOOD RESEARCH DESIGN

1. It is a plan that specifies the objectives of the study and the hypotheses to be

tested.

2. It is an outline that specifies the sources and types of information relevant to the

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research questions.

3. It is a blueprint specifying the methods to be adopted for gathering and?

analyzing the data.

4. It is a scheme defining the domain in general and whether the obtained

information can be generalized to a larger population.

FACTORS AFFECTING RESEARCH DESIGN

A research design is commonly affected by the following factors.

1. The degree of formulation of the problem.

2. The topical scope- breadth and depth - of the study.

3. The research environment

4. The time dimension

5. The mode of data collection

6. The manipulation of variables under study

7. The nature of relationship among variables.

After making decisions on the above questions, a formal research plan is

drafted, incorporating those decisions. The format may vary depending on the

purpose for which the study is undertaken.

EVALUATION OF RESEARCH DESIGN

A research design needs great care. A researcher should decide what

research design would be more appropriate for the study. There can be several

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research designs, each suitable for a different research study. It may be difficult to

state what constitutes an appropriate research design, however the following

aspects may be taken into consideration at the time of deciding about the research

design.

1. Minimum of error: The design in experimentation as well as personal bias

should be minimized to maximize the accuracy and reliability of results.

2. Yield Maximum Information: The design must be capable of yielding

maximum information at minimum cost, effort and time.

3. Flexibility: The research design should be flexible to permit consideration of

a large number of different aspects or phenomena etc.

Much depends upon the nature of the problem to be studied and the object of

inquiry. A design suitable for one research study may or may not be useful for

another. While deciding the research design, therefore, the nature of universe,

objectives of the study, sampling frame, desired standard of accuracy, etc should

be considered. Further, while considering alternative research designs both the

strengths and limitations should be examined. Comments may be invited from

experts and a design appropriate in the given condition should be selected.

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CHAPTER – IV

SAMPLING DESIGN

INTRODUCTION

Empirics, filed studies require collection of first-hand infection or data

pertaining to the units of study from the field. The units of study may include

geographical areas like districts, taluks, cities or villages which are covered by the

study or institutions or households about which information is required or persons

from whom information is available. The aggregate of all the units pertaining to a

study is called the “population” or the “universe”. Population is the target group to

be studied. It is the total collection of elements about which we wish to make

inferences. A member of the population is "an element". It is the subject on which

measurement is taken. It is the unit of study. A part of the population is known as a

sample. The process of drawing a sample from a larger population is called

sampling. The list of sampling units from which sample is taken is called the

sampling frame e.g., a map, a telephone directory, a list of industrial undertakings,

a list of car licensees etc.,

CENSUS VS SAMPLING METHOD

The process of designing a field study among other things involves a

decision to use sampling or not. The researcher must decide whether he should

cover all the units or a sample of units. When all the units are studied such a

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complete coverage is called as census survey. When only a sample of the universe

is studied the study is called sample survey.

In making this decision of census or sampling the following factors are considered.

1. The size of the population

If the population to be studied is relatively small, say 50 institutions or 200

employees or 150 households, the Investigator may decide to study the entire

population. The task is easily manageable and the sampling may not be required.

But if the population to be studied is quite large, sampling is warranted. However

size is a large matter. Whether the population is warranted. However 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.

2. Amount of fund and budget for the study

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.

3. Facilities

The extent of facilities available - staff, access to computer and accessibility to

population elements - is another factor to be considered in deciding to sample or

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not. When the availability of these facilities is extensive, census survey may be

manageable. Otherwise sampling is preferable.

4. Time

The time limit within which the study should be completed is another important

factor to be considered in deciding the question of census or sample survey. This in

fact, is a primary reason for using sampling by academic and other researchers.

PRINCIPLES OF SAMPLING

Sampling is based on two premises. They are,

1. There is much similarity among the elements in a population that a few of

these elements will adequately represent the characteristics of the total population.

2. While the sample value of some sample units may be more than the

population value the sample value of other sample units may be less than the

population value. When the sample is drawn properly, these differences tend to

counteract each other. With the result a sample value is generally close to the

population value.

SAMPLING TECHNIQUES OR METHODS

Sampling techniques or methods may be classified into two generic types.

a) Probability or random sampling and

b) Non-probability or Non-random sampling

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a) Probability sampling is of the following types

a) Simple random sampling

b) Stratified random sampling

c) Systematic random sampling

d) Cluster sampling

e) Area sampling

f) Multi-stage and sub- sampling

g) Random sampling with probability proportional to size,

h) Double sampling and multiphase sampling

i) ) Replicated or interpenetrating sampling.

b)Non-Probability sampling methods are classified as

a) Convenience or accidental sampling

b) Purposive sampling

c) Quota sampling

d) Snow-ball sampling

PROBABILITY SAMPLING METHODS

1. SIMPLE RANDOM SAMPLING

This sampling gives each element an equal and independent chance of being

selected Equal chance means equal probability of being selected e.g., in a

population of 300 each element theoretically has 1/300th chance of being selected.

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Equal probability selection method is described as Epsom sampling. An

independent chance means that the draw of one element will not affect the chances

of other elements being selected.

Where some elements are purposely excluded from the sample the resulting

sample is not a random one. Hence all elements should be in included in the

sample frame to draw a random sample.

Procedure

The procedure of drawing a simple random sample consists of

(i) Enumeration of all elements in the population.

(ii) Proportion of a list of all elements, giving those numbers in serial order like

1,2, 3...and so on.

(iii) Drawing sample numbers by using (a) lottery method (b) a table of random

numbers or (c) a computer.

Suitability

The simple random sampling is suitable only for a small homogeneous

population. If may yield a representative sample under the following conditions.

(1) Where the population is a homogeneous group with reference to the specified

characters (e.g. students studying in fifth standard in a boys school form a

homogeneous group as regards level of education and age group)

(2) Where the population is relativity small; and

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(3) Where a complete list of all elements is available or can be prepared.

The simple random sampling is not suitable for drawing a sample from a large

heterogeneous population as it may a representative sample of such population.

ADVANTAGES

Some advantages of simple random sampling are

1. All elements in the population have an equal chance of being selected.

2. Of all the probability sampling techniques, simple random sampling is the

easiest to apply.

3. It is the simplest type of probability sampling to understand.

4. It does not require prior knowledge of the true composition of the population.

5. The amount of sampling error associated with any sample drawn can easily

be computed.

DISADVANTAGES

The simple random sampling technique suffers from certain draw backs.

1. The use of simple random sampling may be wasteful because we fail to use

all of the known information about the population.

2. This technique does not ensure proportionate representation to various groups

constituting the population.

3. Sampling error in the sampling is greater than that in other probability

samples of the same size, because it is less precise than other methods.

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4. The size of the sample required to ensure its representatives is usually larger

under this type of sampling than under other random sampling techniques.

5. A simple random design may be expensive in terms of time and money.

These problems have led to the development of alternative superior random

sampling designs like stratified random sampling, systematic sampling etc.

2. 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; the employees of a business undertaking may be divided into managers

and non-managers and each of those two groups may be subdivided into salary-

grade-wise strata.

NEED FOR STRATIFICATION

Stratification is necessary for,

1. Increasing a sample's statistical efficiency

2. Providing adequate data for analyzing the various sub-populations and

3. Applying different methods for different strata.

Stratification ensures representation to all relevant sub-groups of the

population. It is thus more efficient statistically than simple random sampling.

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Stratification is essential, when the researcher wants to study the characteristics of

population subgroups, e.g., male and female employees of an organization.

Stratification is also useful when different methods of data collection are

used for different parts of the population e.g., interviewing for workers and self-

administered questionnaire for executives.

Thus the stratified random sampling method is appropriate for a large

heterogeneous population.

STRATIFICATION PROCESS

This involves three major decisions

1. The stratification base or bases to be used should be decided. The ideal base

would be the principal variable understudy. For example, if the size of firms is a

primary variable, the firms may be stratified on the basis of the total capital

employed.

2. The number of strata. What should be the number of strata?

There is no precise answer to this question. Larger the number of strata greater

may be the degree of representativeness of the sample. The decision may be based

on the number of sub-population groups to be studied and the cost of stratification.

Cochran suggests that there is little to be gained in estimating overall population

values when the number of strata exceeds set

3. Strata sample sizes

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There are two alternatives; first the strata sample sizes may be proportionate to

strata's share in the total population. Second they may be disproportionate to

strata's shares.

3. SYSTEMATIC SAMPLING

This method of sampling is an alternative to random selection. It consists of

taking every Kth

item in the population after a random start with an item from I to

K.

As the interval between sample units is fixed, this method is also known as

fixed interval method.

APPLICATIONS

Systematic selection can be applied to various populations such as students

in a class, houses in a street, telephone directory, customers of a bank, assembly

line output in a factory, members of an association and so on.

ADVANTAGES

The major advantages of systematic sampling are

1. It is much simpler than random sampling. It is easy to use.

2. It is easy to instruct the field investigators who use this method.

3. This method may require less time. A researcher operating on a limited time

schedule will prefer this method.

4. This method is cheaper than simple random sampling.

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5. It is easy to check whether every „K‟th

item has been included in the sample.

6. Sample is spread evenly over the population.

DISADVANTAGES

1. This method ignores all elements betweeritwo 'K1* elements selected. Further,

except the first element, other selected elements are not chosen at random.

Hence this sampling cannot be considered to be a probability sampling in the

strict sense of the term.

2. As each element does not have an equal chance of being selected the resulting

sample is not a random one. For studies aiming at estimation or generalization,

this disadvantage would be a serious one.

3. This method may sometimes give a biased sample. If by chance, several „K‟th

elements chosen represent a particular group that group would be over-

represented in the sample.

4. CLUSTER SAMPLING

Cluster sampling means random selection of sampling units from "clusters"

consisting of population elements. Each such-sampling unit is a cluster of

population elements. Then from each such sampling unit a cluster of population

elements is' selected. Then from each related sampling unit is sample of population

elements is drawn by either simple random selection or stratified random selection.

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FEATURES

What makes a desirable cluster depends on the survey's situation and

resources. The individual elements are determined by the survey objectives. For

example, for an opinion poll, the individual person is a population element, but for

a socio economic survey of households or a consumer behavior survey, a

household may be the population element or unit of study.

CLUSTER SAMPLING PROCESS

The process of cluster sampling involves the following steps

1. IDENTIFY CLUSTERS

What can be appropriate clusters for a population? This depends on the nature of

the study and the distribution of the population relating to it. The appropriate

cluster may be area unit's or organizations/organizational units.

2. EXAMINE THE NATURE OF CLUSTERS

How homogenous is the cluster? Clusters should not be homogenous in internal

characteristics. A sample drawn from such clusters cannot fully represent the

overall population. Hence clusters should be constructed in a way as to increase

intra-cluster variance. For example, villages/city blocks that contain different

income/social groups may be combined into one cluster.

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3. DETERMINE THE NUMBER OF STAGES

Shall we use single-stage or multistage cluster? This depends primarily on

the geographical area of the study, the scale of the study, the size of the population

and the consideration of costs. Depending on these factors, the following

alternatives are possible.

(a) single stage sampling

Select cluster on a random basis and study all elements in each of the sample

cluster.

(b) Two stage sampling

Select cluster and then select elements from each selected cluster.

(c) Multi stage sampling

Sampling is carried out in two to more stages. The population is regarded as being

composed of a number of first stage sampling units. Each of them is made up of a

number of second stage units and so forth. That is, at each stage, a sampling unit is

a cluster of sampling units of the subsequent stage.

5. AREA SAMPLING

This is an important form of cluster sampling. In larger field surveys,

clusters consisting of specific geographical areas like districts, taluks, villages, or

blocks in a city are randomly drawn as the geographical areas and selected for

sampling. It is not a separate method of sampling, but forms part of cluster

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sampling. Area sampling invariably involves multi-stage sampling and sub-

sampling.

6. MULTI-STAGE SAMPLING AND SUB SAMPLING

Multi-stage method of sampling is carried out in one or more stages. The

population is regarded as being composed of a number of first stage sampling

units. Each of item is made up of a number of second stage units and so forth. A

sampling unit is a cluster of the sampling units of a subsequent stage. First sample

of the first stage sampling unit is a drawn, then from each of the selected first stage

sampling units, a sample of 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.

USAGE

Multi-stage sampling 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.

ADVANTAGES

The crucial advantages of multi-stage sampling are

1. It results in concentration of field work in compact, small areas and

consequently in a saving of time, labour and money.

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2. It is more convenient, efficient and flexible than single-stage sampling.

3. It obviates the necessity of having a sampling frame covering the entire

population.

DISADVANTAGES

The major disadvantages of the multistage sampling are that the procedure

for estimating sampling error and cost is complicated. It is difficult for non-

statisticians to follow this estimation procedure.

SUB SAMPLING

Sub-sampling is part of a multi stage sampling process. In multi stage

sampling, the sampling in second and subsequent stage frames is called sub-

sampling. Suppose that from a population of 40, 00 households in 800 streets of a

city, we want to select a sample about 400 households. We can select a sample of

400 individual households (elements) that would be scattered over the city, but the

cluster sample would be confined to 8 streets. Clustering reduces survey costs, but

increases the sampling error. Sub-sampling balances those two conflicting effects

of clustering.' In the above case, first a sample of say 80 streets may be drawn.

From each of the selected street a 10% sub-sample of households may be drawn. In

each of the above stages, an appropriate probability sampling-simple

random/stratified random sampling/ systematic random sampling - may be

adopted.

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7. SAMPLING WITH PROBABILITY SIZE (PPS)

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 given 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 major advantages of 'PPS' are,

1. Cluster of various sizes get proportionate representations.

2. PPS leads to greater precision than would a simple random sample of cluster

and a constant sampling fraction at the second stage.

3. Equal-sized samples from each selected primary cluster are convenient for

field work. If one interviewer is assigned to each cluster, the interviewers have

equal workloads.

APPLICATION

Since in practice primary sampling units generally vary considerably in size,

sampling with PPS is used in all multistage sampling.

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LIMITATION

PPS cannot be used if the sizes of the primary sampling clusters are not

known.

DOUBLE SAMPLING AND MULTI-PHASE SAMPLING

Double (or two phase) sampling "refers to the subsection of the final sample

from a respected larger sample, that provided information for improving the final

selection". When this procedure is extended to more than two phase of section, 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. Additional information from

sub samples of the full sample may be collected at the same time or later.

USAGE

Double or multi phase sampling is a compromise solution for a dilemma

posed by undesirable extremes. "The statistics based on the sample 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 „n‟1 which includes the final sample 'n'”.

Multiphase sampling is appropriate when it is more convenient and

economical to collect certain items of general information on the whole of the units

of a sample and other item of special information from a sub-sample of cases

possessing a given set of characteristics.

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9) REPLICATED OR INTERPENETRATING SAMPLING

Replicated sampling can be used with any basic sampling technique, simple

or stratified, single or multi-stage or single or multiphase sampling.

ADVANTAGES

The major advantages of replicated sampling are

1. It provides a simple means of resolving replicated sampling error.

2. It is practical if the size of the total sample is too large. To get the result

ready in time, one or more of the replicates can be used to get advance

results.

3. The replicated samples can throw light on variable non-sampling errors.

If a different set of interviewers are used, an estimation of inter-

interviewer variation for each of the sub-samples can be obtained.

DISADVANTAGES

A disadvantage of replicated sampling is that it limits the amount of

stratification that can be employed. This limitation is a real drawback to the use of

repHcated sampling in a multi-stage sampling plan.

NON PROBABILITY SAMPLING METHODS

1. Convenience Sampling

2. Purpose Sampling

3. Quota Sampling

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4. Accidental Sampling

5. Snow-ball Sampling

1. CONVENIENCE SAMPLING

This is non-probability sampling. If 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.

USEFULNESS

Though convenience sampling has no status, it may be used for simple

purposes such as testing ideas or gaining ideas or rough impressions about a

subject of interest. It lays the groundwork for a subsequent probability sampling.

Sometimes it may have to be necessarily used.

ADVANTAGES

1. Convenience sampling is the cheapest and simplest.

2. It does not require a list of population.

3. It does not require any statistical expertise.

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DISADVANTAGES

a) Convenience sampling is highly biased, because of the researcher's

subjectivity and suit ability does not yield a representative sample.

b) It is the least reliable sampling method. There is no way of estimating the

representativeness of the sample

c) The findings cannot be generalized.

2. PURPOSIVE or JUDGEMENT SAMPLING

This method means deliberate selection of sample units that conform to

some pre-determined criteria. This is also known as judgement sampling This

Involves selection of cases, which we judge as the most appropriate ones for the

given study. It is based on judgement of the researcher or some expert. It does not

aim at securing a cross section of a population.

APPLICATION

The method is appropriate when what is important is the typicality and

specific relevance of the sampling units to the study and not their overall

representatives of the population.

ADVANTAGES

The advantages of purposive or judgement sampling are.

1. It is less costly and more convenient

2. It guarantees inclusion of relevant elements in the sample. Probability

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sampling plans cannot give such a guarantee.

DISADVANTAGES

1. The demerits of judgement sampling are

2. This does not ensure the representativeness of the sample

3. This is less efficient for generalizing when compared with random sampling

4. This method requires more prior extensive information about the population

one plans to study.

5. This method does not lend itself to the use of inferential statistics because

this sampling does not satisfy the underlying assumption of randomness.

3. QUOTA SAMPLING

This is a form of convenient sampling involving the selection of quota

groups of accessible sampling units by traits such as sex, age, social class, etc.,

When the population is known it is easy to choose various categories like sex, age,

religion social class, etc., in specific proportions. Each investigator may be given

an assignment of quota groups pre-determined in the area assigned to him.

APPLICATION

Quota sampling is used in studies like marketing surveys, opinion polls, and

readership survey and which do no aim at precision but to get quickly crude

results.

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MERITS

The major advantages of quota sampling are

1. It is considerably less costly than probability sampling

2. It takes less time

3. There is no need for a list of population

4. Field work can easily be organized. Strict supervisions not be required.

SHORT COMINGS

1. It may not yield a precise or representative sample, and it is impossible

to estimate the sampling error. The finding, therefore is not generalizable

to any significant extent.

2. Interviewers may tend to choose the most accessible persons; they may

ignore slums or areas difficult to reach. Thus they may fail to secure

representative samples within the quota groups.

3. Strict control of field work is difficult

4. It is difficult to sample more than three variable dimensions.

5. The quota of sampling is subject to a higher degree of classification error,

because the investigator is likely to base their classification of respondent's

social status or economic status mostly on the impressions about them.

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4. SNOWBALL SAMPLING

This is the colorful name for techniques of building up a list or a sample of a

special population by using an initial set of its members as informants. For g

example if a researches wants to study the problems faced by Indians in another

country, say, he may identify an initial group of Indians through some source like

the Indian embassy. Then he can ask each one of them to supply names of others

Indians known to them, and continue this procedure unit he gets an exhaustive list

from which he can draw a sample or make a census survey.

ADVANTAGES

The advantages sampling are

1. It is very useful in studying social groups or formal groups in formal

organizations, and diffusion of information among professionals of various

kinds.

2. It is useful for smaller populations for which no frames are readily available.

DISADVANTAGES

1. The major disadvantage of snowball sampling is that it does not allow the use

of probability statistical methods. Elements included are dependent on the

subjective choice of the original selected respondents.

2. It is difficult to apply this method when the population is large.

3. It does not ensure the inclusion of all elements in the list.

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SAMPLING AND NON-SAMPLING ERRORS.

A survey aims at estimating or inferring selected population characteristics

or parameters by studying either the entire population or a sample of the

population. The research results may differ from the true values of the parameters

under study. Such differences are known as errors and biases.

The errors of a survey may be classified into (a) sampling errors, (b) sampling

biases (c) non-sampling errors and (d) non-sampling biases.

SAMPLING ERRORS

The errors which arise because of studying only a part of the total population

are called sampling errors. These may arise due to non-representativeness of the

samples and the inadequacy of sample size. When several sample are drawn from a

population the results would not be identical. The degree of variations of sample

results is measured by standard deviation and it is known as the standard error of

the concerned statistics. As sample size increases the magnitude. Thus sample size

and sampling of error are negatively corrected sampling error also increases.

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SAMPLING BIAS

The average of the estimate of a population parameter received from an

infinite number of samples is called as expected value of the estimators. The

differences between this value and the true value of the parameter are the bias. Bias

may arise

1. If the sampling is done by a non-random method,

2. If the sampling frame is incomplete or inaccurate and

3. If some sections of the population are not available or refuse to co-operate.

Any of these factors will cause non-compensating errors, which cannot be reduced

by an increase in sample size. The only sure way of avoiding bias arising through

the sampling method Is to use a random method. Randomness is an essential part

of the protection against sampling bias.

NON SAMPLING ERRORS

These are errors, which arise from sources other than sampling. They

include errors of observation, errors of measurements and errors of responses. Data

are collected through the methods of observation or interviewing. The physical

procedure of observations or interviewing is subject to imperfection, which cause

errors.

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Measurement error consists of error in processing and analysis. Errors of

response include incorrect responses of the respondents or mistakes in noting their

responses etc.

NON SAMPLING BIAS

These biases pose problems for scientific measurement. They affect both the

population sample values and account for the difference between the population

value and the true value. They consist of biases of observation and non-

observation, response biases and process biases. Obtaining and recording

observations incorrectly cause biases of observation. Non-observations biases arise

from failure to obtain observations on some segments of the population due to

either non coverage or non-response. The latter may be due to refusals, not-at-

homes, lost forms, etc., Response biases consist of biases arising from

imperfections in field observations or interviewing. Processing biases are produced

during coding, tabulating and computing.

TOTAL ERROR

In sampling theory, a populous model combines sampling and non-

sampling errors and biases into its total error. This total error is the square root of

the sums of squares of variables errors and squares of bias.

Total = √

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The variable errors are caused only by sampling errors, and v e equals the

standard error of sampling. Bias is mostly caused by measurement biases (i.e. non-

sampling errors).

Total Error Biases

Sampling error

The total error depends on the length of both the legs. The sampling error/

standard error leg can be shortened by improving the sample design and by

increasing the sample size. The length of biases leg may be reduced by in proving

the tools of data collection, the precision of methods of data collection, field work,

coding, processing and analysis.

The measurement of sampling errors does not pose much problem, but the

measurement of non sampling errors requires special procedures and it is costly.

Hence the reduction of non-sampling errors is a challenge to the researchers.

SELECTION OF A SAMPLE

A sample design is a definite plan for obtaining a sample from a given

population. It refers to the technique or the procedure the researcher would adopt

in selecting items for the sample. Sample design may as well lay down the number

of items to be included in the sample i.e., the size of the sample.

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(A) STEPS IN SAMPLING DESIGN

The following steps have to be followed while developing a sampling design. (1)

(1)Outlining the universe

The first step is clearly outlining the set of the objects to be studied. It may be

infinite or finite. In a finite universe, the number of items is certain for example the

number of workers in a factory me number of dealers of a company etc.

But in case of an infinite universe the number of items is infinite. For example,

number of stars in the sky, the number of TV viewers etc.

(2) Defining sampling unit

The second step is to identify the sampling unit of the universe. For eg. it may be

based on geographical basis such as state, district or village etc.

(3) Sampling frame

Once the sampling unit is drawn, the third step is preparing the source list to cover

all the samples. This list contains the names of all the items of a universe, if the list

is not ready, the researcher has to prepare it.

(4) Size of sample

The fourth step is fixing the number of items to be selected from the universe to

constitute a sample. It may be 10% or 20% or any percentage of the universe. The

size of sample should neither be excessively large, nor too small. It should be

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optimum. An optimum sample is one which Mills the requirements of efficiency,

representativeness, reliability and flexibility.

Budgetary constraints must invariably be taken into consideration when we decide

the sample size.

(B) CHARACTERISTICS OF A GOOD SAMPLE DESIGN

The following are the characteristics of a good sample design

a) Sample design must result in a truly representative sample

b) Sample deign must be one which results in a small sampling error.

c) Sample deign must be viable in the context of funds available for the

research study

d) Sample design must be such that the systematic bias can be controlled in a

better way.

(e) samples should be such that results of the sample study can be

applied, in general, for the universe with reasonable confidence.

(C) CRITERIA FOR EVALUATING AN IDEAL SAMPLE DESIGN

Three criteria should be borne in mind in the construction of a sample design.

1. It is important that measurable or known probability sampling techniques

should be used. Sampling procedures which do not provide a good basis for the

estimation of a sampling error should be avoided.

2. Due care should be exercised to use single, straight forward, workable

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methods suitably adapted to available techniques and personnel.

3. Every effort should be made to achieve maximum reliability of results for the

expenditure incurred as a result of the application of the sampling procedure.

SELECTING SAMPLE SIZE BY TRADITIONAL METHODS

Generally there are four traditional approaches to determining sample size.

First, the analyst can simply select a size either arbitrarily or on the basis of some

judgmentally based criterion. Similarly, there may be instances where the size of

sample represents all that were available at the time e.g., when a sample composed

of members of some organization and data collection occurs during a meeting of

the organization. Second, analysis consideration may enter and the sample size is

determined from the minimum cell size needed. For example if the critical aspect

of the analysis requires a breakdown of three variables which creates 12 cells, and

it is felt that there should be at least 30 observations in a cell, them the absolute

minimum sample size needed would be 360. Third, the budget may determine the

sample size. If, for example, the research design for a survey calls for personal

interviews, and the cost of, each interview is estimated to be Rs.50, and the

budgets allotted to data collection is Rs.10, 000, than the sample size would be

200.

It may appear that these methods are for non probability samples. While this

certainly is true, these methods are also applicable to probability samples and have

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occasionally been used for such samples. For probability samples, the precision

must be determined after data collection.

The fourth approach to sample size determination is based on specifying the

precision of estimation the desired in advance and then applying the appropriate

standard error formula to calculate the sample sizes. This is the approach of

traditional inference. Two major classes of procedures are available for estimating

sample sizes within the context of traditional inference. The first and better known,

of these is based on the idea of constructing confidence intervals around sample

means or proportions. This can be called the confidence-interval approach.

The second approach makes use of both type I (rejecting a true null

hypothesis) and type II (accepting a false null hypothesis) error risks and can be

called the hypothesis-testing approach.

However, two points must be made. First as with the other approaches, the analyst

must still calculate the standard error after data collection in order to know what it

is for the actual sample that provided data. Second, the size of sample that results

from traditional inference refers to the obtained (or resultant) sample.

Depending on the data collection method used, the original sample may have to be

much larger. For example, suppose that the size of the desired sample is 582. A

mail survey is used for data collection and past experience has shown that the

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response rate would be around 25%. Then original sample size in this case would

have be 2,328 in order that 582 responses would be obtained.

CRITERIA OF A GOOD SAMPLE

Whether the result obtained from a sample survey would be accurate or not

depends upon the quality of the sample. The characteristics of a good sample are

described below.

1. REPRESENTATIVENESS

A sample must be representative of the population. Probability sampling

techniques yield representative samples. In measurement terms, the sample must

be valid. The validity of a sample depends upon its accuracy and precision.

2. ACCURACY

Accuracy is defined as the degree to which bias is absent from the sample. An

accurate sample is one, which exactly represents the population. It is free from any

influence that causes any difference between sample value and population value.

3. PRECISION

The sample must yield precise estimates. Precision is measured by the

standard error or standard deviation of the sample estimate. The smaller the

standard error or estimate, the higher is the precision of the sample.

4. SIZE

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A good sample must be adequate in size in order to be reliable. The sample

should be of such size that the inferences drawn from the sample are accurate to a

given level of confidence.

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CHAPTER – V

SCALING TECHNIQUES

SCALING TECHNIQUES: MEANING

Scaling denotes the theory and practice of associating numbers with objects

by ranking the qualitative aspects such as character, ability, personality, etc. This

makes rigorous statistical analysis possible. Scaling can be done either by the

subject himself or by the interviewer. Ranking of candidates after a viva voce

examination scaling the items/objects/individuals in terms of certain attributes are

examples of scaling.

Broadly, techniques for registering differences in degree are of two types. In

the first type one makes a judgement about some characteristics of an individual

and places him directly on a scale defined in terms of that characteristic.

A scale is a continuum consisting of the highest point and the lowest point

there being several intermediate points between these two poles.

These scale point positions are so related to each other that the second point

indicates a higher degree in terms of a given characteristic compared to a third

point and the third point indicates a higher degree compared to the fourth and so

on.

The second type of technique for registering difference in degree consists of

a questionnaire constructed in such a way that the score of individual's responses

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assigns him a place on a scale. Both the rating scales and attitudes have the object

of assigning individuals to numerical positions to make possible the distinctions of

degree.

TYPES OF SCALE

Scales may be classified in many ways: in terms of,

a) subject matter,

b) scaling methods or techniques,

c) scale function

d) level of measurement, and

e) number of dimensions

However, there is no widely accepted system of classification,

a) Subject matter: Scales are designed to measure 1) attitude, 2) social distance,

3) socio-economic status, and 4) other variables.

b) Scaling techniques: In terms of techniques, scales may be classified into 1)

Arbitrary scales,2)Judgement scales,3) 'Item analysis' scales.4) Rating scales,5)

Ranking scales, 6) Cumulative scales, and 7) Factorial scales.

c) Scale Function: Scales may have their predictive powers or reproductivity.

Scales which have powers to predict an external criterion are „Productive scales‟.

Aptitude tests and prediction of marital adjustment are examples of this type of

scales. Reproductive scales are designed to arrange sets of data that if the research

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is given a single score, he can produce all items on the scale. Louis Guttman has

designed scales of this type.

d) Levels of Measurement: In terms of this property, scales may be classified as

1) nominal, 2) ordinal, 3) Interval or ratio scales.

e) Number of Dimensions: Scales are either 1) unidimensional or 2)

multidimensional.

Unidimensional scale measures only one attribute of the respondent or

object ,e.g. attitude, opinion, job satisfaction, so on. Most of the scales used in

research are of this type. Multidimensional scale measures several dimensions of

an object.

Following are the types of scales used in social science research.

(1) Graphic rating scale

This is perhaps the most widely used rating scale. In this type the rater

indicates his rating by simply making a mark at the appropriate point on a line that

runs from one extreme of the attribute or characteristic in question to the other

extreme. Scale points with brief descriptions may be indicated along the line their

junction being to help the rater in localizing his rating.

One of the major advantages of these scales is that they are relatively easy to use

and provide opportunity for fine discriminations of degree.

(2) Itemized rating scale

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These are also known as numerical scales. In this type the rater selects one

of a limited number of categories that are ordered in terms of their scale position.

Scales with five or seven categories have generally been employed but some have

used even as many as eleven points. Barker Dembo and Lewin in their study of the

effects of frustration on consecutiveness of play of young children constructed a

seven point scale for rating consecutiveness. They draw specific illustrations of the

activities that were judged to fall at various points on the scale. The first point on

the scale indicating least abstractiveness in the above study was the "toys are

examined superficially- the fourth point indicating moderate constructiveness was

"definitely more complicated and elaborate manipulation of the toys" and the

seventh point indicating the highest degree of constructiveness was -play showing

more then usual originality".

(3) Comparative Rating Scales

In this category of rating scales, the positions on the ratings scale are

expressly defined in terms of a given population, a group or in terms of people

with known characteristics.

The rater, for example, may be called upon to incite whether an individuals

problem-solving skill or some other attribute most closely resembles that of Mr. X

or of Mr. Y or of Mr. Z etc. - all of whom are known to him (the rater) in respect of

the skill or attribute. Or again, a rater may be asked to estimate the ability of an

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individual to do a certain kind of work as compared to the total group of persons

engaged in the above kind of work as compared to the total group of persons

engaged in the above kind of work and whom the rater has known. The rater then

may indicate whether the individual is more capable than 10% of them or 20% of

them, etc.

(4) Rank Order Scale

Here the rater is required to rank subjects/persons specifically in relation to

one another. The indicator which person rates the highest in terms of the

characteristic being measured which person is highest and so on. We have said that

In the rating scales the rater may himself be the subject to be rated This is called

self-rating have certain advantages indeed. The individual (rater himself)is often in

a better position to observe and report his feelings, opinion etc. than anyone else. Is

port if the individuals is not aware, as is not unusual, of his beliefs or feelings but

close not wish to express them for certain reasons(such as fear), then self-rating

procedure is of little value.

(5) Attitude Scales

In this approach, the individual does not directly describe himself in the

terms of his position (rater himself) on the dimension in question; rather he express

his agreement or disagreement with a number of statements relevant to the issue.

On the basis of his responses he is assigned a score. The score a person gets

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indicates his position on the dimension. Since this technique has been generally

used in the measurement of attitudes the students can understand the rationale

behind entitling these sections-attitude scales.

If has been said that the attitudes scales are constituted of various statement

items relevant to an issue (like nationalization of banks, co-education, inter-caste

marriages, etc). The individual subjects respond in a particular manner to these

statements. To these modes of response, particular scores are assigned. The way in

which a scale discriminates among individuals depends on the way in which the

scale is formulated and the method of scoring. In some scales, the statements/items

form a gradation of such a nature that the individual agrees with only one or two of

these and disagrees with the remaining statements on either side of those agreed to.

Such scales in which a person's response fixes his position, are called the

'differential scales'. In other scales, the individual indicates his agreement or

disagreement with all statements and his total score is computed by adding scores

assigned to his responses to all separate statements. Such scales may be called

summated scales. Yet another type of scales is set up in such a way that the

statements or items form a cumulative series, principally, an individual whose

attitude is at a certain point on the dimension being measured will answer

favourably all the items on one side of that point and answer unfavourably ail those

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on the other side of this point. They are called cumulative scales. Let us discuss

each of these types of scales hereunder.

a) Differential scales

These scales for measurement of attitudes are closely associated with the

name of L.L. Thurstone, hence often called Thurstone-type scale. This scale

consists of a number of statements whose position on the scale has been

determined by judges. The judges being persons whose judgements as to the

relative rank of different statements along dimensions can be relied on. The

method of equal-appearing intervals is most commonly used in the construction of

this scale. This method is described as under.

In selecting the statements for the scale and assigning scores to them, the

following procedure is used: (a) the researcher gathers a large number of

statements conceived as related to the attitude being, investigated, (b) A large

number of judges (up to 300) working independently are requested to classify these

statements into eleven groups or piles. The judges are requested to place in the first

pile the statements which they think are most favourable to the issue (or most

progressive or permissive, depending upon the dimension along which the

statements are to be placed) in the second pile those they consider next most

favourable and like this, in the eleventh pile the statements they consider most

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unfavourable. The sixth position is defined as the point at which the attitude is

neutral.

(c) The scale value of a statement is computed as the mean or 'median' position to

which it is assigned by the group of judges. Statements that have too broad a

scatter, that is, whose evaluation by different judges varies very widely, are

discarded as ambiguous or irrelevant.

(d) A final selection is made taking evaluated items that spread out evenly along

the scale from one extreme position to the other (scale values like, 10.3, 9.4, 8.4,

5.5, 4.7, 3.3, 2.6 and 1.7)

The resulting scale is thus a series of statements usually about twenty, the

position of each statement on the scale having been determined by the judges'

classification. The subjects are asked during the administration of the questionnaire

to check the statement or statements with which they agree or to check two or three

statements those are closest to their position.

b) Summated scales

A summated scale like the differential scale just discussed, consists of a

series of statements to which the subject is asked to react. The main difference

between the two is that unlike the differential scales, only statements that seem to

be either definitely favorable or definitely unfavourable are used in the scale (the

intermediate shades being excluded). The respondent / subject indicates his

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agreement or disagreement and degree thereof with each item. Each response is

given a numerical score indicating its favorableness or favourableness. The

summation of the scores of individual responses to all the separate statements gives

his total score. This score represents his position on the continuum of favourable -

unfavourableness towards an issue.

The type of summated scale most frequently used in the study of social

attitudes follows the pattern devised by Likert. Therefore, it is referred to as the

Likert type scale. In this scale, the subjects are asked to respond to each of the

statements in terms of several degrees of agreement or disagreement.

The procedure for constructing a Likert type scale is as follows, (a) The

investigator assembles a large number of statements considered relevant to the

attitude being investigated that are either clearly favorable or clearly unfavorable,

(b) The statements are administered to a small sample of subjects /representatives

of those on whom the questionnaire is to be finally administered. The subjects then

rate their response to each item by checking one of the categories of approval or

disapproval on the scale below each statement, (c) The responses to various items

are scored in such a way that a response indicative of the most favorable attitude is

given the highest score. It is important that the responses are scored consistently in

terms of the attitudinal direction they indicate whether 'approve' or' disapprove' is a

favorable response depends on the content and working of the statement.

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The Likert-type scale has several advantages over the Thurstone scale.

1. It permits the use of items that are not manifestly related to the attitude being

studied. This is so because in the Likert method any item that is found empirically

consistent with the total score can be included. Unlike Thurstone-type scale, there

is no necessity that agreement among judges that restricts the items (statements)

content which is obviously relates to the attitude being studied. As was indicated

earlier, it is a great advantage to be able to use themes that do not, on the face of it,

appear to have a direct relationship to the attitude being studied.

2. The Likert type scale is generally considered simpler to construct. At least the

procedure of construction is less cumbersome.

3. It is likely to be more reliable than a Thurstone-type scale for some items.

The Likert-type scale permits the expression of several degrees (usually five) of

agreement-disagreement; whereas the Thurstone-type scale allows a choice

between only two alternative responses, i.e., acceptance or rejection.

4. The range of responses permitted to a statement in the Likert-type scale

provides more precise information about the individual's opinion on the issue.

5. One disadvantage of the Likert scale is that often, the total score of an

individual has little clear meaning since many patterns of response to

the various statements may produce the same score.

c) Cumulative scales

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Cumulative scales like the earlier scales are made up of a series of items

with which the respondent indicates agreement or disagreement The distinctive

feature of a cumulative scale is that the items here are ordered or related to one

another in such a way that an individual who replies favourably to item No.3 also

repeats favourably to item No.2 and No.1 and one who replies favorably to items

No2 and No.1 and one who replies favourably to item No.4 also replies favourably

to items No 3,2 and land so on. Thus, all individuals who answer a given item

favourably have higher scores on the total scale than the individuals who answer

that item unfavourably.

The individuals score is calculated by counting the number of items he

answers favourably. This score places him on the sale of favourable-unfavourable

attitude provided by the relationship of the items to one another. One of the earlier

scales for measurement of attitudes, the Bogardus social distance scale, was

intended to be of a cumulative type. The social distance scale' which has become a

classic technique in the measuring of attitudes toward ethnic or racial groups tests a

number of relationships to which members of a given ethnic group might be

admitted. The respondent is asked to indicate for relationships to which he would

be willing to admit members of each of these groups. This attitude is measured by

the closeness of relationship that he is willing to accept or the distance that he

would wish to maintain.

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Technique developed by Guttmann known as 'scale analysis' or the

"Scalogram method', has, for its main purpose, to ascertain whether the attitude or

characteristic being studied actually involves only a single dimension. In the

Guttmann procedure, a universe of content (the attitude or characteristic under

study) is considered to be unidimensional only if it yields a perfect or almost

perfect cumulative scale.

Samuel Stouffer points out the characteristic feature of the Guttmann

technique thus; "it must be possible to order the items such that, persons who

answer a given question favourably all have higher ranks than persons who answer

the same question unfavourably... the response to any item provides a definition of

the respondent's attitude.

The Guttmann technique may rightly be regarded as a method of

determining whether a set of statements forms an unidimensional scale. It affords

no guidance for selecting statements that are likely to form an unidimensional

scale. The scale discrimination technique developed by Edwards and Kilpatrick is

a method of selecting a set of items likely to form a unidimensional scale.

The procedure suggested is as under

1. A large number of statements dealing with the issue of study are collected.

Items that are ambiguous, irrelevant neutral or too extreme are eliminated by

inspection.

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2. As in the Thurstone method a large number of Judges are requested to place

the remaining statements in eleven piles, according to their degree of favourable

ness- unfavourablness toward the issue. The un reliable items are discarded and each

of the remaining items is assigned a scale-value (Median position)

3. These statements are then transformed into a Likert-type scale by providing

for expression of various degrees of agreement-disagreement in response to each

item. This scale is administered to a large group of subjects and their responses

analysed to determine which of the items discriminate most clearly between the high

scores and the low scores on the total scale. Such items which have the highest

discriminatory co-efficient in their scale interval are selected in twice the number

that is actually wanted for use in the final scale. For each scale interval, an equal

number of items is selected.

4. The statement or items in the resulting list are arranged in order of their scale

value. The list is sub-divided into two counter part questionnaire.

5. But the one-dimensional scales do suffer from certain limitations which we

would do well to note.

6. One-dimensional scales hardly constitute a reliable basis for assessing

attitudes of persons towards complex objects or phenomena or for predicting the

behavioural responses of individuals towards each object or phenomena. For

instance' war' is a complex concept; hence the one-dimensional scale does not quite

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help us to measure the attitudes of men toward war. It is of course, possible to

construct and use one-dimensional scales for the implications of war on economy,

health, morality etc., in an independent study.

7. Although these dimensions of war do effect the final shape of a person's

attitudes towards war, independent enquiries basing their assessment on

unidimensional scale hardly effort us a total perspective on persons attitude toward

'war' in all its many-sided connotations.

Secondly a scale may be unidimensional for some person but not so for

others. In our discussion of cumulative scales, we have shown how the items on

the scale do not constitute a cumulative series for one and all. Differences in

educational levels and experience are reflected in a person's subjective evaluation

of the scale items.

1. Harding and Hogrefe have in their study demonstrated how a single scale did not

in effect function as a unidimensioned scale on the three different categories of

workers.

SCALE CONSTRUCTION TECHNIQUES

Following are the five main techniques by which scales can be developed.

1. Arbitrary approach

It is an approach where scale is developed on ad-hoc basis. This is the most

widely used approach. It is presumed that such scales measure the concepts for

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which they have been designed, though there is little evidence to support such an

assumption.

2. Consensus approach

Here a panel of judges evaluates the items chosen for inclusion in the

instrument in terms of whether they are relevant to the topic area and unambiguous

in implication.

3. Item analysis

Under this a number of individual items are developed into a test which is

given to a group of respondents. After administering the test, the scores are

calculated for everyone. Individuals are then analysed to determine which items

discriminate between persons or objects with high total scores and those with low

scores.

4. Cumulative scales

The cumulative scales are chesen on the ba* of their conforming to the

ranking of items with ascending and descending discriminating pewer. For

instance, in such a scale the endorsement of an item representing an extreme

position should also result in the endorsement of all items indicating a less extreme

position.

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5. Factor scales

These scales may be constructed on the basis of inter correlations of items

which indicate that a common factor accounts for the relationship between items.

This relationship is typically measured through factor analysis method.

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CHAPTER – VI

DATA COLLECTION

INTRODUCTION

Once the researcher has decided on the research design, the next job is data

collection .For data to be useful, our observations need to be organized so that one

can get some patterns and come to logical conclusions.

Statistical investigation requires systematic collection of data, so that all relevant

groups are represented in the data.

DATA - Meaning

"Data are facts, figures and other relevant materials, past and present,

serving as bases for study and analysis."

To determine the potential market for a new product, for example, the

researcher might study 500 consumers in a certain geographical area. People

represent variables such as income level, race, education and neighborhood. The

quality of data will greatly affect the conclusions and hence, utmost importance

must be given to this process and every possible precaution should be taken to

ensure accuracy while gathering and collecting data.

TYPES OF DATA

Depending upon the sources utilized, viz whether the data has come from

actual observations, or from records that are kept for normal purposes, statistical

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data can be classified into two categories as primary and secondary. However, the

data needed for a social science research may be broadly classified into

a) data pertaining to human beings

b) data relating to organizations, and

c) data pertaining to territorial areas

Personal data or data related to human beings consists of,

1. demographic and socio-economic characteristics of individuals: age, sex,

social class, religion, marital status, education, occupation, income, family

size, location of the house hold, life style etc.

2. Behavioural variables: attitudes, opinions, awareness, knowledge, practice,

intentions, etc.

Organisational data consists of data relating to an organisation's origin,

ownership, objectives, resources, functions, performance, and growth.

Territorial data is related to geophysical characteristics, resources

endowment, population, occupational pattern, Infrastructure, economic structure,

degree of development, etc. of spatial divisions like villages, cities, taluks, districts,

states and the nation.

SOURCES OF DATA

The two main sources of data in social science research are „people‟ and

„paper‟. The responses to questions put to people contribute the major source of

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data in social research. This source is labeled as the primary source of data. A large

amount of data is already available in the form of „paper‟ sources.

These incudes documents, historical records, diaries, biographies, statistical

records, and the like. The paper sources are commonly known as secondary

sources of data or 'available data sources'.

When a researcher decides to collect data through the primary source, he has

three options, namely observation, interview and questionnaire and, in case he opts

for the secondary source of data, he uses the methods of content analysis.

PRIMARY DATA

Primary data is one which is collected by the investigator himself for the

purpose of a specific inquiry or study. Such data is original in character and is

generated by surveys. These data are collected for the first time and have not been

collected earlier. Primary data are first hand information collected through various

methods such as observation, interview, mailing etc.

SECONDARY DATA

When an investigator uses the data which has already been collected by

others, such data is called secondary data. This data is primary data for the agency

that collects it and becomes secondary data for someone else who uses this data for

his own purposes. The secondary data can be obtained from journals, reports,

government publications, publication of professional and research organizations

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and so on. For example if a researcher desires to analyze the weather conditions of

different regions, he can get the required information or data from the records of

the meteorology department.

DATA COLLECTION METHODS

OBSERVATION

Observation is the basic method of obtaining information about social

phenomena under investigation. Observation becomes a method of data collection

when it is planned in accordance with the purpose of research and records

systematically, keeping ,in mind the validity and reliability of observed data.

Information is collected by observing the process at work.

The method can be used to study sales techniques, customer movements,

customer response, etc. However the customers/consumer's state of mind, their

buying motives their linguiations are not revealed. Their income and education is

also not known. It also takes time for the investigator to wait for particular

situations to take place.

SURVEY

According to Hutton (1990) survey research is the method of collecting

information by asking a set of pre formulated questions in a predetermined

sequence in a structured questionnaire to a sample of individuals drawn so as to be

representative of a defined population.

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Questionnaires and interviews are both types of survey; surveys are probably

the most common forms of research method, the primary function of surveys is to

collect information which can then be analyzed to produce conclusions. In order, to

evaluate different survey methods it is necessary first to look at the purpose to

which the information will be put. The first purpose of surveys Is to describe what

is going on; to obtain all the relevant facts about something; and to state those facts

as quantifiable data. More sophisticated descriptive surveys try to identify areas

where problems occur. Or where changes are required, other may seek to measure

the extent and nature of known problems.

In addition to explaining past change, surveys, can be used to predict future

changes. The changes may be beyond the control of the surveyor in which case the

survey seeks to isolate the magnitude, nature and timing of the change.

Accordingly, the surveyor may be in control of the change, in which case the

survey is more likely to be concerned with predicting the outcomes of the change

or examining the merits of different policy options. Once the changes have been

made, the surveyor may be called upon to evaluate the results of the change,

perhaps even suggesting further changes which might be necessary. Generally

surveys are very flexible and widely used research methods. Given a small amount

of common sense they can be used by relatively inexperienced researchers to

produce useful results.

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QUESTIONNAIRE

By questionnaire is meant a set of questions developed in an organized and

ordered manner for finding information from the people in relation to a given

problem. Questionnaire method of data collection is the tool which is more

frequently used in mail survey research than any other method of data collection.

Mail survey research is that branch of scientific investigation which studies the

universe by selecting a sample from it and uses the mail media for collecting data.

In survey research one attempts to discover the reliable incidence, distribution and

interrelation of sociological and psychological variables. Though the approach and

technique of survey research can be used on any set of objects that can be well

defined, survey research mainly focuses on people, the vital facts of people, and

their beliefs, opinion,, attitudes, mutations, memory, behaviors, actions,

interactions and even future plans. The questionnaire is the best tool for collecting

information in all the above areas, though besides it from time to time, other types

of methods, like interview method based upon schedule, or projective techniques

have also been used.

INTERVIEW SCHEDULE

Questionnaire and Schedule are increasingly used for the collection of varied

and diverse data in survey research. Their popularity and utility are enhancing day

by day. The reason for the growing popularity is the recognition by social science

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researchers of the importance quantitative measurement of qualitative social

behavior. The features of the interview schedule are given below.

1. The schedule is generally filled out by the research worker of the enumerator,

who can interpret questions where necessary.

2. Collection of data through schedule is relatively expensive due to the

appointment of enumerators and imparting training to them.

3. In schedule method, the response rate is very high because they are filled by

enumerators who are able to get answers to all questions.

4. In case of schedule the identity of respondents is known.

5. In case of schedule the information is collected well in time as they are filled in

by enumerators.

6. Direct personal contact is established with respondents in the schedule method.

7. The information can be gathered even when the respondents happen to be

illiterate.

8. In respect of schedules there will be difficulty in sending enumerators over

relatively wider areas.

9. The information collected If generally complete and accurate as enumerators,

can remove the difficulties faced by respondents in correctly understanding the

questions.

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10. In case of schedule much depends upon the honesty and competence of

enumerators.

11. The difficulty with schedules is that they are to be filled in by enumerators and

not by respondents and the responses may not be the respondents.

12. Observation method can also be used along with schedules.

The schedule is especially more useful and economical in situations where the

respondents' geographical dispersal is wide. Schedule also isolates the respondent

from external Influence. The respondent is totally free to express his views

according to his knowledge, views and attitudes in an unbiased manner. Data

obtained without external influence is more valid and reliable.

EFFECTIVE INTERVIEW TECHNIQUES

Despite the fact that the interview schedules have a lot of advantages, they

may be made more effective by adopting the following techniques.

1. Before asking questions, introduce yourself or have a mutual friend Introduce

you.

2. Briefly explain the purpose of the survey and cite the relevant authorities who

have approved the-survey. Do not deviate from the study objectives which was

developed for the survey. Offer proof of interviewer status or letter of

authorization If necessary.

3. Be confident, not afraid or wary. You are a trained professional on an

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assignment for an important purpose. If you convey insecurity or hesitancy to

the respondent; he will immediately suspect your motives.

4. Use an informal manner of speaking, natural to you, aimed at putting the

respondents at ease. Know the questions so well that you never sound as though

you're reading them formally.

5. Never get involved in a lengthy explanation of the study. Use the standard

responses that have been provided.

6. Follow the instructions for interviews explicitly. Do not change the sequence

of the questions or deviate or skip and filter instructions.

7. Ask the questions exactly as they are written on the questionnaire (or with any

minor wordings changes that were agreed on in training). You must ask the

questions exactly as all the other interviewers do so that results can be

combined and interpreted meaningfully.,

8. Ask the questions in a respectful manner, and do not imply that some answers

are "better" than others. A respondent may deny that she would like to have

more children if she believes that the interviewer associates large families with

poor family welfare.

9. Do not by word or action indicate surprise, pleasure, or disapproval at any

answer. Even a slight change in expression will tell a respondent that you have

reacted to his answer.

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10. When an answer is unclear repeat the question exactly as written, or ask it in a

slightly different way (if agreed during training) being careful not to change the

meaning or lead the respondent to a particular response. If a respondent seems

confused by a question, even after repetition, record whatever answer is given.

11. For personal or cultural reasons, people may not want to talk about

sensitive issues like contraceptive use or the death of a child. In such

cases you will needed to be particularly sensitive and respectful in the

way you ask questions.

12. Record open ended questions verbatim. Do not paraphrase or interpret. The

exact words people use to describe opinions and5 attitudes are important.

13. Always take the blame for faulty communication. If the respondent stumbles,

say that perhaps you did not read the question clearly or read it too rapidly.

Never let the respondent feel the questions are too difficult for her.

14. Check over the questionnaire before leaving the interview to make sure all the

questions were asked and the answers recorded legibly.

LIMITATIONS OF INTERVIEW METHOD

In the interview method the information obtained is often difficult to

analyze. It may be possible to use quantitative methods for some of it, but the

method will probably have been chosen to collect information which, by its very

nature, is not susceptible to this form of analysis.

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The main problem is that of ensuring a high degree of consistency in the

presentation of the interview. Trained interviewers can overcome many of the

problems associated with bias; becoming too personally involved with the

interview, or simply the tiring experience of coming into direct contact with a large

number of strangers every day.

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CHAPTER – VII

TOOLS OF DATA COLLECTION

CONSTRUCTING QUESTONNAIRE

A questionnaire is a proforma containing a sequence of questions to elicit

information from the interviewees. The questionnaire is used for personal

Interview. At the same time the questionnaire is also mailed to individuals who are

requested to write the answers against each question and to return the completed

proforma by post. Thus, it is especially true of a mail questionnaire which

essentially has to speak for itself. If it is not clean not only the replies may be

vague and of little value but many potential respondents may not bother returning

the questionnaire at all.

A questionnaire may be said to possess three main aspects

1. the general form

2. the question sequence

3. the question wording

1. The general form

The form of a questionnaire will depend partly on the type of data being

sought and partly on the data collection method to be used. The choice lies

between two extremes. On the one hand, there is the highly structured

questionnaire in which all questions and answers are specified and comments, in

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the respondents' own words are held to a, minimum. At the other end is the

unstructured questionnaire in which the interviewer is provided with a general

brief on the sort of information to be obtained but the exact question is largely his

own responsibility.

The unstructured questionnaires are useful in carrying out in depth

interviews where the aim is to probe for attitudes and reasons. They may also be

effectively employed in pre-testing, the result of which can be used as a basis for

constructing a structured questionnaire at a later stage. Thus, in order to ascertain

the expectations of the television viewers about a programme, interviews may be

conducted with an unstructured questionnaire. The resulting range of answers can

then be used to prepare a structured questionnaire for use in the main part of the

study.

The main disadvantage with an unstructured questionnaire is that it requires

a personal interview. It cannot be used in the mailed questionnaire method of data

collection.

A structured questionnaire usually has fixed alternative answers to each

question. They are simple to administer and relatively inexpensive to analyse. The

questionnaires have, however, their own limitations. It is not possible to record the

responses made by the respondents in their own words. They are considered

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inappropriate in investigations where the aim happens to be to probe for attitudes

and feelings.

2. The question sequence

The introduction to the questionnaire should be as short and simple as

possible. The introductory letter accompanying the mailed questionnaire should

also be made very brief. The introduction lays the foundation for establishing

rapport with the respondent in addition to making the interview possible.

Once the rapport is established the questions will generally seek substantive

information of value to the study. As a general rule, questions that put too great a

strain on the memory or the intellect should be reserved till later. Likewise,

questions relating to personal wealth and personal character should be avoided in

the beginning.

Following the opening phase, should come the questions that are really vital

to the interview. Even here, substantive questions should be surrounded by more

interesting ones in order that the attention does not slip. Awkward questions, which

create the risk the respondent discontinuing the interview, are usually relegated

toward the end. By the time the interview has been terminated, some information is

available with the interviewer.

Ideally, the question sequence should conform to the respondents' way of

thinking, and this is where unstructured interviews are highly advantageous. The

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interviewer can rearrange the order of the questions to fit the discussion in each

particular case. With a structured questionnaire the best that can be done is to

determine with pre-testing the question sequence which is likely to produce good

rapport with most people.

3. The question wording

It has been stated that the question wording and formulation are more of an

art than a science. Science does enter, however, in testing the stability and the

adequacy of replies for business and management decisions. The wording of the

questions should be impartial so as not to give a biased picture of the true state of

affairs. Colourful adjectives and undue descriptive phrases should be avoided. In

general the questions should be worded such that a) they are easily understood b)

they are simple c) they are concrete and conform to the respondents' way of

thinking.

Multiple choice questions constitute the basis of a structured questionnaire,

particularly in a mailed questionnaire method. But in addition to these questions

various open ended questions are generally inserted to provide a more complete

picture of the respondent's feelings and attitudes.

Thus construction of a questionnaire is of great importance in social

research. The following points are to be kept in mind while constructing a

questionnaire.

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• The most important aspect of questionnaire design is to define the

research problem, to be tackled and framing the questions as per the

requirement. Lengthy and rambling questionnaires are demoralizing for

the interviewer and the respondent. The questionnaire should not be

longer than is absolutely necessary for the purpose.

• The questionnaire must introduce each topic in such a way that it appeals

to the perceptions of the respondent It must meet the respondent's

intention for reasonableness and logic.

• The questions should be practicable. No attempt should be made to

ask a person's opinion about something he does not understand or about matters

which are personals emotional.

• Unrealistic assumptions should not be made. Questionnaires should not

assume that the respondent necessarily possess any knowledge or an opinion on the

survey subject.

• The vocabulary and syntax of the questionnaire should offer the respondent

maximum opportunity for complete and accurate communication of ideas between

interviewer and respondent. The language of the questionnaire must be close to the

respondents.

• Questions should consist of simple words, which will convey the exact

meaning. Ambiguous and vague words should be avoided.

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• It is not desirable to use leading questions. Questions should be placed in such

a way that they do not contain any suggestions as to the most appropriate response.

• Respondents should not be compelled by such questions to give socially

unacceptable responses.

• As for as possible each question should be concerned with a single issue or a

single reference.

• Questions should be so arranged that they make the most sense to the

respondents. The sequence of questions may be determined by the “formal

method”. Panel method is an approach of asking the most general, broad and

unrestricted questions first and allowing it with successively narrow or restricted

questions.

• Some respondent like to remain unidentified and unrecognized. The

respondents feel free to answer if anonymity / confidentiality is maintained.

• A questionnaire designer has ensure that all the necessary items are duly

incorporated in the questionnaire. The researcher may take the help of a standard a

check list to see that all the required items are included in the questionnaire. A

check list can also be prepared by the researcher himself.

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FORMAT OF A GOOD QUESTIONNAIRE

The following specimen questionnaire incorporates most of the qualities of a

good questionnaire. The first questionnaire relates to Spencer Plaza. It is aimed at

evolving better ways of providing shopping facilities to the consumers.

ADVANTAGES OF A QUESTIONNAIRE

One cannot know by observation, why a buyer makes particular purchases or

what his opinion about a product is. Compared with either direct observation or

experimentation, surveys yield a broader range of information and are effective for

producing information on socio-economic characteristics, attitudes, opinions,

motives etc and to gather information for planning product features, advertising

copy, advertising media, sales promotions, channels of distribution and other

marketing variables. Questioning is much more cheaper than observation.

DISADVANTAGES

1. Unwillingness of respondents to provide information. This requires

salesmanship on the part of the interviewer. The interviewer may assure that the

information will be kept secret. Motivating respondents with some token gifts

often yields results.

2. Inability of the respondents to provide information this may be due to

i. Lack of knowledge

ii. Lapse of memory

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iii. Inability to identify their motives and provide 'reasons why' for their actions.

3. Human biases of the respondent: i.e., Ego etc.

4. Semantic difficulties: It is difficult, if not impossible to state a given question

in such a way that it will mean exactly the same thing to every respondent.

Similarly, two different wordings of the same questions will frequently generate

quite different results. These limitations can be controlled to some extent by:

i. Careful phrasing of questions.

ii. Careful control of data gathering by employing specially trained

investigators who will observe carefully and report on the suitable reactions

of the person interviewed.

iii. Cautions interpretation by clearly accesting of the limitations e data and an

understanding of what exactly the data represents. This is especially true of

responses to questions like.

"What price would you be willing to pay for this product?"

iv. Looking at facts in related rather than absolute terms. A survey showed that

60% of families in the middle income group used toothpaste. Taken by

itself, in the absolute sense, the results of the survey are in some doubt

because the question asked encountered an obvious bias. But if this 60% is

looked at on a relative basis, viz. the corresponding figure of 60% for upper

income group families, a more meaningful and significant interpretation can

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be made, even though the individual figure for each group may be slightly

inflated.

ADVANTAGES OF SCHEDULES

Both questionnaire and Schedule are popularly used methods of collecting

data in research surveys. They have much resemblance but from the technical point

of view, there is some difference between the two and the following are some of

the advantages of a schedule.

Higher Responses

In case of a schedule, since a research worker is present and he can explain

and persuade the respondent, response rate is high. In case of any mistake in the

schedule the researcher can rectify it.

Saving Of Time

While filling the schedule the research may use abbreviations or short terms

for answers, He may also generate a template. All these steps help in saving of

time in data collection.

Personal Context

In the schedule method there is a personal context between the respondent

and the field worker. The behavior and character of the respondent obviously

facilitates the research work.

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Human Touch

Sometimes reading something does not impress as much as when the same is

heard or spoken by experts as they are able to lay the right emphasis. This greatly

improves the response.

Deeper probe

Through this method it is possible to probe deeper into the personality,

living conditions, values, etc, of the respondents.

Defects in sampling are detected

If there are some defects in the schedule during sampling, it easily comes to

the notice of the researcher and can be rectified by the researcher.

Removal of doubts

Presence of the enumerator removes the doubts in the minds of respondents

on the one hand and helps avoid artificial replies from the respondent owing to fear

of cross checking on the other hand.

Human elements make the study more reliable and dependable.

The presence of human elements makes the situation more attractive and

interesting which helps in making the interview useful and reliable.

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LIMITATIONS OF A SCHEDULE

In spite of the advantages, schedules have the following limitations.

1. Costly and Time Consuming

This method is costly and time consuming because of its basic requirements

of interviewing all the respondents. This becomes a serious limitation when

respondents are not found in a particular region but are scattered over a wide area.

2. Need for Trained Field Workers

The schedule method requires involvement of well trained and experience

field workers. This involves great cost and sometimes workers are not easily

available forcing engagement of inexperienced hands. This defeats the purpose of

the research.

3. Adverse Effect of Personal Presence

Sometimes the personal presence of enumerators becomes an inhibiting

factor. Many people despite knowing certain facts may not say them in the

presence of other persons.

4. Organisational Difficulties

If the field of research is dispersed, it becomes difficult to organize it.

Getting trained manpower, assigning them duties and then administering the

research is a very difficult task.

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PILOT STUDY

It is difficult to plan a major study or project without adequate knowledge of

its subject matter, the population it is to cover, their level of knowledge and

understanding and the like. What are the issues involved? What are the concepts

associated with the subject matter? How can they be operationalised? What method

of study is appropriate? How long will the study take? How much; | money will it

cost? These and other related questions call for a good deal of knowledge on the

subject matter of the study and its dimensions. In order to gain such pre-knowledge

of the subject matter of an extensive study, a preliminary investigation is

conducted. This is called a „Pilot study‟.

A pilot study is a "small scale replica" of the main study. It is the rehearsal

of the main study. It covers the entire process of research viz. preparation of a

broad plan of the study, construction of tools, collection of data, processing and

analysis of data and reporting.

FUNCTIONS OF A PILOT STUDY

A pilot study fulfills one or more of the following purposes

1. It provides a better knowledge of the problem under study and its

dimensions.

2. It provides guidance on conceptualization.

3. It assists in discovering the nature of relationship between variables and in

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formulating hypothesis.

4. It shows the nature of the population to be surveyed and the variability

within it.

5. A pilot study shows whether the available sampling frame from which

sampling is to be drawn is adequate, complete, accurate, up to date and

convenient.

6. It shows the adequacy of the tool for data collection.

7. It helps the researcher to develop an appropriate plan of analysis.

8. It provides data on the relative suitability of alternative methods of collection

of data, their relative cost, accuracy and response rates to make a sensible choice.

9. it helps the researcher to identify field problems to be encountered.

10. it provides information for estimating the probable cost and duration of the

main study and of its various stages.

11. Above all, it helps the researcher to determine whether or not a more

substantial study is warranted.

SIZE AND DESIGN OF PILOT STUDY

The size, scope and design of the pilot study are a matter of convenience

time and money. It should be large enough to fulfill the above functions and the

sample should be of a comparable structure to that of the main study. It should be

designed so as to ensure a testing of alternative methods of data collection,

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ordering of the questions, wording and the like. It should succeed in disclosing the

significant difficulties to be guarded against.

In the light of the outcome of the pilot study, if it is found that the main

study is worth understanding, then it is adequately designed on the basis of the

results of the pilot study and the lessons drawn from its experience.

PRE-TESTING

While a pilot study is a full fledged miniature study of a problem, pre-test is

a trial test of a specific aspect of the study such as method of data collection or data

collection instrument- interview schedule, mailed questionnaire or measurement

scale.

Before the final form of the questionnaire is adopted it is desirable to carry

out a preliminary experiment on a sample basis. When questionnaires are to be

distributed on a large scale, it is absolutely essential to pre-test them. There are

many advantages of pre-testing the questionnaire, such as:

1. The investigator can find out what are the drawbacks of the questionnaire,

i.e., which questions ought to be deleted and which more might to be added.

2. An idea can be formed about the extent of non-responses likely to happen.

3. Greater co-operation of the informants can be secured. Even persons most

allergic to writing can, with proper inducement, be prevailed upon to answer the

questionnaire. It is the surveyor‟s job to find out what these appeals are.

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While pre-testing the questionnaire, it is important always to cover a cross-section

of the population eventually to be surveyed. When the sample is drawn, it should

be broken down into various sub-samples by taking, for instance, every tenth or

every hundredth case from the entire list.

The work of pre-testing the questionnaire must be done with utmost care and

caution, otherwise unnecessary and unwanted changes may be introduced. Proper

testing, revising and re-testing the questionnaire would yield high dividends.

If time and budget permit, a second pilot study should be undertaken on a

fresh sample of respondents to further improve the final document.

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CHAPTER - VIII

ANALYSIS AND PROCESSING OF DATA

MEANING

After the collection of research data, an analysis of the data and the

interpretation of the results are necessary. Analysis of data comes prior to

interpretation. But these two operations are so lined up that they cannot be

regarded as two separate operations. There is something more crucial than the facts

and figures in research. The purpose of analysis is to build up a set of empirical

models where the relationships involved are carefully brought out so that some

meaningful inferences can be drawn. Analysis of data is to be made with reference

to the purpose / objectives of the study and its possible bearing on the scientific

discovery. An analysis is made with reference to the research problem at hand or

the hypothesis. Some authors consider processing a necessary prerequisite for

analysis. But many maintain that analysis of data involves processing. In other

words, these two operations can be simultaneously done.

DATA ANALYSIS

The first step in the analysis of data is the critical examination of the

gathered data during the study. The analysis is made with a view to find out some

significance for a systematic theory and some basis for a broader generalization.

For analyzing data, a context type analysis process is helpful. Analysis involves the

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verification of the hypothesis or the problem. Without proper analysis, data

remains a meaningless heap of materials. Analysis involves the representation of

data, which can be done by tabulation sequentially and analysis requires logical

organization of data; otherwise, logical results cannot be achieved.

EDITING

The first step in processing of data is editing of complete schedules /

questionnaires. Editing is a process of checking to detect and correct errors and

omissions. Editing is done at two stages; first at the field work level and second at

office level.

Field Editing

During the process of interviewing, the interviewer cannot always record

responses completely and legibly. Therefore after each interview is over, he should

review the schedule to complete abbreviated responses, rewrite illegible responses

and correct omissions.

Office Editing

All completed schedules / questionnaires should be thoroughly checked in

the office for completeness, accuracy and uniformity. The first aspect to check is to

see whether there is an answer to every question. If there is an omission, the editor

can deduce the proper answer from other related data on the instrument. Apart

from checking for omissions, the accuracy of each recorded answer should be

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checked. Arithmetic errors can be easily corrected. Clear inconsistencies should be

rectified. In editing another keen lookout is for any lack of uniformity in

interpretation of questions and instructions by the interviewers. Errors of this type

should be carefully examined and rectified. In order to examine the relationship

between answers to different questions and to detect inconsistencies, it is better to

edit the schedule / questionnaire as a whole. In making the corrections, original

entries should not be erased but just crossed out with a single line. Corrections may

be made in some distinctive colour and should be initiated.

As the editing involves a careful scrutiny of the questionnaires/ schedules,

they should be checked for 1) completeness.2) leglbllty.3) comprehensibility, 4)

consistency, 5) uniformity, 6) relevance.

Categorisation and classification

The edited data are classified and coded. The responses are classified into

meaningful categories so as to bring out their essential pattern. By this method,

several hundred responses are reduced to five or six appropriate categories

containing critical information needed for analysis. The collected data are not

amenable to analysis, unless an appropriate scheme of classification is used. The

set of categories is referred to as a 'coding frame'.

Classification can be done at any phase prior to the tabulation. Certain items

like sex, age, type of house and the like are structured and pre-classified in the data

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collection form itself. The responses to the open ended questions are classified at

the processing stage. It is preferable to include many categories rather than a few,

since reducing the number later is 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.

CODING

Coding means assigning numerals or other symbols to the categories or

responses. The purpose of symbols is to translate raw data into information that

may be counted and tabulated. The task of a coder is to give proper codes to the

responses. The respondent himself is a coder when he is denoting his position in a

particular claim or category (e.g., unemployed or employed) secondly, coding can

be done by the observers the interviewer at the time of data collection. Thirdly, the

officially appointed coder may perform the job of coding. There may be many

difficulties in coding due to the inadequacies of data, inefficiency of the coder and

lack of editing or scrutiny of the available data. Editing can be very helpful for

coding and for the improvement of the quality of data collection.

The coding instructions with their assigned symbols together with specific

coding instructions may be assembled in a book. The code book will identify

specific items of a variable/ observation and the code number assigned to each

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category of that item. If the data are to be transferred to machine punch-cards, the

code book will also identify the column in which it is to be entered.

There are many things that may operate to make the judgements of coders

unreliable. Some of the factors may arise from the data to be categorized, some

from the nature of the categories that are to be applied and still others, from the

coders themselves. Many of the difficulties that occur in coding result from the

inadequacies of data. Frequently, the data do not supply enough relevant

information for reliable coding. This could be due to inadequate data collection

procedures. These difficulties, however, can generally be overcome by careful

editing of data.

TRANSCRIPTION

When only a few schedules are processed and hand-tabulated, tabulation can

directly be made from the schedules. On the other hand, direct tabulation from the

edited schedules/ questionnaires is difficult if the number of the schedules and the

number of response in them are large. Suppose an interview schedule contains 180

responses requiring tabulation and 210 simple and cross tables are to be

constructed, each schedule has to be handled at least 210 times for tabulation. This

will result in mutilation of the schedule, and omissions and commissions may

easily occur in tabulation. In order to avoid these drawbacks, data contained in

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schedules/ questionnaires are transferred to another material for the purpose of

tabulation. This intermediary process is called 'transcription'.

The materials to be used for transcription depend on the method of

tabulation-manual or mechanical. Long work sheets, sorting cards or sorting strips

are used for transcription when tabulation is done manually, and punch cards or

magnetic tape (or discs) are used in a system of machine sorting and tabulation.

DIAGRAMS

Diagrams are generally more attractive, fascinating and impressive than the

any of numerical data. They are more appealing to the eyes and leave a much

lasting impression on the mind compared to the dry and uninteresting statistical

figures. Even a lay man who has no statistical background can understand them

easily. They are more catchy and as such are extensively used to present statistical

figures and facts in most of the exhibitions, trade or industrial fairs, public

functions, statistical reports, etc. The human mind has a natural craving and love

for beautiful pictures and this psychology of the human mind has been thoroughly

by the modern advertising agencies who bring out their advertisements in the shape

of attractive and beautiful pictures.

Accordingly diagrams and graphs have universal applicability. They register

a meaningful impression on the mind almost before we think. They also show a

number of them as very little effort is required to group them and draw meaningful

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inferences from them. An individual may not like to through a set of numerical

figures but may pause for a white to have a glance at the diagrams or pictures. It is

for this reason that diagrams, graphs and charts find a place almost daily in

financial / business columns of the newspapers, economic and business journals,

annual reports of business houses etc. Graphs reveal the trends, present in the data

more avidly than the tabulated numerical figures and also exhibit the way in which

the trends change. Although this information is exhibited in a table it may be quite

different and time consuming (and sometimes may be impossible) to determine the

existence and nature of trends from the tabulation of data.

TABULATION

After the transcription of data is over, data are summarized and arranged in a

compact form for further analysis. This process is called „tabulation‟. Tabulation is

a process of orderly arrangements of data into series of rows and columns where

they can be read in two dimensions; Tabulation is essential to represent a particular

result of enquiry investigation. As has already been mentioned, a mass of data

termed as 'raw materials' can express no meaning without being tabulated. In

reality, tabulation is a process of representation of data.

Sometimes a question is asked, why is tabulation done? The reasons are manifold

i. it simplifies the comparison of data,

ii. it makes the study complete and accurate in every respect,

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iii. it makes the comparison easier together with saving a lot of time and energy.

iv. it makes statistical treatment a possibility and

v. finally, tabulation makes the 'affairs' easily intelligible; which, in the absence

of tabulation, becomes not only difficult to understand but almost impossible

to give a practical shape.

Construction of Tables

After the data have been tabulated, they are arranged in statistical tables in

vertical columns and horizontal rows according to some classification. 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 give a visual picture of relationships between variables and

categories; they facilitate summation of items and detection of errors and

omissions; and they provide a basis for computations.

It is important to make a distinction between the general purpose tables and

the special 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 derivative ones which demonstrate

significant relationships in data or the results of statistical analysis. Tables in

reports of government on population, vital statistics, of agriculture, industries etc.

are of the general purpose type. They represent extensive repositories of statistical

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information. Special purpose tables are found in monographs, research reports and

articles, and are used as instruments of analysis. In research we are primarily

concerned with special purpose tables.

Components of Tables The major components of a table are:

A. Heading

1. Table Number

2. Title of the table

3. Designation of units

B. Body

Sub-head- headings of all rows or blocks of sub items Boxhead - headings of all

columns or main captions and their sub-captions

3. Field or body - the cells in rows and columns

C. Notations

1. Footnotes, if necessary

2. Source

Principle of Table construction

There are certain generally accepted principles of rules relating to the

construction of tables. They are

1. Every table should have a title.

2. Every table should be identified by a number to facilitate easy reference.

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3. The captions should be clear and brief.

4. The units of measurement should always be indicated.

5. Any explanatory footnote concerning the table itself should be are placed

beneath the table

6. The specific source of the data should be indicated just below the table.

7. The columns may be numbered to facilitate reference.

8. All columns should be properly aligned.

9. The arrangement of the categories in a table may be chronological,

geographical, alphabetical or according to magnitude. Abbreviations should be

avoided.

10. The table should be made as logical, clear, accurate and simple as possible.

Types of Tables

Normally two types of tables are used to represent the data in research viz.

one-way tables and Two-way tables.

One way tables

Frequency tables present the distribution of cases on only a single dimension

or variable. For example, distribution of respondents by sex, religion, socio-

economic status and the like are shown in one way tables. Following is an

illustration of a one way table.

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Distribution of respondents by sex

Sex No. of respondents %

Males 130 65

Females 70 35

Total 200 100

Two way tables

Distribution in terms of two or more variables and the relationship between

two variables are shown in two-way tables. The categories of the one variable are

presented, one below another, on the left margin of the table and those of another

variable at the upper part of the table, one by the side of another. The cells

represent particular combinations of both variables. To compare the distribution of

cases raw numbers are converted into percentages based on the number of cases in

each category as shown below.

Socio-economic status of the respondents

Extent of participation

Low Medium High

Categories No. of %

Respon-

dents

No. of %

respon-

dents

No. of %

respondents

Total

Rural area 65 41.9 83 56.8 2 1.3 155 Urban area 4 10.3 33 84.6 2 5.1 39

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TYPES AND GENERAL RULES

The most commonly used graphic forms may be grouped into the following

categories.

1. Line graphs and charts

2. Bar charts

3. Segmental representation

4. Pictographs.

The general rules to be followed in graphic representation 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.

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 the left to right on the horizontal line and from

bottom to top on the vertical line.

6. Each curve or bar on the chart should be labeled. In case of more than one

such curve /bar, they should be clearly differentiated from one another by a distinct

pattern or colour.

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7. The zero point should be represented and the scale intervals should be equal.

8. Graphic forms should be used sparingly. Too many forms detract from the

representation.

9. Graphic forms should follow, not precede, the related textual discussion.

LINE GRAPHS

The line graph is useful for showing changes in data relationships 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'. The 'O' is the origin of coordinates. The two lines divide the region of the

plane into four sections known as quadrants, which are numbered anticlockwise.

Measurements to the right and above '0' are positive (plus), and measurements to

the left and below 'O' are negative (minus).The independent variable is represented

by 'X' axis and the other variable by 'Y' axis. The following Illustration shows this

type of graph.

Y

M I P

L

x O M ABSCISSA x

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Histogram

This is another form of the line chart used for presenting a frequency

distribution.

It is constructed by erecting vertical lines on the limits of the class intervals

marked on the base line. The vertical lines are drawn from a series of contiguous

rectangles or columns. The width of each rectangle represents its class interval, and

the height represents class frequency. The following is an illustration of a

Histogram.

70

60

50

40

30

20

10

0

90 100 110 120 130 140 150 160 170

FREQUENCY POLYGONS

It is often more convenient to draw a frequency polygon instead of drawing

a histogram of a distribution. In laying out a frequency polygon the frequency of

each class is located at the midpoint of the interval and the plotted points are then

connected by straight lines. If two or more series are shown on then same graph,

the curves can be made with different kinds of ruling. If the total number of cases

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in the two series is of different sizes, the frequencies are often reduced to

percentages. The frequency polygon is particularly appropriate for portraying

continuous series.

It is sometimes desirable to portray the data by a smoothed curve. The chart

is then called a frequency curve.

Frequency polygon gives an instant picture of a frequency distribution and

shows whether the distribution is normal or otherwise. For example, if the peak of

the frequencies occurs in the middle, the distribution is relatively a normal one. If

the peak occurs towards one end or the other, then the distribution is skewed.

OGIVE

The Ogive is a line chart plotted on arithmetic graph paper from a

cumulative frequency distribution which may be cumulated downward or upward.

It is useful in representing population, per capita income, per capita earnings etc.

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Two or more distributions may be compared by converting the data of the

distributions to percentages of the total, then cumulating the percentages and

plotting the ogives on the same grid. The differences in steepness and shape of the

ogives facilitate comparative observations as shown below.

LORENZ CURVE

The Lorenz curve is a line chart used to compare the proportionality of two

quantitative variables. It is commonly used to show the degree by which the

distribution of income per family departs from the distribution of the number of

families. It shows that a disparate proportion of the income goes to a few families.

The curve is plotted from cumulative percentages of the total of each of the

variables. The independent variable viz. the cumulative percentages of the total

number of employees are marked along the base line and the cumulative

percentages total income (i.e. the dependent variable) are plotted on the vertical

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line. The "line of equal distribution" is a line which would have been obtained if

the two distributions had been proportional. The line designated 'actual

distribution' reveals that the distribution of income is unequal compared to the

distribution of the number of employees. The more the 'actual distribution' line

departs from the 'line of equal distribution', the more unequal is the Y variable in

terms of the X variable and the more disproportionate is the relationship.

BARCHARTS

These charts consist of either vertical or horizontal bars to represent

variables. The length of the bars varies correspondingly to the values of the

variable. Bar charts are a most effective pictorial device for comparing data. The

bars may be depicted on solid blocks or in patterns of dots, dishes etc. They may

be of different forms; 1) linear or one-dimensional, 2) areal or two-dimensional,

and 3) Cubic or three dimensional. The actual numerical values may be shown on

the X- axis or Y- axis, as the case may be, or at the immediate ends of the bars.

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Vertical Bar charts: They consist of vertical bars or columns erected on the

horizontal line and the values of the bars are shown on the Y-axis as illustrated

below. The vertical charts are commonly used for presenting time series data.

Horizontal Bar charts are commonly used for presenting qualitative and

geographical distributions as shown below.

Component Bar charts This is employed to show comparison involving two or

more variables on a single chart. This may consist of either horizontal bars or

vertical bars. This type of chart shows not only variations in total values, but also

components of the respective totals.

0

2

4

6

1stQuarter

2ndQuarter

3rdQuarter

4thQuarter

East

West

North

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Principles in designing Bar charts In constructing bar charts the following

principles should be adopted.

1. The bars should be arranged in some systematic order; in chronological

order in a presentation of time series; according to magnitude, starting

with the largest, etc.

2. The bars should be of uniform width and properly adapted to the overall

size, proportion, and other features of the chart.

3. A scale should be included in every bar chart. The number of intervals on the

scale should be adequate for measuring distances but not too numerous to cause

confusion. The intervals should be indicated in round numbers.

4. The stubs or designations for the various categories of a bar chart should be

clearly indicated to the left of the vertical base line.

Pie or circle charts

The circle or pie chart is a component parts chart. The component parts form

the segments of the circle. The circle chart is usually a percentage chart. The data

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are converted to percentage of the total; and the proportional segments, therefore,

give a clear picture of the relationship among the component parts.

PICTOGRAMS

A pictogram is a variation of the bar chart. In it the values are represented by

the identical symbols or pictures. Each one represents a fixed size of the variable.

The symbols used may be appropriate to the type of data. For example, pictures of

human beings can be used for depicting population data. The pictogram is used for

qualitative distributions and for time series distribution as well.

Whatever the graphic devices we use to represent our data, we must draw them

accurately; otherwise we may allow the readers to infer unintended meanings.

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CHAPTER – IX

TEST OF SIGNIFICANCE

INTRODUCTION

An investigator is interested in ascertaining before he uses the results as to

what extent the results are reliable and may be used for drawing conclusions in a

given situations. For this, either the results may be compared with some parameters

or they may be compared with the results of some other sample study covering a

similar field of investigation done under similar conditions. If the two results are

identical or there is very little difference between the two, this may be taken as an

indication of their reliability. However, this cannot be taken as the conclusive

proof of their reliability. Tests of significance are used for testing the reliability of

the results under such conditions.

For the purpose of testing the significance of sample results certain tests are

used. These tests, depending upon the nature of data, are broadly put into two

categories - tests for the variables and tests for the attributes.

TESTS OF SIGNIFICANCE OF VARIABLES

Variables are features or items, which can be measured and expressed in

quantities. For instance the production of a crop in a month, height of a person,

income of an institution, sales revenue of a company, etc. for testing the

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significance of the sample results. In case of variables these are classified as large

sample and small sample.

TESTING THE SIGNIFICANCE

After obtaining the results on the basis of analysis of data or observations, it

becomes necessary to test its significance. No valid conclusion can be drawn if

results are not significant. The sequence of steps followed in the process of testing

the significance of the results is

• Formulation of null hypothesis or alternative hypothesis.

• Specification of the level of significance

• Selection of the test statistics

• Establishment of the decision criteria

• Computation of the test statistics of the sample data

• Decision about the results including, accepting or rejecting the hypothesis.

For testing the significance of results, two types of methods are available

viz. Parametric methods and Non-parametric methods.

ASSUMPTIONS ABOUT PARAMETRIC AND NON-PARAMETRIC

TESTS

Parametric tests The classical statistical methods were based on the

conditions of normal distribution. Parametric methods were thus developed on the

assumption that the underlying distribution was normal, exponential etc. Important

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parametric tests used for testing the significance are “t” test, “f” test, “z” test etc.

With these tests the observed values and their distribution and conclusions are

drawn on the basis of the nature and extent of difference between the two.

Non-parametric tests: Non-parametric methods, also known as distribution free

methods, have no assumption about the underlying distribution. These methods can

be used regardless of the shape of underlying distribution. For small sized samples,

non-parametric tests may be the only alternative. Non-parametric tests can be

applied even in case of nominal scale and ordinal scaled data. These tests are also

easy to apply.

Important non-parametric tests used for testing the significance are.

• Median test

• Wald-Wolfowitz Runs test

• Sign test

• Wiicoxon matched-pairs test, (signed Rank test)

• Chi-square test

• Mann-Whitney 'IT test

• Kolmogorov Smirnov test (KS test)

• Kruskal Wallis test, etc.

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PARAMETRIC TESTS

‘t’ Test : Large sample

In case of large samples the significance of a single measure or two

measures can be tested. In case of two measures, a comparison can also be made

between two measures. This can be illustrated taking an example of mean or any

other static* In case of single measure a comparison can be made between the

actual and the expected or the sample measure and the parameter, whereas in case

of two measures a comparison can be made between these two variables. The

measures of significance known as ratio of significance is determined in case of

mean as under.

' 'x

tx

Where t=ratio of significance

x = sample mean.

= mean of universe

x = standard error of mean.

Small samples

In case of small samples also, to test the significance of sample measures, test of

significance are applied. The ratio of significance is used as a measure of

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significance in case of small sample also. The ratio of significance („t‟) in case of

small samples is determined as

' ' sxt

n

Where, t = ratio of significance,

x = mean of sample

= mean of universe

Standard Error

The standard deviation of a sampling distributions is termed as standard

error. It is a measure of variability in the sample results due to chance. To

understand standard error we should understand the sampling distribution. If a

large number of random samples are selected from a large population of definite

size and some statistic mean, standard deviation, correlation, etc., is calculated for

each sample and these values are put in the form of a frequency distribution, the

distribution so formed is known as sampling distribution.

Uses of standard error

The standard error is used for testing the significance of the results in the

following manner.

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1. To Test Given Hypothesis

The hypothesis is generally tested at 5% or at 1 % level of significance. If

the difference between the observed and the expected results is more than 1.96 or

2.58 times the standard error, then the difference is not accepted as arising due to

chance at 5% or at 1 % level of significance. The difference is taken as significant

or due to reasons other than chance, putting a question mark on the reliability of

the data.

2. Measures of Reliability

The standard error is used as a measure of reliability of the sample. Greater

the standard error, lesser is the reliability of the sample. To double the reliability,

therefore, the number of observations shall have to be increased 4 times.

3. Determination of limits of variation

The standard error is used to determine the limits within which the

parameter values are expected to lie. Thus x 2σ x gives the two limits within which

99.73% of the cases are expected to lie. Likewise, x + 1.96 σ x gives the limits of

variation within which 95% of the cases are expected to lie.

CRITICAL VALUES

Critical value is a value in normal distribution which indicates the number

of times of the standard error in a normal distribution expected to cover a certain

area of the universe.

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Standard Error of Difference between Two Sample Means

When the two is not correlated,

1. When parameter standard deviation (σ p) is given

1 2

1 2

1 1( ) px x

n n

3. When (σ p) is not given

In case the parameter standard deviation is not given, either its value may be

estimated from the given information or, the combined standard deviation may be

taken as the best estimate of the parameter standard deviation. Thus,

Estimated value of

(i) Combined standard deviation 2

2 2

1 21 2

1

( )xn n

3. When the difference between the average of the two sample means is

to be compared with the mean of anyone sample

22 21 12 2

1 1 2) 1 1 2

( ) , ( ) ( ) ( )( ( )

p p

n nx or x

n n n n n n

1 2

1 1.p s

n n

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Where , 2 2

1 1 2 2( ) ( )p x x x x

4.In case the variables have correlation

2 2

1 2 1 21 2

1 2 1 2

( ) 2x rn n n n

STANDARD ERROR OF DIFFERENCE BETWEEN TWO STANDARD

DEVIATIONS

1. When 2

1 2 1 1 2( ) / 2(1/ 1/ )n n or

1 2(1/ 1/ )/ 2

p

p

n nn

2. When p is not given

2 2

1 2 1 1 2 2( ) / 2 / 2n n

3. When the standard deviation of one sample is to be compared with the

combined standard deviation of the two samples.

1 2 1 2 1 2( ) / 2 ( / )( )n n n n

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Ratio of significance can also be used for testing the significance of difference in

two standard deviations in above cases.

Ratio of significance

1 2

1 2)(

Small Samples

In case of large samples while the values of normal distribution may be

directly used, these cannot be used, for testing the significance, for small samples.

To meet this situation Prof.William Gusset, who was student of Prof. R.A. Fisher,

had in a paper published in 1905 derived a theoretical distribution which came to

be known as student's “t” distribution. It approximates normal distribution as the

size of the sample increases.

The “t” distribution can be used for testing the significance of results in case

of small samples. The test is applied in the same way as in large samples.

The equation is stated as

( )' '

x rt n

s

where, x = sample mean

s r = mean of universe

2

2 1 1 2/ 2( / )( )n n n n

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s = standard of deviation of the sample, when

2( )

1

x Xs

n

n= number of observations.

The distribution curve of the statistic “t” is

0

2( )

1(1 )

2

yF t

t v

v

Where , v= degree of freedom, and yo is such that the total area is unity.

As v tends to infinity, 2

1

1(1 / )

2

vt r

tends to e-t1/2

and hence the “t” distribution

approximates normal distribution.

Properties of "t" distribution

The following are the important properties of the T distribution.

“t” distribution ranges from -a to a, just as does a normal distribution.

“t” distribution is symmetrical with mean, median and mode=0

't' distribution has greater dispersion than the normal distribution. As the n

becomes large, it approaches the normal distribution. Where n is 30 or large,

the difference is very small as shown in the following figure

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t" distribution in case of different sizes of sample

There are two types of t-tests, one is called t-test for independent samples,

and the other is called paired t-test. The first test is used for the scores of the

other group.

That means there is no logical relationships between the scores that have

been obtained for one group when compared with the other group. However, both

the tests are used to assess significance of difference.

THE PAIRED T-TEST

The data for this example is taken from school social workers. The school

social workers want to list the effects of intervention to improve the self-esteem of

children. A group of 10 children was randomly selected from a class. Rating scales

before the intervention assessed the self esteem of children. The scale had a

scoring range from 5 to 15. After the intervention, the school social worker

administered the same rating scales. The pre-intervention and post-intervention

scores are shown below in the given table.

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z Pre-test

scores(x)

Post-test

scores(y)

D=(y-x) D2

1 1 9 12 3 9 2. 8 10 2 4 3 15 15 0 0 4 12 14 2 4 5 8 14 6 36

6 4 11 7 49 7 6 10 4 16 8 3 8 5 25 9 3 8 5 25

10 2 8 6 36 N=10 ED=40 D

2=204

The social work researcher established a null hypothesis that there is no

difference between pre-intervention and post-intervention scores to list whether

there is a statistically significant difference. The research hypothesis is that post-

intervention scores will show improvement in self-esteem over pre-intervention.

Step Procedure Application

1 Find out the difference (D)

between the post-intervention

and pre-intervention scores.

D=Y-X

2. Compute mean difference D

xn

3. Compute square of difference D2

4. Find the sum of squares of

difference

"D2

5. Compute sum of squares (ss)

22"

DSs D

n

6. Find out degree of freedom (df) Df=n-1

7. Compute variance (s2) 2 ss

sdf

8. Compute‟t‟ by using the formula 2

' 'x

ts

n

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Now that the value of the paired T-test has been calculated, we have to see if the

null hypothesis can be rejected. It is assumed that the intervention is likely to

improve the post-intervention scores. That mean there is directionality in the data.

Hence, we use one-tailed test.

The T-test for independent samples

The T-test for two independent samples examines the difference between

their means to see how close or apart they are. Let us consider the data given in the

table below.

The data compares scores obtained by two groups of students say, for

example, a group of master's degree students and a group of bachelor's degree

students on a scale designed to measure attitude towards AIDS patients. A social

work teacher is interested in studying whether there is significant difference among

the two groups of students with regard to their attitude toward AIDS patients. The

social work teacher selects two groups of students randomly and administers the

list. The scores at the interval level of measurement are presented in the table

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S.No.

Scores

obtained by

masters

degree

students (x)

X2

Scores

obtained by

Bachelor's

Degree

student(y)

Y2

64 12

12

1 8 144 2 11 121 9 81

3 9 81 6 36

4 12 144 5 25

5 16 256 8 64

6 10 100 12 144

7 7 49 11 121

8 16 256 10 100

9 6 36 10 100 10 5 25 7 49

N=10 ?x=100 ?x2=1132 ?x=90 ?y

2=864

The first step in calculating T-test for two independent samples is to square

the scores of X and Y columns to get x2and y

2 values.

The next step is to sum up the columns to find out “x”,”y”,”x2”,. This is followed

by calculation of means of x and y columns, that is x and y.

Before we take the decision we have to check whether the scores of two groups

show any directionality. Since there is no indication that either set of scores

support the hypothesis. Thus, we will have to look for critical value of “t” for two -

tailed tests at .05 level of significance for 1.8 degree of freedom.

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Difference Test

In case of paired data where the same sample is studied under two different

situations, before and after a treatment to know the effect of the treatment, the

difference test is applied. Here, there is only one sample and its two situations are

observed. Some typical examples of this type are, performance of students before

and after coaching etc. In such cases, first the two observations are recorded and

their differences are determine Taking these differences as basic data their mean

and standard deviation is determined. Then taking the hypothesis of no difference,

“t” test is applied.

SIGNIFICANCE OF DIFFERENCE BETWEEN TWO SAMPLE MEANS

The “t” test can also be used for testing the significance of difference

between the means of two independent random samples. In this case, the

hypothesis is taken that the samples are drawn from population identical both as to

their means and the standard deviations. It should be marked that this approach is

to be adopted only in case where two independent samples are drawn at random

from the same population. In case the same sample is divided into two parts; the

earlier approach should be adopted.

1 2

1 21/ 1/

x xt

s n n

Where, t= ratio of significance.

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1x = mean of first sample

2x = mean of second sample

n1 = number of items in first sample

n2 = number of items in second sample.

S = standard deviation of difference between two sample results.

2 21 21 2

1 2

( ) ( )

2

x x x xS

n n

LIMITATIONS OF TESTS OF SIGNIFICANCE

In testing statistical significance the following points must be noted

i) They should not be used mechanically

Tests of significance are simply the raw materials from which to make decisions,

not decisions in themselves. There may be situations where real differences exist

but do not produce evidence that they are statistically significant or the other way

round. In each case it is absolutely necessary to exercise great care before taking

decisions.

ii) Conclusions are to be given in terms of possibilities and not certainties

When a test shows that a difference was statistically significant ft suggests that the

observed difference is probably not due to chance. Thus statements are not made

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with certainty but with knowledge of probability". Unusual" events do happen once

in a while.

iii) They do not tell us "why" the difference exists

Though tests can indicate that a difference has statistical significance, they do not

tell us why the difference exists. However, they do suggest the need for further

investigation in order to reach definite answers.

iv) Serious violations of assumptions

Many, hypothesis tests are performed even though the assumptions that would

validate the procedure used are not met. There might be a difference. In addition,

an indented simple random sample can in fact turn out to be a non-probability

sample or perhaps, the populations being samples may not be normally distributed

or they may not have equal variances.

v) Non-publication of non-significant results

Results of hypothesis tests that are statistically non-significant are unlikely to be

published. This situation may have serious consequences.

Consider a null hypothesis that is in fact true and that is tested independently by

many ways at 5% level of significance.

‘F’TEST

The F-Test of the variance Ratio Test

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The F-test is named in honour of the great statistician R.A Fisher. The object

of the F-test is to find out whether the two independent estimates of population

variance differ significantly, or whether the two samples may be regarded as drawn

from the normal populations having the same variance. For carrying out the test of

significance, we calculate the ratio F. F is defined as

where

212 1

1

1

( )

1

x xS

n

and

222 2

2

2

( )

1

x xS

n

It should be noted that s12is always the larger estimate of variance: le s1

2> s2

2

Larger estimate of variance/smaller estimate of variance

F =

Smaller estimate of variance

V1= n1-1 and V2=n2-1

V1 = degrees of freedom for sample having larger variance.

V2 = degrees of freedom for sample having smaller variance.

The calculated value of F is compared with the table value for V1 and V2 at

5% or 1 % level of significance. If calculated, value of F is greater than the table

value then the F ratio is considered significant and the null hypothesis is rejected.

On the other hand, if the calculated value of F is less than the table value the null

2

1

2

2

SF

S

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hypothesis is accepted and it is inferred that both the samples have come from the

population having same variance.

Since “t” test is based on the ratio of two variances it is also known as the

variance ratio test. The ratio of two variances follows a distribution called the “f“

distribution named after the famous statistician R.A Fisher.

Assumption in F-Test: the F-test is based on the following assumptions.

1. Normality, i.e.: the values in each group are normally distributed.

2. Homogeneity; i.e.: the variance with in each group. This assumptions is

needed in order to combine or pool the variances within the group into a single

"with in groups" source of variation.

3. Independence of error, it states that the error (variation of each value

around its own group mean) should be independent for each value.

Z-TEST

Professor Ronald Fisher has given a method for testing the significance of

correlation of small samples. In this method, the sample „r‟ is transformed into Z.

That is why the test is called the Z test. This test may be applied in two situations.

i. To test the significance of difference between the observed and hypothetical

value of r.

Where,

11/ 2log

1e

rZ

r

,

1

3z

n

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Co-efficient of significance

s p

z

Z Zt

ii. To test the significance of difference between the values of r of two samples.

Where, s p

z

Z Zt

1 2

1log

1e

rZ Z

r

1 2

1 1

3 3z

n n

Note: Log e is base of natural logs and is taken as equal to 1.1513 - log10.s

"Z" test is used to measure the difference between any variable value (x) and the

mean of all variable values or "x" values, which is indicated by "m", divided by the

standard deviation (s). it is based on the normal probability distribution. In

following cases, we find the usage of Z-test.

1. To judge the significance of statistical measures, particularly the mean. This

is done by comparing the observed value (test statistic) with the probable value

(table value) at a specified level of significance.

2. It is used to compare the mean of a sample with some hypothesized mean of

the population.

3. It is also used to judge the significance of difference between means of two

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independent samples.

4. It can also be used for judging the significance of difference between sample

and population proportion or proportions of two independent samples.

5. Finally this test can also be used for measuring the significance of medium,

mode, co-efficient of correlation and other measures.

Z = Observed value of statistic (x)-expected value (μ)

Standard error of the estimate

NON-PARAMETRIC TESTSs

INTRODUCTION

Sometimes, the rigid requirement of parametric tests is not satisfied by the

data. This is particularly so in case of nominal scaled or ordinal scaled data. Under

such a situation non-parametric tests, also known as distribution free methods, can

be used for testing the significance of the results obtained from the statistical

analysis of such data. The non-parametric tests are useful under the following

conditions

1. When the nature of the population distribution from which the samples were

drawn is not known or is known to be not normal.

2. The variables are expressed in

• nominal form, i.e., classified in categories and represented by frequency 10

units or,

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• ordinal form, i.e., ranked in order and expressed as 1,2,3,..etc.

Use of non-parametric methods

There are situations where the basic assumptions underlying the parametric

tests are not valid or one does not have the knowledge of the distribution of the

population parameters being tested. In studies involving human behaviour such as

in psychological or market research studies, such a problem is encountered. Non-

parametric tests can handle these problems. Some of the typical situations for using

non-parametric tests are

1. Not-Normal distribution

In survey, many times the responses are not distributed normally. Instead of

parametric tests based on the assumptions of normal distribution, non-parametric

tests may prove useful for testing the hypothesis.

2. Nominal scaled data

Sometimes, the responses are not numerically expressed but are given In

terms of yes or no, and mere names which cannot be treated as numbers.

3. Incomplete or partial data

Sometimes, In case of mailed questionnaires, all the information are not

filled In. Non-parametric tests can extract maximum Information-even from such

Incomplete data set by making the necessary adjustments.

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4. Very small sample

Size of sample is significant in case of parametric tests. However, non-

parametric tests can provide reasonable good results even for very small samples.

a. One sample tests

• Runs test for randomness

• Sign test

• Chi-square test

• Kolmogorow-Smirnov one sample test

b. Two sample tests

• Median test

• Mann-Whitney U test

• Wald Wolfowitz runs test

c. Two matched pairs test

• Sign test

• Wileoxon matched pairs signed ranks test

d. K sample tests

• Median test

• Kruskal Wallis test

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Advantages of Non-parametric tests

1. Non-parametric tests are distribution free: i.e., they do not require any

assumption to be made about population following normal or any other

distribution.

2. Generally they are simple to understand and easy to apply when the sample

sizes are small.

3. Most non-parametric tests do not require lengthy and laborious, computations

and hence are less time-consuming. If a significant result is obtained, no further

work is necessary.

4. Non- parametric tests are applicable to all types of data- qualitative, data in

rank form as well as data that have been measured more precisely.

5. Many non-parametric tests make it possible to work with very small samples.

This is particularly helpful to the researcher collecting pilot study data or to

medical researcher working with a rare disease.

6. Non-parametric methods make fewer and less stringent assumptions than do

the classical procedures.

CHI-SQUARE TEST

Defined as: The x2 test (pronounced as chi-square Test) is one of the

simplest and most widely used. The symbol x2 is the Greek Letter chl. The x

2 test

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was first used by Karl Pearson In the year 1990. the quantity x2 describes the

magnitude of the discrepancy between theory and observation. It is defined as

22 ( )O E

xE

Where, O refers to the observed frequencies and E refers to the expected

frequencies

The x2 test has the following steps

i) State the null hypothesis and calculate the numbers in each category.

ii) Determine the level of significance the researcher is prepared to take.

iii) Calculate x2, as follows

22 ( )O E

xE

iv) Find the critical value of x2 against the number of degrees of freedom for the

specified level of significance.

v) Compare the calculated value of x2 with the tabulated value and determine the

region of reflection.

A chi-square test can be used when the data satisfies four conditions

i) There must be two observed sets of data or one observed set of data and one

expected set of data.

ii) The two sets of data must be based on the same sample size.

iii) Each cell in the data contains the observed or expected count of five or large?

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iv) The different cells in a row or column most have categorical variables (Gender-

male, female or younger than 20 years of age, 25 years of age, older than 25 years

of age etc)

APPLICATION AREAS

When tests are undertaken to examine whether the sample data supports the

hypothetical distribution, such problems are called "test of goodness of fit", I.e.,

• To test whether the sample differences among various sample proportions are

significant or can they be attributed to chance.

• To test the independence of two variables in a contingency table.

• To use it as a test of goodness of fit.

The chi-square distribution typically looks like a normal distribution which is

skewed to the right with a long tail to the right. It is a continuous distribution with

only positive values.

CONDITIONS FOR APPLYING X2 TESTS

The following conditions should be satisfied before applying the x2 test

1. In the first place N must be reasonably large to ensure the similarity between

theoretically correct distribution and our sampling distribution of x2, the chi-square

statistic. It is difficult to say what constitutes largeness, but as a general rule x2 test

should not be used when N is less than 50, however few the cells.

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2. No theoretical cell frequency should be small when the compacted frequencies

are too small; the value of x2 will be overestimated and will result in too many

rejections of the null hypothesis. To avoid making incorrect inferences, a general

rule is followed that expected frequency of less than 5 in one cell of a contingency

table is too small to use.

3. The constraints on the cell frequencies, if any, should be linear, i.e., they

should not involve square and higher powers of the frequencies such as

YATE'S CORRECTIONS

One of the conditions for the application of x2 test is that no cell frequency

should be less than 5 in any case, though 10 is better. This requirement is to avoid

inflated chi-square values due to the division of the square values due to the

division of the squared differences by a small size of the expected frequency.

When the theoretical frequencies are less than 10 and especially less than 5, the

ordinary table values of x2 are less reliable. This is especially true for one degree of

freedom; it is true to a lesser extent for two or three degrees of freedom. However

the error is negligible for more than three degrees of freedom.

The Yates's corrections, also called as Yates corrections for continuity are

introduced because theoretical chi-square distribution is continuous where as the

tabulated values are based on the distribution of discrete x2 statistic. The

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corrections has the effect of reducing the calculated value of x2 as continuous

compared to the corresponding value without correction.

In a special case of 2x2 contingency table the approximation may be

improved, and bias arising out of the use of small theoretical frequencies may be

reduced, by means of a correction proposed by F.

The correction involves the reduction of the deviation of observed from

theoretical frequencies which of course reduced the value of x2. The working rule

for the application of the correction is

Adjust the observed frequency in each cell of the 2x2 table in such a way as

to reduce the absolute deviation of the observed from the theoretical frequency for

that cell by 1/2, adjustments for all the cells are to be made without changing the

marginal totals. This operation will increase F that is observed frequency, by 1/2 in

each of two cells, and will reduce F by 1/2 in each of two cells.

USES OF X 2 TESTS

The x2 test is one of the most popular statistical inference procedures today.

It is applicable to a very large number of problems in practice which can be

summed up under the following heads.

i) x2 test as a test of independence

With the help of x2 test we can find out whether two or more attributes are

associated or not. Suppose we have N observations classified according to some

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attributes we may ask whether the attributes are related or independent. Thus, we

can find out whether quinine is effective in controlling fever or not whether there is

any association between marriage and failure. In order to test whether or not the

attributes are associated we take the null hypothesis that there is a association in

the attributes under study, or in other words, the two attributes are independent. It

should be noted that x2

is not a measure of the degree or form of relationship, it

only tells us whether two principles of classification are or are not significantly

related, without reference to any assumptions concerning the form of relationship.

ii) x2 test as a test pf goodness of fit

X2 test is very popularly known as test of goodness of fit for the reason that it

enables us to ascertain how appropriately the theoretical distributions such as

Binomial, Poisson, normal, etc, fit empirical distributions i.e., those obtained from

sample data. A test of the concordance of the two can be made just by inspection,

but such a test is obviously inadequate. Precision can be scored by a applying the

x2 test.

The following are the steps in testing the goodness of fit.

1. A null hypothesis is established and a significance level is selected for

rejection of the null hypothesis.

2. A random sample of observations is drawn from a relevant statistical

population.

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3. A set of expected or theoretical frequencies is derived under the assumption

that the null hypothesis is true.

4. The observed frequencies are compared with the expected or theoretical

frequencies.

5. If the calculated value of x2 is less than the table value at a certain level of

significance and for certain degrees of freedom the fit is considered to be good.

The divergence between the actual and expected frequencies is attributed to

fluctuations of simple sampling.

(iii) x2 test as a test of homogeneity

The x2 test of homogeneity is an extension of the chi-square test of

independence. Tests of homogeneity are designed to determine whether two to

more independent random samples are drawn from the some population or from

different populations. Instead of one sample as we use with independence problem

we shall now have two or more samples.

It should be noted that both types of tests i.e., test of independence and

homogeneity are concerned with cross-classified data,

ADDITIVE PROPERTY OF X2

One of the merits of x2 test as an instrument of research is that it is possible

to combine the independently derived values of x2 relating to samples of similar

data by the simple process of addition. It enables a better test than could be made

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using the data of any sample by itself. The sum of the x2 values thus combined will

itself have a x2 distribution with a degree of freedom equal to the sum of the

degrees of freedom of the separate x2 values. However, while adding x

2 values two

points must be remembered

1. The combined result in a single inclusive test is appropriate when the samples

are independent; and

2. When x2values are to be added, Yates's corrections should not be applied

because the addition theorem holds only for uncorrected constituent items.

Chi-square test for specified value of population variance

When we want to test if a random sample of x1, x2.., x n has been drawn from

a normal population with mean p and a specified variance 622, the statistic is.

22

2

0

( )xi xx

Where S is the standard deviation of the sample follows chi-square distribution

with (n-1) degrees of freedom.

By comparing the calculated value of x2 with the tabulated value (n-1) at certain

level of significance, we may accept or reject the null hypothesis.

It should be noted that this test can be applied only if the population is normal.

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MISUSE OF CHISQUARE TEST

The most common mistake in the application of the chi-square statistic and

yet the most critical for its correct application is the violation of the independence

between measures or events. This assumption of independence is not to be

confused with the chi-square as a test of independence.

The assumption of independence refers to the individual observations or

frequencies and means that the occurrence of one event has no effect upon the

occurrence of any other event. Some other sources of error in the application of x2

test as revealed in a survey of research papers published in the journal of

experimental psychology are

i. Small theoretical frequencies.

ii. Neglect of frequencies of non-occurrence.

iii. Failure to equalize the sum of observed frequencies and the sum of the

theoretical frequencies.

iv. Incorrect or questionable categorizing.

v. Use of non-frequency data.

vi. incorrect determination of the number of degrees of freedom.

vii. Incorrect computation.

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LIMITATIONS OF THE USE OF X2 TEST

X2 test la very widely used In practice however, in order to avoid the mis-

application of the test its following limitations should be kept in mind.

1. Frequencies of non-occurrence should not be omitted for binomial or

multinomial events.

2. The formula presented for x2 statistics is in terms of frequencies. Hence an

attempt should not be made to compute on the basis of proportions or other derived

measures.

3. The formula presented in this is not appropriate for cases in which repeated

measurements on the same or matched group are represented in one table, when

data from questionnaires and similar devices are analysed.

MANN-WHITNEY U TEST

The Mann-Whitney U test (1947) was designed to test the significance of

difference between the results of two samples drawn at random from the same

population but administered different treatments. It is a more powerful test than the

median test, for the observations are expressed in at least ordinal scale, i.e., in

ordinal or in ratio scale and then reduced to ranks, it can be used as an alternative

either to t tests when parametric assumptions are not satisfied.

a. Small samples (n1 + n2 £20)

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The Mann-Whitney U test for small samples (n1 + n2 £20) follows the following

sequences

1. Hypothesis

Ho - there is no difference between the results of two samples.

H1 - there is difference between the results of two sample.

2. Ranking: All values of both the samples are taken together and ranked

from the lowest to the highest. In case two or more values are equal,

average of rank values is given to all. The rank assigned to the values of the

two groups is summed up separately.

3. computation of U value: computation of U value is done separately for each

sample (U1 and u2) by the relationship.

i.

ii.

Where, n1 = number of items in first group.

N2 = sum of ranks in second group.

"R1 = sum of ranks in first group.

"R2 = sum of ranks in second group.

Alternatively, value of any one U(u1 or u2) may be compared and then the other

one may be determined by the relationship. U1=(n1 n2)-U2

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4. test of significance: to test the significance, the value of U is compared with the

critical value of U for Mann-Whitney test. If the computed value is greater than the

critical value,H0 is rejected. But if it is equal to or less than greater than the critical

value, H0 is accepted.

b. Large samples (n>20)

In case the size of samples is greater than 20, U distribution can be approximated

by the standard normal distribution. In such cases the decision has two steps

1. Determination of Z value: first taking smaller of the two values of U, value of z

is to be determined by the formula

( )

u

U E UZ

Where, E(U)=(n1 *n2)/2 and 1 2 1 2{( )( 1)}/12u n n n n

If thus, Zu =

1 2

1 2 1 2

( ) / 2

{( )( 1)}/12

U n nZu

n n n n

2.test of significance: the observed value of z is compared with the critical value

taking null hypothesis or alternative hypothesis at 5% or 1 % level of significance.

If the observed value of z is equal to or less than the critical value, the null

hypothesis is accepted and the difference between the two sample results is

considered not significant otherwise, the null hypothesis is rejected and the

alternative hypothesis is accepted.

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KRUSKAL WALLIS TEST

If several independent samples are involved, analysis of variance is the usual

procedure. Failure to meet the assumptions needed for analysis of variance makes

its value doubtful. An alternative technique was developed called the Kruskal

Wallis one way analysis of variance or the H-test. This test helps in testing the null

hypothesis that "K " independent random samples come from identical populations

against the alternative hypothesis that the means of these samples are not all equal.

The test follows the following steps

• hypothesis

one of the following two hypotheses is taken

Ho = the difference between the observations of different samples is not significant.

H1 the difference between the observations of different samples is significant

• Ranking of observations

All the elements (observations) of all the samples are pooled together as ff they

were a single sample and ranked the lowest getting rank 1 and so on. Incase of tie

the values of ranks are equally divided.

Test statistic

The statistic is given by

H Why =

2

1

123*( 1)

*( 1)

ki

i i

rn

n n n

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Where, n=total number of elements of K samples,

• Test of significance

Kruskal - Wallis test used X2 test to test the hypothesis. The value of x

2 distribution

for (k-1) degrees of freedom is determined. If the value of H is equal to or less than

x2, the null hypothesis is accepted. In case the value of H is greater than x

2, the null

hypothesis is rejected and it is concluded that test results of different samples are

not similar and the difference is significant.

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CHAPTER – X

STATISTICAL ANALYSIS

INTRODUCTION TO ANNOVA

The analysis of variance frequently referred to by the contraction ANOVA is

a statistical technique specially designed to determine whether the means of more

than two quantitative populations are equal. The analysis of variance technique,

developed by R.A.Fisher in 1920's, is capable of fruitful application to a diversity

of practical problems. Basically, it consists of classifying and cross - classifying

statistical results and testing whether the means of a specified classification differ

significantly. In this way it is determined whether the given classification is

important in affecting the results by cross-classification, it could be determined

whether the mean qualities of the outputs of the various machines differed

significantly. Also it could independently be determined whether the mean

qualities of the outputs of the various machines differed significantly. Analysis of

variances thus enables us to analyse the total variation of our data into components

which may be attributed to various "source" or "causes" of variation.

Technique of analysis of variance

For the sake of clarity the technique of analysis of variance has been

discussed separately for (a) one-way classification, and (b) two - way

classification.

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ONE-WAY CLASSIFICATION THE DATA ARE CLASSIFIED

ACCORDING TO ONLY ONE CRITERION

sall the means are not equal that is, the arithmetic means of

populations from which the k samples were randomly drawn are equal to one

another" the steps in carrying out the analysis are

1. Calculate variance between the samples

The variance between the samples measures the differences between the

samples mean of each group and the overall mean weighted by the number of

observations in each group. The variance between samples takes into account the

random variations from observation to observation. It also measures difference

from one group to another. The sum of squares between samples is denoted by

SSC for calculating variance between the samples. We take the total of the square

of the deviations of the means of various samples from the grand average and

divide this total by the degrees of freedom. Thus the steps in calculating variance

between samples will be

a) Calculate the mean of each sample; i.e x1.,, x2.etc.,

b) calculate the grand average ^, pronounced" x double bar" its value is

obtained as follows

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1 2 2

1 2 3

......

......

x x xx

n n n

c) Take the difference between the means of the various samples and the

grand average.

d) Square these deviations and obtain the total which will give sum of squares

between the samples; and.

e) Divide the total obtained in step (d) by the degrees of freedom. The degrees

of freedom will be one less than the number of samples.

2. Calculate variance within the samples

The variance within samples measures those inter-samples difference due to

chance only. It is denoted by SSE. The variance with in samples measures

variability around the means of each group. Since the variability is not affected by

group differences it can be considered a measure of the random variation of values

with in a group. For calculating the variance within the samples we take the total of

the sum of squares of the deviation of various items from the mean values of the

respective samples and divide this total by the degree of freedom. Thus the steps in

calculating variance with in samples will be

a) Calculate the mean value of each sample.

b) Take the deviations of the various items in a sample from the mean values of

the respective samples.

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c) Square these deviations and obtain the total which gives the sum of squares

within the sample and.

d) Divide the total obtained in step (c) by the degrees of freedom, the degrees of

freedom is obtained by deduction from the total number of items the number of

samples, i.e. (V=N-K), where, K refers to the number of samples.

e) N refers to the total number of all the observations.

3. Calculate the ratio 'f as follows

Between - column variance.

f =

Within - column variance.

The „f‟ distribution measures the ratio of the variance between groups to the

variance within groups. The variance between the samples means is the

numerator and the variance within the sample means is the denominator. If

these is no real difference from group to group, any sample difference will be

explained by random variation and the variation between groups should be

close to the variance within groups. However, if there is a real difference between

the groups, the variance groups will be significantly larger than the variance

with in groups.

4. Compare the calculated value of "f with the table value of “f” for the degrees

of freedom at a certain level. If the calculated value of “f” is greater than the

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table value, it is concluded that the difference in sample means is significant

or, in other words, the samples do not come from the sample population. On

the other hand, if the calculated value of f is less than the table value, the

difference is not significant and has arisen to fluctuations of sampling.

It is customary to summarise calculations for sums of squares, together with the

r numbers of degrees of freedom and mean squares in a table called the analysis of

variance table, generally abbreviated ANOVA. The specimen of a ANOVA table is

given below.

Source of

variation

Ss (Sum of

squares)

v-degrees of

freedom

Ms-mean square Variance ratio

of "F"

Between

samples

SSC V1=c-1 MSC=SSC/C-1

Within

samples

SSE V2=n-c MSE=SSE/(N-

C)

MSC/MSE

Total SST n-1

SST = total sum of squares of variation.

SSC = sum of squares between samples (columns)

SSE = sum of squares with in samples (rows)

MSC = mean sum of squares between samples.

MSE = mean sum of squares within samples.

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Coding of Data

While making out an analysis of variance, it should be noted that the final

quantity tested is a ratio and so dimensionless. This means that the original

measurement can be coded to simplify calculations without the need for any

subsequent adjustments of the results.

TWO-WAY CLASSIFICATIONS MODEL

When it is believed that two Independent factors might have an effect on the

response variable of Interest, it is possible to design the test, so that an analysis of

variance can be used to test for the effects of the two factors simultaneously. Such

a test is called a two factor analysis of variance. With the two-factor analysis of

variance, we can test two sets of hypothesis with the same data at the same time.

In a two-way classification, the data are classified according to two different

factors. The procedure for analysis of variance is somewhat different than the one

followed while dealing with problems of one-way classifications. In a two-way

classification the analysis of variance table takes the following form

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Source of

variation

(Sum of

squares)

Degrees

of

freedom

Ms-mean square Variance ratio

of “F”

Between

samples

SSC (c-1) MSC=SSC/(c-1)

Between rows SSR (M) MSR=SSR/(r-1) MSR/MSE

Residual or

error

SSE (c-1)(r-1) MSE=SSE/ (r-1)(c-1)

Total SST n-1

SSC = sum of squares between columns

SSR = sum of Squares between rows.

SSE = sum of squares due to error.

SST = total sum of squares.

The sum of squares for the sources "Residual" is obtained by subtracting

from the total, sum of squares between columns and rows,

i.e., SSE=SST-[SSC+SSR].

The total number of degrees of freedom= n-1 or cr-1

Where c refers to number of columns, and r refers to number of rows, Number of

degrees of freedom between columns= (c-1), Number of degrees of freedom

between rows= (r-1), Number of degrees of freedom for residual- (c-1) (r-1)

The total sum of squares, sum of squares "between columns" and sum of squares

for "between rows" are obtained in the same way as before.

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Residual or error sum of square= total sum of squares- sum of squares between

columns- sum of squares between rows.

The F values are calculated as follows

Where v1 =(c-1) and v2=(c-1) (r-1)

1 2( )MSR

F v vMSE

Where v1 =(r-1) and v2=(c-1) (r-1)

It should be carefully noted that v1 may not be same In both cases -in one case

v1=(c-1) and another case v1=(v-1)

The calculated values of F are compared swith the table values. If calculated values

of "f is greater than the table value at pre-assigned level of significance, the null

hypothesis is rejected, otherwise accepted.

It would be clear from above that in problems involving two-way classification,

"Residual", is the measuring rod for testing significance. It represents the

magnitude of variation due to forces called "chance".

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MULTIVARIATE ANALYSIS OF DATA

INTRODUCTION

Variables can be analyzed in three different ways as given below

Univariate: whema single variable is analysed alone, e.g., sample statistic

such as "mean" which might refer to the average consumption of a particular kind

of food or the age of a certain group of students or people, it is known as univariate

analysis.

Bivariate: when some association is measured between two variables

simultaneously, e.g., cross-classification of age group and cross-consumption of

two products, it is know as bivariate analysis.

Multivariate Analysis: it is a logical extension of the univariate analysis.

Two or more independent variables form the basis for estimating the values of

dependent variables. The statistical technique used in multi-variate analysis is

called multivariate technique.

VARIOUS MULTIVARIATE TECHNIQUES

FUNCTIONAL MULTIVARIATE TECHNIQUES

These techniques help the researcher to develop the predictive methods.

Where one tries to relate two data sets consisting of dependent and

independent variables. On the basis of specific means used in measuring)' the

variables, the different dependence methods can be further classified as follows.

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CORRELATION AND REGRESSION ANALYSIS

Correlation analysis tries to measure the magnitude and direction of

relationship between two variables. Multiple and partial correlation analysis

extends the same notion between a single variable and a set of variables.

Regression analysis is used to determine the functional relationship between a

dependent variable and a host of predictors.

DISCRIMINATE ANALYSIS

It involves a situation where the relationship between a categorical

dependent variable and number of independent variable is derived.

ANALYSIS OF VARIANCE

This is used to analyse some experimental data when there is a metric

dependent measure and a set of experimental independent variables which measure

even on nominal or ordinal scales.

All the above mentioned analysis are extended to multivariate level when

there is more than one dependent variable. The statistical formula for estimation of

parameters will, however, change.

STRUCTURAL MULTIVARIATE TECHNIQUES

Techniques which help in reducing large and complex data into meaningful groups

are known as structural multivariate techniques.

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In some cases there is no prior basis for distinguishing between criterion and

predictor variables. But the researcher may be interested in analyzing their

interdependence or underlying relationship. Techniques belonging to

interdependence methods applicable in marketing situations are as follows

Factor analysis: here a few basis dimensions are extracted by examining the

correlations among the variables. The information from a large number of

interrelated variables is summarized into few factors.

Cluster analysis: it tries to group a set of objects into smaller sets based on

their profiles similarities.

Co joint analysis: It is a set of techniques used to derive a set of weightage

for some discrete levels of variables which are prior selected to study

consumers' choice decision.

Characteristic features of multivariate analysis

With the availability of a wide range of useful data and knowledge of statistical

techniques and their applications, multivariate techniques have become popular.

Basic characteristics of these techniques are

1. More than two variables: in multivariate analysis of data the observations are

related with more than two variables, where one or more of these are dependent

variables and the rest are explanatory or independent variables.

2. Simultaneous consideration of relationship existing: The relationship or

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interdependence existing among all the variables under observation is considered

at the same time, rather than separately for each set of variables.

3. Composite view: multivariate analysis transforms the mass of observations

into a small number of composite scores and presents a composite view of the

mass of data. This facilitates their interpretation and use in decision making.

4. Require wide range of observations: for obtaining objective result in case of

multivariate analysis, a wide range of observations are required.

5. Useful for long range planning: the result of multi-variate analysis are useful

for long range planning, as they provide a composite view of the situation, rather

than compartmentalized view, as s large number of variables are considered.

Limitations of multivariate techniques

Multivariate techniques have great potential in research. Analysis of data is

possible with their use in greater depth. Sometimes, the results are highly revealing

and provide right direction to the research. However, in spite of all the beneficial

and positive aspects of multivariate techniques, their use is still not wide-spread

due to their following limitations.

• Laborious computations are involved in multivariate analysis. Advent

of high speed computers and software packages has greatly facilitated their

use. Enlarging the scope of their application considerably, it has made it

possible to use multivariate data in research. However, still this remains out

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of reach of most researchers in developing countries.

• Cost of analysis is another limitation. This technique may not be cost

effective in all cases. An individual researcher has to depend upon his own

resources.

• All these techniques are data-specific and result specific. For their use,

without expert knowledge, validity of results cannot be ensured The utility

of results may be much less in many cases as compared to the effort needed.

CORRELATION

INTRODUCTION

Correlation measures the nature and extents of proximity in the relationship

of two or more variables in a field of inquiry, where cause and effect relationship is

existing among them. This provides a very useful basis for statistical analysis of

data. Correlation analysis is very useful in research. It provides a useful basis in

case of multivariate analysis.

For correlation analysis, the variables considered are classified into two

broad groups, viz., dependent variables and independent variables. The variables

whose value depends on some other variable are known as dependent variables,

and the variables influencing the behaviour of other variables are known as

regressors, explanatory variables or independent variable.

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On the basis of the number of variables considered, the correlation analysis?, can

be termed as simple or multiple.

Simple correlation

An analysis wherein relationship existing between two variables, one

dependent and the other Independent, is examined, is known as simple correlation

analysis. The statistical measure of simple correlation is known as "co-efficient of

linear correlation" with symbol „r‟.

The co-efficient of correlation can be either a positive or negative value,

depending upon the direction of change in one variable with the change in the

other variable. If the direction of change is similar, H signifies positive correlation.

But if it is in reverse direction it signifies negative correlation.

The value of co-efficient of correlation is expected to range from minus one

to plus one Simple correlation measures the strength and type of relationship

between two variables on the assumption that no other variables comes into play

and as such, is not to be taken into account. It is therefore, also called „zero order

correlation co-efficient‟.

Linear and non-linear correlation

The distinction between linear and non-linear correlation is based on the

ratio of change between variables. If the degree of change in one variable tends to

bear a constant ratio to the degree of change in other variable, then the correlations

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said to be linear. However, in case the degree of change in one variable does not

bear a constant ratio to the degree of change in the other variable, it is called non-

linear correlation.

Assumption of correlation analysis

• Cause and effect relationship exists between the variables, such that one is an

independent (cause) variable, and the other is a dependent (effect) variable.

• The relationship between the variables is linear.

• The variables under observation are so influenced in a large number of

cases that they form a normal distribution.

Testing the significance of correlation

Co-efficient of simple correlation, though a measure of great value, provides

only a broad indication of the nature and extent of relationship existing between

two variables. Sometimes, it may give a illusory and false indication. The size of

the sample also influences this.

For testing the significance of simple correlation or zero order correlation standard

error is determined. The standard error of simple correlation is

Where, n indicates the number of observations in the sample.

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PARTIAL CORRELATION

In case it is suspected that the relationship between two variables, say x and

y, is being distorted due to their relationship with some other variables, then partial

correlation is determined between these two variables after the effect of one or

more other distracting variable, if any has been eliminated.

In multivariate analysis, partial correlation analysis proves useful

eliminating the distracting influence of variables and in judging the depth of

relationship between each set of variables. Partial correlation provides the basis for

determining the multiple correlation.

The measure of partial correlation is known as co-efficient of partial

correlation of the first, second and third order, and so on, depending upon how

many disturbing variables have been taken into account.

Determination of partial correlation is essential to understand the cause and

effect relationship between the variables under observation.

PARTIAL CORRELATION INCASE OF THREE VARIABLES

In a study of three variables, determination of correlation between any two

variables eliminating the effect of third variable can be* measured as

a) between y and x2 keeping x1 constant

2 1 1 22 1

2 2

1 2

( )( ).

(1 1) (1 )

ryX ryX rX Xryx x

r y r x x

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Where, ryx1 = simple correlation between y and x1

ryx2 = simple correlation between y and x2

rx1x2 = simple correlation between x1 and x2

It may be noted in the above that the two variables under observation are identified

by the subscript before the dot, and the variabie held constant is identified by the

subscript after the dot.

PARTIAL CORRELATION IN CASE OF FOUR VARIABLES

In case of four variables, one dependent variable and three independent

variables, there can be twelve possible first order co-efficient of partial correlation.

These will be determined as shown here under

i)

3 2 1 2 2 33 1 2

2 2

1 2 2 1

. ..

1 . 1 .

ryx x ryx x rx xryx x x

r yx x r yx x

ii) 1 2 2 1

1 22 2

2 2 1

( )( ).

(1 ) (1 )

ryx ryx rx xryx x

r yx r x x

iii)

3 1 1 33 1

2 2

1 1 3

( )( ).

(1 ) (1 )

ryx ryx rx xryx x

r yx r x x

Second order co-efficient of partial correlation may be determined from the order

co-efficient as under

Between y and x1 eliminating x2 and x3

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(i)

21.2 1.3 2.31 2 3 2.32

1.3

(11

ry ry ryry x x ry

r y

Between y and x2 eliminating x1 and x

3

(ii)

2 3 1 2 1 32 1 3

2 2

1 2 1 3

. ..

1 . 1 .

ryx x ryx x rx xryx x x

r yx x r yx x

Between y and x3 eliminating x1 and x

3

(iii)

3 2 1 2 2 33 1 2

2 2

1 2 2 1

. ..

1 . 1 .

ryx x ryx x rx xryx x x

r yx x r yx x

The main object of determining partial co-efficient is to eliminate the

disturbance of other variables, while determining the correlation between two

variables. However, whether removing the effect of other variables will strengthen

or weaken correlation between two variables, will depend upon the nature and

extent of correlation with the disturbing variables and cannot be foretold by a

general value.

Significance test for partial correlation

Standard error of co-efficient of partial correlation is used to test the

significance of partial correlation. The standard error of co-efficient of partial

correlation is determined as under.

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S.E = 2

2 2

( ) ( ) ( )

( ) ( )

a x b xy n x

x n x

21

(3 )

r

n NVE

Where n= number of pairs of observation of variables considered.

NVE= number of variables eliminated.

MULTIPLE CORRELATION

Co-efficient of multiple correlation measures the nature and extent of

proximity in the relationship between one dependent variable and two or more

independent or explanatory variables. The statistical measure of such a relationship

is known as co-efficient of multiple correlation, with symbol R.

Co-efficient of multiple correlation as a measure has a large number of uses.

Important ones among them are

• It provides a measure of correlation between one dependent variable and large

number of independent variables.

• The co-efficient of determination provides a measure of change in dependent

variable explained by the change in independent variables.

• In multi variable analysis, the co-efficient of multiple correlation is a basic

measure useful in Identification of variables for the model where y is the

dependent variable and x1 and x

2 are regressors, the co-efficient of multiple

correlation indicated as RY.X, X, (or) RY.12 will be calculated as

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2 2

1 2 1 2 121.2

2

12

( (2 )

(1 ( ) )

ry ry ry ry rRY

r

Determination of correlation by least square method

Correlation among variables can alternatively be determined by the least square

method. Under the least square method, the co-efficient of correlation is

determined as under

2

2 2

( ) ( ) ( )

( ) ( )

a y b xy n yR

y n y

or

2

2 2

( ) ( ) ( )

( ) ( )

a x b xy n x

x n x

The co-efficient of correlation can also be determined by first determining the

ssunexplained and total variance of regression. In this case the co-efficient$1

correlation will be determined as

R=1-exp var

var

Un ected ianceofregression

total ianceofregression

The co-efficient of correlation can also be determined as

( )2 /

1( )2 /

cy y nr

y y n

or

2

2

( )1

( )

cy y

ry y

The coefficient of correlation can also be determined as

2

2

( )1

( )

cy y

ry y

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Where, Yc= expected value of Y for the given values of X.

y c = mean of expected values of Y.

y s = mean of Y

KARL PEARSON'S COEFFICIENT OF CORRELATION

The Karl Pearson's method is popularly known as Pearson's co-efficient of

correlation. The Pearson coefficient of correlation is denoted by the symbol 'r'. The

formula for computing Pearsonian „r‟ is

' '

x y

xyr

N

( ) : ( )x x x y y y

x =S.D of series x

y =S.D of series y

N = number of pairs of observation.

This method is applied only where deviations of items are taken from actual mean

and not from assumed mean.

Direct method of finding out correlation co-efficient

Correlation co-efficient can also be calculated without taking deviations of

items either from actual mean or assumed mean. The formula is

2 2 2 2

( )( )' '

( ) ( )

N xy x yr

N x x N y y

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When deviations are taken from an assumed mean

When actual means are in fractions, the calculation of correlation by the

method would take a lot of time. In such a case we make use of the assumed mean

method for finding out correlation. When deviations are taken from an assumed

mean the following formula is applicable

2 2 2 2( ) ( )

x y x yN fd d fd fdR

N fdx fdx N fdy fdy

Where,

dx - refers to deviations of x series from an assumed mean.

dy- refers to deviations of y series from an assumed mean.

"dx2 = sum of the squares of the deviations of x series from an assumed mean.

"dy2 = sum of squares of the deviation of y series.

"dx = sum of deviations of x series from an assumed mean.

"dy = sum of deviations of y series from an assumed mean.

Correlation of grouped data

When the number of observations is large, the data are often classified into

two-way frequency distribution called a correlation table.

The class intervals for y are listed in the captions or column headings and

those for x are listed in the stubs at the left of the table. The order also be reversed.

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The frequencies for each cell of the table are determined by either tallying or card

sorting just as In the case of frequency distribution of a single variable.

The formula is

2 2 2 2( ) ( )

x y x yN fd d fd fdR

N fdx fdx N fdy fdy

Assumption of the Pearson's co-efficient

Karl Pearson's co-efficient of correlation is based on the following assumptions

(1) there is linear relationship between the variables.

(2) The two variables under study are affected by a large number of independent

causes so as to form a normal distribution.

(3) There is a cause and effect relationship between the forces affecting

trie distribution of the items in the two services.

Merits and limitations of the Pearsonian coefficient

Amongst the mathematical methods used for measuring the degree of relationship,

Kari Pearson's method is most popular. The correlation co-efficient summarizes in

one figure not only the degree of correlation but also the direction.

The limitations are

• The correlation co-efficient always assumes linear relationship regards of the

fact whether that assumption is correct or not.

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• Great care must be exercised in the value of this co-efficient as very often

the coefficient is misinterpreted. The value of the co-efficient is unduly

affected by the extreme items.

• As compared with other methods this method takes more times to compute

the value of correlation co-efficient.

Properties of the co-efficient of correlation

The following are the important properties of the correlation co-efficient, r

1. the co-efficient of correlation lies between -1 and +1, symbolically, -d"

r+1or|r|d"|

2. the co-efficient of correlation is independent of change of scale and origin. of

the variable X and Y.

3. the co-efficient of correlation is the geometric mean of two regression co-

efficient.

4. the degree of relationship between the two variables is symmetric.

RANK CORRELATION CO-EFFICIENT

It is possible to avoid making any assumptions about the populations being

studied by ranking the observations according to size and basing the calculations

on the ranks rather than upon the original observations. It does not matter which

way the items are ranked. Item number one may be the larger or it may be smallest.

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Using ranks rather than actual observations gives the co-efficient of rank

correlation. Spearman's rank correlation co-efficient is defined as

6 21

( 2 1)

DR

n n

or

6 21

3

D

n n

Where, 'R' denotes rank co-efficient of correlation and „D‟ refers to the difference

of rank between paired items in two series.

FEATURES OF SPEARMAN'S CORRELATION CO-EFFICIENT

• the sum of the difference of ranks between two variables shall be zero.

• Spearman's correlation co-efficient is distribution free or non-parametric because

no strict assumptions are made about the form of population from which sample

observations are-drawn.

• The spearman's correlation co-efficient is nothing but Karl Pearson's correlation

co-efficient between the ranks.

In rank correlation we may have two types

• When ranks are given.

• When ranks are not given.

Where ranks are given

When actual ranks are given to us the steps required for computing rank correlation

are

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(i) take the difference of the two ranks i.e., (R1-R2) and denote these differences

by D.

(ii) square these difference and obtain the total "D2

(iii) apply the formula R=1 -

Where ranks are not given

When we are given the actual data and the ranks, it will be necessary to

assign the ranks. Ranks can be assigned by taking either highest value as 1 or the

lowest value as 1 but whether we start with the lowest value or the highest value

we must follow the same method in case of both the variables.

Equal Ranks

In some cases it may be found necessary to rank two or more individuals or

entries as equal. In such a case it is customary to give each individual an average

rank. If there is more than one group of items with a common rank, this value is

added as many times the number of such groups. The formula can thus be written.

6{ 2 1/12( 3 ) 1/12( 3 ) ....}1

3

D m m m mR

N N

s

When to use rank correlation co-efficient?

The rank method has principal uses

• the initial data are in the form of ranks.

• If N is/fairly small (say, not more than 25 or 30). Rank method is sometimes

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applied to interval data as an approximation to the more time-consuming r.

REGRESSION ANALYSIS

INTRODUCTION

Regression is a technique for approximating the relationship existing among

variables and using the same for estimating or predicting the value of one of these

variables on the basis of observed values of other related variable.

As a statistical measure 'regression' is based on 'correlation' i.e., existence of cause

and effect relationship among the variables under study. Regression analysis

provides a useful basis for estimation and forecasting the value of dependent

variable from the given values of one or more independent variables.

Regression is the measure of the average relationship between two or more

variables in terms of the original units pf the data.

Uses of regression analysis

Regression analysis is a branch of statistical theory that is widely used in

almost all the scientific disciplines. In economics, it is the basic technique for

measuring or estimating the relationship among economic variables that constitute

the essence of economic theory and economic life. The regression analysis

attempts to accomplish the following.

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1. Regression analysis provides estimates of values of the dependent variables

from values of the independent variables. The device used to accomplish this

estimation procedure is the regression line.

2. A second goal of regression analysis is to obtain a measure of the error

involved in using the regression line as a basis for estimation. For this

purpose the standard error of estimate is calculated.

3. With the help of regression co-efficient we can calculate the correlation co-

efficient. The square of correlation co-efficient R, called co-efficient of

determination measures the degree of association of correlation that exists

between two variables.

Difference between correlation and regression analysis

1. Whereas co-efficient is a measure of degree of co variability between X and

Y, the objective of regression analysis is to study the nature of relationship

between the variables so that we may be able to predict the value of one on the

basis of another.

2. Correlation is merely a tool of ascertaining the degree of relationship between

two variables. In regression analysis one variable is taken as dependent while the

other as independent. In correlation analysis there is a measure of direction and

degree of relationship between two variables. In regression analysis the regression

co-efficient is not symmetric and hence it definitely makes a difference as to which

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variable is dependent and which is independent.

3. There may be non-sense correlation between two variables which is purely

due to chance and has no practical relevance such as increase in income and

increase in weight of a group of people.

4. Correlation co-efficient is independent of change of scale and origin.

Regression co-efficient are independent of change of origin but not of

scale.

REGRESSION EQUATIONS

Regression equations, also known as estimating equations, are algebraic

expressions of the regression lines. Since there are two regression lines, there are

two regression equations.

Regression equation of Y on X

The regression equation of Y on X is expressed as follows

Y= a+bX

It may be noted that in this equation „y‟ is a dependent variables, „x‟ is independent

variable.

'a' and 'b' in the equation are called numerical constants because for any given

straight line, their value does not change.

If the values of the constants 'a' and 'b' are obtained, the line is completely

determined.

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A straight line fitted by least squares has the following characteristics

1. It gives the best fit to the data in the sense that it makes the sum of the

squared deviations from the line, "(Y-YC)2, smaller than they would be from any

other straight line. This property accounts for the name "Least Squares".

2. The deviations above the line equal those below the line on the average. This

means that the total of the positive and negative deviations is zero, or"(Y-YC)=0.

3. The straight line goes through the overall mean of the data ().

4. When the data represent a sample from a large population the least squares

line is a best estimate of the population regression line.

"y=Na+b"x

"xy=a"x+b"x2

These equations are usually called the normal equations.

Regression equation of X on Y

The regression equation of X on Y is expressed as follows.

Xc = baby

To determine the values of a and b, the following two normal equations are to be

solved simultaneously

"x=Na+b"y

"xy=a"y+b"y2

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Deviations taken from arithmetic means of X and Y

The above method of finding out regression equation is tedious. The calculations

can very much be simplified if instead of dealing with the actual values of X and Y

we take the deviations of X and Y series from their respective means. In such a

case the two regression equations are written as follows

(i) Regression equation of x on y

(ii) X- =R(y-)

is the mean of x series.

is the mean of y series,

r = is known as the regression co-efficient of x on y.

The regression co-efficient of X on Y is denoted by the symbol bxy or b1 It

measures the change in X corresponding to a unit change in Y.

Bxy or r =

(ii) Regression equation of Y on X

( )y

Y y r x Xx

it measures the change in Y corresponding to a unit change in X. when deviations

are taken from actual means, the regression are taken from actual means, the

regression co-efficient of Y on X can be obtained as follows.

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2

x xyr

y x

Deviations taken from assumed means

When actual mean of X and Y variables are in fractions the calculations can be

simplified by taking the deviations from the assumed means. When deviations are

taken from assumed means, the entire procedure of finding regression equations

remains the same. The two regression equations are

( )x

x X r y yy

The value of x

ry

will now be obtained as follows

2 2

( . )

( )

y N dxdy dx dyr

x N dY dy

Dx=(X-A) and dy =(Y-As)

Similarly, the regression equation of Y on X is

( )y

Y y r x Xx

Limitations of Regression Analysis

In making an estimate from a regression equation, it is important to

remember that an assumption is being made that relationship has not changed since

the regression equation was computed. Another point worth remembering is that

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the relationship shown by the scatter diagram may not be the same if the equation

is extended beyond the values used in computing the equation. For example, there

may be a close linear relationship between the yield of a crop and the amount of

fertilizer applied, with the yield increasing as the amount of fertilizer is increased.

It would not be logical, however, to extend this equation beyond the limits of the

experiment for it is quite likely that if the amount of fertilizer were increased

indefinitely, the yield would eventually decline as too much fertilizer was applied.

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CHAPTER – XI

REPORT WRITING

SIGNIFICANCE OF A RESEARCH REPORT

Report writing makes the final stage of research study. After the collected

data has been analyzed and interpreted, and various generalizations have been

drawn the report has to be prepared. The report of a survey is thus, the statement

that contains in brief the procedure adopted and the findings arrived at by the

investigator of a research problem. It is not a complete description of what has

been done during the period of survey. Reporting may appear simple, but it

requires considerable thought, skill, effort, patience and presentation. This unit

discusses the purpose of research reports, types of reports, principles of writing,

drafting and finalizing the report and evaluation of a research report.

Research report is considered a major component of the research study for

the research task remains incomplete till the report has been presented and / or

written. As a matter of fact even the most brilliant hypothesis, highly well designed

and conducted research study, and the most striking generalization and findings are

of little value unless they are effectively communicated to others. The purpose of

research is not well served unless the findings are made known to others. Research

results must Invariably enter the general store of knowledge. AH this explains the

significance of writing research report. There are people who do not consider

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seriously the research process. But the general opinion is in favor of treating the

presentation of research results or the writing of report as part and parcel of the

research project. Writing of report is the last step in a research study and requires a

set of skills somewhat different from those called for in respect of the earlier stages

of research. This task should be accomplished by the researcher with utmost care;

he may seek the assistance and guidance of experts for the purpose.

STEPS IN REPORT WRITING

Research reports are the product of slow, painstaking, accurate inductive

work. The usual steps involved in report writing are

1. logical analysis of the subject matter

2. preparation of the final outline

3. preparation of the rough draft

4. rewriting and polishing

5. preparation of the final bibliography

6. writing the final draft.

Though all the steps are self explanatory, yet a brief mention of each one of these

will be appropriate for better understanding.

1. Logical Analysis of the subject matter; It is the first step which is primarily

concerned with the development of a subject. There are two ways in which to

develop a subject.

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a) Logically; and b) chronologically; The logical development is made on the basis

of mental connections and associations between one thing and another by means of

analysis. Logical treatment often consists in developing the material from simple

propositions to the most complex structures. ; Chronological development is based

on a connection or sequence in time or occurrence. The directions for doing or

making something usually follow the chronological order.

2. Preparation of the final outline; It is the next step in writing the research report.

"Outlines are the framework upon which long written works are constructed. They

are an aid to the logical organization of the material and a reminder of the points to

be stressed in the report."

3. Preparation of the rough report; This follows the logical analysis of the subject

and the preparation of the final outline. Such a step is of utmost importance. For

the researcher now sits to write down what he has done In the context of his

research study. He will write down the procedure adopted by him in collecting the

material for his study along with various limitations faced by him; the technique of

analysis adopted by him, the broad findings and generalizations and the various

suggestions he wants to offer regarding the problem concerned.

4. Rewriting and polishing the rough draft; This step happens to be the most

difficult part of all formal writing. Usually this step requires more time than the

writing of the rough draft. The careful revision makes the difference between a

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mediocre and a good piece of writing. While rewriting and polishing, one should

check the report for weakness in logical development or presentation. The

researcher should also "see whether or not the material, as it is presented, has unity

and cohesion; does the report stand upright and firm and exhibit a definite pattern,

like a marble arch? Or does it resemble an old wall of moldering cement and loose

bricks." In addition the researcher should give due attention to the fact that in his

rough draft he has been consistent or not. He should check the mechanics of

writing - grammar, spelling and usage.

5. Preparation of a final Bibliography; Next in order comes the task of the

preparation of the final bibliography. The bibliography which is generally

appended to the research report is a list of books in some way pertinent to the

research which has been done. It should contain all those works which the

researcher has consulted. The bibliography should be arranged alphabetically and

may be divided into two parts; the first part may contain names of the books and

pamphlets and the second part may contain the names of the magazines and

newspaper articles. Generally this pattern of bibliography is considered convenient

and satisfactory from the point of view of the reader, though it is not the only way

of presenting bibliography. The entries in bibliography should be made adopting

the following order.

For the books and the pamphlets the order may be as under

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1. Name of the author, with last name first.

2. Title, underlined or the use of italics.

3. Place, publisher and date of publication.

4. Number of volumes.

Example: Subbiah, A.Sethurama, "Research Methodology", Chennai, Esores

Publications, 2005.

For magazines and newspapers the order may be as under

1. Name of the author, last name first.

2. Title of the article, in quotation marks.

3. Name of the periodical; underlined to indicate italics.

4. The volume and number.

5. The date of the issue.

6. The pagination.

Example: Subbiah, A.Sethurama." Who are Children?" Frontline, Chennai,

September 7-15, 2004, pp 34 -37.

WRITING THE FINAL DRAFT

This constitutes the last step. The final draft should be written in concise and

objective style and in simple language, avoiding vague expressions such as "it

seems," "they may be" and the like. While writing the final draft, the researcher

must avoid abstract terminologies and technical jargon. Illustrations and examples

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based on common experiences must be incorporated in the final draft as they

happen to be more effective in communicating the research findings to others. A

research report should not be dull, but must enthuse people and maintain interest

and must show originality, ft must be remembered that every report should be an

attempt to solve some intellectual problem and must contribute to the solution of a

problem and must add to the knowledge of both the researcher and the reader.

LAYOUT OF THE RESEARCH REPORT

Anybody, who is reading the research report, must necessarily be conveyed

enough about the study so that he can place it in its general scientific context,

judge the adequacy of its method and thus form an opinion of how seriously the

findings are to a taken. For this purpose there is the need of proper layout of the

report. The layout of the report means as to what the research report should

contain. A comprehensive layout of the research report should comprise, (a)

Preliminary pages (b) the main text; (c) the end matter.

(a) PRELIMINARY PAGES

In its preliminary pages the report should carry a title and date, followed by

acknowledgements in the form of 'preface' or 'forward'. Then there should be a

table of contents followed by list of tables and illustrations so that the decision

maker or anybody interested in reading the report can easily locate the required

information in the report.

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(b) MAIN TEXT

The main text premises the complete outline of the research report along

with all details. Title of the research study is repeated at the top of the first page of

the main text and then follows the other details on pages numbered consecutively,

beginning with the second page. Each main section of the report should begin on a

new page. The main text of the report should have the following sections, i)

introduction; ii) statement of findings and recommendations iii) the results iv) the

implications drawn from the results v) the summary.

i) Introduction

The purpose of introduction is to introduce the research project to the

readers. It should contain a clear statement of the objectives of research i.e.,

enough background should be given to make clear to the reader why the problem

was considered worth investigation. A brief summary of other relevant research

may also be stated so that the present study can be seen in that context. The

hypothesis of study, if any, and the definition of the major concepts employed ^n

the study should be explicitly stated in the introduction of the report.

The methodology adopted in conducting the study must be fully explained.

The scientific reader would like to know in detail about such things as; how was

the study carried out? What was its basic design? If the study was an experimental

one, then what were the experimental manipulations? If the data were collected by

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means of questionnaires or interviews, then exactly what questions were asked (the

questionnaire or interview schedule is usually given in an appendix) If

measurements were based on observation, then what instructions were based on

observation, then what instructions were given to the observers? Regarding the

sample used in the study the reader should be told; who were the subjects? How

many were there? How were they selected?

All these questions are crucial for estimating the probable*limits of

generalizability of the findings. The statistical analysis adopted must also be

clearly stated. In addition to all this, the scope of the study should be stated and the

boundary lines be demarcated. The various limitations, under which the research

project was completed, must also be narrated.

ii) Statement of findings and recommendations

After introduction, the research report must contain a statement of findings

and recommendation in non-technical language so that it can be easily understood

by all concerned. If the findings happen to be extensive, at this point they should

be put in the summarized form.

iii) Results

A detailed presentation of the findings of the study, with supporting data in

the form of tables and charts together with a validation of results. The next step is

writing the main text of the report. This generally comprises the main body of the

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report, extending over several chapters. The result section of the report should

contain statistical summaries and reductions of the data rather than the raw data; all

the results should be presented in logical sequence and split into readily

identifiable sections. All relevant results must find a place in the report. But how

one is to decide about what is relevant is the basic question. Quite often guidance

comes primarily from the research problem and from the hypothesis, if any, with

which the study was concerned. But ultimately the researcher must rely on his own

judgment in deciding the outline of his report. "Nevertheless, it is still necessary

that he states clearly the problem, the conclusion at which he arrived, and the bases

for his conclusion".

iv) Implications of the Result

Towards the end of the main text, the researcher should again put down the

results of his research clearly and precisely. He should state the implications that

flow from the results of the study, for the general reader who is interested in the

implication. For understanding the human behaviour, such implications may have

these aspects as stated below.

a) A statement of the inference drawn from the present study which may be

expected to apply in similar circumstances.

b) The conditions of the present study which may limit the extent of legitimate

generalization of the inferences drawn from the study.

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c) The relevant questions that still remain unanswered or new questions raised

by the study along with suggestions for the kind of research that would provide an

answer for them.

It is considered good practice to finish the report with a short conclusion

which summarizes and recapitulates the main points of the study. The conclusion

drawn from the study should be clearly related to the hypotheses that were stated in

the introductory section. At the same time, a forecast of the probable future of the

subject and an indication of the kind of research which needs to be done in that

particular field is useful and desirable.

v) The summary

It has become customary to conclude the research report with a very brief

summary, resting in brief the research problem. The methodology, the major

findings and the major conclusion drawn from the research results.

vi) End Matter

At the end of report; appendices should be enlisted in respect of ail technical

data such as questionnaires, sample information, mathematical derivations and the

like. Bibliography of sources consulted should also be given. Index (an

alphabetical listing of names, places and topics along with the numbers of the

pages in a book or report on which they are mentioned or discussed) should

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invariably be given at the end of the report. The value of index lies, in the fact that

it works as a guide to the reader for the contents in the report.

TYPES OF REPORT

Research reports vary greatly in length and type. In each individual's case,

both the length and the form are largely dictated by the problems at hand. For

instance, business firms prefer reports in the letter form, just one or two pages in

length. Banks, insurance organizations and financial institutions are generally fond

of the short balance sheet type of tabulation for their annual reports to their

customers and shareholders. Mathematicians prefer to write the results of their

investigations in the form of algebraic notations. Chemists report their results in

symbols and formulae. Students of literature usually write long reports presenting

the critical analysis of some writer or period or the like with a liberal use of

quotations from the works of the author under discussion. In the field of education

and psychology, the favorite form is the report on the results of experimentation

accompanied by the detailed statistical tabulation. Clinical psychologists and social

pathologists' frequently find it necessary to make use of the case-history form.

New items in the daily papers are also forms of report writing. They represent

firsthand-on-the scene accounts of the events described or compilations of

interviews with persons who were on the scene. In such reports the first paragraph

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usually contains the important information in details and the succeeding paragraph;

contain materials which are progressively less and less important.

Book reviews analyze the content of the book and report on the author's

intention, his success or failure in achieving his aims, his language, his style,

scholarship, bias or his point of view. Such reviews also happen to be a kind of

short report. The reports prepared by governmental bureaus, special commissions,

and other similar organizations are generally very comprehensive records on the

issue Involved. Such reports are usually considered as important research products,

similarly, PhD theses and dissertations are also a form of report-writing, usually

completed by students in academic institutions.

The above narration throws light on the fact that the results of a research

investigation can be presented in a number of ways viz.., a technical report, a

popular report, an article, a monograph or at times even in the form of oral

presentation. The method (s) of presentation to be used in a particular study

depends on the circumstances under which the study arose and the nature of the

results. A technical report is used whenever a full written report of the study is

required whether for record-keeping or for public dissemination. A popular report

is used if the research results have policy implications. We give below a few

details about the said two types of reports.

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(a) TECHNICAL REPORTS

In the technical report the main emphasis is on (I) the method employed, (ii)

assumptions made in the course of the study, (iii) the detailed presentation of the

findings including their limitations and supporting data.

A general outline of a technical report can be follows

1. SUMMARY OF RESULTS

A brief review of the main findings just in two or three pages.

2. NATURE OF THE STUDY

Description of the general objectives of study, formulation of the problem in

operational terms, the working hypothesis, the type of analysis and data required

etc.

3. METHODS EMPLOYED

Specific methods used in the study and their limitations; for instances; in

sampling studies we should give details of sample design vizo sample size, sample

selection, etc.

4. DATA

Discussion of data collected, their sources, characteristics and limitation. If

secondary data are used, their suitability to the problem at hand is fully assessed. In

case of a survey, the manner in which data were collected should be fully

described.

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5. ANALYSIS OF DATA AND PRESENTATION

The analysis of data and presentation and of the findings of the study with

supporting data in the form of tables and charts is fully narrated. This, infect,

happens to be the main body of the report usually extending over several chapters.

6. CONCLUSIONS

A detailed summary of the findings and the policy implications and the

results be explained.

7. BIBLIOGRAPHY

Bibliography of various sources consulted be prepared and attached.

8. TECHNICAL APPENDICES

Appendices are given for all technical matters relating to questionnaire,

mathematical derivations, elaboration on particular technique of analysis and the

like.

9. INDEX

Index must be prepared and be given invariably at the end of the report.

The order presented above only gives a general idea of the nature of a technical

report; the order of presentation may not necessarily be the same in all the

technical reports. This, in other words, it means that the presentation may vary in

different reports; even the different sections outlined above will not always be the

same, nor will all these sections appear in any particular order.

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It should, however, be remembered that even in a technical report, simple

presentation and ready availability of the findings remain an important

consideration and as such the liberal use of charts and diagrams is considered

desirable.

b) POPULAR REPORT

The popular report is one which gives emphasis on simplicity and

attractiveness. The simplification should be sought through clear writing,

minimization of technical, particularly mathematical, details and liberal use of

charts and diagrams. Attractive layout along with large print many subheadings,

even an occasional cartoon now and then is another characteristic feature of the

popular report* Besides, in such a report emphasis is given on practical aspects and

policy implications.

We give below a general outline of a popular report.

1. The findings and their implications

Emphasis in the report is given on the findings that are most practical and on

the implication of these findings

2. Recommendation for action

Recommendations for action on the basis of the findings ofstudy are made in

this section of the report.

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3. Objective of the study

A general review of how the problem arises is presented along with the

specific objectives of the project under study.

4. Methods employed

A brief and non-technical description of the methods and techniques used

including a short review of the data on which the study is based, is given in this

part of the report.

5. Results

This section constitutes the main body of the report where in the results of

the study are presented in clear and non-technical terms with liberal use of all sorts

of illustrations such as charts, diagrams and the like ones.

6. Technical Appendices

More detailed information on methods used, forms, etc is presented in the

form of appendices. But the appendices are often not detailed if the report is

entirely meant for general public.

There can be several variations of the form in which a popular report can be

prepared. The only important thing about such a report is that it gives emphasis on

simplicity and policy implications from the operational point of view avoiding the

technical details of all sorts to the extent possible.

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ORAL PRESENTATIONS

At times oral presentation of the results of the study is considered effective,

particularly in cases where policy recommendations are indicated by project

results. The merit of this approach lies in the fact that it provides an opportunity for

give-and-take decisions which generally lead to a better understanding of the

findings and their implication. But the main demerit of this sort of presentation is

the lack of any permanent record concerning the research details and it may be just

possible that the findings may fade away from people's memory even before any

action is taken. In order to overcome this difficulty, a written report may be

circulated before the oral presentation is made. Presentation is effective when

supplemented by various visual devices. Use of slides, wall charts and guide broad

are quite helpful in contributing to clarity and in reducing the boredom, if any.

Distributing a broad outline, with a few important tables and charts concerning the

research results, makes the listeners attentive who have a ready outline on which to

focus their thinking. This very often happens in academic institutions where the

researcher discusses his research findings and policy implication with others either

in a seminar or in a group discussion. Thus, research results can be reported in

more than one way, but the usual practice adopted, in academic institution

particularly, is that of writing the Technical Report and then preparing several

research papers to be discussed at various forums in one form or the other. But in

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the practical field and with problems having policy implications, the technique

followed is that of writing a popular report. Research done on governmental

account or on behalf of some major public or private organizations is usually

presented in the form of a technical report.

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CHAPTER – XII

MECHANICS OF REPORT WRITING

MECHANICS OF WRITING A RESEARCH REPORT

There are many definite and sets of rules which should be followed in %

actual preparation of the research report or paper. Once the techniques are finally

decided, they should be scrupulously adhered to, and no deviation permitted. The

criteria of format should be decided as soon as the materials for the research paper

have been assembled. The following points deserve mention so far as the

mechanics of writing a report are concerned.

SIZE AND PHYSICAL DESIGN

The manuscript should be written on Unit ruled paper 8.1/2"X11" in size. If

it is to be written by hand, then black or blue-black ink should be used. A margin

of at least one and one-half inches should be allowed at the left hand and of at least

half an inch at the right hand of the paper. There should also be one-inch margins,

top and bottom. The paper should be neat and legible. If the manuscript is to be

typed, then typing should be double-spaced on one side of the page only except for

insertion of the long quotations.

PROCEDURE

While writing the report the steps already mentioned in the earlier chapter

should be strictly adhered to.

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LAYOUT

Keeping in view the objective and nature of the problem, the layout of the

reports should be thought of and decided and accordingly adopted.

TREATMENT OF QUOTATION

Quotations should be placed in quotation marks and double spaced, forming

an immediate part of the text. But if a quotation is of a considerable length then it

should be single - spaced and indented at least half an inch to the right of the

normal text margins.

THE FOOTNOTES

Regarding footnotes one should keep in view the following

a) The footnotes serve two purposes viz., the identification of materials used in

quotation in the report and the notice of materials not immediately necessary to the

body of the research text but still of supplemental value. The modern tendency is to

make the minimum use of footnotes for scholarship does not need to be displayed.

b) Footnotes are placed at the bottom of t he page on which the reference or

quotation which they identify or supplement ends. Footnotes are customarily

separated from the textual materials by a space of half an inch and a line about one

and a half inches long.

c) Footnotes should be numbered consecutively, usually beginning with 1 in

each chapter separately. The number should be put slightly above the line, say at

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the end of a quotation.

d) Footnotes are always typed in single space though they are divided from one

another by double space

DOCUMENTATION STYLE

Regarding documentation, the first footnotes reference to any given work

should be complete in its documentation, giving all the essential facts about the

edition used. Such documentary footnotes follow a general sequence. The common

order may be described as under.

i) Regarding a single volume reference

1. authors name in normal order followed by a comma

2. title of work, underline to indicate italics

3. place and date of publication

4. pagination references

Example

John Gassner, Master of the Drama, New York; Dover publication, Inc

1954, P.315

ii) Regarding multivolume reference

1. Author's name in the normal order

2. Title of work, underlined to indicate italic

3. place and date of publication

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4. number of volume

5. Pagination reference.

iii) Regarding work arranged alphabetically

For works arranged alphabetically such as encyclopedias and dictionaries,

no pagination references are usually needed. In such cases the order is illustrated as

under.

Example 1

"Salamanca", Encyclopedia Britannica, 14th Edition.

Example 2

"Mary Wollstonecraft Godwin, "Dictionary of National biography".

But if there should be a detailed reference to any encyclopedia article, volume and

pagination reference may be found necessary.

vi) Regarding periodicals references

1. name of the author in normal order

2. title of article, in quotation marks

3. name of periodical, underlined to indicate italics

4. volume number

5. date of issuance

6. Pagination

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(vi) Regarding anthologies and collections references

Quotations from anthologies or collections of literacy works must be

acknowledged not only by author, but also by the name of the collector.

vii) Regarding second hand quotations reference

In such cases the documentation should be handled as follows

1. original author and title

2. "quoted or cited in,"

3. Second authors and work. Example

J.F. Jones, Life in Polynesia, P.16. Quoted in History of the Pacific Ocean Area, by

R.B.Abel, P. 191.

viii) Case of multiple authorship

If there are more than two authors or editors, then in the documentation the

name of only the first is given and the multiple authorship is indicated by "etal". or

"and other".

PUNCTUATION AND ABBREVIATIONS IN FOOTNOTES

The first item after the number in the footnotes is the author's name, given in

the normal signature. This is followed by a comma. After the comma, the title of

the book is given; the article (such as "A", "An", "The" etc.) is omitted and only

the first word and proper nouns and adjectives are capitalized. The title is followed

by a comma. The place of publication is then stated; it may be mentioned in an

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abbreviated form, if the place happens to be a famous one such as LON. for

London, N.Y for NEW York, N.D for New Delhi and so on. This entry is followed

by a comma. Then the name of the published is mentioned, and this entry is closed

by a comma. It is followed by the date of publication if the date is given on the title

page. If the date appears in the copyright notice on the reverse side of the title page

or else where in the volume the comma should be omitted and the date enclosed in

square brackets (d 978), [1978].

Certain English and Latin abbreviations are quite often used in

bibliographies and footnotes to eliminate tedious repetition. The following is a

partial fist of the most common abbreviations frequently used in report-writing,

(the researcher should learn to recognize them as well as he should learn to use

them)

Anon., anonymous

Ante, before

Art., article

Aug., augmented

Bk.,book

Bull., bulletin

Cf., compare

Ch., chapter

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Col., column

Discs, dissertation

Ed., editor, edition, edited

Edacity, edition cited

e.g., example gratis; for example

eng., enlarged

etal; and others

ex; example

f., ff., and the following

fig(s). figures

fir. footnotes

ibid. bidden in the same place

USE OF STATISTICS CHARTS AND GRAPHS

A judicious use of statistics in research reports is often considered a virtue

for it contributes a great deal towards the clarification and simplification of the

materials and research results. One may well remember that a good picture is often

worth more than a thousand words. Statistics are usually presented in the form of

tables, charts, bars Shd line-graphs and pictograms.

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THE FINAL DRAFT

Revising and rewriting the rough draft of the report should be done with

great care before writing the final draft. For the purpose, the researcher should put

to himself questions like; Are the sentences written in the report dear? Are they

grammatically correct? Do they say what is meant? Do the various points

incorporated in the report fit together logically? Having at least one colleaque read

the report just before the final revision is extremely helpful. Sentences that seem

crystal-clear to the writer may prove quite confusing to people; a connection that

had seemed self evident may strike other as not so evident

BIBLIOGRAPHY

Bibliography should be prepared and appended to the research report as

discussed earlier.

PREPARATION OF THE INDEX

At the end of the report an index should invariably be given, the value of

which lies in the fact t hat it acts as a good guide to the reader. Index may be

prepared both as subject index and as author index. The former gives the name of

the subject topics along with the number of pages on which they have appeared or

discussed in the report, whereas the latter gives the similar information regarding

the names of authors. The index should always be arranged alphabetically. Some

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people prefer to prepare only one index common for names of authors, subject

topics, concepts and the like ones.

NORMS FOR USING TABLES, CHARTS AND DIAGRAMS

The following general norms should be followed while constructing the charts and

diagrams.

1. Title: Every chart/ diagram must be given a suitable title. The title should

convey in as few words as possible the main idea that the diagrams intend to

portray. Title should be given either at the top or at the bottom of the chart /

diagram.

2. Proportion between width and height: If either the width or height of the

diagram / chart is too short or too long in proportion, it would giwsan ugly look.

While there are no fixed rules about the dimensions, Lutz suggested "Root-Two"

norm, which may be adopted for general use. It means, a ratio of 1 (short side) to

1.414 (long side).

3. Selection of scale: The scale showing the values should be in even numbers or

in multiples of five or ten. E.g. 25, 50,75 or 20,30,40. Etc. Odd numbers like 1,3,5,

7, should be avoided.

4. Foot notes: In order to clarify certain doubts about the diagram / art, foot

notes may be given at the bottom of the diagram /chart.

5. Index: An index illustrating different types of lines or different shades, colour

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should be given so that the reader can easily make out the meaning of the diagram.

6. Neatness and cleanliness: The chart / diagram should be absolutely neat and

clean.

7. Simplicity: Diagrams should be simple so that the reader can understand

clearly and easily. It is important that too many materials should not be loaded in a

single diagram which may confuse the reader.

Tables and figures may be profitably employed to present the data more

clearly and more concisely than would be possible if the same information were

presented in text form. Most style manuals provide examples of commonly used

types of tables and figures and instruction for their construction. A well

constructed table can give the reader a concise overview of the data.

The tables one has constructed in the conduction of the study cannot usually

be incorporated directly into the report. For example, at the completion of a study

one may have an alphabetical list of the subjects in one's study and their scores on

criterion measures. Rather than present this list as it stands one should construct a

table with the information in summary form.

It is desirable to arrange the tables in such a way that they illustrate the

relationship of the data to the hypothesis of the study. Beginning researchers are

frequently tempted to include the data in both forms. This merely makes the report

longer and more tedious. A better approach is to present the data in tables and

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figures accompanied by sufficient text to point out the most important and

interesting findings. It is especially important to relate the information in the tables

to the hypothesis.

APPENDIX, INDEX AND BIBLIOGRAPHY

The appendix contains pertinent materials that are not important enough to

be included in the body of the report, but which may be of value to some readers.

Such materials may include complete copies of locally devised texts or

questionnaires, together with the instructions and scoring key for such instruments,

item analysis data for measurements used, pertains instructions to subjects, and

tables that are very long or of only minimum importance of the study.

The following materials may be incorporated in the appendices.

1. original data, 2. long tables, 3. Long quotations, 4. supportive legal decisions,

documents, 5. illustrative material, 6. questionnaires, 7.case studies, 8. schedules or

forms, 9. transcripts of interviews.

The appendices may be serialized with capital letters (eg. Appendix-A, Appendix-

B) to differentiate from the chapter or table numbers,

In the text, reader's attention is drawn to the appendices as in the case of tables.

All the appendices are listed in the table of contents.

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INDEX

An index is seldom required in a research report, but one should be included

if it would be useful to the reader. Index is otherwise called as glossary. A

glossary/ index is a short dictionary giving definitions and examples of terms and

phrases which are technical, used in a special connotation by the author, unfamiliar

to the reader, or foreign to the language in which the book is written. It is listed as

a major section in all capital letters in the table of contents.

The glossary appears after the bibliography. It may also appear in the introductory

pages of a book after the lists of tables and illustrations.

Items are listed in alphabetical and normal order.

BIBLIOGRAPHY

The bibliography must include all sources mentioned in the text or footnotes.

Most universities insist that only these be listed, but a few insist that pertinent

references not specifically mentioned also be listed. The style manual previously

stated will give complete details on the method of listing references. It is important

to follow these rules rigorously and completely. In fact, it is a good strategy to

learn them before carrying out the search through the literature for the proposal. By

listing each reference in the correct form as it is encountered, one can avoid the

extra time involved in finding the references again in order to have them in

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complete form for the bibliography. It is advisable to list them on cards so that they

can be filed in alphabetical order.

The bibliography comes after the appendices section and is separated from it

by a division sheet written 'BIBLIOGRAPHY'. It is listed as a major section in all

capital letters in the table of content. The title of a bibliography should indicate

what type of items is listed. Some common varieties of bibliographies are given

below.

1. Bibliography of works cited

2. Selected bibliography

3. Annotated Bibliography

When the bibliography is long, items are classified for easy references according to

s1. format like books, periodicals, and news papers, 2. subject or theme, or 3.

chronological order.

If the name of the author is not given, the title of the book or the article

appears first An author's works written in collaboration with others are listed after

the works which he has written alone.

If there are more than three authors, the symbol et al. is used after the first author's

name and other names are omitted.