22538598 introduction-to-research-methodology-acccording-to-jntu-hyd-mba-syllabus

RESEARCH METHODOLOGY & STATISTICAL TOOLS

MASTER OF BUSINESS ADMINISTRATION(JNTU)

A MATERIAL FOR

RESEARCH

METHODOLOGY

AND

STATISTICAL TOOLS(According to JNTU Syllabus)

Prepared by,S. Venkata Siva Kumar;

MBA (HR/MRKTG), MSc (Statistics).

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UNIT-1RESEARCH METHODOLOGY:

An Introduction

Meaning of Research:

Research in common parlance refers to a search for knowledge. Once can also

define research as a scientific and systematic search for pertinent information on a

specific topic. In fact, research is an art of scientific investigation. The advanced

Learner’s Dictionary of current English lays down the meaning of research as “a

careful investigation or inquiry especially through search for new facts in any branch

of knowledge.” Redman and Mory define research as a “systematized effort to gain

new knowledge.” Some people consider research as a movement, a movement from

the known to unknown. It is actually a voyage of discovery.

Research is an academic activity and as such the term should be used in a

technical sense. According to “Clifford Woody, Research comprises defining and

redefining problems, formulating hypothesis or suggested solutions; collecting,

organizing and evaluating data; making deductions and reaching conclusions; and at

last carefully testing the conclusions to determine whether they fit the formulating

hypothesis. D. Slesinger and M. Stephenson in the encyclopedia of Social Sciences

define Research as “the manipulation of things, concepts or symbols for the purpose of

generalizing to extend, correct or verify knowledge, whether that knowledge aids in

construction of theory or in the practice of an art.”

Objectives of Research:

The purpose of Research is to discover answers to questions through the

application of scientific procedures. The main aim of research is to find out the truth

which is hidden and which has not been discovered as yet. Though each research study

has its own specific purpose, we may think of research objectives as falling into a

number of following broad groupings:

1. To gain familiarity with a phenomenon or to achieve new insights into it

(studies with this object in view are termed as exploratory or formulative

research studies);

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2. To portray accurately the characteristics of a particular individual, situation or

a group (studies with this object in view are known as descriptive research

studies);

3. To determine the frequency with which something occurs or with which it is

associated with something else (studies with this object in view are known as

diagnostic research studies);

4. To test a hypothesis of a casual relationship between variables (such studies are

known as hypothesis-testing research studies).

Motivation in Research:

What makes people to undertake research? This is a question of fundamental

importance. The possible motives for doing research may be either one or more of the

following:

1. Desire to get a research degree along with its consequential benefits;

2. Desire to face the challenge in solving the unsolved problems, i.e., concern

over practical problems initiates research;

3. Desire to get intellectual joy of doing some creative work;

4. Desire to be of service to society.

5. Desire to get Respectability.

However, this is not an exhaustive list of factors motivating people to

undertake research studies. Many more factors such as directives of

government, employment conditions, curiosity about new things, desire to

understand casual relationships, social thinking and awakening and the like

may as well motivate (or at times compel) people to perform research

operations.

Types of Research:

The basic types of research are as follows:

1. Descriptive Vs. Analytical Research : Descriptive research includes surveys

and fact-finding enquiries of different kinds. The major purpose of descriptive

research is description of the state of affairs as it exists at present. In social

science and business research we quite often use the term Ex post facto

research for descriptive research studies. The main characteristic of this

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method is that the researcher has no control over the variables; he can only

report what has happened or what is happening. Most ex post facto research

projects used for descriptive studies in which the researcher seeks to measure

such items as, for example, frequency of shopping, preferences of people, or

similar data. Ex post facto studies also include attempts by researchers to

discover causes even when they cannot control the variables. The methods of

research utilized in descriptive research are survey methods of all kinds,

including comparative and co-relational methods. In analytical research, on

the other hand, the researcher has to use facts or information already available,

and analyze these to make a critical evaluation of the material.

2. Applied Vs Fundamental Research : Research can either be applied (or action)

research or fundamental (to basic or pure) research. Applied research aims at

finding a solution for an immediate problem facing a society or an

industrial/business organization, whereas fundamental research is mainly

concerned with generalizations and with the formulation of a theory.

“Gathering knowledge for knowledge’s sake is termed as ‘pure’ or ‘basic’

research.” Research concerning some natural phenomenon or relating to pure

mathematics are examples of fundamental research. Similarly, research studies,

concerning human behavior carried on with a view to make generalizations

about human behavior, are also examples of fundamental research, but research

aimed at certain conclusion (say, a solution) facing a concrete social or

business problem is an example of applied research. Research to identify

social, economic or political trends that may affect a particular institution or

the copy research or the marketing research or evaluation research are

examples of applied research. Thus, the central aim of applied research is to

discover a solution for some pressing practical problem, whereas basic research

is directed towards finding information that has a broad base of applications

and thus, adds to the already existing organized body of scientific knowledge.

3. Quantitative Vs Qualitative Research : Quantitative research is based on the

measurement of quantity or amount. It is applicable to phenomena that can be

expressed in terms of quantity. Qualitative research, on the other hand, is

concerned with qualitative phenomenon i.e., phenomena relating to or

involving quality or kind. For instance, when we are interested in investigating

the reasons for human behavior, we quite often talk of ‘Motivation Research’,

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an important type of qualitative research. This type of research aims at

discovering the underlying motives and desires, using in depth interviews for

the purpose. Other techniques of such research are word association tests,

sentence completion tests, story completion tests and similar other projective

techniques. Attitude or opinion research i.e., research designed to find out how

people feel or what they think about a particular subject or institution is also

qualitative research. Qualitative research is especially important in the

behavioral sciences where the aim is to discover the underlying motives of

human behavior. Through such research we can analyze the various factors

which motivate people to behave in a particular manner or which make people

like or dislike a particular thing. It may be stated, that to apply qualitative

research in practice is relatively a difficult job and therefore, while doing such

research, one should seek guidance from experimental psychologists.

4. Conceptual Vs Empirical Research : Conceptual research is that related to

some abstract idea(s) or theory. It is generally used by philosophers and

thinkers to develop new concepts or to reinterpret existing ones. On the other

hand, empirical research relies on experience or observation alone, often

without due regard for system and theory. It is data-based research, coming up

with conclusions which are capable of being verified by observation or

experiment. We can also call it as experimental type of research. In such a

research it is necessary to get at facts first hand, at their source, and actively to

go about doing certain things to stimulate the production of desired

information. In such a research, the researcher must first provide himself with a

working hypothesis or guess as to the probable results. He then works to get

enough facts (data) to prove or disprove his hypothesis. He then sets up

experimental designs which he thinks will manipulate the persons or the

materials concerned so far to bring forth the desired information. Such research

is thus characterized by the experimenter’s control over the variables under

study and his deliberate manipulation of one of them to study its effects.

Empirical research is appropriate when proof is sought that certain variables

affect other variables in some way. Evidence gathered through experiments or

empirical studies is today considered studies are today considered to be the

most powerful support possible for a given hypothesis.

Nature and Importance of Research:

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“All progress is born of inquiry. Doubt is often better than over-confidence, for it leads

to inquiry, and inquiry leads to invention” is famous Hudson Maxim in context of

which the significance of research can well be understood. Increased amounts of

research make progress possible. Research inculcates scientific and inductive thinking

and it promotes the development of logical habits of thinking and organization.

The role of research in several fields of applied economics, whether related to

business or to the economy as a whole, has greatly increased in modern times. The

increasingly complex nature business and government has focused attention on the use

of research in solving operational problems. Research, as an aid to economic policy,

has gained added importance, both for government ad business.

Research provides the basis for nearly all government policies in our

economic system. For instance, government’s budgets rests in part on an analysis of

the needs and desires of the people and on the availability of revenues to meet these

needs. The cost of needs has to be equated to probable revenues and this is a field

where research is most needed. Through research we van devise alternative policies

and can as well examine the consequences of each of these alternatives. Decision-

making may not be a part of research, but research certainly facilitates the decisions of

the policy maker. Government has also to chalk out programmes for dealing with all

facets of the country’s existence and most of these will be related directly or indirectly

to economic conditions. The plight of cultivators, the problems of big and small

business and industry, working conditions, trade union activities, the problems of

distribution, even the size and nature of defense services are matters requiring

research. Thus, research is considered necessary with regard to the allocation of

nation’s resources.

Research has its special significance in solving various operational and

planning problems of business and industry. Operations research and market research,

along with motivational research, are considered crucial and their results assist, in

more than one way, in taking business decisions. Market research is the investigation

of the structure and development of a market of the purpose of formulating efficient

policies for purchasing, production and sales. Operations research refers to the

application of mathematical, logical and analytical techniques to the solution of

business problems of cost minimization or of profit maximization or what can be

termed as optimization problems. Motivational research of determining why people

behave as they do is mainly concerned with market characteristics.

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In addition to what has been stated above, the significance of research can also be

understood keeping in view the following points:

1. To those students who are to write a master’s or Ph.D.thesis, research may

mean a careerism or a way to attain a high position in the social structure;

2. To professionals in research methodology, research may mean a source of

livelihood.

3. To philosophers and thinkers, research may mean the outlet for new ideas and

insights;

4. To analysts and intellectuals, research may mean the generalizations of new

theories.

Thus, research is the fountain of knowledge for the sake of knowledge and an

important source of providing guidelines for solving different business, governmental

and social problems. It is a sort of formal training which enables one to understand the

new developments in one’s field in a battery way.

RESEARCH PROCESS:

The Research Process consists of series of actions or steps necessary to

effectively carry out research and the desired sequencing of these steps. The following

order concerning various steps provides a useful procedural guideline regarding the

research process:

1. Formulating the Research problem

2. Extensive Literature survey

3. Development of working hypothesis

4. Preparing the Research design

5. Determining the Sample design

6. Collection of data

7. Execution of the project

8. Analysis of data

9. Hypothesis-testing

10. Generalizations and interpretation

11. Preparation of the report or the thesis

1) Formulating the research problem: There are two types of research problems,

viz., those which relates to states of nature and those which relate to relationships

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between variables. At the very outset the researcher must single out the problem he

wants to study i.e., he must decide the general area of interest or aspect of a subject

matter that he would like to inquire into. Initially the problem may be stated in a broad

general way and then the ambiguities, if any, relating to the problem be resolved.

Then, the feasibility of a particular solution has to be considered before a working

formulation of the problem can be set up. The formulation of a general topic into a

specific research problem, thus, constitutes the first step in a scientific enquiry.

Essentially two steps are involved in formulating the research problem, viz.,

understanding the problem thoroughly, and rephrasing the same into meaningful terms

from an analytical point of view.

The best way of understanding the problem is to discuss it with one’s

own colleagues or with those having some expertise in the matter. In an academic

institution the researcher can seek the help from a guide who is usually an

experimented man and has several research problems in mind. Often, the guide puts

forth the problem in general terms and it is up to the researcher to narrow it down and

phrase the problem in operational terms. In private business units or in governmental

organizations, the problem is usually earmarked by the administrative agencies with

which the researcher can discuss as to how the problem originally came about and

what considerations are involved in its possible solutions.

Professor W.A. Neiswanger correctly states that the statement of the

objective is of basic importance because it determines the data which are to be

collected, the characteristics of the data which are relevant, relations which are to be

explored, the choice of techniques to be used in these explorations and the form of the

final report. If there are certain pertinent terms, the same should be clearly defined

along with the task of formulating the problem. In fact, formulation of the problem

often follows a sequential pattern where a number of formulations are set up, each

formulation more specific than the preceding one, each one phrased in more analytical

terms, and each more realistic in terms of the available data and resources.

2) Extensive literature survey: Once the problem is formulated, a brief summary

of it should be written down. It is compulsory for a research worker writing a thesis for

a Ph.D. degree to write a synopsis of the topic and submit it to the necessary

Committee or the Research Board for approval. At this juncture the researcher should

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undertake extensive literature survey connected with the problem. For this purpose, the

abstracting and indexing journals and published or unpublished bibliographies are the

first place to go to. Academic journals, conference proceedings, government reports,

books etc., must be tapped depending on the nature of the problem. In this process, it

should be remembered that one source will lead to another. The earlier studies, if any,

which are similar to the study in hand, should be carefully studied. A good library will

be a great help to the researcher at this stage.

3) Development of working hypothesis: After extensive literature survey,

researcher state in clear terms the working hypothesis or hypotheses. Working

hypothesis is tentative assumption made in order to draw out and test its logical or

empirical consequences. As such the manner in which research hypotheses are

developed is particularly important since they provide the focal point for research.

They also affect the manner in which tests must be conducted in the analysis of data

and indirectly the quality of data which is required for the analysis. In most types of

research, the development of working hypothesis plays an important role. Hypothesis

should be very specific and limited to the piece of research in hand because it has to be

tested. The role of the hypothesis is to guide the researcher by delimiting the area of

research and to keep him on the right track. It sharpens his thinking and focuses

attention on the more important facets of the problem. It also indicates the type of data

required and the type of methods of data analysis to be used.

How does one go about developing working hypothesis? The answer is by

using the following approach:

a) Discussions with colleagues and experts about the problem, its origin and the

objectives in seeking a solution;

b) Examination of data and records, if available, concerning the problem for

possible trends, peculiarities and other clues;

c) Review of similar studies in the area or of the studies on similar problems; and

d) Exploratory personal investigation which involves original field interviews on

a limited scale with interested parties and individuals with a view to secure

greater insight into the practical aspects of the problem.

Thus, working hypothesis arise as a result of a priori thinking about the subject,

examination of the available data and material including related studies and the

counsel of experts and interested parties. Working hypothesis is more useful when

stated in precise and clearly defined terms. It may as well be remembered that

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occasionally we may encounter a problem where we do not need working hypothesis,

especially in the case of exploratory or formulative researches which do not aim at

testing the hypothesis. But as a general rule, specification of working hypothesis in

another basic step of the research process in most research problems.

4) Preparing the research design: The research problem having been formulated

in clear cut terms, the researcher will be required to prepare a research design, i.e., he

will have to state the conceptual structure within which research would be conducted.

The preparation of such a design facilitates research to be as efficient as possible

yielding maximal information. In other words, the function of research design is to

provide for the collection of relevant evidence with minimal expenditure of effort,

time and money. But how all these can be achieved depends mainly on the research

purpose. Research purposes may be grouped into four categories, viz.,

a. Exploration

b. Description

c. Diagnosis

d. Experimentation

A flexible research design which provides opportunity for considering many

different aspects of a problem is considered appropriate if the purpose of the research

study is that of exploration. But when the purpose happens to be an accurate

description of a situation or of an association between variables, the suitable design

will be one that minimizes bias and maximizes the reliability of the data collected and

analyzed. There are several research designs, such as, an experimental and non-

experimental hypothesis testing. Experimental designs can be either informal design

(such as completely randomized design, randomized block design, Latin square

design, simple and complex factorial designs), out of which the researcher must select

one for his own project.

The preparation of the research design, appropriate for a particular research

problem, involves usually the consideration of the following:

I. The means of obtaining the information;

II. The availability and skills of the researcher and his staff (if any);

III. Explanation of the way in which selected means of obtaining information will

be organized and the reasoning leading to the selection;

IV. The time available for research; and

V. The cost factor relating to research, i.e., the finance available for the purpose.

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5) Determining sample design: All the items under consideration in any field of

inquiry constitute a ‘universe’ or ‘population’. A complete enumeration of all items in

the ‘population’ is known as a census enquiry. It can be presumed that in such an

enquiry when all the items are covered no element of chance is left and highest

accuracy is obtained. But in practice this may not be true. Even the slightest element of

bias in such an enquiry will get larger and larger as the number of observations

increases. Moreover, there is no way of checking the element if bias or its extent

except through a resurvey or use of sample checks. Besides, this type of inquiry

involves a great deal of time, money and energy. Not only this, census enquiry is not

possible in practice under many circumstances. For instance, blood testing is done

only on sample basis. Hence, quite often we select only a few items from the universe

for our study purposes. The items so selected continue what is technically called a

sample.

The researcher must decide the way of selecting a sample or what is popularly

known as the sample design. In other words a sample design is a definite plan

determined before any data are actually collected for obtaining a sample from a given

population. Thus, the plan to select 12 of a city’s 200 drugstores in a certain way

constitutes a sample design. Samples can be either probability samples or non-

probability samples. With probability samples each element has a known probability

of being included in the sample but the non-probability samples do not allow the

researcher to determine this probability. Probability samples are those based on simple

random sampling, systematic sampling, stratified sampling, cluster/area sampling

whereas non-probability samples are those based on convenience sampling, judgment

sampling and quota sampling techniques. A brief mention of the important sample

designs is as follows.

1. Deliberate sampling: Deliberate sampling is also known as purposive or non-

probability sampling. This sampling method involves purposive or deliberate

selection of particular units of the universe for constituting a sample which

represents the universe. When population elements are selected for inclusion in

the sample based on the ease of access, it can be called convenience sampling.

2. Simple random sampling: This type of sampling is also known as chance

sampling or probability sampling where each and every item in the population

has an equal chance of inclusion in the sample and each one of the possible

samples, in case of finite universe, has the same probability of being selected.

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For example, if we have to select a sample of 300 items from a universe of

15,000 items, then we can put the names or numbers of all the 15,000 items on

slips of paper and conduct a lottery.

3. Systematic sampling: In some instances the most practical way of sampling is

to select every 15th name on a list, every 10th house on one side of a street and

so on. Sampling of this type is known as systematic sampling.

4. Stratified sampling: if the population from which a sample is to be drawn does

not constitute a homogeneous group, then stratified sampling technique is

applied so as to obtain a representative sample. In this technique, the

population as stratified into a number of non-overlapping subpopulations or

strata and sample items are selected from each stratum. If the items selected

from each stratum is based on simple random sampling the entire procedure,

first stratification and then simple random sampling, is known as stratified

random sampling.

5. Quota sampling: In stratified sampling the cost of taking random samples

from individual strata is often so expensive that interviewers are simply given

quota to be filled from different strata, the actual selection of items for sample

being left to the interviewer’s judgment. This is called quota sampling.

6. Cluster sampling and Area sampling: cluster sampling involves grouping the

population and then selecting the groups or the clusters rather than individual

elements for inclusion in the sample. Suppose some departmental store wishes

to sample its credit card holders. It has issued its cards to 15,000 customers.

The sample size is to be kept say 450. For cluster sample this list of 15,000

card holders could be formed into 100 clusters of 150 card holders each. Three

clusters might then be selected for the sample randomly.

7. Multi-stage sampling: This is a further development of the idea of cluster

sampling. This technique is mean for big enquiries extending to a considerably

large geographical area like an entry country. Under multi-stage sampling the

first stage may be to select large primary sampling units such as states, then

districts, then towns and finally certain families within towns. If the technique

of random sampling is applied at all stages, the sampling procedure is

described as multi-stage random sampling.

8. Sequential sampling: This is some what a complex sample design where the

ultimate size of the sample is not fixed in advance but is determined according

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to mathematical decisions on the basis of information yielded as survey

progresses. This design is usually adopted under acceptance sampling plan in

the context of statistical quality control.

6) Collecting the data: In dealing with any real life problem it is often found that

data at hand are inadequate, and hence, it becomes necessary to collect data that are

appropriate. There are several ways of collecting the appropriate data which differ

considerably in context of money costs, time and other resources at the disposal of the

researcher.

Primary data can be collected either through experiment or through survey. If

the researcher conducts an experiment, he observes some quantitative measurements,

or the data, with the help of which he examines the truth contained in his hypothesis.

But in the case of a survey, data can be collected by any one or more of the following

ways.

1. By observation

2. Through personal interview

3. Through telephone interviews

4. By mailing of questionnaires

5. Through schedulers.

7) Execution of the project: Execution of the project is a very important step in the

research process. If the execution of the project proceeds on correct lines, the data to

be collected would be adequate and dependable. The researcher should see that the

project is executed in a systematic manner and in time. If the survey is to be conducted

by means of structured questionnaires, data can be readily machine-processed. In such

a situation, questions as well as the possible answers may be coded. If the data are to

be collected through interviewers, arrangements should made for proper selection and

training of the interviewers. The training may be given with the help of instruction

manuals which explain clearly the job of the interviewer at each step. Occasional field

checks should be made to ensure that the interviewers are doing their assigned job

sincerely and efficiently. A careful watch should be kept for unanticipated factors in

order to keep the survey as much realistic as possible. This, in other words, means that

steps should be taken to ensure that survey is under statistical control so that the

collected information is in accordance with the pre-defined standard of accuracy. If

some of the respondents do not cooperate, some suitable methods should be designed

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to tackle this problem. One method of dealing with the non-response problem is to

make a list of the non-respondents and take a small sub sample of them, and then with

the help of experts vigorous efforts can be made for securing response.

8) Analysis of data: After the data have been collected, the researcher turns to the

task of analyzing them. The analysis of data requires a number of closely related

operations such as establishment of categories, the application of these categories to

raw data through coding, tabulation and then drawing statistical inferences. The un-

widely data should necessarily be condensed into a few manageable groups and tables

for further analysis. Thus researcher should classify the raw data into some purposeful

and usable categories. Coding operation is usually done at this stage through which the

categories of data are transformed into symbols that nay be tabulated and counted.

Editing is the procedure that improves the quality of the data for coding. With coding

the stage is ready for tabulation. Tabulation is a part of the technical procedure

wherein the classified data are put in the form of tables. The mechanical devices can

be made use of at this juncture. A great deal of data, especially in large inquiries, is

tabulated by computers. Computers not only save time but also make it possible to

study large number of variables affecting a problem simultaneously.

9) Hypothesis-testing: after analyzing the data as stated above, the researcher is in a

position to test the hypothesis, if any, he had formulated earlier. Do the facts support

the hypothesis or they happen to be contrary? This is the usual question which should

be answered while testing hypothesis. Various tests, such as Chi-square test, t-test, F-

test have been developed by statisticians for the purpose. The hypothesis may be tested

through the use of one or more of such tests, depending upon the nature and object of

research inquiry. Hypothesis-testing will result in either accepting the hypothesis or in

rejecting it. If the researcher had no hypothesis to start with, generalizations

established on the basis of data may be stated as hypothesis to be tested by subsequent

researches in times to come.

10) Generalizations and interpretation: If a hypothesis is tested and upheld

several times, it man be possible for the researcher to arrive at generalization, i.e., to

build a theory. As a matter of fact, the real value of research lies in its ability to arrive

at certain generalizations. If the researcher had no hypothesis to start with. He might

seek to explain his findings on the basis of some theory. It is knows as interpretation.

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The process of interpretation may quite often trigger off new questions which in turn

lead to further researches.

11) Preparation of the report or the thesis: Finally, the researcher has to prepare the

report of what has been done by him. Writing of report must be done with great care

keeping in view the following:

1. The layout of report should be as follows:

(i) The preliminary pages;

(ii) The main text, and (iii) The end matter

In its preliminary pages the report should carry title and data followed

acknowledgements and foreword. Then there should be a table of contents followed by

a list of tables and list of graphs and charts, if any, given in the report.

The main text of the report should have the following parts:

(a) Introduction: It should contain a clear statement of the objective of the

research and explanation of the methodology adopted in accomplishing the

research. The scope of the study along with various limitations should as well

be stated in this part.

(b) Summary of findings: after introduction there would appear a statement of

findings and recommendations in non-technical language. If the findings are

extensive, they should be summarized.

(c) Main report: the main body of the report should be presented in logical

sequence and broken-down into readily identifiable sections.

(d) Conclusion: towards the end of the main text, researcher should again put

down the results of his research clearly and precisely. In fact, it is the final

summing up.

At the end of the report, appendices should be enlisted in respect of all

technical data. Bibliography, i.e., list of books, journals, reports, etc.,

consulted, should also be given in the end. Index should also be given specially

in a published research report.

2. Report should be written in a concise and objective style in simple language

avoiding vague expressions such as ‘it seems’, ‘there may be’, and the like.

3. Charts and illustrations in the main report should be used only if they present

the information more clearly and forcibly.

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4. Calculated ‘confidence limits’ must be mentioned and the various constraints

experienced in conducting research operations may as well be stated.

COLLECTION OF DATA

Statistical investigation: An investigation (or) inquiry means a “search for

knowledge”. Statistical investigation means “search for knowledge with the help of

statistical methods”.

Stages of Investigation: A statistical investigation is a comprehensive which passes

through the following steps:

1. Planning the inquiry

2. Collection of data

3. Editing the data

4. Presentation of data

5. Analysis of data

6. Presentation of final report

Collection of data: The first in the conduct of statistical investigation (or) inquiry is “collection of data”. The source of data can be represented as follows:

Internal source: Internal data come from government and business organizations

which generate them in the form of production, purchase, expenses etc.

DATA

INTERNAL DATA

EXTERNAL DATA

PRIMARY DATA

SECONDARY DATA

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External data: When data is collected from outside the organization, then this is

collected from the external source. External data can be divided into two types.

(i) Primary (ii) secondary

(i) Primary data: It refers to the statistical material which the investigator originates

for him for the purpose of the inquiry in hand in other words; it is one which is

collected by the investigator the first time.

(ii) Secondary data: it refers to the statistical material which is not originated by the

investigator himself but obtained from some one else records. This type of data is

generally taken from news papers, magazines, bulletins, reports etc.

Methods of collection of primary data: following methods may be used to collect the

primary data:

1. Direct personal investigation

2. Indirect personal investigation

3. Information through correspondent

4. Questionnaire method

(a) Questionnaire step to post

(b) Questionnaire step to investigators

(1) Direct personal investigation: According to this method, the investigator obtains

the data from personal interview or observation.

Therefore, he contains the source of information directly and personally. He

will contact cash and every possible source of information.

(2) Indirect personal investigation: According to this method the investigator contains

third party’s witnesses who are use to collect the information directly or indirectly and

or capable of supplying the necessary information. This method is generally adapted

by government committees to get views of the people relating to the inquiry.

(3) Information through correspondent: Under this method, the investigator does not

collect the information from the persons directly. He appoints local agents in different

cards of the area under investigation. These local agents are called “correspondents”.

This correspondents collect the information and pass it on to the investigate on time-

to-time.

(4) Questionnaire method: In this method, the necessary information is collected from

the respondent’s through a questionnaire. A questionnaire is a set of questions relating

to the inquiry. The information can be collected through questionnaires in two ways.

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(i) Questionnaires sent to post: in this case, the questionnaire is sent to a person and

the persons he fills the various answers to the various questions asked in it.

(ii) Questionnaires sent to investigator: under this method, the investigators are

appointed and contact the persons and get replace to the questionnaire and tell them in

their own hand writing in the questionnaire form.

Sources of secondary data: sometimes it is not possible to collect information for

resources in terms of money, time etc, in that solution secondary data is used. This

type of data is generally available in magazines, journals etc. This secondary data can

be classified into two categories:

(i) Published data

(ii) Unpublished data

Organization of data: the raw data in the form of unarranged figures are collected

through primary or secondary sources. The raw data practically gives no information

and hence there is a need for organization of data. In organization of data involves the

following ‘3’ stages:

(1) Editing of data

(2) Classification of data

(3) Tabulation of data

(1) Editing of data:

Editing of data refers to detect possible errors and irregulatories committed

during the collection of data.

If the data is not edited, then it may lead to wrong conclusions. Therefore

editing is essential to arrange the data in order.

(2) Classification of data:

The process of arranging the data in groups or classes according to their

common characteristics is technically classified.

Classification is the grouping of related facts into classes.

Types of classification: broadly whole data can be classified into following factors:

1. geographical classification

2. chromo logical classification

3. conditional classification

4. qualitative classification

5. quantitative classification

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1. Geographical classification: Here data are classified on the basic of

geographical area like village, city, states, and regions.

2. Chromo logical classification: Here, this classification is done on the

basis of time likely hourly, daily, weakly, monthly etc.

3. Conditional classification: This classification is done on the basis of

some conditions such as literacy, intelligence, honesty, beauty and ugly

etc.

4. Qualitative classification: Here, this data is classified on the basis of

some attributes (or) quality like literacy, honesty, beauty, intelligence

etc,. In this case the basis of classification is either presence or absence

of a quality.

5. Quantitative classification: When the data classified on the basis of

the characteristics which can be measured such as age, income, marks,

height, weight, product is called “Qualitative classification”.

(3) Tabulation of data: After the collection and classification of data process of

tabulation begins. Tabulation is dependent upon classification. Tabulation is necessary

in order to make the data understandable or organize. By tabulation we make a

systematic arrangement of statistical data in rows and columns. Rows are the

horizontal arrangements of data, where as the columns are the vertical arrangement of

data.

Tabulation tries to give the maximum information contained in the data in

minimum possible space. It is mid way process between the collection of data and

statistical analysis.

QUESTIONNAIRE AS A TOOL OF COLLECTING DATA

This method consists in preparing a questionnaire (a list of questions relating to

the field of enquiry and providing space for the answers to be filled by the

respondents) which is mailed to the respondents with a request for quick response

within the specified time. The questionnaire is the only media of communication

between the investigator and the respondents and as such the questionnaire should be

designed or drafted with utmost care and caution so that all the relevant and essential

information for the enquiry may be collected without any difficulty, ambiguity and

vagueness.

Drafting or Framing the Questionnaire:

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Drafting of a good questionnaire is a highly specialized job and requires great

care, skill, wisdom, efficiency and experience. No hard and fast rules can be laid down

for designing or framing a questionnaire. However, in this connection, the following

general points may be borne in mind:

1. The size of the questionnaire should be as small as possible. The

number of questions should be restricted to the minimum, keeping in view the

nature, objectives and scope of the enquiry. In other words, the questionnaire

should be concise and should contain only those questions which would furnish

all the necessary information relevant for the purpose. Respondents’ time should

not be wasted by asking irrelevant and unimportant questions. A large number of

questions would involve more work for the investigator and thus result in delay

on his part in collecting and submitting the information. These may, in addition,

also necessarily annoy or tire the respondents. A reasonable questionnaire should

contain from 15 to 20-25 questions. If a still larger number of questions is a must

in any enquiry, then the questionnaire should be divided into various sections or

parts.

2. The questions should be clear, brief, unambiguous, non-offending, and

courteous in tone, corroborative in nature and to the point so that not much scope

of guessing is left on the part of the respondents.

3. The questions should be arranged in a natural logical sequence. For

example, to find if a person owns a refrigerator the logical order of questions

would be: “Do you own a refrigerator”? When did you buy it? What is its make?

How much did it cost you? Is its performance satisfactory? Have you ever got it

serviced? The logical arrangement of questions in addition to facilitating

tabulation work would leave no chance for omissions or duplication.

4. The usage of vague and ‘multiple meaning’ words should be avoided.

The vague works like good, bad, efficient, sufficient, prosperity, rarely,

frequently, reasonable, poor, and rich, etc., should not be used since these may

be interpreted by different persons and as such might give unreliable and

misleading information. Similarly the use of words with multiple meanings like

price, assets, capital, income, household, democracy, socialism, etc., should not

be used unless a clarification to these terms is given in the questionnaire.

5. Questions should be so designed that they are readily comprehensive

and easy to answer for the respondents. They should not be tedious nor should

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they tax the respondents’ memory. Further, questions involving mathematical

calculations like percentages, ratios, etc., should not be asked.

6. Questions of a sensitive and personal nature should be avoided.

Questions like “How much money you owe to private parties?” or “Do you clean

your utensils yourself?” which might hurt the sentiments, pride or prestige of an

individual should not be asked, as far as possible. It is also advisable to avoid

questions on which the respondent may be reluctant or unwilling to furnish

information. For example, the questions pertaining to income, savings, habits,

addiction to social evils, age (particularly in case of ladies), etc., should be asked

very tactfully.

7. Typed Questions: Under this head, the questions in the questionnaire

may be broadly classified as follows:

a) Shut Questions: In much questions possible answers are suggested by

the framers of the questionnaire and the respondent is required to tick one of

them. Shut questions can further be sub-divided into the following forms.

(i) Simple Alternative Questions: In such questions, the

respondent has to choose between two clear cut alternatives like ‘Yes’ or

‘No’; ‘Right’ or ‘Wrong’; ‘Either’ or ‘Or’ and so on. For instance, do you

own a refrigerator? – Yes or No. Such questions are also called

dichotomous questions. This technique can be applied with elegance to

situations where two clear cut alternatives exist.

(ii) Multiple Choice Questions: Quite often, it is not possible to define a clear

cut alternative and accordingly in such a situation either the first method

(Alternative Questions) is not used or additional answers between ‘Yes’

or ‘No’ like ‘Do not know’, ‘No opinion’, Occasionally, Casually,

Seldom, etc., are added. For instance to find a person smokes or drinks,

the following multiple choice answers may be used:

Do you smoke?

Yes (Regularly) [ ] No (Never) [ ]

Occasionally [ ] Seldom [ ]

Which of the following modes of cooking you use?

Gas [ ] Coal (Coke) [ ] Wood [ ]

Power (Electricity) [ ] Stove (Kerosene) [ ]

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How do you go to your place of duty?

By bus [ ] By three wheeler scooter [ ]

By your own vehicle [ ] By taxi [ ]

By your own scooter [ ] On foot [ ]

By your own car [ ] Any other [ ]

Multiple choice questions are very easy and convenient for the respondents

to answer. Such questions save time and also facilitate tabulation. This

method should be used if only a selected few alternative answers exist to a

particular question. Sometimes, a last alternative under the category

‘Others’ or ‘Any other’ may be added. However, multiple answer questions

of relatively equal importance to a given question.

b) Open Questions: Open questions are those in which no alternative

answers are suggested and the respondents are at liberty to express their frank

and independent opinions on the problem in their own words. For instance,

‘What are the drawbacks in our examination system?’; ‘What solution do you

suggest to the housing problem in Delhi?’; ‘Which program in the Delhi TV

do you like best?’ are some of the open questions. Since the views of the

respondents in the open questions might differ widely, it is very difficult to

tabulate the diverse opinions and responses.

8) Leading questions should be avoided: For example, the question ‘why do we use a

particular brand of blades, say, Erasmic blades’ should preferably be framed into two

questions.

(i) Which blade do you use?

(ii) Why do you prefer it?

Gives a smooth shave [] Readily available in the market []

Gives more shaves [] Any other []

Price is less (cheaper) []

9) Cross checks: The questionnaire should be so designed as to provide internal

checks on the accuracy of the information supplied by the respondents by including

some connected questions at least with respect to matters which are fundamental to the

enquiry. For example in social survey for finding the age of the mother the question

‘What is your age’? Can be supplemented by additional questions ‘What is your date

of birth?’ or ‘What is the age of your eldest child’? Similarly, the question, ‘Age at

marriage’ can be supplemented by the question ‘The age of the first child’.

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10) Pre-testing the questionnaire: From practical of view it is desirable to try out the

questionnaire on a small scale (i.e., on a small cross-section of the population for

which the enquiry is intended) before using it for the given enquiry on a large scale.

This testing on a small scale (called pre-test) has been found to be extremely useful in

practice. The given questionnaire can be improved or modified in the light of the

drawbacks, shortcomings and problems faced by the investigator in the pre-test. Pre-

testing also helps to decide upon the effective methods of asking questions for

soliciting the requisite information.

11) A covering letter: A covering letter from the organizers of the enquiry should be

enclosed along with the questionnaire for the following purposes:

i. It should clearly explain in brief the objectives and scope of the

survey to evoke the interest of the respondents and impress upon them to

render their full co-operation by returning their schedule/questionnaire duly

filled in within the specified period.

ii. It should contain a note regarding the operational definitions to

the various terms and the concepts used in the questionnaire; units of

measurements to be used and the degree of accuracy aimed it.

iii. It should take the respondents in confidence and ensure them

that the information furnished by them will be kept completely secret and

they will not be harassed in any way later.

iv. In the case of mailed questionnaire method a self-addressed

stamped envelope should be enclosed for enabling the respondents to return

the questionnaire after completing it.

v. To ensure quick and better response the respondents may be

offered awards/incentives in the form of free gifts, coupons, etc.

vi. A copy of the survey report may be promised to the interested

respondents.

12) Mode of tabulation and analysis viz., hand operated, machine tabulation or

computerization should also be kept in mind while designing the questionnaire.

13) Lastly, the questionnaire should be made attractive by proper layout and appealing

get up. We give below two specimen questionnaires for illustration.

A MODEL OF QUESTIONNAIRE IN REGARDS TO CENSUS SURVEY:

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We give below the 1971 Census – Individual Slip which was used for a general

purpose survey to collect:

(i) Social and Cultural data like nationality, religion, literacy, mother tongue, etc.;

(ii) Exhaustive economic data like occupation, industry, class of worker and activity, if

not working;

(iii) Demographic data like relation to the head of the house,

sex, age, marital status, birth place, births and depths and the fertility of women to

assess in particular the performance of the family planning programme.

1971 CENSUS – INDIVIDUAL SLIP

1. Name…………………………………………………..

2. Relationship to the head of the family………………………………………

3. Sex………………………..

4. Age…………………………………..

5. Marital status………………………..

6. For currently married women only:

a) Age at marriage……………

b) Any child born in the last one year……………..

7. Birth place:

a) Place of birth……………

b) Rural or urban…………….

c) District…………………………….

d) State/Country…………………………..

8. Last Residence:

a) Place of last residence…………………………………………

b) Rural/Urban……………………………………….

c) District………………………………….

d) State/Country………………………………………

9. Duration of present residence……………………………………..

10. Religion………………………………………….

11. Scheduled Caste/Tribe………………………………………

12. Literacy………………………………………….

13. Educational level………………………………………..

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14. Mother Tongue…………………………………………..

15. Other Languages, if any……………………………………………………….

16. Main Activity:

a) Broad Category:

(i) Worker

(ii) Non – Worker

b) Place of work (Name of village/town)…………………………..

c) Name of establishment………………………

d) Name of Industry, Trade, Profession or Service…………………

e) Description of work…………………………………..

f) Class of worker………………………………..

17. Secondary work:

a) Broad Category………………………

b) Place of work…………………………….

c) Name of establishment……………………….

d) Nature of Industry, Trade, Profession or

service………………………….

e) Description of work…………………………………..

f) Class of worker……………………………………………..

SCHEDULES AS A TOOL FOR COLLECTING DATA

Before discussing this method it is desirable to make a distinction between a

questionnaire and a schedule. As already explained, questionnaire in a list of questions

which are answered by the respondent himself in this own handwriting while schedule

is the device of obtaining answers to the questions in a form which is filled by the

interviewers or enumerators (the field agents who put these questions) in a face to face

situation with the respondents. The most widely used method of collection of primary

data is the ‘schedules sent through enumerators’. This is so because this method is free

from certain shortcomings inherent in the earlier methods discussed so far. In this the

enumerators go to the respondents personally with the schedule (list of questions), ask

them the questions there in and record their replies. This method is generally used by

big business houses, large public enterprises and research institutions like ‘National

Council of Applied Economic Research (NCAER), Federation of Indian Chambers of

Commerce and Industries (FICCI) and so on and even by the governments – state or

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central – for certain projects and investigations where high degree of response is

desired. Population census, all over the world is conducted by this technique.

Merits:

1. The enumerators can explain in detail the objectives and aims of the enquiry to

the informants and impress upon them the need and utility of furnishing the

correct information.

2. This technique is very useful in expensive enquiries and generally yields fairly

dependable and reliable results due to the fact that the information is recorded

by highly trained and educated enumerators.

3. Unlike the ‘Questionnaire method’, this technique can be used with advantage

even if the respondents are illiterate.

4. As already pointed out in the ‘direct personal investigation’, due to personal

likes and dislikes, different people react differently to different questions and

as such some people might react very sharply to certain sensitive and personal

questions.

Demerits:

1. It is fairly expensive method since the team of enumerators is to be paid for

different services and as such can be used by only those bodies or institutions

which are financially sound.

2. It is also more time consuming as compared with the ‘Questionnaire method’.

3. The success of the method largely depends upon the efficiency and skill of the

enumerators who collect the information. The enumerators have to be trained

properly in the art of collecting correct information by their intelligence,

insight, patience and perseverance, diplomacy and courage. They should

clearly understand the aims and objectives of the enquiry and also the

implications of the various terms, definitions and concepts used in the

questionnaire.

4. Due to inherent variation in the individual personalities of the enumerators

there is bound to be variation, though not so obvious, in the information

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recorded by different enumerators. An attempt should be made to minimize

this variation.

5. The success of this method also lies to a great extent on the efficiency and

wisdom with which the schedule is prepared or drafted. If the schedule is

framed haphazardly and incompetently, the enumerators will find it very

difficult to get the complete and correct desired information from the

respondents.

SAMPLE DESIGN AND SAMPLING PROCEDURES

SAMPLE DESIGN:

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

times to be included in the sample i.e., the size of the sample. Sample design is

determined before data are collected. There are many sample designs from which a

researcher can choose. Some designs are relatively more precise and easier to apply

than others. Researcher must select/prepare a sample design which should be reliable

and appropriate for his research study.

STEPS IN SAMPLE DESIGN:

While developing a sample design, the researcher must pay attention to the following

points:

1. Type of universe: The first step in developing sample design is to clearly

define the set of objects, technically called the Universe, to be studied. The

universe can be finite or infinite. In finite universe the number of items is

certain, but in case of an infinite universe the number of items is infinite i.e.,

we cannot have any idea about the total number of items. The population of a

city, the number of workers in a factory and the like are examples of finite

universes, whereas the number of stars in the sky, listeners of a specific radio

programme, throwing of a dice etc., are examples of infinite universes.

2. Sampling Unit: A decision has to be taken concerning a sampling unit before

selecting sample. Sampling unit may be a geographical one such as state,

district, village, etc., or a construction unit such as house, flat, etc., or it may be

a social unit such as family, club, school, etc., or it may be an individual. The

27


researcher will have to decide one or more of such units that he has to select

for his study.

3. Source List: It is also known as ‘Sampling frame’ from which sample is to be

drawn. It contains the names of all items of a universe (in case of finite

universe only). If source list is not available, researcher has to prepare it. Such

a list should be comprehensive, correct, reliable and appropriate. It is

extremely important for the source list to be as representative of the population

as possible.

4. Size of sample: This refers to the number of items to be selected from the

universe to constitute a sample. This major problem before a researcher. The

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

optimum. An optimum sample is one which fulfills the requirements of

efficiency, representative-ness, reliability and flexibility. While deciding the

size of sample, researcher must determine the desired precision as also an

acceptable confidence level for the estimate.

5. Parameters of interest: In determining the sample design, one must consider

the question of the specific population parameters which are of interest. For

instance, we may be interested in estimating the proportion of persons with

some characteristic in the population, or we may be interested in knowing

some average or the other measure concerning the population. There may also

be important sub-groups in the population about whom we would like to make

estimates. All this has a strong impact upon the sample design we would

accept.

6. Budgetary Constraint: Cost considerations, from practical point of view, have

a major impact upon decisions relating to not only the size of the sample but

also to the type of sample. This fact can even lead to the use of a non-

probability sample.

7. Sampling Procedure: Finally, the researcher must decide the type of sample he

will use i.e., he must decide about the technique to be used in selecting the

items for the sample. In fact, this technique or procedure stands for the sample

design itself. There are several sample designs out of which the researcher

must choose one for his study. Obviously, he must select that design which, for

a given sample size and for a cost, has a small sampling error.

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CHARACTERISTICS OF GOOD SAMPLE DESIGN:

From what has been stated above, we can list down the characteristics of a good

sample design as under:

a) Sample design must result in a truly representative sample.

b) Sample design must be such which results in a small sampling error.

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

study.

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

better way.

e) Sample should be such that the results of the sample study can be applied, in

general, for the universe with a reasonable level of confidence.

CRITERIA OF SELECTING A SAMPLING PROCEDURE:

In this context one must remember that two costs are involved in a sampling

analysis viz., the cost of collecting the data and the cost of an incorrect inference

resulting from the data. Researcher must keep in view the two causes of incorrect

inferences viz., systematic bias and sampling error. Systematic bias results from errors

in the sampling procedures, and it cannot be reduced or eliminated by increasing the

sample size. At best the causes responsible for these errors can be detected and

corrected. Usually a systematic bias is the result of one or more of the following

factors.

1) Inappropriate frame: If the sampling frame is inappropriate i.e., a biased

representation of the universe, it will result in a systematic bias.

2) Defective measuring device: If the measuring device is constantly in error, it will return in

systematic bias. In survey work, systematic bias can result if the questionnaire or the

interviewer is biased. Similarly, if the physical measuring device is defective there will be

systematic bias in the data collected through such a measuring device.

3) Non-respondents: If we are unable to sample all the individuals initially include in the

sample, there may arise a systematic bias. The reason is that in such a situation the likelihood

of establishing contact or receiving a response from an individual is often correlated with the

measure of what is to be estimated.

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4) Indeterminacy principle: Sometimes we find that individuals act different when kept

under observation that what they do when kept in non-observed situations. For instance, if

workers are aware that somebody is observing then in course of a work study on the basis of

which the average length of time to complete a task will be determined and accordingly the

quota will be set for piece work, they generally tend to work slowly in comparison to the

speed with which they work if kept unobserved. Thus, the indeterminacy principle may also be

a cause of a systematic bias.

5) Natural bias in the reporting of data: Natural bias of respondents in the reporting of data

is often the cause of a systematic bias in many inquiries. There is usually a download bias in

the income data collected data by government taxation department, whereas we find an

upward bias in the income data collected by some social organization. People in general

understate their incomes if asked about it for tax purposes, but they overstate the same if asked

for social status or their affluence. Generally in psychological surveys, people tend to give

what they think is the ‘correct’ answer rather than revealing their true feelings.

DIFFERENT TYPES OF SAMPLE DESIGNS:

There are different types of sample designs based on two factors viz., the

representation basis and the element selection technique. On the representation basis

and the element selection technique. On the representation basis, the sample may be

probability sampling or it may be non-probability sampling. Probability sampling is

based on the concept of random selection, whereas non-probability sampling is ‘non-

random sampling. On element selection bias, the sample may be either unrestricted or

restricted. When each sample element is drawn individually from the population at

large, then the sample so drawn is known as ‘unrestricted sample’, whereas all other

forms of sampling are covered under the term ‘restricted sampling’. The following

chart exhibits the sample designs as explained above.

Non-probability sampling: Non-probability sampling is that sampling procedure

which does not afford any basis for estimating the probability that each item in the

population has of being included in the sample. Non-probability sampling is also

known by different names such as deliberate sampling, purposive sampling and

judgment sampling. In this type if sampling, items for the sample are selected

deliberately by the researcher; his choice concerning the items remains supreme. In

other words, under non-probability sampling the organizers of the inquiry purposively

30


choose the particular units of the universe for consulting a sample on the basis that the

small mass that they so select out of a huge one will be typical or representative of the

whole. For instance, if economic conditions of people living in a state are to be

studied, a few towns and villages may be purposively selected for intensive study on

the principle that they can be representative of the entire state. Thus, the judgment of

the organizers of the study plays an important part in this sampling design.

Quota sampling: It is also an example of non-probability sampling. Under quota

sampling the interviewers are simply given quotas to be filled from the different strata,

with some restrictions on how they are to be filled. In other words, the actual selection

of the items for the sample is left to the interviewer’s discretion. This type of sampling

is very convenient and is relatively inexpensive. But the samples so selected certainly

do not possess the characteristic of random samples. Quota samples are essentially

judgment samples and inferences drawn on their basis are not amenable to statistical

treatment in a formal way.

Probability sampling: Probability sampling is also known as ‘random sampling’ or

‘chance sampling’. Under this sampling design, every time of the universe has an

equal chance of inclusion in the sample. It is, so to say, a lottery method in which

individual units are picked up from the whole group not deliberately but by some

mechanical process. Here it is blind chance alone that determines whether one item or

the other is selected. The results obtained from probability or random sampling can be

assured in terms of probability i.e., we can measure the errors of estimation or the

significance of results obtained from a random sample, and this fact brings out the

superiority of random sampling design over the deliberate sampling design. Random

sampling ensures the Law of Statistical Regularity which states that if on an average

the sample chosen is a random one, the sample will have the same composition and

characteristics as the universe. This is the reason why random sampling is considered

as the best technique of selecting a representative sample.

Random sampling from a finite population to that method of sample selection

which gives each possible sample combination an equal probability of being picked up

and each item in the entire population to have an equal chance of being included in the

sample. This applies to sampling without replacement i.e., once an selected for the

sample, it cannot appear in the sample again (sampling with replacement is used less

31


frequently in which procedure the element for the sample is returned to the population

before the next element is selected. In such a situation the same element could appear

twice in the same sample before the second element is chosen).in brief, the

implications of random sampling (or simple random sampling) are:

(a) It gives each element in the population an equal probability of getting into the

sample; and all choices are independent of one another.

(b) It gives each possible sample combination an equal probability of being chosen.

COMPLEX RANDOM SAMPLING DESIGNS:

Probability sampling under restricted sampling techniques, as stated above,

may result in complex random sampling designs. Such designs may as well be called

‘mixed sampling designs’ for many of such designs may represent a combination of

probability and non-probability sampling procedures in selecting a sample. Some of

the popular complex random sampling designs are as follows:

(i) Systematic Sampling: In some instances, the most practical way of sampling is to

select every ith item on a list. Sampling of this type is known as systematic sampling.

An element of randomness is introduced into this kind of sampling by using random

numbers to pick up the unit with which to start. For instance, if a 4 percent sample is

desired, the first item would be selected randomly from the first twenty-five and

thereafter every 25th item would automatically be included in the sample. Thus, in

systematic sampling only the first unit is selected randomly and the remaining units of

the sample are selected at fixed intervals. Although a systematic sample is not a

random sample in the strict sense of the term, but it is often considered reasonable to

treat systematic sample as if it were a random sample.

(ii) Stratified Sampling: If a population from which a sample is to be drawn does not

constitute a homogeneous group, stratified sampling technique is generally applied in

order to obtain a representative sample. Under stratified sampling the population is

divided into several sub-populations that are individually more homogeneous than the

total population a (the different sub-populations are called ‘strata’) and then we select

items from each stratum to constitute a sample. Since each stratum is more

homogeneous than the total population, we are able to get precise estimates for each

stratum and by estimating more accurately each of the component parts; we get a

32


better estimate of the whole. In brief, stratified sampling results in more reliable and

detailed information.

(iii) Cluster Sampling: If the total area of interest happens to be a big one , a

convenient way in which a sample can be taken is to divide the area into a number of

smaller non-overlapping areas and then to randomly select a number of these smaller

areas (usually called clusters), with the ultimate sample consisting of all (or samples of

) units in these small areas of clusters.

Thus in cluster sampling the total population is divided into a number of

relatively small subdivisions which are themselves clusters of still smaller units and

then some of these clusters are randomly selected for inclusion in the overall sample.

Suppose we want to estimate the proportion of machine parts in an inventory which

are defective. Also assume that there are 20000 machine parts in the inventory at a

given point of time, stored in 400 cases of 50 each. Now using a cluster sampling, we

would consider the 400 cases as clusters and randomly select ‘n’ cases and examine all

the machine parts in each randomly selected case.

Cluster sampling, no doubt, reduces cost by concentrating surveys in selected

surveys. But certainly it is less precise than random sampling. There is also not as

much information in ‘n’ observations within a cluster as there happens to be in ‘n’

randomly drawn observations. Cluster sampling is used only because of the economic

advantage it possesses; estimates based on cluster samples are usually more reliable

per unit cost.

(iv) Area Sampling: If clusters happen to be some geographic subdivisions, in that

case cluster sampling is better known as area sampling. In other words, cluster

designs, where the primary sampling unit represents a cluster of units based on

geographic area, are distinguished as area sampling. The plus and minus points of

cluster sampling are also applicable to area sampling.

(v) Multi-stage Sampling: Multi-stage sampling is a further development of the

principle of cluster sampling. Suppose we want to investigate the working efficiency

of nationalized banks in India and we want to take a sample of few banks for this

purpose. The first stage is to select large primary sampling unit such as states in a

country. Then we may select certain districts and interview all banks in the chosen

33


districts. This would represent a two-stage sampling design with the ultimate sampling

units being clusters of districts.

If instead of taking a census of all banks within the selected districts, we select

certain towns and interview all banks in the chosen towns. This would represent a

three-stage sampling design. If instead of taking a census of all banks within the

selected towns, we randomly sample banks from each selected town, then it is a case

of using a four-stage sampling plan. If we select randomly at all stages, we will have

what is known as ‘multi-stage random sampling design’.

Ordinarily multi-stage sampling is applied in inquires extending to a

considerable large geographical area, say, the entire country. There are two advantages

of this sampling design viz., (a) It is easier to administer than most single stage designs

mainly because of the fact that sampling frame under multi-stage sampling in

developed impartial units. (b) A large number of units can be sampled for a given cost

under multistage because of sequential clustering, whereas this is not possible in most

of the sample designs.

(vi) Sampling with probability proportional to size: In case the cluster sampling

units do not have the same number or approximately the same number of elements, it

is considered appropriate to use a random selection process where the probability of

each cluster being included in the sample is proportional to the size of the cluster. For

this purpose, we have to list the number of the elements in each cluster irrespective of

the method of ordering the cluster. Then we must sample systematically the

appropriate number of elements from the cumulative totals.

(vii) Sequential Sampling: This sampling design is some what complex sample

design. The ultimate size of the sample under this technique is not fixed in advance,

but we determined according to mathematical decision rules on the basis of

information yielded as survey progresses. This is usually adopted in case of acceptance

sampling plan in context of statistical quality control. When a particular lot is to be

accepted or rejected on the basis of single sample, it is known as single sampling;

when the decision is to be taken on the basis of two samples, it is known as double

sampling and in case the decision rests on the basis of more than two samples but the

number of samples in certain and decide in advance, the sampling is known as the

34


multiple sampling. But when the number of samples is more than two but it is neither

certain nor decides in advance, this type of system is often referred to as sequential

sampling.

DIAGRAMATIC PRESENTATION OF DATA

General rules for Constructing Diagrams:

(1) Neatness: Diagrams are visual aids for presentation of statistical

data and are more appealing and fascinating to the eye and leave a lasting

impression on the mind. It is, therefore, imperative that they are made very neat,

clean and attractive by proper size and lettering; and the use of appropriate devices

like different colours, different shades (light and dark), dots, dashes, dotted lines,

broken lines, dots and dash lines, etc., for filling the in between space of the bars,

rectangles, circles, etc., and their components.

(2) Title and Footnotes: As in the case of a good statistical table, each

diagram should be given a suitable title to indicate the subject-matter and the

various facts depicted in the diagram. The title should be brief and self

explanatory, clear. If necessary the footnotes may be given at the left hand bottom

of the diagram to explain certain points or facts, not otherwise covered in the title.

(3) Selection of Scale: One of the most important factors in the

construction of diagrams is the choice of an appropriate scale. The same set of

numerical data if plotted on different scales may give the diagrams differing

widely in size and at times might lead to wrong and misleading interpretations.

Hence, the scale should be selected with great caution.

(4) Proportion between Width and Height: A proper proportion

between the dimensions (height and width) of the diagram should be maintained,

consistent with the space available.

(5) Choice of a Diagram: A large number of diagrams are used to

present statistical data. The choice of a particular diagram to present a given set of

numerical data is not an easy one. It primarily depends on the nature of the data,

magnitude of the observations and the type of the people for whom the diagrams

35


are meant and requires great amount of expertise, skill, and intelligence. An

inappropriate choice of the diagram for the given set of data might give a distorted

picture of the phenomenon under study and might lead to wrong and fallacious

interpretations and conclusions.

(6) Source Note and Number: As in the case of tables, source note,

wherever possible should be appended at the bottom of the diagram. This is

necessary as, to the learned audience of statistics; the reliability of the information

varies from source to source. Each diagram should also be given a number for

ready reference and comparative study.

(7) Index: A brief index explaining various types of shades, colors,

lines, and designs used in the construction of the diagram should be given for clear

understanding of the diagram.

(8) Simplicity: Lastly, diagrams should be as simple as possible so that

they are easily understood even by a layman who does not have any mathematical

or statistical background. If too much information is presented in a single complex

diagram it will be difficult to grasp and might even become confusing to the mind.

Hence, it is advisable to draw more simple diagrams than one or two complex

diagrams.

TYPES OF DIAGRAMS:

A large variety of diagrammatic devices are used in practice to present statistical data.

However, we shall discuss here only some of the most commonly used diagrams

which may be broadly classified as follows:

(1) One-dimensional diagrams

(2) Two-dimensional diagrams

(3) Three-dimensional diagrams

(4) Pictograms

(5) Cartograms

1) One-Dimensional Diagrams: These one-dimensional diagrams are classified into

two types. They are:

I. Line Diagrams

II. Bar Diagram

36


a) Line Diagram: This is the simplest of all the diagrams. It consists in drawing

vertical lines, each vertical line being equal to the frequency. The variate (x) values

are presented on a suitable scale along the X-axis and the corresponding

frequencies are presented on a suitable scale along Y-axis. Line diagrams facilitate

comparisons though they are not attractive or appealing to the eye.

0

20

40

60

80

100

1st Qtr 2nd Qtr 3rd Qtr 4th Qtr

East

West

North

b) Bar Diagram: Bar diagrams are one of the easiest and the most commonly used

devices of presenting most of the business and economic data. These are especially

satisfactory for categorical data or series. They consist of a group of equidistant

rectangles, one for each group or category of the data in which the values or the

magnitudes are represented by the length or height of the rectangles, the width of

the rectangles being arbitrary and immaterial. These diagrams are called one-

dimensional because in such diagrams only one dimension viz., height or length of

the rectangles is taken into account to present the given values. There are various

types of Bar Diagrams. They are listed as follows:

(i) Simple bar diagram(ii) Sub-divided or component bar diagram(iii) Percentage bar diagram(iv) Multiple bar diagram(v) Deviation or Bilateral bar diagram

37


0102030405060708090

1st Qtr 2nd Qtr 3rd Qtr 4th Qtr

East

West

North

2) Two-Dimensional Diagrams: Line or Bar diagrams discussed so far are one-

dimensional diagrams since the magnitudes of the observations are represented by

only one of the dimensions viz., height (length) of the bars while the width of the bars

is arbitrary and uniform. However, in two-dimensional diagrams, the magnitudes of

the given observations are represented by the area of the diagram. Thus, in the case of

two-dimensional bar diagrams, the length as well as width of the bars will have to be

considered. Two-dimensional diagrams are also known as “area diagrams or surface

diagrams”. Some of the commonly used two-dimensional diagrams are listed as

follows:

They are:

Rectangles Squares Circles Angular or pie diagrams

3) Three-Dimensional Diagrams: Three-dimensional diagrams, also termed as

‘volume diagrams’ are those in which three dimensions, viz., length, breadth, and

height are taken into account. They are constructed so that the given magnitudes

are represented by the volumes of the corresponding diagrams. The common forms

38


of such diagrams are “cubes, spheres, cylinders, blocks etc”. These diagrams are

specially useful if there are very wide variations between the smallest and the

largest magnitudes to be represented. Of the various three-dimensional diagrams,

‘cubes’ are the simplest and most commonly used devices of diagrammatic

presentation of data.

4) Pictograms: Pictograms is the technique of presenting statistical data through

appropriate pictures and is one of the very popular devices particularly when the

statistical facts are to be presented to a layman without any mathematical

background. In this, the magnitudes of the particular phenomenon under study are

presented through appropriate pictures, the number pictures drawn or the size of

the pictures being proportional to the values of the different magnitudes to be

presented. Pictures are more attractive and appealing to the eye and have a lasting

impression on the mind. Accordingly they are extensively used by government and

private institutions for diagrammatic presentation of the data relating to a variety

of social, business or economic phenomena primarily for display to the general

public or common masses in fairs and exhibitions.

5) Cartograms: in cartograms, statistical facts are presented through maps

accomplished by various types of diagrammatic representation. They are specially

used to depict the quantitative facts on a regional or geographical basis eg., the

population density of different states in a country or different countries in the

world, or the distribution of the rainfall in different regions of a country can be

shown with the help of maps or cartograms. The different regions or geographical

zones are depicted on a map and the quantities or magnitudes in the regions may

be shown by dots, different shades or colors etc., or by placing bars or pictograms

in each region or by writing the magnitudes to be represented in the respective

regions. Cartograms are simple and elementary forms of visual presentation and

are easy to understand. They are generally used when the regional or geographic

comparisons are to be highlighted.

GRAPHIC REPRESENTATION OF DATA

Diagrams are primarily used for comparative studies and can’t be used to study the

relation ship between the variables under study. This is done through graphs.

Diagrams furnish only approximate information and they are not of much utility to a

statistician from analysis point of view. On the other hand, graphs are more obvious,

precise and accurate than diagrams and can be effectively used for further statistical

39


analysis, viz., to study slopes, rates of change and for forecasting wherever possible.

Graphs are drawn on a special type of paper, known as “graph paper”.

Before discussing these graphs we shall briefly describe the technique of

constructing graphs and the general rules for drawing graphs.

TECHNIQUE OF CONSTRUCTION OF GRAPHS:

QUADRANT II 5- QUADRANT I

X-Negative 4- X-Positive

Y-Positive 3- Y-Negative

(-X, +Y) 2- (+X, +Y)

1-

-5 -4 -3 -2 -1 0 1 2 3 4 5

QUADRANT III -1- QUADRANT IV

X-Negative -2- X - Positive

Y-Positive -3- Y - Negative

(-X, -Y) -4- (+X, -Y)

-5-

Graphs are drawn on a special type of paper known as “Graph Paper”, which

has a fine network of horizontal and vertical lines; the thick lines for each division of a

centimeter or an inch measure and thin lines for small parts of the same. In a graph of

any size, two simple lines are drawn at right angle to each other, intersecting at point

‘O’ which is known as origin or zero of reference. The two lines are known as co-

ordinate axes. The horizontal line is called X – axis and is denoted by X’OX. The

vertical line is called the Y – axis and is usually denoted by YOY’. Thus the graph is

divided into four sections, known as four quadrants.

General Rules for Graphing: The following guidelines may be kept in mind for

drawing effective and accurate graphs.

1. Neatness

2. Title and Footnote

3. Structural Framework

4. Scale

40


5. False Base Line

6. Ratio or Logarithmic Scale

7. Line designs

8. Source Note and Number

9. Index

10. Simplicity

TYPES OF GRAPHS: A large number of graphs are used in practice. But they can

be broadly classified under the following two heads:

(i) Graphs of frequency distributions.

(ii) Graphs of time series.

1) Graphs of Frequency Distributions: The reasons and the guiding principles

for the graphic representation of the frequency distributions are precisely the same

as for the diagrammatic and graphic representation of other types of data. The so-

called frequency graphs are designed to reveal clearly the characteristic features of

a frequency data. Such graphs are more appealing to the eye than the tabulated data

and are readily perceptible to the mind. They facilitate comparative study of two or

more frequency distributions regarding their shape and pattern. The most

commonly used graphs for charting a frequency distribution for the general

understanding of the details of the data are:

A) Histogram B) Frequency Polygon

C) Frequency Curve D) “Ogive” or Cumulative Frequency CurveThe choice of a particular graph for a given frequency distribution largely depends on

the nature of the frequency distribution, viz., discrete or continuous.

A) HISTOGRAM: It is one of the most popular and commonly used devices for

charting continuous frequency distribution. It consists in erecting a series of

adjacent vertical rectangles on the sections of the horizontal axis (X-axis), with

bases (sections) equal to the width of the corresponding class intervals and heights

are so taken that the areas of the rectangles are equal to the frequencies of the

corresponding classes.

The Histogram can be constructed in two cases. They are:

Case (i): Histogram with equal classes.

Case (ii): Histogram with un-equal classes.

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B) FREQUENCY POLYGON: Frequency polygon is other device of graphic

presentation of a frequency distribution (continuous, grouped or discrete). In case

of discrete frequency distribution, frequency polygon is obtained on plotting the

frequencies on the vertical axis (Y-axis) against the corresponding values of the

variable on the horizontal axis (X-axis) and joining the points so obtained by

straight lines.

C) FREQUENCY CURVE: A frequency curve is a smooth free hand curve

drawn through the vertices of a frequency polygon. The object of smoothing of the

frequency polygon is to eliminate, as far as possible, the random or erratic

fluctuations that might be present in the data. The area enclosed by the frequency

curve is same as that of the histogram or frequency polygon but its shape is smooth

one and not with sharp edges. Frequency curve may be regarded as a limited form

of the frequency polygon as the number of observations (total frequency) becomes

very large and class intervals are made smaller and smaller.

Types of frequency curves:

Though different types of data may give rise to a variety of frequency curves, we

shall discuss below only some of the important curves which, in general, describe

most of the data observed in practice, viz., and the data relating to natural, social,

economic and business phenomena.

i) Curves of Symmetrical Distribution

ii) Moderately Asymmetrical (skewed) frequency distribution

curves

iii) Extremely asymmetrical or J – shaped curves

iv) U – curve

v) Mixed curves

D) “OGIVE” OR CUMULATIVE FREQUENCY CURVE: Ogive, pronounced

as “Ojive”, is a graphic presentation of the cumulative frequency (C.F)

distribution of continuous variable. It consists in plotting the cumulative frequency

(along the Y – axis) against the class boundaries (along the X – axis). Since there

are two types of cumulative frequency distributions viz., “LESS THAN C.F” and

“MORE THAN C.F”. We have accordingly two types of ogives, viz., (i) Less than

ogive (ii) More than ogive.

42


(i) Less than Ogive: This consists in plotting the ‘less than’ cumulative

frequencies against the upper class boundaries of the respective classes. The points

so obtained are joined by a smooth free hand curve to give “Less than Ogive”.

Obviously, “less than ogive” is an increasing curve, sloping upwards from left to

right and has the shape of an elongated S.

(ii) More than Ogive: Similarly, in “more than ogive”, the “more than”

cumulative frequencies are plotted against the lower class boundaries of the

respective classes. The points so obtained are joined by a smooth ‘free hand’ curve

to give “more than ogive”. “More than Ogive” is a decreasing curve and slopes

downwards from left to right and has the shape of an elongated S, upside down.

2) Graphs of Time Series: The Time Series data are represented geometrically by

means of times series graph which is also known as “Histogram”. The various types

of Time Series graphs are:

i) Horizontal Line Graphs or Histograms

ii) Silhouette or Net Balance Graphs

iii) Range or Variation Graphs

iv) Components or Band Graphs

TABULATION OF DATA

Meaning and Importance of Tabulation: By Tabulation we mean the symmetric

presentation of the information contained in the data, in rows and columns in

accordance with some salient features or characteristics. Rows are horizontal

arrangements and columns are vertical arrangements. In the words of A.M. Tuttle.

“A Statistical table is the logical listing of related quantitative data in vertical

columns and horizontal rows of numbers with sufficient explanatory and qualifying

words, phrases and statements in the form of titles, headings and notes to make clear

the full meaning of data and their origin”.

Professor Bowley, in his manual of statistics prefers to Tabulation as “the

intermediate process between the accumulation of data in what ever form they are

obtained, and the final reasoned account of the result shown by the statistics”.

Tabulation is one of the most important and ingenious device of the presenting

the data in a condensed and readily comprehensible form and attempts to furnish the

43


maximum information contained in the data in the minimum possible space, without

sacrificing the quality and usefulness of the data. It is an intermediate process between

the collection of the data on one hand and statistical analysis on the other hand. In

fact, Tabulation is the final stage in collection and compilation of the data and forms

the gateway for further statistical analysis and interpretations. Tabulation makes the

data comprehensible and facilitates comparisons (by classifying data into suitable

groups), and the work of further statistical analysis, averaging, correlation, etc. It

makes the data suitable for further Diagrammatic and Graphic representation.

GENERAL RULES FOR CONSTRUCTING A TABLE

The various parts of a table vary from problem to problem depending upon the nature

of the data and the purpose of the investigation. However, the following are a must in

a good statistical table:

1. Table Number

2. Title

3. Head Notes (or) Prefatory Notes

4. Captions and Stubs

5. Body of the Table

6. Foot-Note

7. Source Note

FORMAT OF A BLANK TABLE

Table No: # TITLE

[Head Note or Prefatory Note (if any)]

Caption

Sub Heads Sub Heads

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Stub Heading

TotalColumn

HeadColumn

HeadColumn

HeadColumn

HeadColumn

Head

Body

Total

Foot Note:Source Note:

TYPES OF TABULATION: The Tables are constructed in many ways.

1. Objectives and Scope of the enquiry.

General Purpose or Reference Table

(ii) Special Purpose or Summary Table

2. Nature of Enquiry.

(i) Original or Primary Table

(ii) Derived or Derivative Table

3. Extent of Coverage given in the Enquiry.

Simple Table

(ii) Complex Table

45


SPSS (STATISTICAL PACKAGE FOR THE SOCIAL SCIENCES)

SPSS (Statistical Package for the Social Sciences) has now been in development for

more than thirty years. Originally developed as a programming language for

conducting statistical analysis, it has grown into a complex and powerful application

with now uses both a graphical and a syntactical interface and provides dozens of

functions for managing, analyzing, and presenting data. Its statistical capabilities alone

range from simple percentages to complex analyses of variance, multiple regressions,

and general linear models. You can use data ranging from simple integers/binary

variables to multiple response or logarithmic variables. SPSS also provides extensive

data management functions, along with a complex and powerful programming

language.

STATISTICS PROGRAM

SPSS (originally, Statistical Package for the Social Sciences) was released in its first

version in 1968 after being developed by Norman H. Nie and C. Hadlai Hull. Norman

Nie was then a political science postgraduate at Stanford University, and now

Research Professor in the Department of Political Science at Stanford and Professor

Emeritus of Political Science at the University of Chicago. SPSS is among the most

widely used programs for statistical analysis in social science. It is used by market

researchers, health researchers, survey companies, government, education researchers,

marketing organizations and others. The original SPSS manual (Nie, Bent & Hull,

TYPES OF

TABLES

OBJECTIVES AND THE SCOPE OF

THE ENQUIRIES

NATURE OF THE

ENQUIRY

EXTENT OF COVERAGE

GIVEN IN THE

ENQUIRY

General Purpose or Reference

Table

Special Purpose or Summary

Table

Original or Primary Table

Derived or Derivative

TableSimple Table Complex Table

46

http://en.wikipedia.org/wiki/Social_science

http://en.wikipedia.org/wiki/Statistics

http://en.wikipedia.org/wiki/University_of_Chicago

http://en.wikipedia.org/wiki/Emeritus

http://en.wikipedia.org/wiki/Professor

http://en.wikipedia.org/wiki/Stanford_University

http://en.wikipedia.org/wiki/Postgraduate

http://en.wikipedia.org/wiki/Political_science

http://en.wikipedia.org/wiki/Norman_Nie

http://en.wikipedia.org/wiki/Norman_Nie

http://en.wikipedia.org/wiki/Norman_H._Nie


1970) has been described as 'Sociology's most influential book'. In addition to

statistical analysis, data management (case selection, file reshaping, creating derived

data) and data documentation (a metadata dictionary is stored in the data file) are

features of the base software.

Statistics included in the base software:

• Descriptive statistics: Cross tabulation, Frequencies, Descriptive, Explore,

Descriptive Ratio Statistics

• Bi-variate statistics: Means, t-test, ANOVA, Correlation (bi-variate, partial,

distances), Nonparametric tests

• Prediction for numerical outcomes: Linear regression

• Prediction for identifying groups: Factor analysis, cluster analysis (two-step,

K-means, hierarchical), Discriminant

The many features of SPSS are accessible via pull-down menus or can be programmed

with a proprietary 4GL command syntax language. Command syntax programming

has the benefits of reproducibility; simplifying repetitive tasks; and handling complex

data manipulations and analyses. Additionally, some complex applications can only be

programmed in syntax and is not accessible through the menu structure. The pull-

down menu interface also generates command syntax, this can be displayed in the

output though the default settings have to be changed to make the syntax visible to the

user; or can be paste into a syntax file using the "paste" button present in each menu.

Programs can be run interactively or unattended using the supplied Production Job

Facility. Additionally a "macro" language can be used to write command language

subroutines and a Python programmability extension can access the information in the

data dictionary and data and dynamically build command syntax programs. The

Python programmability extension, introduced in SPSS 14, replaced the less functional

SAX Basic "scripts" for most purposes, although Sax Basic remains available. In

addition, the Python extension allows SPSS to run any of the statistics in the free

software package R. From version 14 onwards SPSS can be driven externally by a

Python or a VB.NET program using supplied "plug-ins".

SPSS places constraints on internal file structure, data types, data processing and

matching files, which together considerably simplify programming. SPSS datasets

47

http://en.wikipedia.org/wiki/Data_processing

http://en.wikipedia.org/wiki/Data_types

http://en.wikipedia.org/wiki/VB.NET

http://en.wikipedia.org/wiki/R_(programming_language)

http://en.wikipedia.org/wiki/Free_software

http://en.wikipedia.org/wiki/Free_software

http://en.wikipedia.org/wiki/Visual_Basic

http://en.wikipedia.org/wiki/Python_(programming_language)

http://en.wikipedia.org/wiki/Subroutines

http://en.wikipedia.org/wiki/Default_(computer_science)

http://en.wikipedia.org/wiki/4GL

http://en.wikipedia.org/wiki/Pull-down_menus

http://en.wikipedia.org/wiki/Discriminant_analysis_(in_marketing)

http://en.wikipedia.org/wiki/Cluster_analysis

http://en.wikipedia.org/wiki/Factor_analysis

http://en.wikipedia.org/wiki/Linear_regression

http://en.wikipedia.org/wiki/Nonparametric

http://en.wikipedia.org/wiki/Correlation

http://en.wikipedia.org/wiki/ANOVA

http://en.wikipedia.org/wiki/T-test

http://en.wikipedia.org/wiki/Mean

http://en.wikipedia.org/wiki/Statistical_frequency

http://en.wikipedia.org/wiki/Cross_tabulation

http://en.wikipedia.org/wiki/Data

http://en.wikipedia.org/wiki/Metadata


have a 2-dimensional table structure where the rows typically represent cases (such as

individuals or households) and the columns represent measurements (such as age, sex

or household income). Only 2 data types are defined: numeric and text (or "string").

All data processing occurs sequentially case-by-case through the file. Files can be

matched one-to-one and one-to-many, but not many-to-many.

The graphical user interface has two views which can be toggled by clicking on one of

the two tabs in the bottom left of the SPSS window. The 'Data View' shows a

spreadsheet view of the cases (rows) and variables (columns). Unlike spreadsheets, the

data cells can only contain numbers or text and formulas cannot be stored in these

cells. The 'Variable View' displays the metadata dictionary where each row represents

a variable and shows the variable name, variable label, value label(s), print width,

measurement type and a variety of other characteristics. Cells in both views can be

manually edited, defining the file structure and allowing data entry without using

command syntax. This may be sufficient for small datasets. Larger datasets such as

statistical surveys are more often created in data entry software, or entered during

computer-assisted personal interviewing, by scanning and using optical character

recognition and optical mark recognition software, or by direct capture from online

questionnaires. These datasets are then read into SPSS.

SPSS can read and write data from ASCII text files (including hierarchical files), other

statistics packages, spreadsheets and databases. SPSS can read and write to external

relational database tables via ODBC and SQL.

Statistical output is to a proprietary file format (*.spv file, supporting pivot tables) for

which, in addition to the in-package viewer, a stand-alone reader can be downloaded.

The proprietary output can be exported to text or Microsoft Word. Alternatively,

output can be captured as data (using the OMS command), as text, tab-delimited text,

PDF, XLS, HTML, XML, SPSS dataset or a variety of graphic image formats (JPEG,

PNG, BMP and EMF).

Add-on modules provide additional capabilities. The available modules are:

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http://en.wikipedia.org/wiki/Windows_Metafile

http://en.wikipedia.org/wiki/Windows_and_OS/2_bitmap

http://en.wikipedia.org/wiki/Portable_Network_Graphics

http://en.wikipedia.org/wiki/JPEG

http://en.wikipedia.org/wiki/XML

http://en.wikipedia.org/wiki/HTML

http://en.wikipedia.org/wiki/Microsoft_Excel#File_formats

http://en.wikipedia.org/wiki/PDF

http://en.wikipedia.org/wiki/Microsoft_Word

http://en.wikipedia.org/wiki/Pivot_table

http://en.wikipedia.org/wiki/Proprietary_format

http://en.wikipedia.org/wiki/SQL

http://en.wikipedia.org/wiki/ODBC

http://en.wikipedia.org/wiki/Table_(database)

http://en.wikipedia.org/wiki/Databases

http://en.wikipedia.org/wiki/Spreadsheets

http://en.wikipedia.org/wiki/ASCII

http://en.wikipedia.org/wiki/Online_questionnaires

http://en.wikipedia.org/wiki/Online_questionnaires

http://en.wikipedia.org/wiki/Optical_mark_recognition

http://en.wikipedia.org/wiki/Optical_character_recognition

http://en.wikipedia.org/wiki/Optical_character_recognition

http://en.wikipedia.org/wiki/Computer-assisted_personal_interviewing

http://en.wikipedia.org/wiki/Data_entry

http://en.wikipedia.org/wiki/Statistical_survey

http://en.wikipedia.org/wiki/Spreadsheet

http://en.wikipedia.org/wiki/Graphical_user_interface

http://en.wikipedia.org/wiki/String_(computer_science)


• SPSS Programmability Extension (added in version 14). Allows Python

programming control of SPSS.

• SPSS Data Validation (added in version 14). Allows programming of logical

checks and reporting of suspicious values.

• SPSS Regression Models - Logistic regression, ordinal regression, multinomial

logistic regression, and mixed models.

• SPSS Advanced Models - Multivariate GLM and repeated measures ANOVA

(removed from base system in version 14).

• SPSS Classification Trees. Creates classification and decision trees for

identifying groups and predicting behavior.

• SPSS Tables. Allows user-defined control of output for reports.

• SPSS Exact Tests. Allows statistical testing on small samples.

• SPSS Categories

• SPSS Trends

• SPSS Conjoint

• SPSS Missing Value Analysis. Simple regression-based imputation.

• SPSS Map

• SPSS Complex Samples (added in Version 12). Adjusts for stratification and

clustering and other sample selection biases.

SPSS Server is a version of SPSS with client/server architecture. It has some features

not available in the desktop version, such as scoring functions.

REPORT PRESENTATION

Significance of Report Writing:

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 generalizations 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. All this explains the significance of writing research

report. Writing of report is the last step in a research study and requires a set of skills

some what different from those called for in respect of the earlier stages of the

49

http://en.wikipedia.org/wiki/Score_(statistics)

http://en.wikipedia.org/wiki/GLM

http://en.wikipedia.org/wiki/Multilevel_models


research. This task should be accomplished by the researcher with at-most care; he

may seek the assistance and guidance of experts for the purpose.

Different steps in writing Report: Research reports are the product of slow,

painstaking, accurate inductive work. The usual steps involved in writing report are as

follows:

1) Logical analysis of the subject-matter.

2) Preparation of the final outline.

3) Preparation of the rough draft.

4) Re-writing and Polishing.

5) Preparation of the final Bibliography.

6) Writing the final draft.

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 specific context, judge the

adequate of its adequacy of its methods and thus form an opinion of how seriously the

findings are to be 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. Let us deal with them separately.

(A) PRELIMINARY PAGES: In its preliminary pages the report should carry a

“title and date”, followed by acknowledgments in the form of ‘Preface’ or

‘Foreword’. 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.

(B) MAIN TEXT: the main text provides the completely 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; and (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

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reader why the problem was considered worth investigating. 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 definitions of the major

concepts employed in the study should be explicitly stated in the introduction of

the report.

(ii) Statement of findings and recommendations: After

introduction, the research report must contain a statement of findings and

recommendations in non-technical language so that it can be easily understood by

all concerned. The findings happen to be extensive; at this point they should be put

in the summarized form.

(iii) Results: A detailed presentation of the finding of the study,

with supporting data in the form of tables and charts together with a validation of

results, is the next step in writing the main text of the report. This generally

comprises the main body of the report, extended 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

splitted into readily identifiable sections.

(iv) Implications of the Results: Toward 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 is interested in the implications for understanding the human

behavior. Such implications may have three aspects as stated below:

A statement of the inferences drawn from the present

study which may be expected to apply in similar circumstances.

The conditions of the present study which may limit

the extent of legitimate generalizations of the inferences drawn from the

study.

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 answers for them.

(v) 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 conclusions drawn from the research results.

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(C) END MATTER: At the End of the Report, appendices should be enlisted in

respect of all technical data such as questionnaires, sample information, mathematical

derivations and the like ones. 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

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 REPORTS: Types of reports are classified into two types:

(A) Technical report

(B) Popular report

(A) Technical report: In the technical report the main emphasis is on (i) the methods

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 as follows:

1. Summary of results

2. Nature of the study

3. Methods employed

4. Data

5. Analysis of data and presentation of findings

6. Conclusions

7. Bibliography

8. Technical Appendices

9. Index

(B) Popular report: The popular 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

2. Recommendations for Action

3. Objective of the Study

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4. Methods Employed

5. Results

6. Technical Appendices

MECHANICS OF WRITING A RESEARCH REPORT

There are very definite and set rules which should be followed in the 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 the concerned.

1. Size and Physical design: The manuscript should be written on un ruled

paper1/2”x 11” in size. If it is 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 all typing should be double-

spaced on one side of the page only except for the insertion of the long

quotations.

2. Procedure: Various steps in writing the report should be strictly adhered (all

such steps have already been explained earlier in this chapter).

Keeping in view the objective and nature of the problem, the layout of

the report should be thought of and decided and accordingly adopted (The

layout of the research report and various types of reports have been described

in this chapter earlier which should be taken as a guide for report-writing in

case of particular problem).

3. Treatment of quotations: 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 (more than four or five type written lines) then it should be

single-spaced and intended at least half an inch to the right of the normal text

margin.

4. The footnotes: Regarding footnotes one should keep in view the followings:

a) The footnotes serve two purposes viz., the identification of materials

used in quotations in the report and the notice of materials not

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immediately necessary to the body of the research text but still of

supplemental value.

b) Footnotes are placed at the bottom of the page on which the reference

or quotation which they identify or supplement ends. Footnotes are

customarily separated from the textual material by a space or 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 the end of a quotation. At the foot of the page, again, the

footnote number should be intended and typed a little above the line.

d) Footnotes are always typed in single space though they are divided

from one another by double space.

5. Documentation style: Regarding documentation, the first footnote 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 the single-volume reference:

1. Author’s name in normal order (and not beginning

with the last name as in a bibliography) followed by comma;

2. Title of work, underlined to indicate italics;

3. Place and date of publication;

4. Pagination references (The page number).

Example: John Gassner, Master of the Drama, New York: Dover

Publications, Inc. 1954, p. 315.

(ii) Regarding Multi-volume reference:

1. Author’s name in the normal order;

2. Title of work, underlined to indicate italics;

3. Place and date of publication;

4. Number of volume;

5. Pagination references (The page number).

(iii) Regarding works arranged alphabetically: For works arranged

alphabetically such as encyclopedias and dictionaries, no pagination

reference is usually needed. In such cases the order is illustrated as under:

Example: “Salamanca,” Encyclopedia Britannica, 14th Edition.

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Example: “Mary Wollstonecraft Godwin,” Dictionary of national

biography.

But if there should be a detailed reference to a long encyclopedia article,

volume and pagination reference may be found necessary.

(iv) Regarding periodicals reference:

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;

(v) Regarding anthologies and collections reference: Quotations from

anthologists and collections of literary works must be acknowledged not

only by author, but also by the name of the collector.

(vi) 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 author and work;

Example: J.F. Jones, life in Ploynesia, p.16, quoted in History of the

Pacific Ocean Area, by R.B. Abel, p.191.

(vii) Case of Multiple authorship: If there are more than two authors or

editors, then in the documentation the name of only the first is given and

the multiple authorship is indicated by “et al.” or “and “others”.

Subsequent references to the same work need not be as detailed as stated

above. If the work is cited again without any other work intervening, it

may be indicated as ibid, followed by a comma and the page number. A

single page should be referred to as p., but more than one page be

referred to as pp. If there are several pages referred to at a stretch, the

practice is to use often the page number, for example, pp.190ff, which

means page number 190 and the following pages; but only for page 190

and the following page ‘190f’.

6. Punctuation and Abbreviations in footnotes: The first item after the number

in the footnote is the author’s name, given in the normal signature order. This is

55


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.

Anon., anonymous

Ante., before

Art., article

Aug., augmented

Bk., book

Bull., bulletin

Cf., compare

Ch., chapter

Col., column

Diss., dissertation

Ed., editor, edition, edited

Ed.cit., edition cited

e.g., exempli sratia: for example

eng., enlarged

et.al., and others

et seq., et sequens: and the following

ex., example

f.,ff., and the following

fig(s)., figure(s)

fn., footnote

ibid.,ibidem., in the same place (when two or more successive

footnotes refer to the same work, it is not

necessary to repeat complete reference for the

second footnote. Ibid. may be used. If different

pages are referred to, pagination must be shown).

Id.,idem., the same

Ill.,illus.,or illust(s). illustrated, illustration(s)

Intro.,intro., introduction

L, or ll, line(s)

loc.cit., in the place cited; used as op.cit.,(when new

reference

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loco citato is made to the same pagination as cited in the

previous note)

MS., MSS., Manuscript or Manuscripts

N.B.,notabene: note well

N,d., no date

n.p., no place

no pub., no publisher

no(s) number(s)

o.p., out of print

op.cit: in the work cited (if reference has been made to a

work

Opera citato and new reference is to be made, ibid., may be

used, if intervening reference has been made to

different works, op.cit. Must be used. The name

of the author must proceed.

p.or pp., page(s)

Passim: here and there

Post: after

rev., revised

tr.,trans., translator,translated,translation

vid or vide: see, refer to

viz., namely

Vol. or vol(s) volume(s)

vs., versus: against

7. 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 material and research results. One may

well remembered that a good picture is often worth more than a thousand words.

Statistics are usually presented in the form of tables, charts, bars and line-graphs

and pictograms. Such presentation should be self explanatory and complete in

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itself. It should be suitable and appropriate looking to the problem at hand.

Finally, statistical presentation should be neat and attractive.

8. The Final Draft: Revising and re-writing 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 clear? 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 colleague read the report just before the final revision is extremely

helpful. Sentences that seem crystal-clear to the writer may prove quite

confusing to other people; a connection that had seemed self evident may strike

others as a non-sequitur. A friendly critic, by pointing out passages that seem

unclear or illogical, and perhaps suggesting ways of remedying the difficulties,

can be an invaluable aid in achieving the goal of adequate communication.

9. Bibliography: Bibliography should be prepared and appended to the research

report as discussed earlier.

10. Preparation of the Index: At the end of the report, an Index should invariably

be given, the value of which lies in the fact that 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 names of the subject-topics or concepts 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 people prefer to

prepare only one index common for names of authors, subject-topics, concepts

and the like ones.

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