Q.1 (a) Explain the general characteristics of observation.

Answer.

'Observation' has been derived from the Greek words 'ob' and 'servare'. The above

two words stand for the meanings 'to keep' and 'before the mind' respectively. The

knowledge derived by placing something before the mind leads to observational

knowledge. Usually the perceptual knowledge is considered as the observational

knowledge. But in respect of the inductive reasoning 'observation' has been

defined as regulated perception with a definite purpose.

Three factors are involved in the case of an observation.

There must be some object to be observed;

the sense organs to observe the object and;

the mind to become aware of it.

Characteristics of observation:

1. Observation is the case of regulated perception of events. Observations

are made by help of sense organs. So it is basically perceptual. Perception

may be either external or internal. Perception of natural events or

occurrences is external perception. To know something directly by

introspection without using the sense organs is called internal perception.

Feeling of sorrow, joy, happiness etc. is internal perception.

2. Observation should be systematic and selective. Observation excludes the

cases of careless and stray perceptions. When the purpose of observation is

decided we select those instances, which have got relevance with the

purpose. Suppose we want to observe the colour of the crows. Then out of

the different types of birds we select only crows to observe. Hence

perception should not be careless or a casual one. The aim of perceptions

is to establish some generalized truths. A general truth cannot be derived

from stray or casual perception. The perception should be systematic and

selective.

3. Observation should be impartial and free from any bias. It means that the

observation should be strictly objective. Sometimes in order to establish a

definite conclusion we overlook certain instances, which are not

favourable to the conclusion. For example, when a sales representative

demonstrates the utilities of a particular product he only shows us some of

the suitable utilities of it. He overlooks those instances, which are not

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favourable for the purpose of demonstration. This is an example of biased

observation. Such types of biased observation should be avoided.

4. Observations should be objective and be neutral. If the neutrality is not

maintained it may lead to fallacious observations. For example, while

evaluating the answer scripts if the examiner thinks that he is evaluating

the scripts of brilliant students then the mistakes present in the answer

script may be overlooked. A prejudiced mind cannot make observation

neutral. If a person is biased, then his observation will not be true or

objective.

5. Observation is the active process of knowing the truth. Knowledge

through observation is always active. The involvement of sense organs

makes it active. Of course, the experiments are more active as compared to

observations. But observations are not passive.

6. Observations should be simple and direct observations help in knowing

the uncontroversial truths. Since the aim of observation is to obtain right

knowledge and to establish the material truth of a general proposition it

should be simple and direct.

Q.1 (b). What is the utility of observation in business research?

Answer.

In business research, observation is a systematic process of recording behavioral

patterns of people, objects, and occurrences as they happen. No questioning or

communicating with people is needed. Researchers who use observation as a

method of data collection either witness and record information while watching

events take place or take advantage of some tracking system such as check-out

scanners or Internet activity records. These tracking systems can observe and

provide data such as whether or not a specific consumer purchased more products

on discount or at regular price or how long an employee takes to complete a

specific task.

Observation becomes a tool for scientific inquiry when it meets several

conditions:

• The observation serves a formulated research purpose.

• The observation is planned systematically.

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• The observation is recorded systematically and related to general

propositions, rather than

simply reflecting a set of interesting curiosities.

• The observation is subjected to checks or controls on validity and

reliability.

Utility of observation in business research

Observation is suitable for a variety of research purposes. It may be used for

studying for the following:

i. 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;

ii. The behaviour of other living creatures like birds, animals etc;

iii. Physical characteristics of inanimate things like stores, factories,

residences etc;

iv. Flow of traffic and parking problems 

v. movement of materials and products through a plant.

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

Answer:

An interview is a conversation between two people (the interviewer and the

interviewee) where questions are asked by the interviewer to obtain information

from the interviewee.

The qualitative research interview seeks to describe and the meanings of central

themes in the life world of the subjects. The main task in interviewing is to

understand the meaning of what the interviewees say. (Kvale,1996)

The interview process consists of the following stages:

Preparation

Introduction

Developing rapport

Carrying the interview forward

Recording the interview

Closing the interview

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 travelling. 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.

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

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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. 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.

Developing Rapport

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

relationship with the respondent. 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.”

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:

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

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

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

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

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

Talk all answers naturally, never showing disapproval or surprise. 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.

Neither argue nor dispute. Show genuine concern and interest in the ideas expressed by the respondent;

at the same time, maintain an impartial and objective attitude. 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.”

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.

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.

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?”

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.

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?”

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.

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

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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.

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.

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.

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.

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Q.2 (b) What are the problems encountered in Interview?

Answer:

In personal interviewing, the researcher must deal with two major problems,

inadequate response, non-response and interviewer’s bias.

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.

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.

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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.

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.

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.

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.

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

Data is an integral part of all business processes. It is the invisible backbone that

supports all the operations and activities within a business. Without access to

relevant data, businesses would get completely paralyzed. This is because quality

data helps formulate effective business strategies and fruitful business decisions. 

Data processing refers to the process of converting data from one format to

another. It transforms plain data into valuable information and information into

data. Clients can supply data in a variety of forms, be it .xls sheets, audio devices,

or plain printed material. Data processing services take the raw data and process it

accordingly to produce sensible information. The various applications of data

processing can convert raw data into useful information that can be used further

for business processes.

Data processing in simplest way, we can draw like this; DATA --> Processing

on Data --> Converted to Information.

As we have seen above, data processing means a process of converting data into

information. This processing is done through computers which accept raw data as

input and provide information as output.

Steps in Data Processing

Here are the steps that are included in data processing:

1. Collecting

First step is to collect the raw data which you want to process. From which data

do you want information? This is first question before you start.

2. Sorting

Relevance of data is very important while processing the data. There are various

irrelevant data which decrease the perfection of the information. So from the

bunch of collected data, sorting is needed to get relevant output information. Data

must be in proper categorization.

3. Editing

There is a big difference between data and useful data. While there are huge

volumes of data available on the internet, useful data has to be extracted from the

huge volumes of the same. Extracting relevant data is one of the core procedures

of data processing. When data has been accumulated from various sources, it is

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edited in order to discard the inappropriate data and retain relevant data.

4. Coding

Even after the editing process, the available data is not in any specific order. To

make it more sensible and usable for further use, it needs to be aligned into a

particular system. The method of coding ensures just that and arranges data in a

comprehendible format. The process is also known as netting or bucketing.

5. Data Entry

After the data has been properly arranged and coded, it is entered into the software

that performs the eventual cross tabulation. Data entry professionals do the task

efficiently.

6. Validation

After the cleansing phase, comes the validation process. Data validation refers to

the process of thoroughly checking the collected data to ensure optimal quality

levels. All the accumulated data is double checked in order to ensure that it

contains no inconsistencies and is utterly relevant.

7. Tabulation

This is the final step in data processing. The final product i.e. the data is tabulated

and arranged in a systematic format so that it can be further analyzed. 

Q. 3 (b) How is data editing is done at the Time of Recording of Data?

Answer:

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:

Is the coding frame complete?

Is the documentary material sufficient for the methodological description

of the study?  

Is the storage medium readable and reliable.

Has the correct data set been framed?

Is the number of cases correct?

Are there differences between questionnaire, coding frame and data? 

Are there undefined and so-called “wild codes”?

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Comparison of the first counting of the data with the original documents

of the researcher.

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

as created by the researcher.

Completeness: The first step of editing is to check whether there is an answer to

all the questions/variables set out in the data set. If there were any omission, the

researcher sometimes would be able to deduce the correct answer from other

related data on the same instrument. If this is possible, the data set has to rewritten

on the basis of the new information. For example, the approximate family income

can be inferred from other answers to probes such as occupation of family

members, sources of income, approximate spending and saving and borrowing

habits of family members’ etc. If the information is vital and has been found to be

incomplete, then the researcher can take the step of contacting the respondent

personally again and solicit the requisite data again. If none of these steps could

be resorted to the marking of the data as “missing” must be resorted to. 

Accuracy: Apart from checking for omissions, the accuracy of each recorded

answer should be checked. A random check process can be applied to trace the

errors at this step. Consistency in response can also be checked at this step. The

cross verification to a few related responses would help in checking for

consistency in responses. The reliability of the data set would heavily depend on

this step of error correction. While clear inconsistencies should be rectified in the

data sets, fact responses should be dropped from the data sets.

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

uniformity, in interpretation of questions and instructions by the data recorders.

For instance, the responses towards a specific feeling could have been queried

from a positive as well as a negative angle. While interpreting the answers, care

should be taken as a record the answer as a “positive question” response or as

“negative question” response in all uniformity checks for consistency in coding

throughout the questionnaire/interview schedule response/data set.

The final point in the editing of data set is to maintain a log of all corrections that

have been carried out at this stage. The documentation of these corrections helps

the researcher to retain the original data set.

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

Answer:

When you collect behavioural measurements, data is initially unorganized

and raw. One of the many uses of statistics is to make sense out of the senseless

and randomness of data; that is, organize raw data. This chapter is still dealing

with raw data; we are not converting raw data into any type of statistic; we are

simply taking the raw data and consolidating it so that it is easier to understand.

 Simple Frequency Distributions

 The easiest method for organizing raw data is to create a frequency distribution.

In a frequency distribution each possible value in the range between the high

score and the low score in the data set is listed with its frequency of occurrence.

That is, each value in the data set is listed with the number of times it was

recorded across all of the subjects.

Creating a frequency distribution is easy, but some rules must be followed to

preserve clarity. First, list each value in the range of obtained scores from high to

low in one column by placing the highest value at the top of this column and

working your way down to the lowest score. It is important to not skip values that

were not obtained in your data.

Next, count the number of times each value occurs in the data set. The “counts” of

each value is the frequency of each value. List the frequency of each value in a

new column labeled “f” that should be positioned to the right of the X column. It

is good to list the total frequency (n), that is, the number of scores in the data set

at the bottom of this column. This is just to be sure that the individual frequencies

of each value add up to the total number of people measured. If not, go back and

check. Except for some more information that we’ll deal with in the next section,

that’s it! You’ve created a frequency distribution and organized a messy set of raw

data:

 Relative Frequencies and Relative Percentages

 There is a limitation to using only the frequencies of values to organize raw data. The

raw frequency of any value doesn't tell you anything about the rest of the distribution, or

how frequent a value is with respect to the rest of the distribution. That is, the raw

frequency of a value does not tell you anything about the impact that a value has on the

rest of the distribution.

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We need a measurement that tells us of the “relative importance” or “impact” of each

value on the rest of the distribution. That is, the frequency of a value relative to the

distribution and to have this value be standardized, such that higher values on this

relative measure always indicate a higher frequency in the distribution and lower values

indicate a lesser frequency.  The relative frequency of a value is calculated by dividing

the frequency of a value by the total frequency (n) and then listing that resulting value in

a column to the right of the frequency column:

Cumulative Frequencies, Cumulative Relative Frequencies, Cumulative Relative Percentages

Frequency distributions are also useful for determining the “place” or “rank” of a

value relative to the other values in a distribution; that is, the number and/or

proportion of scores above or below a certain value. To do this we must generate

several other columns in our frequency distribution.

 The cumulative frequency (cf) of a value is the frequency of that value added to

the frequencies of all the values less than that value. For example, the cumulative

frequency of a value of 4 is its frequency (f = 2) added to the frequency for the

values less than 4: 3 (f = 4), 2 (f = 3) and 1 (f = 0), which results in a cumulative

frequency of 9 for a value of 4. This indicates there are 9 scores less than or equal

to 4.

The cumulative frequency of a value does not say anything about the impact of a value

on the distribution; that is, what proportion of scores are less than or equal to a certain

value.

Grouped Frequency Distributions for Quantitative Data

 Although frequency distributions are helpful for organizing data, they are not

useful if you have a wide range of scores. In the example in the preceding section

there were 11 possible values that could be obtained. But consider the

hypothetical case where you have a 100-point exam that can, theoretically, range

from a low score of 0 to a high score of 100, in increments of 1-point. There are

101 potential values that can be obtained, including zero; hence, listing each

possible value in that range with its frequency would make a cumbersome

distribution. Also, for such a wide range unless you had a very large class taking

the exam, it is highly unlikely that all possible values will be obtained, leaving

many values with frequencies of zero.

 

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In a grouped frequency distribution, similar values are combined into groups and

the frequencies of each value in a group are added to provide a frequency for that

group. By doing this, the grouped frequency distribution is more manageable and

more readable.  

Given below a few considerations that must be taken into account when

creating a grouped frequency distribution:

The size (interval, i) of each group should be equal.

The size of the group interval (i) should include 2 values, 3 values, or any multiple

of 5 values.

The number of groups should reflect the range of values. Importantly, the number

of groups should consolidate the data and make it more understandable, but

maintain precision. A rule of thumb is to include between 5 and 15 groups with 7

groups being optimal. When the range is small use about 5 and when the range is

large use about 15.

Frequency Distributions for Qualitative Variables

In the preceding sections, each distribution was included for quantitative data. It

can be the case that the data you want to create a frequency distribution from is

qualitative. For example, you may want determine the frequencies of people that

like different hockey teams. Say that I ask 50 people whether they like the Buffalo

Sabers, Philadelphia Flyers, Detroit Red Wings, Calgary Flames, or none of those

teams, and I determine the frequency of each category/team. Notice that each

entry (under Favourite Team) is qualitative (nominal) data. When creating a

frequency distribution for qualitative data most of the steps involved are identical

to those when creating a frequency distribution with quantitative data; that is,

place each category name into a column, determine the frequency for each

category, and then determine the relative frequency and percentage for each

category. 

There are two differences between frequency distributions with quantitative data

and one with qualitative data. First, you cannot create grouped frequency

distributions, because groups must be based on numerical values. Second,

cumulative frequency, cumulative relative frequency, and cumulative percentages

cannot be created in a frequency distribution with a qualitative variable.

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Q.4(b) What are the types and general rules for graphical representation of

data?

Answer

The data which has been shown in the tabular form may be displayed in pictorial

form by using a graph. A well-constructed graphical presentation is the easiest

way to depict a given set of data.

Types of graphical representation of data

The following forms are used for graphical representation of data:

Histogram

Bar Diagram or Bar Graph

Frequency Polygon

Cumulative Frequency curve or Ogive

Line Graphs or Charts

Bar Charts

Segmental presentations.

Scatter plots

Bubble charts

Stock plots

Pictographs

Chesnokov Faces

General rules to be followed in graphic representations are:

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

2. The title should be clear, concise and simple and should describe the

nature of the data presented.

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

accompanying table.

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

vertical line the measured variable.

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

bottom to top on the vertical.

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

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

differentiated from one another by distinct patterns or colours.

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

should be equal.

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

than illuminating the presentation.

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

discussion.

 

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

Case study method enables a researcher to closely examine the data within a

specific context. In most cases, a case study method selects a small geographical

area or a very limited number of individuals as the subjects of study. Case studies,

in their true essence, explore and case study as a research method to investigate

contemporary real-life phenomenon through detailed contextual analysis of a

limited number of events or conditions, and their relationships. Yin (1984:23)

defines the case study research method “as an empirical inquiry that investigates a

contemporary phenomenon within its real-life context; when the boundaries

between phenomenon and context are not clearly evident; and in which multiple

sources of evidence are used.”

A case study is a unique way of observing any natural phenomenon which exists

in a set of data. By unique it is meant that only a very small geographical area or

numbers of subjects of interest are examined in detail. Unlike quantitative analysis

which observes patterns in data at the macro level on the basis of the frequency of

occurrence of the phenomena being observed, case studies observe the data at the

micro level.

Since case study method receives criticism in terms of its lack of robustness as a

research tool, crafting the design of case studies is of paramount importance.

Researchers can adopt either a single-case or multiple-case design depending on

the issue in question.

Case studies as scientific research

Scientific research requires a high level of study in the subject area. It is important

to remember that with science subjects you cannot progress to these higher levels,

without studying at the level below. Scientific study relies on building on what

you already know. So if you’re interested in working in an area like scientific

research, studying science at a London college would be a very good place to

start.

Case studies cannot be considered scientific since they do not subscribe to most of

the hallmarks of scientific research. Though they may be purposive and

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parsimonious, they are not rigorous. Testability and replicability are difficult and

generalizability is virtually non-existent since each case situation is unique.

There are three types of arguments against case study research.

First, case studies are often accused of lack of rigour. Yin (1984:21) notes that

“too many times, the case study investigator has been sloppy, and has allowed

equivocal evidence or biased views to influence the direction of the findings and

conclusions”.

Second, case studies provide very little basis for scientific generalisation since

they use a small number of subjects, some conducted with only one subject. The

question commonly raised is “How can you generalise from a single case?” (Yin,

1984:21).

Third, case studies are often labelled as being too long, difficult to conduct and

producing a massive amount of documentation (Yin, 1984). In particular, case

studies of ethnographic or longitudinal nature can elicit a great deal of data over a

period of time. The danger comes when the data are not managed and organised

systematically.

A common criticism of case study method is its dependency on a single case

exploration making it difficult to reach a generalizing conclusion (Tellis, 1997).

Yin (1993) considered case methodology ‘microscopic’ because of the limited

sampling cases. To Hamel et al. (1993) and Yin (1994), however, parameter

establishment and objective setting of the research are far more important in case

study method than a big sample size.

One of the biggest disadvantages to using the case study method has to do with

external vs. internal validity. Using the case study method, the researcher often

does not have control over certain variables and events and, therefore, cannot

control them as the researcher could in a lab experiment. Consequently, the

researcher using the case study method must be content that his/her findings may

only be applicable to similar cases. What the case study gains in internal validity,

it loses in external validity.

Many researchers using the case study method make the mistake of relying too

heavily on interpretation to guide findings and recommendations. Essentially the

researcher becomes part of the research itself and knowing the expected results,

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may unknowingly guide the subjects to those results thereby confirming the

expected results. This is known as the self-fulfilling prophecy or Pygmalion

effect.

Some members of the scientific community frown upon the case study method

because researchers using it often violate the principle of falsification. In modern

post-positivist scientific thought (Popper, 1959), the researcher takes the role of

the disinterested observer; he/she has no vested interest in whether the research

turns out one way or the other.

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

Case study method enables a researcher to closely examine the data within a

specific context. In most cases, a case study method selects a small geographical

area or a very limited number of individuals as the subjects of study. Case studies,

in their true essence, explore and case study as a research method to investigate

contemporary real-life phenomenon through detailed contextual analysis of a

limited number of events or conditions, and their relationships.

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.

Case studies are usually interesting because of the unusualness of the case (Three

Faces of Eve, Mind of a Mnemonist) and/or the detail and apparent insightfulness

of the conclusions drawn by the writer (e.g., Freud’s cases such as ‘Little

Hans’). The major problem with case studies is the problem of objectivity.  The

person who is presenting the case usually has some theoretical orientation.  It is

acceptable for a theoretical orientation to affect one’s interpretation of events.   In

a case study the theoretical orientation can also lead to the selection of the facts to

include in the case.  It is not surprising that case studies often seem to provide

very compelling evidence for a theory.  (I discovered this when I tried to provide

alternative interpretations of classic cases described by Freud, Adler, and Jung.)

Case studies can therefore assist psychology by illustrating how a theory could be

applied to a person or events and by assisting with the development of hypotheses

for more systematic testing, e.g., Piaget’s case studies of the cognitive

development of his three children.

Case study and descriptive approach to research

Case study and Descriptive approach are two different aspects of any research

conducted in a given field. It is important to know that both these aspects differ in

terms of their study and presentation.

A case study though is conducted in several fields it is quite commonly seen in the

field of social science. It consists in a kind of deep investigation carried out in the

behavior of a single group or individual or event for that matter. As a matter of

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fact a case study can be either descriptive or explanatory in character. Any single

instance or an event is taken for study and it will be investigated for months by

adhering to a protocol. Limited number of variables will be thoroughly examined

too in the case of a case study.

On the other hand descriptive approach involves more statistical study than

investigation. Descriptive approach is the foundation for conducting a survey

investigation. It involves the use of averages, frequencies and other statistical

calculations. The subject of mathematical statistics and probability play a vital

role in the descriptive approach of research study. In short it can be said that

descriptive approach deals with anything that can be counted and studied. This is

the main difference between a case study and descriptive approach.

A case study is more of a research strategy whereas descriptive approach is not

looked upon as a research strategy but as a part of research. Empirical inquiry is

the backbone of a case study whereas statistical calculation is the backbone of

descriptive approach. Case study contributes to qualitative research whereas

descriptive approach contributes to quantitative research. Both the aspects of

research should be conducted to bring out fruitful results to strengthen a given

field. These are the differences between case study and descriptive approach.

Q.6.b. Distinguish between research methods & research Methodology. Answer:

Research methods are the various procedures, schemes, algorithms etc. 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 particularly scientific research methods call for explanations

based on particularly scientific 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

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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.

Research Methods vs Research Methodology

Research Methods and Research Methodology are two terms that are often

confused as one and the same. Strictly speaking they are not so and they show

differences between them. One of the primary differences between them is that

research methods are the methods by which you conduct research into a subject or

a topic. On the other hand research methodology explains the methods by which

you may proceed with your research.

Research methods involve conduct of experiments, tests, surveys and the like. On

the other hand research methodology involves the learning of the various

techniques that can be used in the conduct of research and in the conduct of tests,

experiments, surveys and critical studies. This is the technical difference between

the two terms, namely, research methods and research methodology.

In short it can be said that research methods aim at finding solutions to research

problems. On the other hand research methodology aims at the employment of the

correct procedures to find out solutions.

It is thus interesting to note that research methodology paves the way for research

methods to be conducted properly. Research methodology is the beginning

whereas research methods are the end of any scientific or non-scientific research.

Let us take for example a subject or a topic, namely, ‘employment of figures of

speech in English literature’. In this topic if we are to conduct research, then the

research methods that are involved are study of various works of the different

poets and the understanding of the employment of figures of speech in their

works.

On the other hand research methodology pertaining to the topic mentioned above

involves the study about the tools of research, collation of various manuscripts

related to the topic, techniques involved in the critical edition of these manuscripts

and the like.

If the subject into which you conduct a research is a scientific subject or topic then

the research methods include experiments, tests, study of various other results of

different experiments performed earlier in relation to the topic or the subject and

the like.

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On the other hand research methodology pertaining to the scientific topic involves

the techniques regarding how to go about conducting the research, the tools of

research, advanced techniques that can be used in the conduct of the experiments

and the like. Any student or research candidate is supposed to be good at both

research methods and research methodology if he or she is to succeed in his or her

attempt at conducting research into a subject.

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