RM Assignment 01/02

8/2/2019 RM Assignment 01/02

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Assignment Set- 1

Q 1. Why should a manger know about research when the job

entails managing people, products, events, environments, and

the like?

Ans.:-Research simply means a search for facts answers to questions and solutions to

problems. It is a purposive investigation. It is an organized inquiry. It seeks to find

explanations to unexplained phenomenon to clarify the doubtful facts and to correct

the misconceived facts. Research is the organized and systematic inquiry or

investigation which provides information for solving a problem or finding answers to a

complex issue.

Research in business:Often, organization members want to know everything about their products, services,

programs, etc. Your research plans depend on what information you need to collect in

order to make major decisions about a product, service, program, etc. Research

provides the needed information that guides managers to make informed decisions to

successfully deal with problems.

The more focused you are about your resources, products, events and environmentswhat you want to gain by your research, the more effective and efficient you can be in

your research, the shorter the time it will take you and ultimately the less it will cost

you.

Managers role in research programs of a company:

Managing people is only a fraction of a manager's responsibility - they have to

manage the operations of the department, and often have responsibilities towards theprofitability of the organization. Knowledge of research can be very helpful for a good

manager.

Q 2. a. How do you evolve research design for exploratory

research? Briefly analyze.

Ans.:-

Research simply means a search for facts answers to questions and solutions to

problems. It is a purposive investigation. It is an organized inquiry. It seeks to find

explanations to unexplained phenomenon to clarify the doubtful facts and to correct


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the misconceived facts. Although any typology of research is inevitably arbitrary,

Research may be classified crudely according to its major intent or the methods.

It is also known as formulating research. It is preliminary study of an unfamiliar

problem about which the researcher has little or no knowledge. It is ill-structured and

much less focused on pre-determined objectives. It usually takes the form of a pilot

study. The purpose of this research may be to generate new ideas, or to increase the

researchers familiarity with the problem or to make a precise formulation of the

problem or to gather information for clarifying concepts or to determine whether it is

feasible to attempt the study. Katz conceptualizes two levels of exploratory studies.

At the first level is the discovery of the significant variable in the situations; at the

second, the discovery of relationships between variables.

b. Briefly explain Independent dependent and extraneous

variables in a research design.

Ans.:-

The research designer understandably cannot hold all his decisions in his head. Even

if he could, he would have difficulty in understanding how these are inter-related.

Therefore, he records his decisions on paper or record disc by using relevant symbols

or concepts. Such a symbolic construction may be called the research design or

model. A research design is a logical and systematic plan prepared for directing a

research study.

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; whereas 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


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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.

Extraneous variable:

The independent variables which are not directly related to the purpose of the study

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

assume that a researcher wants to test the hypothesis that there is relationship

between childrens 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 isnot 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.

Q 3. A. Differentiate between Census survey and SampleSurvey.

Ans.:-

Census Survey Sample SurveyA census measures absolutely everyone

in the whole country. This obviously

means that a census survey is a much

bigger exercise in nature and

procedures

A part of the population is known as

sample

Census survey also is a very time

consuming exercise as information

needs to be collected from each and

every individual from the population.

On the other hand, sample survey is

easier as a representative sample is

taken from the population and the

results obtained are extrapolated to fit

the entire population.


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There are times and requirements

where governments have to indulge in

census survey even if it is time

consuming and very expensive as it

needs to formulate policies and welfare

programs for the population. For

example, when a government has to

count heads of the population

Sample surveys cannot count the

number of people in the country but

when government is planning on a

welfare program for cancer patients, it

can conduct a sample survey of some of

the cancer patients and then

extrapolate the results on the section of

the population that is undergoing

treatment for cancer.Census survey is more accurate. there is margin for error in sample

survey

b. Analyze multi-stage and sequential sampling.

Ans.:-

Multi-stage sampling:

In multi-stage sampling method, sampling is carried out in two or more stages. The

population is regarded as being composed of a number of second stage units and so

forth. That is, at each stage, a sampling unit is a cluster of the sampling units of the

subsequent stage. First, a sample of the first stage sampling units is drawn, then from

each of the selected first stage sampling unit, a sample of the second stage sampling

units is drawn. The procedure continues down to the final sampling units or population

elements. Appropriate random sampling method is adopted at each stage. It is

appropriate where the population is scattered over a wider geographical area and no

frame or list is available for sampling. It is also useful when a survey has to be made

within a limited time and cost budget. The major disadvantage is that the procedure

of estimating sampling error and cost advantage is complicated.

Sequential sampling:

Sequential sampling is a non-probability sampling technique wherein the researcher

picks a single or a group of subjects in a given time interval, conducts his study,

analyses the results then picks another group of subjects if needed and so on. This

sampling technique gives the researcher limitless chances of fine tuning his research

methods and gaining a vital insight into the study that he is currently pursuing. There

is very little effort in the part of the researcher when performing this sampling

technique. It is not expensive, not time consuming and not workforce extensive.

This sampling method is hardly representative of the entire population. Its only hope

of approaching representative ness is when the researcher chose to use a very large

sample size significant enough to represent a big fraction of the entire population.


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Due to the aforementioned disadvantages, results from this sampling technique

cannot be used to create conclusions and interpretations pertaining to the entire

population.

Q 4. List down various measures of central tendency and explain

the difference between them?

Ans.:-

Measures of Central Tendency:

The term central tendency refers to the "middle" value or perhaps a typical value of

the data, and is measured using the mean, median, or mode. Each of these

measures is calculated differently, and the one that is best to use depends upon the

situation.

Analysis of data involves understanding of the characteristics of the data. The

following are the important characteristics of a statistical data:

Central tendency

Dispersion

Skew ness

Kurtosis

In a data distribution, the individual items may have a tendency to come to a central

position or an average value. For instance, in a mark distribution, the individual

students may score marks between zero and hundred. In this distribution, many

students may score marks, which are near to the average marks, i.e. 50. Such a

tendency of the data to concentrate to the central position of the distribution is called

central tendency. Central tendency of the data is measured by statistical averages.

Averages are classified into two groups.

1. Mathematical averages

2. Positional averages Statistical Averages

Mathematical averages Positional averages

Arithmetic mean Median

Geometric mean Mode

Harmonic mean

Arithmetic mean, geometric mean and harmonic mean are mathematical averages.

Median and mode are positional averages. These statistical measures try to

understand how individual values in a distribution concentrate to a central value like


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average. If the values of distribution approximately come near to the average value,

we conclude that the distribution has central tendency.

Difference between Mean and Median:

Mean (Mathematical averages) Median (Positional averages)

When the sample size is large and doesnot include outliers, the mean score

usually provides a better measure of

central tendency.

The median may be a better indicator ofthe most typical value if a set of scores

has an outlier. An outlier is an extreme

value that differs greatly from other

values.The mean is the most commonly-used

measure of central tendency. When we

talk about an "average", we usually are

referring to the mean

The median often is used when there are

a few extreme values that could greatly

influence the mean and distort what

might be considered typical. The mean is simply the sum of the

values divided by the total number of

items in the set

The median is determined by sorting the

data set from lowest to highest values

and taking the data point in the middle

of the sequence

Q.5. Select any topic for research and explain how you will use bothsecondary and primary sources to gather the required information.

Ans.:-

For performing research on the literacy levels among families, the primary and

secondary sources of data can be used very effectively. More specifically the primary

sources of data collection is suggested in this regard. Because personal data or data

related to human beings consist of:

1. Demographic and socio-economic characteristics of individuals: Age, sex, race,social class, religion, marital status, education, occupation income, family size,

location of the household life style etc.

2. Behavioral variables: Attitudes, opinions, awareness, knowledge, practice,

intentions, etc.

3. Organizational data consist of data relating to an organizations origin,

ownership, objectives, resources, functions, performance and growth.

4.

Territorial data are related to geo-physical characteristics, resource endowment,population, occupational pattern infrastructure degree of development, etc. of

spatial divisions like villages, cities, talukas, districts, state and the nation.


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The data serve as the bases or raw materials for analysis. Without an analysis of

factual data, no specific inferences can be drawn on the questions under study.

Inferences based on imagination or guess work cannot provide correct answers to

research questions. The relevance, adequacy and reliability of data determine the

quality of the findings of a study.

Data form the basis for testing the hypothesis formulated in a study. Data also

provide the facts and figures required for constructing measurement scales and

tables, which are analyzed with statistical techniques. Inferences on the results of

statistical analysis and tests of significance provide the answers to research

questions. Thus, the scientific process of measurements, analysis, testing and

inferences depends on the availability of relevant data and their accuracy. Hence, theimportance of data for any research studies

The sources of data may be classified into:

a. Primary sources

b. Secondary sources.

Primary Sources of Data:

Primary sources are original sources from which the researcher directly collects data

that have not been previously collected e.g.., collection of data directly by the

researcher on brand awareness, brand preference, brand loyalty and other aspects of

consumer behaviour from as ample of consumers by interviewing them,. Primary data

are first-hand information collected through various methods such as observation,

interviewing, mailing etc.

Advantage of Primary Data:

It is original source of data

It is possible to capture the changes occurring in the course of time.

It flexible to the advantage of researcher.

Extensive research study is based of primary data

Disadvantage of Primary Data:

Primary data is expensive to obtain

It is time consuming


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It requires extensive research personnel who are skilled.

It is difficult to administer

Methods of Collecting Primary Data:

Primary data are directly collected by the researcher from their original sources. In

this case, the researcher can collect the required date precisely according to his

research needs, he can collect them when he wants them and in the form he needs

them. But the collection of primary data is costly and time consuming. Yet, for several

types of social science research required data are not available from secondary

sources and they have to be directly gathered from the primary sources.

In such cases where the available data are in appropriate, inadequate or obsolete,

primary data have to be gathered. They include: socioeconomic surveys, social

anthropological studies of rural communities and tribal communities, sociological

studies of social problems and social institutions. Marketing research, leadership

studies, opinion polls, attitudinal surveys, readership, radio listening and T.V. viewing

surveys, knowledge-awareness practice (KAP) studies, farm managements studies,

business management studies etc. There are various methods of data collection. A

Method is different from a Tool while a method refers to the way or mode of

gathering data, a tool is an instruments used for the method. For example, a schedule

is used for interviewing. The important methods are (a) observation, (b) interviewing,

(c)mail survey,(d)experimentation,(e) simulation and (f) projective technique. Each of

these methods is discussed in detail in the subsequent sections in the later chapters.

Secondary Sources of Data:

These are sources containing data which have been collected and compiled for

another purpose. The secondary sources consists of readily compendia and alreadycompiled statistical statements and reports whose data may be used by researchers

for their studies e.g., census reports , annual reports and financial statements of

companies, Statistical statement, Reports of Government Departments, Annual

reports of currency and finance published by the Reserve Bank of India, Statistical

statements relating to Co-operatives and Regional Banks, published by the NABARD,

Reports of the National sample survey Organization, Reports of trade associations,

publications of international organizations such as UNO, IMF, World Bank, ILO, WHO,etc., Trade and Financial journals newspapers etc.


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Secondary sources consist of not only published records and reports, but also

unpublished records. The latter category includes various records and registers

maintained by the firms and organizations, e.g., accounting and financial records,

personnel records, register of members, minutes of meetings, inventory records etc.

Features of Secondary Sources:

Though secondary sources are diverse and consist of all sorts of materials, they have

certain common characteristics. First, they are readymade and readily available, and

do not require the trouble of constructing tools and administering them

Second, they consist of data which a researcher has no original control over collection

and classification. Both the form and the content of secondary sources are shaped by

others. Clearly, this is a feature which can limit the research value of secondarysources. Finally, secondary sources are not limited in time and space. That is, the

researcher using them need not have been present when and where they were

gathered

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 maybe 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,


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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., and serve as major data sources for

such research studies

Advantages of Secondary Data:

Secondary sources have some advantages:

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.

Event he tediousness of copying the data from the source can now be avoided, thanks

to Xeroxing facilities.

Wider geographical area and longer reference period may be covered without much

cost. Thus, the use of secondary data extends the researchers space and time reach.

The use of secondary data broadens the data base from which scientific

generalizations can be made.

Environmental and cultural settings are required for the study.

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 needs 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.

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.

The available data may not be as accurate as desired. To assess their

accuracy we need to know how the data were collected.

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 two or three years later after

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

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


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the capital city, and they are not within the easy reach of researchers

based in far off places.

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?

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 and other comments that were made in the original source.

3) Data Completeness:The completeness refers to the actual coverage of the published data. This depends

on the methodology and sampling design adopted by the original organization. Is the


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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 organizations 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 researchers study.

Q 6. a. Explain the role of Graphs and Diagrams?

Ans.:-

In presenting the data of frequency distributions and statistical computations, it isoften desirable to use appropriate forms of graphic presentations. In additions to

tabular forms, graphic presentation involves use of graphics, charts and other pictorial

devices such as diagrams. These forms and devices reduce large masses of statistical

data to a form that can be quickly understood at the glance. The meaning of figures in

tabular form may be difficult for the mind to grasp or retain. Properly constructed

graphs and charts relieve the mind of burdensome details by portraying facts

concisely, logically and simply. They, by emphasizing new and significantrelationship, are also useful in discovering new facts and in developing hypothesis.

The device of graphic presentation is particularly useful when the prospective readers

are non-technical people or general public. It is useful to even technical people for

dramatizing certain points about data; for important points can be more effectively

captured in pictures than in tables. However, graphic forms are not substitutes for

tables, but are additional tools for the researcher to emphasize the research findings.

Graphic presentation must be planned with utmost care and diligence. Graphic forms

used should be simple, clear and accurate and also be appropriate to the data. In

planning this work, the following questions must be considered.

a. What is the purpose of the diagram?

b. What facts are to be emphasized?

c. What is the educational level of the audience?

d. How much time is available for the preparation of the diagram?

e. What kind of chart will portray the data most clearly and accurately?


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Role of Graphs:

Because graphs provide a compact, rhetorically powerful way of representing

research findings, recent theories of science have postulated their use as a

distinguishing feature of science. Studies have shown that the use of graphs in journal

articles correlates highly with the hardness of scientific fields, both across disciplines

and across subfields of psychology.

Role of Diagrams:

Recent technological advances have enabled the large-scale adoption of diagrams in

a diverse range of areas. Increasingly sophisticated visual representations are

emerging and, to enable effective communication, insight is required into how

diagrams are used and when they are appropriate for use. The pervasive, everydayuse of diagrams for communicating information and ideas serves to illustrate the

importance of providing a sound understanding of the role that diagrams can, and do,

play. Research in the field of diagrams aims to improve our understanding of the role

of diagrams, sketches and other visualizations in communication, computation,

cognition, creative thought, and problem solving. These concerns have triggered a

surge of interest in the study of diagrams.

The study of diagrammatic communication as a whole must be pursued as an

interdisciplinary endeavor. Diagrams attract a large number of researchers from

virtually all related fields, placing the conference as a major international event in the

area.

b. What are the Types and General rules for graphical representation of

data?Graphical representation is done of the data available. This is very important step of

statistical analysis. We will be discussing the organization of data. The word 'Data' is

plural for 'datum'; datum means facts. Statistically the term is used for numerical

facts such as measures of height, weight and scores on achievement and intelligence

tests.

Graphs and diagram leave a lasting impression on the mind and make intelligible and

easily understandable the salient features of the data. Forecasting also becomes


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easier with the help of graph. Thus it is of interest to study the graphical

representation of data.

The graphical representation of data is categorized as basic five types:

1) Bar graph

2) Pie graph

3) Line graph

4) Scatter plot

5) Histogram

Examples of graphical representation of data:

Let us see some examples of graphical representation of data.

1) Bar chart:

A Bar chart (or diagram) is a graphical representation of data using bars (rectangles

of same width).

It is one dimensional in which case only the height of the rectangle matters.

Solution: scale: Y axis 1 cm = 1000 years

2) Graphical Representation of Histogram:

year193

1

194

1

195

11961 1971 1981

population of aplace

6000

7600

8900

12000

13500

18000


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A histogram (or rectangular diagram or block diagram) is a graphical representation of

a frequency distribution in the form of rectangles one after the other with height

proportional to the frequencies.

It is two dimensional in which case the height as well as width of the

rectangle matters.

Que: Represent the following data by means of a Histogram:

Age( in years) 20-25 25-

30

30-

35

35-

40

40-

45

45-

50

50-

55Number of workers 3 4 5 6 5 4 3

3) Frequency Polygon of a Line Graph:

A frequency polygon can be constructed for a grouped frequency distribution, with

equal-interval, in two different ways:

Method I:

Represent the class-marks along the x-axis.

Represent the frequencies along y-axis. Join these points, in order, by straight lines.

The points at each end is joined to the immediate higher(or lower) class mark at

zero

Frequency so as to complete the polygon.

Method II:

Represent a histogram of the given data.

Join the mid points of the tops of the adjacent rectangles by straight lines.

The mid points at each end are joined to the immediate higher (or lower) at zero

frequency so as to complete the polygon.

The two classes, one at each end, are to be included.


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Construct a frequency polygon for the following data:

Monthly pocket

expenses of a student

0-5 5-10 10-15 15-20 20-25 25-30 30-35 35-40

Number of students 10 16 30 42 50 30 16 12

Solution: Here we have

Monthly pocket expenses of a

student(in $)class- marks

Number of

students

0-5 2.5 10

5-10 7.5 16

10-15 12.5 30

15-20 17.5 42

20-25 22.5 50

25-30 27.5 30

30-35 32.5 16

35-40 37.5 12

4) Cumulative Frequency Curve(ogive):

The Cumulative frequency curve for a grouped frequency distribution is obtained by

plotting the points and then joining them by a free-hand smooth curve.

This is also known as olive.

Method:


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Form the cumulative frequency table.

Mark the upper class limits along the x-axis.

Mark the cumulative frequencies along the y-axis.

Plot the points and join them by a free-hand smooth curve.

Draw a cumulative frequency curve for the following data:

Marks 0-4 4-8 8-12 12-16 16-20

Number of

students4 6 10 8 4

The cumulative frequency table is as follows:

Marks Number of students cumulative frequency

0-4 4 4

4-8 6 4+6=10

8-12 10 10+10=20

12-16 8 20+8=28

16-20 4 28+4=32

Total 32

Joining these points by a free-hand smooth curve, we have the following cumulative

frequency curve:

5) Pie-chart or Pie-graph:

It is drawn by first drawing a circle of a suitable radius and then dividing the angle of

360 degree at its centre in proportion to the figures given under various heads.

Solution:


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Survey recipients may have a variety of backgrounds so use simple language. For

example, "What is the frequency of your automotive travel to your parents' residents

in the last 30 days?" is better understood as, "About how many times in the last 30

days have you driven to your parent's home?"

Relax your grammar

Relax your grammatical standards if the questions sound too formal. For example, the

word "who" is appropriate in many instances when "whom" is technical correct.

Assure a common understanding

Write questions that everyone will understand in the same way. Don't assume that

everyone has the same understanding of the facts or a common basis of knowledge.

Identify even commonly used abbreviations to be certain that everyone understands.

Start with interesting questions

Start the survey with questions that are likely to sound interesting and attract the

respondents' attention. Save the questions that might be difficult or threatening for

later. Voicing questions in the third person can be less threatening than questions

voiced in the second question. For example, ask: "How do your colleagues feel about

management?" rather than "How do you feel about management?"

Don't write leading questions

Leading questions demand a specific response. For example: the question "Which day

of the month is best for the newly established company-wide monthly meeting?" leads

respondents to pick a date without first determining if they even want another

meeting.

Avoid double negatives

Respondents can easily be confused deciphering the meaning of a question that uses

two negative words.

Balance rating scales

When the question requires respondents to use a rating scale, mediate the scale so

that there is room for both extremes.

Don't make the list of choices too long


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If the list of answer categories is long and unfamiliar, it is difficult for respondents to

evaluate all of them. Keep the list of choices short.

Avoid difficult concepts

Some questions involve concepts that are difficult for many people to understand.

Avoid difficult recall questions

People's memories are increasingly unreliable as you ask them to recall events farther

and farther back in time. You will get far more accurate information from people if you

ask, "About how many times in the last month have you gone out and seen a movie in

a movie theater or drive-in?" rather than, "About how many times last year did you go

out and see a movie in a movie theater or drive-in?"

Use Closed-ended questions rather than Open-ended ones

Most questionnaires rely on questions with a fixed number of response categories

from which respondents select their answers. These are useful because the

respondents know clearly the purpose of the question and are limited to a set of

choices where one answer is right for them.

An open-ended question is a written response. For example: "If you do not want a

company picnic, please explain why". If there are an excessive number of writtenresponse questions, it reduces the quality and attention the respondents give to the

answers. However, Info Poll allows you to use a wide variety of other types of

questions.

Put your questions in a logic order

The issues raised in one question can influence how people think about subsequent

questions. It is good to ask a general question and then ask more specific questions.For example, you should avoid asking a series of questions about a free banking

service and then question about the most important factors in selecting a bank.

Pre-test your survey

t is better to identify a problem during the pretest than after you have published the

survey. Before sending a survey to a target audience, send it out as a test to a small

number of people. After they have completed the survey, brainstorm with them to see

if they had problems answering any questions. It would help if they explained what

the question meant to them and whether it was valid to the questionnaire or not.


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Naming your survey

Some people discard an electronic message based entirely on its subject or sender.

You should consider other titles that will pique the interest of the recipients. Here are

examples of survey names that might be successful in getting attention:

-Memo from the Chief Executive Officer

-Evaluation of Services of the Benefits Office

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Cover memo or introduction

Once a recipient opens your survey, you may still need to motivate him or her to

complete it. The cover memo or introduction offers an excellent place to provide the

motivation. A good cover memo or introduction should be short and includes:

-Purpose of the survey

-Why it is important to hear from the correspondent

-What may be done with the results and what possible impacts may occur with theresults.

-Address identification

-Person to contact for questions about the survey.

-Due date for response

Q 2. What do you mean by primary data? What are the various methods of

collecting primary data?Ans.:-

Primary sources are original sources from which the researcher directly collects data

that have not been previously collected e.g.., collection of data directly by the

researcher on brand awareness, brand preference, brand loyalty and other aspects of

consumer behavior from a sample of consumers by interviewing them,. Primary data

are first hand information collected through various methods such as observation,

interviewing, mailing etc.

Methods of Collecting Primary Data


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Primary data are directly collected by the researcher from their original sources. In

this case, the researcher can collect the required date precisely according to his

research needs, he can collect them when he wants them and in the form he needs

them. But the collection of primary data is costly and time consuming. Yet, for several

types of social science research required data are not available from secondary

sources and they have to be directly gathered from the primary sources.

In such cases where the available data are inappropriate, inadequate or obsolete,

primary data have to be gathered. They include: socio economic surveys, social

anthropological studies of rural communities and tribal communities, sociological

studies of social problems and social institutions. Marketing research, leadership

studies, opinion polls, attitudinal surveys, readership, radio listening and T.V. viewing

surveys, knowledge-awareness practice (KAP) studies, farm managements studies,business management studies etc.

There are various methods of data collection. A Method is different from a Tool

while a method refers to the way or mode of gathering data, a tool is an instruments

used for the method. For example, a schedule is used for interviewing. The important

methods are

(a) observation, (b) interviewing, (c) mail survey, (d) experimentation,

(a) observation: Observation means viewing or seeing. Observation may be defined

as a systematic viewing of a specific phenomenon in its proper setting for the specific

purpose of gathering data for a particular study. Observation is classical method of

scientific study.

(b) Interviewing: Interviewing is one of the prominent methods of data collection. It

may be defined as a two way systematic conversation between an investigator and aninformant, initiated for obtaining information relevant to a specific study. It involves

not only conversation, but also learning from the respondents gesture, facial

expressions and pauses, and his environment. Interviewing requires face to face

contact or contact over telephone and calls for interviewing skills. It is done by using a

structured schedule or an unstructured guide.

(c) Mail survey: The mail survey is a data collection process for researchers.

Research practitioners should recognize that this is a viable means of collecting

specific market data.

(d) Experimentation: The popularity of experimentation in marketing research has


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much to do with the possibilities of establishing cause and effect. Experiments can be

configured in such a way as to allow the variable causing a particular effect to be

isolated. Other methods commonly used in marketing research, like surveys, provide

much more ambiguous findings. In fact, experimentation is the most scientific method

employed in marketing research.

Q 3.a. Analyze the case study and descriptive approach to research.

Ans.:-

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 thefield 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 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.


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b. Distinguish between research methods & research Methodology.

Ans.:-

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 poetsand 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


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

like.

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.

Q 4. Explain the important concepts in Research design?

Ans.:-

It is important to be familiar with the important concepts relating to research design.They are:

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

variable. The concept may assume different quantitative values, like height, weight,

income, etc. Qualitative variables are not quantifiable in the strictest sense of

objectivity. However, the qualitative phenomena may also be quantified in terms of

the presence or absence of the attribute considered. Phenomena that assume

different values quantitatively even in decimal points are known as continuousvariables. 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. Forexample, if demand depends upon price, then demand is a dependent variable, while

price is the independent variable. And if, more variables determine demand, like

income and prices of substitute commodity, then demand also depends upon them in

addition to the own price. Then, demand is a dependent variable which is determined

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

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

the purpose of the study but affect the dependent variable are known as extraneous

variables. For instance, assume that a researcher wants to test the hypothesis that


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there is relationship between childrens 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 ofextraneous 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.


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Treatments: Treatments are referred to the different conditions to which the

experimental and control groups are subject to. In the example considered, the two

treatments are the parents with regular earnings and those with no regular earnings.

Likewise, if a research study attempts to examine through an experiment regarding

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

then the three types of fertilizers would be treated as the three treatments.

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

hypothesis relating to a given research problem. For instance, experiment may be

conducted to examine the yield of a certain new variety of rice crop developed.

Further, Experiments may be categorized into two types namely, absolute experiment

and comparative experiment. If a researcher wishes to determine the impact of achemical 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 worthmentioning here that such experimental units must be selected with great caution.

Q 5. What are the differences between observation and interviewing asmethods of data collection? Give two specific examples of situations where

either observation or interviewing would be more appropriate.

Ans.:-

Observation means viewing or seeing. Observation may be defined as a systematic

viewing of a specific phenomenon in its proper setting for the specific purpose of

gathering data for a particular study. Observation is classical method of scientific

study.

Observation as a method of data collection has certain characteristics.


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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 behavior 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 researchand such as observation schedule, social metric scale etc., and precision instruments,

if any.

Interviewing is one of the prominent methods of data collection. It may be defined as

a two way systematic conversation between an investigator and an informant,

initiated for obtaining information relevant to a specific study. It involves not only

conversation, but also learning from the respondents gesture, facial expressions andpauses, and his environment. Interviewing requires face to face contact or contact

over telephone and calls for interviewing skills. It is done by using a structured

schedule or an unstructured guide. Interviewing may be used either as a main method

or as a supplementary one in studies of persons. Interviewing is the only suitable

method for gathering information from illiterate or less educated respondents. It is

useful for collecting a wide range of data from factual demographic data to highly

personal and intimate information relating to a persons opinions, attitudes, values,

beliefs past experience and future intentions. When qualitative information is required

or probing is necessary to draw out fully, and then interviewing is required. Where the


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area covered for the survey is a compact, or when a sufficient number of qualified

interviewers are available, personal interview is feasible.

Interview is often superior to other data-gathering methods. People are usually more

willing to talk than to write. Once report is established, even confidential information

may be obtained. It permits probing into the context and reasons for answers to

questions. Interview can add flesh to statistical information. It enables the investigator

to grasp the behavioral context of the data furnished by the respondents.

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

(a) The behavior of human beings in purchasing goods and services.: life style,

customs, and manner, interpersonal relations, group dynamics, crowd behavior,

leadership styles, managerial style, other behaviors and actions;(b) The behavior 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.

Q 6.Strictly speaking, would case studies be considered as scientific

research? Why or why not?Ans.:-

A carefully controlled case study would generally be considered research, even careful

research, but not generally scientific research.

One of the reasons is that the subjects are not randomly assigned to conditions. Many

case studies are post hoc.

Many case studies are dependent on subject reporting of experiences.

It is very difficult to do statistical analysis on case studies, which usually do not have

many subjects involved.

Earlier (in the early to mid 20th century) research" with an n of one" (meaning one

subject in the test) was common. Then social science got into numbers and

percentages and such, and the case study fell away as not useful enough to learn

from.


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However, especially in the "soft" sciences (psychology, sociology, anthropology,

ethologic, etc.) that have to do with people rather than formulas or numbers or the

like, case studies can be helpful and important, especially if they are well-constructed,

or longitudinal, or are in depth on their topic.

RM Assignment 01/02

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