MB0050

Sikkim Manipal University - MBA - MB0050 – Research Methodology

Semester: 3

MB0050 – Research Methodology (Set 1)

1.a. Differentiate between nominal, ordinal, interval and ratio scales, with an example of each.

Ans. Measurement may be classified into four different levels, based on the characteristics of order, distance

and origin.

1. Nominal measurement

This level of measurement consists in assigning numerals or symbols to different categories of a variable.

The example of male and female applicants to an MBA program mentioned earlier is an example of nominal

measurement. The numerals or symbols are just labels and have no quantitative value. The number of cases

under each category are counted. Nominal measurement is therefore the simplest level of measurement. It

does not have characteristics such as order, distance or arithmetic origin.

2. Ordinal measurement

In this level of measurement, persons or objects are assigned numerals which indicate ranks with respect to

one or more properties, either in ascending or descending order.

Example

Individuals may be ranked according to their “socio-economic class”, which is measured by a combination of

income, education, occupation and wealth. The individual with the highest score might be assigned rank 1,

the next highest rank 2, and so on, or vice versa.

The numbers in this level of measurement indicate only rank order and not equal distance or absolute

quantities. This means that the distance between ranks 1 and 2 is not necessarily equal to the distance

between ranks 2 and 3.

Ordinal scales may be constructed using rank order, rating and paired comparisons. Variables that lend

themselves to ordinal measurement include preferences, ratings of organizations and economic status.

Statistical techniques that are commonly used to analyze ordinal scale data are the median and rank order

correlation coefficients.

3. Interval measurement

This level of measurement is more powerful than the nominal and ordinal levels of measurement, since it has

one additional characteristic – equality of distance. However, it does not have an origin or a true zero. This

implies that it is not possible to multiply or divide the numbers on an interval scale.

Example

Name Valerie Roshini RodriguesRoll No 521051574


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The Centigrade or Fahrenheit temperature gauge is an example of the interval level of measurement. A

temperature of 50 degrees is exactly 10 degrees hotter than 40 degrees and 10 degrees cooler than 60

degrees.

Since interval scales are more powerful than nominal or ordinal scales, they also lend themselves to more

powerful statistical techniques, such as standard deviation, product moment correlation and “t” tests and “F”

tests of significance.

4. Ratio measurement

This is the highest level of measurement and is appropriate when measuring characteristics which have an

absolute zero point. This level of measurement has all the three characteristics – order, distance and origin.

Examples

Height, weight, distance and area.

Since there is a natural zero, it is possible to multiply and divide the numbers on a ratio scale. Apart from

being able to use all the statistical techniques that are used with the nominal, ordinal and interval scales,

techniques like the geometric mean and coefficient of variation may also be used.

The main limitation of ratio measurement is that it cannot be used for characteristics such as leadership

quality, happiness, satisfaction and other properties which do not have natural zero points.

The different levels of measurement and their characteristics may be summed up.

In the table below –

Levels of measurement Characteristics

Nominal No order, distance or origin

Ordinal Order, but no distance or origin

Interval Both order and distance, but no origin

Ratio Order, distance and origin

b. What are the purposes of measurement in social science research?

Ans. Measurement has several purposes –

• The researcher constructs theories to explain social and psychological phenomena (e.g. labor unrest,

employee satisfaction), which in turn are used to derive hypotheses or assumptions. These hypotheses can

be verified statistically only by measuring the variables in the hypotheses.


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• Measurement makes the empirical description of social and psychological phenomena easier.

Example – When conducting a study of a tribal community, measuring devices help the researcher in

classifying cultural patterns and behaviors.

• Measurement also makes it possible to quantify variables and use statistical techniques to analyze the data

gathered.

• Measurement enables the researcher to classify individuals or objects and to compare them in terms of

specific properties or characteristics by measuring the concerned variables.

Examples –

Comparison of male and female students’ performance in college exams or of length of stay on the job of

older and younger employees.

2. a. What are the sources from which one may be able to identify research problems?

Ans. The selection of one appropriate researchable problem out of the identified problems requires

evaluation of those alternatives against certain criteria, which may be grouped into:

A. Internal Source -

Internal Criteria consists of:

1) Researcher’s interest: The problem should interest the researcher and be a challenge to him. Without

interest and curiosity, he may not develop sustained perseverance. Even a small difficulty may become an

excuse for discontinuing the study. Interest in a problem depends upon the researcher’s educational

background, experience, outlook and sensitivity.

2) Researcher’s competence: A mere interest in a problem will not do. The researcher must be competent

to plan and carry out a study of the problem. He must have the ability to grasp and deal with int. he must

possess adequate knowledge of the subject-matter, relevant methodology and statistical procedures.

3) Researcher’s own resource: In the case of a research to be done by a researcher on his won,

consideration of his own financial resource is pertinent. If it is beyond his means, he will not be able to

complete the work, unless he gets some external financial support. Time resource is more important than

finance. Research is a time-consuming process; hence it should be properly utilized.

B. External Source –


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1) Research-ability of the problem: The problem should be researchable, i.e., amendable for finding

answers to the questions involved in it through scientific method. To be researchable a question must be one

for which observation or other data collection in the real world can provide the answer.

2) Importance and urgency: Problems requiring investigation are unlimited, but available research efforts

are very much limited. Therefore, in selecting problems for research, their relative importance and

significance should be considered. An important and urgent problem should be given priority over an

unimportant one.

3) Novelty of the problem: The problem must have novelty. There is no use of wasting one’s time and

energy on a problem already studied thoroughly by others. This does not mean that replication is always

needless. In social sciences in some cases, it is appropriate to replicate (repeat) a study in order to verify the

validity of its findings to a different situation.

4) Feasibility: A problem may be a new one and also important, but if research on it is not feasible, it cannot

be selected. Hence feasibility is a very important consideration

5) Facilities: Research requires certain facilities such as well-equipped library facility, suitable and

competent guidance, data analysis facility, etc. Hence the availability of the facilities relevant to the problem

must be considered.

6) Usefulness and social relevance: Above all, the study of the problem should make significant

contribution to the concerned body of knowledge or to the solution of some significant practical problem. It

should be socially relevant. This consideration is particularly important in the case of higher level academic

research and sponsored research.

7) Research personnel: Research undertaken by professors and by research organizations require the

services of investigators and research officers. But in India and other developing countries, research has not

yet become a prospective profession. Hence talent persons are not attracted to research projects.

Each identified problem must be evaluated in terms of the above internal and external criteria and the most

appropriate one may be selected by a research scholar.

b. Why literature survey is important in research?

Ans. Frequently, an exploratory study is concerned with an area of subject matter in which explicit hypothesis

have not yet been formulated. The researcher’s task then is to review the available material with an eye on

the possibilities of developing hypothesis from it. In some areas of the subject matter, hypothesis may have

been stated by previous research workers. The researcher has to take stock of these various hypotheses

with a view to evaluating their usefulness for further research and to consider whether they suggest any new

hypothesis. Sociological journals, economic reviews, the bulletin of abstracts of current social sciences

research, directory of doctoral dissertation accepted by universities etc afford a rich store of valuable clues. In

addition to these general sources, some governmental agencies and voluntary organizations publish listings


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of summaries of research in their special fields of service. Professional organizations, research groups and

voluntary organizations are a constant source of information about unpublished works in their special fields.

3.a. What are the characteristics of a good research design?

Ans. Characteristics of a Good Research Design

1. It is a series of guide posts to keep one going in the right direction.

2. It reduces wastage of time and cost.

3. It encourages co-ordination and effective organization.

4. It is a tentative plan which undergoes modifications, as circumstances demand, when the study

progresses, new aspects, new conditions and new relationships come to light and insight into the study

deepens.

5. It has to be geared to the availability of data and the cooperation of the informants.

6. It has also to be kept within the manageable limits

b. What are the components of a research design?

Ans. Components of Research Design-

1. Dependent and Independent variables: A magnitude that varies is known as a variable. The

concept may assume different quantitative values, like height, weight, income, etc. Qualitative

variables are not quantifiable in the strictest sense of objectivity. However, the qualitative phenomena

may also be quantified in terms of the presence or absence of the attribute considered. Phenomena

that assume different values quantitatively even in decimal points are known as „continuous

variables‟. But, all variables need not be continuous. Values that can be expressed only in integer

values are called „non-continuous variables‟. In statistical term, they are also known as „discrete

variable‟. For example, age is a continuous variable; where as the number of children is a non-

continuous variable. When changes in one variable depends upon the changes in one or more other

variables, it is known as a dependent or endogenous variable, and the variables that cause the

changes in the dependent variable are known as the independent or explanatory or exogenous

variables. For example, if demand depends upon price, then demand is a dependent variable, while

price is the independent variable.

And if, more variables determine demand, like income and prices of substitute commodity, then

demand also depends upon them in addition to the own price. Then, demand is a dependent variable

which is determined by the independent variables like own price, income and price of substitute.

2. Extraneous variable: The independent variables which are not directly related to the purpose of the

study but affect the dependent variable are known as extraneous variables. For instance, assume


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that a researcher wants to test the hypothesis that there is relationship between children‟s school

performance and their self-concepts, in which case the latter is an independent variable and the

former, the dependent variable. In this context, intelligence may also influence the school

performance. However, since it is not directly related to the purpose of the study undertaken by the

researcher, it would be known as an extraneous variable. The influence caused by the extraneous

variable on the dependent variable is technically called as an „experimental error‟. Therefore, a

research study should always be framed in such a manner that the dependent variable completely

influences the change in the independent variable and any other extraneous variable or variables.

3. Control: One of the most important features of a good research design is to minimize the effect of

extraneous variable. Technically, the term control is used when a researcher designs the study in

such a manner that it minimizes the effects of extraneous independent variables. The term control is

used in experimental research to reflect the restrain in experimental conditions.

4. Confounded relationship: The relationship between dependent and independent variables is said to

be confounded by an extraneous variable, when the dependent variable is not free from its effects.

Research hypothesis: When a prediction or a hypothesized relationship is tested by adopting scientific

methods, it is known as research hypothesis. The research hypothesis is a predictive statement which relates

a dependent variable and an independent variable. Generally, a research hypothesis must consist of at least

one dependent variable and one independent variable. Whereas, the relationships that are assumed but not

be tested are predictive statements that are not to be objectively verified are not classified as research

hypothesis.

Experimental and control groups: When a group is exposed to usual conditions in an experimental

hypothesis-testing research, it is known as „control group‟. On the other hand, when the group is exposed to

certain new or special condition, it is known as an „experimental group‟. In the afore-mentioned example, the

Group A can be called a control group and the Group B an experimental one. If both the groups A and B are

exposed to some special feature, then both the groups may be called as „experimental groups‟. A research

design may include only the experimental group or the both experimental and control groups together.

Treatments: Treatments are referred to the different conditions to which the experimental and control

groups are subject to. In the example considered, the two treatments are the parents with regular earnings

and those with no regular earnings. Likewise, if a research study attempts to examine through an experiment

regarding the comparative impacts of three different types of fertilizers on the yield of rice crop, then the three

types of fertilizers would be treated as the three treatments.

Experiment: An experiment refers to the process of verifying the truth of a statistical hypothesis relating to

a given research problem. For instance, experiment may be conducted to examine the yield of a certain new


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variety of rice crop developed. Further, Experiments may be categorized into two types namely, absolute

experiment and comparative experiment. If a researcher wishes to determine the impact of a chemical

fertilizer on the yield of a particular variety of rice crop, then it is known as absolute experiment. Meanwhile, if

the researcher wishes to determine the impact of chemical fertilizer as compared to the impact of bio-

fertilizer, then the experiment is known as a comparative experiment.

Experiment unit: Experimental units refer to the predetermined plots, characteristics or the blocks, to

which the different treatments are applied. It is worth mentioning here that such experimental units must be

selected with great caution.

4. a. Distinguish between Doubles sampling and multiphase sampling.

Ans. Double Sampling and Multiphase Sampling

Double sampling refers to the subsection of the final sample form a pre-selected larger sample that provided

information for improving the final selection. When the procedure is extended to more than two phases of

selection, it is then, called multi-phase sampling. This is also known as sequential sampling, as sub-sampling

is done from a main sample in phases. Double sampling or multiphase sampling is a compromise solution for

a dilemma posed by undesirable extremes. “The statistics based on the sample of ‘n’ can be improved by

using ancillary information from a wide base: but this is too costly to obtain from the entire population of N

elements. Instead, information is obtained from a larger preliminary sample nL which includes the final

sample n. extraneousDouble sampling refers to the subsection of the final sample form a pre-selected larger

sample that provided information for improving the final selection. When the procedure is extended to more

than two phases of selection, it is then, called multi-phase sampling. This is also known as sequential

sampling, as sub-sampling is done from a main sample in phases. Double sampling or multiphase sampling

is a compromise solution for a dilemma posed by undesirable extremes. “The statistics based on the sample

of ‘n’ can be improved by using ancillary information from a wide base: but this is too costly to obtain from the

entire population of N elements. Instead, information is obtained from a larger preliminary sample nL which

includes the final sample n.

b. What is replicated or interpenetrating sampling?

Ans. Replicated or Interpenetrating Sampling

It involves selection of a certain number of sub-samples rather than one full sample from a population. All the

sub-samples should be drawn using the same sampling technique and each is a self-contained and adequate

sample of the population. Replicated sampling can be used with any basic sampling technique: simple or

stratified, single or multi-stage or single or multiphase sampling. It provides a simple means of calculating the

sampling error. It is practical. The replicated samples can throw light on variable non-sampling errors. But

disadvantage is that it limits the amount of stratification that can be employed.


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5. a. How is secondary data useful to researcher?

Ans. Use of Secondary Data

The second data may be used in three ways by a researcher. First, some specific information from secondary

sources may be used for reference purpose. For example, the general statistical information in the number of

co-operative credit societies in the country, their coverage of villages, their capital structure, volume of

business etc., may be taken from published reports and quoted as background information in a study on the

evaluation of performance of cooperative credit societies in a selected district/state.

Second, secondary data may be used as bench marks against which the findings of research may be tested,

e.g., the findings of a local or regional survey may be compared with the national averages; the performance

indicators of a particular bank may be tested against the corresponding indicators of the banking industry as

a whole; and so on.

Finally, secondary data may be used as the sole source of information for a research project. Such studies as

securities Market Behaviour, Financial Analysis of companies, Trade in credit allocation in commercial banks,

sociological studies on crimes, historical studies, and the like, depend primarily on secondary data. Year

books, statistical reports of government departments, report of public organizations of Bureau of Public

Enterprises, Censes Reports etc, serve as major data sources for such research studies.

Advantages of Secondary Data

Secondary sources have some advantages:

1. Secondary data, if available can be secured quickly and cheaply. Once their source of documents and

reports are located, collection of data is just matter of desk work. Even the tediousness of copying the data

from the source can now be avoided, thanks to Xeroxing facilities.

2. Wider geographical area and longer reference period may be covered without much cost. Thus, the use of

secondary data extends the researcher’s space and time reach.

3. The use of secondary data broadens the data base from which scientific generalizations can be made.

4. Environmental and cultural settings are required for the study.

5. The use of secondary data enables a researcher to verify the findings bases on primary data. It readily

meets the need for additional empirical support. The researcher need not wait the time when additional

primary data can be collected.

Disadvantages of Secondary Data

The use of a secondary data has its own limitations.

1. The most important limitation is the available data may not meet our specific needs. The definitions

adopted by those who collected those data may be different; units of measure may not match; and time

periods may also be different.


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2. The available data may not be as accurate as desired. To assess their accuracy we need to know how the

data were collected.

3. The secondary data are not up-to-date and become obsolete when they appear in print, because of time

lag in producing them. For example, population census data are published tow or three years later after

compilation, and no new figures will be available for another ten years.

4. Finally, information about the whereabouts of sources may not be available to all social scientists. Even if

the location of the source is known, the accessibility depends primarily on proximity. For example, most of the

unpublished official records and compilations are located in the capital city, and they are not within the easy

reach of researchers based in far off places.

b. What are the criteria used for evaluation of secondary data?

Ans. Evaluation of Secondary Data

When a researcher wants to use secondary data for his research, he should evaluate them before deciding to

use them.

1. Data Pertinence

The first consideration in evaluation is to examine the pertinence of the available secondary data to the

research problem under study. The following questions should be considered.

What are the definitions and classifications employed? Are they consistent ?

What are the measurements of variables used? What is the degree to which they conform to the

requirements of our research?

What is the coverage of the secondary data in terms of topic and time? Does this coverage fit the needs of

our research?

On the basis of above consideration, the pertinence of the secondary data to the research on hand should be

determined, as a researcher who is imaginative and flexible may be able to redefine his research problem so

as to make use of otherwise unusable available data.

2. Data Quality

If the researcher is convinced about the available secondary data for his needs, the next step is to examine

the quality of the data. The quality of data refers to their accuracy, reliability and completeness. The

assurance and reliability of the available secondary data depends on the organization which collected them

and the purpose for which they were collected. What is the authority and prestige of the organization? Is it

well recognized? Is it noted for reliability? It is capable of collecting reliable data? Does it use trained and well

qualified investigators? The answers to these questions determine the degree of confidence we can have in

the data and their accuracy. It is important to go to the original source of the secondary data rather than to

use an immediate source which has quoted from the original. Then only, the researcher can review the

cautionary ands other comments that were made in the original source.


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3. Data Completeness

The completeness refers to the actual coverage of the published data. This depends on the methodology and

sampling design adopted by the original organization. Is the methodology sound? Is the sample size small or

large? Is the sampling method appropriate? Answers to these questions may indicate the appropriateness

and adequacy of the data for the problem under study. The question of possible bias should also be

examined. Whether the purpose for which the original organization collected the data had a particular

orientation? Has the study been made to promote the organization’s own interest? How the study was

conducted? These are important clues. The researcher must be on guard when the source does not report

the methodology and sampling design. Then it is not possible to determine the adequacy of the secondary

data for the researcher’s study.

6. What are the differences between observation and interviewing as methods of data collection?

Give two specific examples of situations where either observation or interviewing would be more

appropriate.

Ans. Observation vs interviewing as Methods of Data Collection

Collection of data is the most crucial part of any research project as the success or failure of the project is

dependent upon the accuracy of the data. Use of wrong methods of data collection or any inaccuracy in

collecting data can have significant impact on the results of a study and may lead to results that are not valid.

There are many techniques of data collection along a continuum and observation and interviewing are two of

the popular methods on this continuum that has quantitative methods at one end while qualitative methods at

the other end. Though there are many similarities in these two methods and they serve the same basic

purpose, there are differences that will be highlighted in this article.

Observation

Observation, as the name implies refers to situations where participants are observed from a safe distance

and their activities are recorded minutely. It is a time consuming method of data collection as you may not get

the desired conditions that are required for your research and you may have to wait till participants are in the

situation you want them to be in. Classic examples of observation are wild life researchers who wait for the

animals of birds to be in a natural habitat and behave in situations that they want to focus upon. As a method

of data collection, observation has limitations but produces accurate results as participants are unaware of

being closely inspected and behave naturally.

Interviewing

Interviewing is another great technique of data collection and it involves asking questions to get direct

answers. These interviews could be either one to one, in the form of questionnaires, or the more recent form

of asking opinions through internet. However, there are limitations of interviewing as participants may not

come up with true or honest answers depending upon privacy level of the questions. Though they try to be

honest, there is an element of lie in answers that can distort results of the project.

http://www.differencebetween.com/category/science-nature/nature/animals/

http://www.differencebetween.com/category/technology/it/internet/

http://www.differencebetween.com/category/science-nature/nature/birds/


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Though both observation and interviewing are great techniques of data collection, they have their own

strengths and weaknesses. It is important to keep in mind which one of the two will produce desired results

before finalizing.

Observation vs Interviewing

• Data collection is an integral part of any research and various techniques are employed for

this purpose.

• Observation requires precise analysis by the researcher and often produces most

accurate results although it is very time consuming

• Interviewing is easier but suffers from the fact that participants may not come up with

honest replies.

Interview format:

Interviews take many different forms. It is a good idea to ask the organisation in advance what format

the interview will take.

Competency/criteria based interviews:

These are structured to reflect the competencies or qualities that an employer is seeking for a particular job,

which will usually have been detailed in the job specification or advert. The interviewer is looking for evidence

of your skills and may ask such things as: µGive an example of a time you worked as part of a team to

achieve a common goal.

Technical interviews:

If you have applied for a job or course that requires technical knowledge, it is likely that you will be asked

technical questions or has a separate technical interview. Questions may focus on your final year project or

on real or hypothetical technical problems. You should be prepared to prove yourself, but also to admit to

what you do not know and stress that you are keen to learn. Do not worry if you do not know the exact

answer - interviewers are interested in your thought process and logic.

The Screening Interview:

Companies use screening tools to ensure that candidates meet minimum qualification requirements.

Computer programs are among the tools used to weed out unqualified candidates. (This is why you need a

digital resume that is screening-friendly. See our resume centre for help.) Sometimes human professionals

are the gatekeepers. Screening interviewers often have honed skills to determine whether there is anything

that might disqualify you for the position. Remember they do not need to know whether you are the best fit for


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the position, only whether you are not a match. For this reason, screeners tend to dig for dirt. Screeners will

hone in on gaps in your employment history or pieces of information that look inconsistent. They also will

want to know from the outset whether you will be too expensive for the company.

The Informational Interview:

On the opposite end of the stress spectrum from screening interviews is the informational interview. A

meeting that you initiate, the informational interview is underutilized by job-seekers who might otherwise

consider themselves savvy to the merits of networking. Jobseekers ostensibly secure informational meetings

in order to seek the advice of someone in their current or desired field as well as to gain further references to

people who can lend insight. Employers that like to stay apprised of available talent even when they do not

have current job openings, are often open to informational interviews, especially if they like to share their

knowledge, feel flattered by your interest, or esteem the mutual friend that connected you to them. During an

informational interview, the jobseeker and employer exchange information and get to know one another

better without reference to specific job opening.

MB0050 – Research Methodology (Set 2)

1. a. Explain the General characteristics of observation.


Semester: 3

Ans. General Characteristics of Observation Method

Observation as a method of data collection has certain characteristics.

1. It is both a physical and a mental activity: The observing eye catches many things that are present. But

attention is focused on data that are pertinent to the given study.

2. Observation is selective: A researcher does not observe anything and everything, but selects the range

of things to be observed on the basis of the nature, scope and objectives of his study. For example, suppose

a researcher desires to study the causes of city road accidents and also formulated a tentative hypothesis

that accidents are caused by violation of traffic rules and over speeding. When he observed the movements

of vehicles on the road, many things are before his eyes; the type, make, size and colour of the vehicles, the

persons sitting in them, their hair style, etc. All such things which are not relevant to his study are ignored and

only over speeding and traffic violations are keenly observed by him.

3. Observation is purposive and not casual: It is made for the specific purpose of noting things relevant to

the study. It captures the natural social context in which persons behaviour occur. It grasps the significant

events and occurrences that affect social relations of the participants.

4. Observation should be exact and be based on standardized tools of research and such as

observation schedule, social metric scale etc., and precision instruments, if any.

b. What is the Utility of Observation in Business Research?

Ans. Utility of Observation in Business Research

Observation is suitable for a variety of research purposes. It may be used for studying (a) The behaviour of

human beings in purchasing goods and services.: life style, customs, and manner, interpersonal relations,

group dynamics, crowd behaviour, leadership styles, managerial style, other behaviours and actions; (b) The

behaviour of other living creatures like birds, animals etc. (c) Physical characteristics of inanimate things like

stores, factories, residences etc. (d) Flow of traffic and parking problems

(e) movement of materials and products through a plant.

2. a. Briefly explain Interviewing techniques in Business Research?

Ans. Interviewing techniques in Business Research

The interview process consists of the following stages:

· Preparation

· Introduction

· Developing rapport

· Carrying the interview forward

· Recording the interview

· Closing the interview


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1 Preparation

The interviewing requires some preplanning and preparation. The interviewer should keep the copies of

interview schedule/guide (as the case may be) ready to use. He should have the list of names and addresses

of respondents, he should regroup them into contiguous groups in terms of location in order to save time and

cost in traveling. The interviewer should find out the general daily routine of the respondents in order to

determine the suitable timings for interview. Above all, he should mentally prepare himself for the interview.

He should think about how he should approach a respondent, what mode of introduction he could adopt,

what situations he may have to face and how he could deal with them. The interviewer may come across

such situations as respondents; avoidance, reluctance, suspicion, diffidence, inadequate responses,

distortion, etc. The investigator should plan the strategies for dealing with them. If such preplanning is not

done, he will be caught unaware and fail to deal appropriately when he actually faces any such situation. It is

possible to plan in advance and keep the plan and mind flexible and expectant of new development.

2 Introduction

The investigator is a stranger to the respondents. Therefore, he should be properly introduced to each of the

respondents. What is the proper mode of introduction? There is no one appropriate universal mode of

introduction. Mode varies according to the type of respondents. When making a study of an organization or

institution, the head of the organization should be approached first and his cooperation secured before

contacting the sample inmates/employees. When studying a community or a cultural group, it is essential to

approach the leader first and to enlist cooperation. For a survey or urban households, the research

organization’s letter of introduction and the interviewer’s identity card can be shown. In these days of fear of

opening the door for a stranger, residents cooperation can be easily secured, if the interviewer attempts to

get him introduced through a person known to them, say a popular person in the area e.g., a social worker.

For interviewing rural respondents, the interviewer should never attempt to approach them along with

someone from the revenue department, for they would immediately hide themselves, presuming that they are

being contacted for collection of land revenue or subscription to some government bond. He should not also

approach them through a local political leader, because persons who do not belong to his party will not

cooperate with the interviewer. It is rather desirable to approach the rural respondents through the local

teacher or social worker.

After getting himself introduced to the respondent in the most appropriate manner, the interviewer can follow

a sequence of procedures as under, in order to motivate the respondent to permit the interview:

1. With a smile, greet the respondent in accordance with his cultural pattern.

2. Identify the respondent by name.

3. Describe the method by which the respondent was selected.

4. Mention the name of the organization conducting the research.

5. Assure the anonymity or confidential nature of the interview.

6. Explain their usefulness of the study.


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7. Emphasize the value of respondent’s cooperation, making such statements as “You are among the few in

a position to supply the information”. “Your response is invaluable.” “I have come to learn from your

experience and knowledge”.

3 Developing Rapport

Before starting the research interview, the interviewer should establish a friendly relationship with the

respondent. This is described as “rapport”. It means establishing a relationship of confidence and

understanding between the interviewer and the respondent. It is a skill which depends primarily on the

interviewer’s commonsense, experience, sensitivity, and keen observation.

Start the conversation with a general topic of interest such as weather, current news, sports event, or the like

perceiving the probable of the respondent from his context. Such initial conversation may create a friendly

atmosphere and a warm interpersonal relationship and mutual understanding. However, the interviewer

should “guard against the over rapport” as cautioned by Herbert Hyman. Too much identification and too

much courtesy result in tailoring replied to the image of a “nice interviewer.” The interviewer should use his

discretion in striking a happy medium.

4 Carrying the Interview Forward

After establishing rapport, the technical task of asking questions from the interview schedule starts. This task

requires care, self-restraint, alertness and ability to listen with understanding, respect and curiosity. In

carrying on this task of gathering information from the respondent by putting questions to him, the following

guidelines may be followed:

1. Start the interview. Carry it on in an informal and natural conversational style.

2. Ask all the applicable questions in the same order as they appear on the schedule without any elucidation

and change in the wording. Ask all the applicable questions listed in the schedule. Do not take answers for

granted.

3. If interview guide is used, the interviewer may tailor his questions to each respondent, covering of course,

the areas to be investigated.

4. Know the objectives of each question so as to make sure that the answers adequately satisfy the question

objectives.

5. If a question is not understood, repeat it slowly with proper emphasis and appropriate explanation, when

necessary.

6. Talk all answers naturally, never showing disapproval or surprise. When the respondent does not meet the

interruptions, denial, contradiction and other harassment, he may feel free and may not try to withhold

information. He will be motivated to communicate when the atmosphere is permissive and the listener’s

attitude is non judgmental and is genuinely absorbed in the revelations.

7. Listen quietly with patience and humility. Give not only undivided attention, but also personal warmth. At

the same time, be alert and analytic to incomplete, non specific and inconsistent answers, but avoid


Semester: 3

interrupting the flow of information. If necessary, jot down unobtrusively the points which need elaboration or

verification for later and timelier probing. The appropriate technique for this probing is to ask for further

clarification in such a polite manner as “I am not sure, I understood fully, is this….what you meant?”

8. Neither argue nor dispute.

9. Show genuine concern and interest in the ideas expressed by the respondent; at the same time, maintain

an impartial and objective attitude.

10. Should not reveal your own opinion or reaction. Even when you are asked of your views, laugh off the

request, saying “Well, your opinions are more important than mine.”

11. At times the interview “runs dry” and needs re-stimulation. Then use such expressions as “Uh-huh” or

“That interesting” or “I see” “can you tell me more about that?” and the like.

12. When the interviewee fails to supply his reactions to related past experiences, represent the stimulus

situation, introducing appropriate questions which will aid in revealing the past. “Under what circumstances

did such and such a phenomenon occur?” or “How did you feel about it and the like.

13. At times, the conversation may go off the track. Be alert to discover drifting, steer the conversation back

to the track by some such remark as, “you know, I was very much interested in what you said a moment ago.

Could you tell me more about it?”

14. When the conversation turns to some intimate subjects, and particularly when it deals with crises in the

life of the individual, emotional blockage may occur. Then drop the subject for the time being and pursue

another line of conversation for a while so that a less direct approach to the subject can be made later.

15. When there is a pause in the flow of information, do not hurry the interview. Take it as a matter of course

with an interested look or a sympathetic half-smile. If the silence is too prolonged, introduce a stimulus saying

“You mentioned that… What happened then?”

5 Additional Sittings

In the case of qualitative interviews involving longer duration, one single sitting will not do, as it would cause

interview weariness. Hence, it is desirable to have two or more sittings with the consent of the respondent.

6 Recording the Interview

It is essential to record responses as they take place. If the note taking is done after the interview, a good

deal of relevant information may be lost. Nothing should be made in the schedule under respective question.

It should be complete and verbatim. The responses should not be summarized or paraphrased. How can

complete recording be made without interrupting the free flow of conversation? Electronic transcription

through devices like tape recorder can achieve this. It has obvious advantages over note-taking during the

interview. But it also has certain disadvantages. Some respondents may object to or fear “going on record”.

Consequently the risk of lower response rate will rise especially for sensitive topics.

If the interviewer knows short-hand, he can use it with advantage. Otherwise, he can write rapidly by

abbreviating word and using only key words and the like. However, even the fast writer may fail to record all


Semester: 3

that is said at conversational speed. At such times, it is useful to interrupt by some such comment as “that

seems to be a very important point, would you mind repeating it, so that I can get your words exactly.” The

respondent is usually flattered by this attention and the rapport is not disturbed.

The interviewer should also record all his probes and other comments on the schedule, in brackets to set

them off from responses. With the pre-coded structured questions, the interviewer’s task is easy. He has to

simply ring the appropriate code or tick the appropriate box, as the case may be. He should not make

mistakes by carelessly ringing or ticketing a wrong item.

7 Closing the Interview

After the interview is over, take leave off the respondent thanking him with a friendly smile. In the case of a

qualitative interview of longer duration, select the occasion for departure more carefully. Assembling the

papers for putting them in the folder at the time of asking the final question sets the stage for a final

handshake, a thank-you and a good-bye. If the respondent desires to know the result of the survey, note

down his name and address so that a summary of the result could be posted to him when ready.

8 Editing

At the close of the interview, the interviewer must edit the schedule to check that he has asked all the

questions and recorded all the answers and that there is no inconsistency between answers. Abbreviations in

recording must be replaced by full words. He must ensure that everything is legible. It is desirable to record a

brief sketch of his impressions of the interview and observational notes on the respondent’s living

environment, his attitude to the survey, difficulties, if any, faced in securing his cooperation and the

interviewer’s assessment of the validity of the respondent’s answers.

b. What are the problems encountered in Interview?

Ans. Interview Problems

In personal interviewing, the researcher must deal with two major problems, inadequate response, non-

response and interviewer’s bias.

1 Inadequate response

Kahn and Cannel distinguish five principal symptoms of inadequate response. They are:

· partial response, in which the respondent gives a relevant but incomplete answer

· non-response, when the respondent remains silent or refuses to answer the question

· irrelevant response, in which the respondent’s answer is not relevant to the question asked

· inaccurate response, when the reply is biased or distorted and

· verbalized response problem, which arises on account of respondent’s failure to understand a question or

lack of information necessary for answering it.


Semester: 3

2 Interviewer’s Bias

The interviewer is an important cause of response bias. He may resort to cheating by ‘cooking up’ data

without actually interviewing. The interviewers can influence the responses by inappropriate suggestions,

word emphasis, tone of voice and question rephrasing. His own attitudes and expectations about what a

particular category of respondents may say or think may bias the data. Another source of response of the

interviewer’s characteristics (education, apparent social status, etc) may also bias his answers. Another

source of response bias arises from interviewer’s perception of the situation, if he regards the assignment as

impossible or sees the results of the survey as possible threats to personal interests or beliefs he is likely to

introduce bias.

As interviewers are human beings, such biasing factors can never be overcome completely, but their effects

can be reduced by careful selection and training of interviewers, proper motivation and supervision,

standardization or interview procedures (use of standard wording in survey questions, standard instructions

on probing procedure and so on) and standardization of interviewer behaviour. There is need for more

research on ways to minimize bias in the interview.

3 Non-response

Non-response refers to failure to obtain responses from some sample respondents. There are many sources

of non-response; non-availability, refusal, incapacity and inaccessibility.

4 Non-availability

Some respondents may not be available at home at the time of call. This depends upon the nature of the

respondent and the time of calls. For example, employed persons may not be available during working hours.

Farmers may not be available at home during cultivation season. Selection of appropriate timing for calls

could solve this problem. Evenings and weekends may be favourable interviewing hours for such

respondents. If someone is available, then, line respondent’s hours of availability can be ascertained and the

next visit can be planned accordingly.

5 Refusal

Some persons may refuse to furnish information because they are ill-disposed, or approached at the wrong

hour and so on. Although, a hardcore of refusals remains, another try or perhaps another approach may find

some of them cooperative. Incapacity or inability may refer to illness which prevents a response during the

entire survey period. This may also arise on account of language barrier.

6 Inaccessibility

Some respondents may be inaccessible. Some may not be found due to migration and other reasons. Non-

responses reduce the effective sample size and its representativeness.


Semester: 3

7 Methods and Aims of control of non-response

Kish suggests the following methods to reduce either the percentage of non-response or its effects:

1. Improved procedures for collecting data are the most obvious remedy for non-response.

Improvements advocated are (a) guarantees of anonymity, (b) motivation of the respondent to co-

operate (c) arousing the respondents’ interest with clever opening remarks and questions,

(d) advance notice to the respondents.

2. Call-backs are most effective way of reducing not-at-homes in personal interviews, as are repeated

mailings to no-returns in mail surveys.

3. Substitution for the non-response is often suggested as a remedy. Usually this is a mistake because

the substitutes resemble the responses rather than the non-responses. Nevertheless, beneficial

substitution methods can sometimes be designed with reference to important characteristics of the

population. For example, in a farm management study, the farm size is an important variable and if

the sampling is based on farm size, substitution for a respondent with a particular size holding by

another with the holding of the same size is possible.

Attempts to reduce the percentage or effects on non-responses aim at reducing the bias caused by

differences on non-respondents from respondents. The non-response bias should not be confused with the

reduction of sampled size due to non-response. The latter effect can be easily overcome, either by

anticipating the size of non-response in designing the sample size or by compensating for it with a

supplement. These adjustments increase the size of the response and the sampling precision, but they do

not reduce the non-response percentage or bias.

3. a. What are the various steps in processing of data?

Ans. The various steps in processing of data may be stated as:

• Identifying the data structures

• Editing the data

• Coding and classifying the data

• Transcription of data

• Tabulation of data.

Objectives:

After studying this lesson you should be able to understand:

Checking for analysis

Editing

Coding

Classification

Transcription of data

Tabulation


Semester: 3

Construction of Frequency Table

Components of a table

Principles of table construction

Frequency distribution and class intervals

Graphs, charts and diagrams

Types of graphs and general rules

Quantitative and qualitative analysis

Measures of central tendency

Dispersion

Correlation analysis

Coefficient of determination

1 Checking for Analysis

In the data preparation step, the data are prepared in a data format, which allows the analyst to use modern

analysis software such as SAS or SPSS. The major criterion in this is to define the data structure. A data

structure is a dynamic collection of related variables and can be conveniently represented as a graph where

nodes are labelled by variables. The data structure also defines and stages of the preliminary relationship

between variables/groups that have been pre-planned by the researcher. Most data structures can be

graphically presented to give clarity as to the frames researched hypothesis. A sample structure could be a

linear structure, in which one variable leads to the other and finally, to the resultant end variable.

The identification of the nodal points and the relationships among the nodes could sometimes be a complex

task than estimated. When the task is complex, which involves several types of instruments being collected

for the same research question, the procedures for drawing the data structure would involve a series of steps.

In several intermediate steps, the heterogeneous data structure of the individual data sets can be harmonized

to a common standard and the separate data sets are then integrated into a single data set. However, the

clear definition of such data structures would help in the further processing of data.

2 Editing

The next step in the processing of data is editing of the data instruments. Editing is a process of checking to

detect and correct errors and omissions. Data editing happens at two stages, one at the time of recording of

the data and second at the time of analysis of data.

a. Data Editing at the Time of Recording of Data

Document editing and testing of the data at the time of data recording is done considering the following

questions in mind.

Do the filters agree or are the data inconsistent?

Have missing values been set to values, which are the same for all research questions?

Have variable descriptions been specified?


Semester: 3

Have labels for variable names and value labels been defined and written?

All editing and cleaning steps are documented, so that, the redefinition of variables or later analytical

modification requirements could be easily incorporated into the data sets.

b. Data Editing at the Time of Analysis of Data

Data editing is also a requisite before the analysis of data is carried out. This ensures that the data is

complete in all respect for subjecting them to further analysis. Some of the usual check list questions that can

be had by a researcher for editing data sets before analysis would be:

1. Is the coding frame complete?

2. Is the documentary material sufficient for the methodological description of the study?

3. Is the storage medium readable and reliable.

4. Has the correct data set been framed?

5. Is the number of cases correct?

6. Are there differences between questionnaire, coding frame and data?

7. Are there undefined and so-called wild codes?

8. Comparison of the first counting of the data with the original documents of the researcher.

The editing step checks for the completeness, accuracy and uniformity of the data as created by the

researcher.

Completeness: The first step of editing is to check whether there is an answer to all the questions/variables

set out in the data set. If there were any omission, the researcher sometimes would be able to deduce the

correct answer from other related data on the same instrument. If this is possible, the data set has to

rewritten on the basis of the new information. For example, the approximate family income can be inferred

from other answers to probes such as occupation of family members, sources of income, approximate

spending and saving and borrowing habits of family members etc. If the information is vital and has been

found to be incomplete, then the researcher can take the step of contacting the respondent personally again

and solicit the requisite data again. If none of these steps could be resorted to the marking of the data as

missing must be resorted to.

Accuracy: Apart from checking for omissions, the accuracy of each recorded answer should be checked. A

random check process can be applied to trace the errors at this step. Consistency in response can also be

checked at this step. The cross verification to a few related responses would help in checking for consistency

in responses. The reliability of the data set would heavily depend on this step of error correction. While clear

inconsistencies should be rectified in the data sets, fact responses should be dropped from the data sets.

Uniformity: In editing data sets, another keen lookout should be for any lack of uniformity, in interpretation of

questions and instructions by the data recorders. For instance, the responses towards a specific feeling could

have been queried from a positive as well as a negative angle. While interpreting the answers, care should

be taken as a record the answer as a positive question response or as negative question response in all


Semester: 3

uniformity checks for consistency in coding throughout the questionnaire/interview schedule response/data

set.

The final point in the editing of data set is to maintain a log of all corrections that have been carried out at this

stage. The documentation of these corrections helps the researcher to retain the original data set.

3 Coding

The edited data are then subject to codification and classification. Coding process assigns numerals or other

symbols to the several responses of the data set. It is therefore a pre-requisite to prepare a coding scheme

for the data set. The recording of the data is done on the basis of this coding scheme.

The responses collected in a data sheet varies, sometimes the responses could be the choice among a

multiple response, sometimes the response could be in terms of values and sometimes the response could

be alphanumeric. At the recording stage itself, if some codification were done to the responses collected, it

would be useful in the data analysis. When codification is done, it is imperative to keep a log of the codes

allotted to the observations. This code sheet will help in the identification of variables/observations and the

basis for such codification.

The first coding done to primary data sets are the individual observation themselves. This responses sheet

coding gives a benefit to the research, in that, the verification and editing of recordings and further contact

with respondents can be achieved without any difficulty. The codification can be made at the time of

distribution of the primary data sheets itself. The codes can be alphanumeric to keep track of where and to

whom it had been sent. For instance, if the data consists of several public at different localities, the sheets

that are distributed in a specific locality may carry a unique part code which is alphabetic. To this alphabetic

code, a numeric code can be attached to distinguish the person to whom the primary instrument was

distributed. This also helps the researcher to keep track of who the respondents are and who are the

probable respondents from whom primary data sheets are yet to be collected. Even at a latter stage, any

specific queries on a specific responses sheet can be clarified.

The variables or observations in the primary instrument would also need codification, especially when they

are categorized. The categorization could be on a scale i.e., most preferable to not preferable, or it could be

very specific such as Gender classified as Male and Female. Certain classifications can lead to open ended

classification such as education classification, Illiterate, Graduate, Professional, Others. Please specify. In

such instances, the codification needs to be carefully done to include all possible responses under Others,

please specify. If the preparation of the exhaustive list is not feasible, then it will be better to create a

separate variable for the Others please specify category and records all responses as such.

Numeric Coding: Coding need not necessarily be numeric. It can also be alphabetic. Coding has to be

compulsorily numeric, when the variable is subject to further parametric analysis.

Alphabetic Coding: A mere tabulation or frequency count or graphical representation of the variable may be

given in an alphabetic coding.


Semester: 3

Zero Coding: A coding of zero has to be assigned carefully to a variable. In many instances, when manual

analysis is done, a code of 0 would imply a no response from the respondents. Hence, if a value of 0 is to be

given to specific responses in the data sheet, it should not lead to the same interpretation of non response.

For instance, there will be a tendency to give a code of 0 to a no, then a different coding than 0 should be

given in the data sheet. An illustration of the coding process of some of the demographic variables is given in

the following table.

Question Variable Response categories Code

Number observation

1.1 Organisation Private Pt

Public Pb

Government Go

3.4 Owner of Vehicle Yes 2

No1

4.2 Vehicle performs Excellent 5

Good 4

Adequate 3

Bad 2

Worst 1

5.1 Age Up to 20 years 1

21-40 years 2

40-60 years 3

5.2 Occupation Salaried S

Professional P

Technical T

Business B

Retired R

Housewife H

Others =

= Could be treated as a separate variable/observation and the actual response could be recorded. The new

variable could be termed as other occupation

The coding sheet needs to be prepared carefully, if the data recording is not done by the researcher, but is

outsourced to a data entry firm or individual. In order to enter the data in the same perspective, as the

researcher would like to view it, the data coding sheet is to be prepared first and a copy of the data coding

sheet should be given to the outsourcer to help in the data entry procedure. Sometimes, the researcher might

not be able to code the data from the primary instrument itself. He may need to classify the responses and

then code them. For this purpose, classification of data is also necessary at the data entry stage.


Semester: 3

4 Classifications

When open ended responses have been received, classification is necessary to code the responses. For

instance, the income of the respondent could be an open-ended question. From all responses, a suitable

classification can be arrived at. A classification method should meet certain requirements or should be guided

by certain rules.

First, classification should be linked to the theory and the aim of the particular study. The objectives of the

study will determine the dimensions chosen for coding. The categorization should meet the information

required to test the hypothesis or investigate the questions.

Second, the scheme of classification should be exhaustive. That is, there must be a category for every

response. For example, the classification of martial status into three category viz., married Single and

divorced is not exhaustive, because responses like widower or separated cannot be fitted into the scheme.

Here, an open ended question will be the best mode of getting the responses. From the responses collected,

the researcher can fit a meaningful and theoretically supportive classification. The inclusion of the

classification Others tends to fill the cluttered, but few responses from the data sheets. But others

categorization has to carefully used by the researcher. However, the other categorization tends to defeat the

very purpose of classification, which is designed to distinguish between observations in terms of the

properties under study. The classification others will be very useful when a minority of respondents in the

data set give varying answers. For instance, the reading habits of newspaper may be surveyed. The 95

respondents out of 100 could be easily classified into 5 large reading groups while 5 respondents could have

given a unique answer. These given answer rather than being separately considered could be clubbed under

the others heading for meaningful interpretation of respondents and reading habits.

Third, the categories must also be mutually exhaustive, so that each case is classified only once. This

requirement is violated when some of the categories overlap or different dimensions are mixed up.

The number of categorization for a specific question/observation at the coding stage should be maximum

permissible since, reducing the categorization at the analysis level would be easier than splitting an already

classified group of responses. However the number of categories is limited by the number of cases and the

anticipated statistical analysis that are to be used on the observation.

5 Transcriptions of Data

When the observations collected by the researcher are not very large, the simple inferences, which can be

drawn from the observations, can be transferred to a data sheet, which is a summary of all responses on all

observations from a research instrument. The main aim of transition is to minimize the shuffling proceeds

between several responses and several observations. Suppose a research instrument contains 120

responses and the observations has been collected from 200 respondents, a simple summary of one

response from all 200 observations would require shuffling of 200 pages. The process is quite tedious if

several summary tables are to be prepared from the instrument. The transcription process helps in the


Semester: 3

presentation of all responses and observations on data sheets which can help the researcher to arrive at

preliminary conclusions as to the nature of the sample collected etc. Transcription is hence, an intermediary

process between data coding and data tabulation.

a. Methods of Transcription

The researcher may adopt a manual or computerized transcription. Long work sheets, sorting cards or sorting

strips could be used by the researcher to manually transcript the responses. The computerized transcription

could be done using a data base package such as spreadsheets, text files or other databases.

The main requisite for a transcription process is the preparation of the data sheets where observations are

the row of the database and the responses/variables are the columns of the data sheet. Each variable should

be given a label so that long questions can be covered under the label names. The label names are thus the

links to specific questions in the research instrument. For instance, opinion on consumer satisfaction could be

identified through a number of statements (say 10); the data sheet does not contain the details of the

statement, but gives a link to the question in the research instrument though variable labels. In this instance

the variable names could be given as CS1, CS2, CS3, CS4, CS5, CS6, CS7, CS8, CS9 and CS10. The label

CS indicating Consumer satisfaction and the number 1 to 10 indicate the statement measuring consumer

satisfaction. Once the labelling process has been done for all the responses in the research instrument, the

transcription of the response is done.

b. Manual Transcription

When the sample size is manageable, the researcher need not use any computerization process to analyze

the data. The researcher could prefer a manual transcription and analysis of responses. The choice of

manual transcription would be when the number of responses in a research instrument is very less, say 10

responses, and the numbers of observations collected are within 100. A transcription sheet with 100x50

(assuming each response has 5 options) row/column can be easily managed by a researcher manually. If, on

the other hand the variables in the research instrument are more than 40 and each variable has 5 options, it

leads to a worksheet of 100x200 sizes which might not be easily managed by the researcher manually. In the

second instance, if the number of responses is less than 30, then the manual worksheet could be attempted

manually. In all other instances, it is advisable to use a computerized transcription process.

c. Long Worksheets

Long worksheets require quality paper; preferably chart sheets, thick enough to last several usages. These

worksheets normally are ruled both horizontally and vertically, allowing responses to be written in the boxes.

If one sheet is not sufficient, the researcher may use multiple rules sheets to accommodate all the

observations. Heading of responses which are variable names and their coding (options) are filled in the first

two rows. The first column contains the code of observations. For each variable, now the responses from the

research instrument are then transferred to the worksheet by ticking the specific option that the observer has


Semester: 3

chosen. If the variable cannot be coded into categories, requisite length for recording the actual response of

the observer should be provided for in the work sheet.

The worksheet can then be used for preparing the summary tables or can be subjected to further analysis of

data. The original research instrument can be now kept aside as safe documents. Copies of the data sheets

can also be kept for future references. As has been discussed under the editing section, the transcript data

has to be subjected to a testing to ensure error free transcription of data.

A sample worksheet is given below for reference.

Transcription can be made as and when the edited instrument is ready for processing. Once all

schedules/questionnaires have been transcribed, the frequency tables can be constructed straight from

worksheet. Other methods of manual transcription include adoption of sorting strips or cards.

In olden days, data entry and processing were made through mechanical and semi auto-metric devices such

as key punch using punch cards. The arrival of computers has changed the data processing methodology

altogether.

6 Tabulation

The transcription of data can be used to summarize and arrange the data in compact form for further

analysis. The process is called tabulation. Thus, tabulation is a process of summarizing raw data displaying

them on compact statistical tables for further analysis. It involves counting the number of cases falling into

each of the categories identified by the researcher.

Tabulation can be done manually or through the computer. The choice depends upon the size and type of

study, cost considerations, time pressures and the availability of software packages. Manual tabulation is

suitable for small and simple studies.

b. How is data editing is done at the Time of Recording of Data?

Ans. Data Editing at the Time of Recording of Data

Document editing and testing of the data at the time of data recording is done considering the following

questions in mind.

Do the filters agree or are the data inconsistent?

Have missing values been set to values, which are the same for all research questions?

Have variable descriptions been specified?

http://resources.smude.edu.in/slm/wp-content/uploads/2010/09/clip-image003.jpg


Semester: 3

Have labels for variable names and value labels been defined and written?

All editing and cleaning steps are documented, so that, the redefinition of variables or later analytical

modification requirements could be easily incorporated into the data sets.

4. a. What are the fundamental of frequency Distribution?

Ans. Frequency Distribution

Variables that are classified according to magnitude or size are often arranged in the form of a frequency

table. In constructing this table, it is necessary to determine the number of class intervals to be used and the

size of the class intervals.

A distinction is usually made between continuous and discrete variables. A continuous variable has an

unlimited number of possible values between the lowest and highest with no gaps or breaks. Examples of

continuous variable are age, weight, temperature etc. A discrete variable can have a series of specified

values with no possibility of values between these points. Each value of a discrete variable is distinct and

separate. Examples of discrete variables are gender of persons (male/female) occupation (salaried,

business, profession) car size (800cc, 1000cc, 1200cc)

In practice, all variables are treated as discrete units, the continuous variables being stated in some discrete

unit size according to the needs of a particular situation. For example, length is described in discrete units of

millimetres or a tenth of an inch.

b. What are the types and general rules for graphical representation of data?

Ans. The most commonly used graphic forms may be grouped into the following categories:

a) Line Graphs or Charts

b) Bar Charts

c) Segmental presentations.

d) Scatter plots

e) Bubble charts

f) Stock plots

g) Pictographs

h) Chesnokov Faces

The general rules to be followed in graphic representations are:

1. The chart should have a title placed directly above the chart.

2. The title should be clear, concise and simple and should describe the nature of the data presented.

3. Numerical data upon which the chart is based should be presented in an accompanying table.

4. The horizontal line measures time or independent variable and the vertical line the measured variable.

5. Measurements proceed from left to right on the horizontal line and from bottom to top on the vertical.

6. Each curve or bar on the chart should be labelled.

7. If there are more than one curves or bar, they should be clearly differentiated from one another by


Semester: 3

distinct patterns or colours.

8. The zero point should always be represented and the scale intervals should be equal.

9. Graphic forms should be used sparingly. Too many forms detract rather than illuminating the

presentation.

10.Graphic forms should follow and not precede the related textual discussion.

5. Strictly speaking, would case studies be considered as scientific research? Why or why not?

Case studies are a tool for discussing scientific integrity. Although one of the most frequently used tools for

encouraging discussion, cases are only one of many possible tools. Many of the principles discussed below

for discussing case studies can be generalized to other approaches to encouraging discussion about

research ethics.

Cases are designed to confront readers with specific real-life problems that do not lend themselves to easy

answers. Case discussion demands critical and analytical skills and, when implemented in small groups, also

fosters collaboration (Pimple, 2002). By providing a focus for discussion, cases help trainees to define or

refine their own standards, to appreciate alternative approaches to identifying and resolving ethical problems,

and to develop skills for analyzing and dealing with hard problems on their own. The effective use of case

studies is comprised of many factors, including:

appropriate selection of case(s) (topic, relevance, length, complexity)

method of case presentation (verbal, printed, before or during discussion)

format for case discussion (Email or Internet-based, small group, large group)

leadership of case discussion (choice of discussion leader, roles and responsibilities for discussion

leader)

outcomes for case discussion (answers to specific questions, answers to general questions, written

or verbal summaries)

Research methods don't seem so intimidating when you're familiar with the terminology. This is important

whether you're conducting evaluation or merely reading articles about other studies to incorporate in your

program. To help with understanding, here are some basic definitions used.

Variable: Characteristics by which people or things can be described. Must have more than one

level; in other words, to be able to change over time for the same person/object, or from person to

person, or object to object. Some variables, called attributes, cannot be manipulated by the

researcher (e.g., socioeconomic status, IQ score, race, gender, etc.). Some variables can be


Semester: 3

manipulated but are not in a particular study. This occurs when subjects self-select the level of the

independent variable, or the level is naturally occurring (as with ex post facto research).

Manipulation: Random assignment of subjects to levels of the independent variable (treatment

groups).

Independent variable: The treatment, factor, or presumed cause that will produce a change in the

dependent variable. This is what the experimenter tries to manipulate. It is denoted as "X" on the

horizontal axis of a graph.

Dependent variable: The presumed effect or consequence resulting from changes in the

independent variable. This is the observation made and is denoted by "Y" on the vertical axis of a

graph. The score of "Y" depends on the score of "X."

Population: The complete set of subjects that can be studied: people, objects, animals, plants, etc.

Sample: A subset of subjects that can be studied to make the research project more manageable.

There are a variety of ways samples can be taken. If a large enough random samples are taken, the

results can be statistically similar to taking a census of an entire population--with reduced effort and

cost.

Case Study:

A case study is conducted for similar purpose as the above but is usually done with a smaller sample size for

more in-depth study. A case study often involves direct observation or interviews with single subjects or

single small social units such as a family, club, school classroom, etc. This is typically considered qualitative

research.

Purpose: Explain or Predict

Type of Research to Use: Relational Study

In a relational study you start with a research hypothesis, that is, is what you're trying to "prove."

Examples of research hypotheses for a relational study:

The older the person, the more health problems he or she encounters.

4-H members attending 4-H summer camp stay enrolled in 4-H longer.


Semester: 3

The greater the number of money management classes attended, the greater the amount of annual

savings achieved.

Types of relational studies include correlational studies and ex post facto studies.

Correlational Study:

A correlational study compares two or more different characteristics from the same group of people and

explains how two characteristics vary together and how well one can be predicted from knowledge of the

other.

A concurrent correlational study draws a relationship between characteristics at the same point in time. For

example, a student's grade point average is related to his or her class rank.

A predictive correlational study could predict a later set of data from an earlier set. For example, a student's

grade point average might predict the same student's grade point average during senior year. A predictive

correlational study could also use one characteristic to predict what another characteristic will be at another

time. For example, a student's SAT score is designed to predict college freshman grade point average.

Ex Post Facto (After the Fact) Study:

An ex post facto study is used when experimental research is not possible, such as when people have self-

selected levels of an independent variable or when a treatment is naturally occurring and the researcher

could not "control" the degree of its use. The researcher starts by specifying a dependent variable and then

tries to identify possible reasons for its occurrence as well as alternative (rival) explanations such

confounding (intervening, contaminating, or extraneous) variables are "controlled" using statistics.

This type of study is very common and useful when using human subjects in real-world situations and the

investigator comes in "after the fact." For example, it might be observed that students from one town have

higher grades than students from a different town attending the same high school. Would just "being from a

certain town" explain the differences? In an ex post facto study, specific reasons for the differences would be

explored, such as differences in income, ethnicity, parent support, etc. It is important to recognize that, in a

relational study, "cause and effect" cannot be claimed. All that can be claimed is that that there is a

relationship between the variables.

For that matter, variables that are completely unrelated could, in fact, vary together due to nothing more than

coincidence. That is why the researcher needs to establish a plausible reason (research hypothesis) for why

there might be a relationship between two variables before conducting a study. For instance, it might be


Semester: 3

found that all football teams with blue uniforms won last week. There is no likely reason why the uniform color

had any relationship to the games' outcomes, and it certainly was not the cause for victory. Similarly, you

must be careful about claiming that your Extension program was the "cause" of possible results.

6. a. Analyse the case study and descriptive approach to research?

a) Case Study and descriptive approach to research:

Descriptive research, also known as statistical research, describes data and characteristics about the

population or phenomenon being studied. Descriptive research answers the questions who, what, where,

when and how...

Although the data description is factual, accurate and systematic, the research cannot describe what caused

a situation. Thus, Descriptive research cannot be used to create a causal relationship, where one variable

affects another. In other words, descriptive research can be said to have a low requirement for internal

validity.

The description is used for frequencies, averages and other statistical calculations. Often the best approach,

prior to writing descriptive research, is to conduct a survey investigation. Qualitative research often has the

aim of description and researchers may follow-up with examinations of why the observations exist and what

the implications of the findings are.

In short descriptive research deals with everything that can be counted and studied. But there are always

restrictions to that. Your research must have an impact to the lives of the people around you e.g. finding the

most frequent disease that affects the children of a town. The reader of the research will know what to do to

prevent that disease thus; more people will live a healthy life.

Descriptive research does not fit neatly into the definition of either quantitative or qualitative research

methodologies, but instead it can utilize elements of both, often within the same study. The term descriptive

research refers to the type of research question, design, and data analysis that will be applied to a given

topic. Descriptive statistics tell what is, while inferential statistics try to determine cause and effect.

A case study is a research method common in social science. It is based on an in-depth investigation of a

single individual, group, or event. Case studies may be descriptive or explanatory. The latter type is used to

explore causation in order to find underlying principles. They may be prospective, in which criteria are

established and cases fitting the criteria are included as they become available, or retrospective, in which

criteria are established for selecting cases from historical records for inclusion in the study.


Semester: 3

Rather than using samples and following a rigid protocol (strict set of rules) to examine limited number of

variables, case study methods involve an in-depth, longitudinal (over a long period of time) examination of a

single instance or event: a case. They provide a systematic way of looking at events, collecting data,

analyzing information, and reporting the results. As a result the researcher may gain a sharpened

understanding of why the instance happened as it did, and what might become important to look at more

extensively in future research. Case studies lend themselves to both generating and testing hypotheses.

Another suggestion is that case study should be defined as a research strategy, an empirical inquiry that

investigates a phenomenon within its real-life context. Case study research means single and multiple case

studies, can include quantitative evidence, relies on multiple sources of evidence and benefits from the prior

development of theoretical propositions. Case studies should not be confused with qualitative research and

they can be based on any mix of quantitative and qualitative evidence. Single-subject research provides the

statistical framework for making inferences from quantitative case-study data

b. Distinguish between research methods & research Methodology.

Ans.

Research Methods Research MethodologyResearch methods are the various procedures,

schemes, algorithms, etc. used in research. All

the methods used by a researcher during a

research study are termed as research methods.

They are essentially planned, scientific and

value-neutral. They include theoretical

procedures, experimental studies, numerical

schemes, statistical approaches, etc. Research

methods help us collect samples, data and find

a solution to a problem. Particularly, scientific

research methods call for explanations based on

collected facts, measurements and observations

and not on reasoning alone. They accept only

those explanations which can be verified by

experiments.

Research methodology is a systematic way to

solve a problem. It is a science of studying how

research is to be carried out. Essentially, the

procedures by which researchers go about their

work of describing, explaining and predicting

phenomena are called research methodology. It

is also defined as the study of methods by which

knowledge is gained. Its aim is to give the work

plan of research.

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