55640691 Assignment MB0050 Research Methodology Set 1

8/6/2019 55640691 Assignment MB0050 Research Methodology Set 1

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Sikkim Manipal University - MBA - MB0050 Research Methodology

Semester: 3 - Assignment Set: 1

Question 1: Why should a manger know about research when the job entails

managing people, products, events, environments, and the like?

Answer:

Research simply means a search for facts answers to questions and solutions to problems. Itis 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 withproblems.

The more focused you are about your resources, products, events and environments what youwant 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 theoperations of the department, and often have responsibilities towards the profitability of the

organization. Knowledge of research can be very helpful for a good manager.

Question 2:a. How do you evolve research design for exploratory research? Briefly analyze.b. Briefly explain Independent, dependent and extraneous variables in a research

design.

Answer:

a. Research design for exploratory research:

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

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

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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 thestudy. 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 ofrelationships between variables.

b. Independent and dependent and extraneous variables in a research design:

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, herecords 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 alogical 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 differentquantitative 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 adependent or endogenous variable, and the variables that cause the changes in the

dependent variable are known as the independent or explanatory or exogenous variables.For example, if demand depends upon price, then demand is a dependent variable, while

price is the independent variable.

And if, more variables determine demand, like income and prices of substitute commodity,then demand also depends upon them in addition to the own price. Then, demand is a

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

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, assumethat 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 is not directly related to the purpose ofthe study undertaken by the researcher, it would be known as an extraneous variable. Theinfluence 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 independentvariable and any other extraneous variable or variables.



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Question 3:

a. Differentiate between Census survey and Sample Surveyb. Analyse multi-stage and sequential sampling.

Answer:

a. Difference between Census survey and Sample Survey

Census Survey Sample Survey

A census measures absolutely everyone inthe whole country. This obviously means

that a census survey is a much biggerexercise in nature and procedures

A part of the population is known assample

Census survey also is a very timeconsuming exercise as information needs

to be collected from each and every

individual from the population.

On the other hand, sample survey iseasier as a representative sample is taken

from the population and the results

obtained are extrapolated to fit the entirepopulation.

There are times and requirements where

governments have to indulge in census

survey even if it is time consuming andvery expensive as it needs to formulate

policies and welfare programs for the

population. For example, when a

government has to count heads of thepopulation

Sample surveys cannot count the number

of people in the country but when

government is planning on a welfareprogram 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 populationthat is undergoing treatment for cancer.

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

b. Analyse multi-stage and sequential sampling:

Multi-stage sampling:

In multi-stage sampling method, sampling is carried out in two or more stages. The populationis 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, asample 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 continuesdown 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 widergeographical 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 theprocedure of estimating sampling error and cost advantage is complicated.



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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 resultsthen 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 intothe 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 notworkforce extensive.

This sampling method is hardly representative of the entire population. Its only hope ofapproaching representativeness is when the researcher chose to use a very large sample size

significant enough to represent a big fraction of the entire population. Due to theaforementioned disadvantages, results from this sampling technique cannot be used to create

conclusions and interpretations pertaining to the entire population.

Question 4: List down various measures of central tendency and explain thedifference between them?

Answer:

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 calculateddifferently, 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 theimportant 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 scoremarks 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 thecentral 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



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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, talluks, districts, state and the nation.

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. Therelevance, 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 thefacts 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 ofmeasurements, analysis, testing and inferences depends on the availability of relevant data

and their accuracy. Hence, the importance of data for any research studies

The sources of data may be classified into:a. Primary sourcesb. 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 brandawareness, brand preference, brand loyalty and other aspects of consumer behaviour from asample 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

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 cancollect them when he wants them and in the form he needs them. But the collection of primary



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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 theprimary sources. In such cases where the available data are in appropriate, inadequate or

obsolete, primary data have to be gathered. They include: socioeconomic surveys, socialanthropological 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. Thereare 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. Forexample, 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 anotherpurpose. The secondary sources consists of readily compendia and already compiled 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, Reportsof Government Departments, Annual reports of currency and finance published by the Reserve

Bank of India, Statistical statements relating to Co-operatives and Regional Banks, publishedby 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.

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 andorganizations, 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 certaincommon 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 andclassification. 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 secondary sources. 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, theircoverage 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 ofperformance of cooperative credit societies in a selected district/state.



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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 thenational averages; the performance indicators of a particular bank may be tested against the

corresponding indicators of the banking industry as a whole; and so on.

Finally, secondary data may be used as the sole source of information for a research project.Such studies as securities Market Behaviour, Financial Analysis of companies, Trade in credit

allocation in commercial banks, sociological studies on crimes, historical studies, and the like,depend primarily on secondary data. Year books, statistical reports of government

departments, report of public organizations of Bureau of Public Enterprises, Censes Reportsetc., 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 ofdocuments 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 toXeroxing 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 generalizationscan 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 needsnot 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. Thedefinitions 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 needto 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 arepublished 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 arelocated in the capital city, and they are not within the easy reach of researchers based

in far off places.

Evaluation of Secondary Data:



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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 availablesecondary 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 onhand 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 isto 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 theorganization 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 iscapable 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 andtheir 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 canreview 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 methodologysound? 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 problemunder 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 studybeen 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 themethodology and sampling design. Then it is not possible to determine the adequacy of the

secondary data for the researchers study.

Question 6:a. Explain the role of Graphs and Diagrams?

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

Answer:



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a) Role of Graphs and Diagrams:

In presenting the data of frequency distributions and statistical computations, it is often

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 canbe 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 ofburdensome details by portraying facts concisely, logically and simply. They, by emphasizing

new and significant relationship, are also useful in discovering new facts and in developinghypothesis.

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 certainpoints 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?

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 thehardness 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 enableeffective communication, insight is required into how diagrams are used and when they are

appropriate for use. The pervasive, everyday use 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 ourunderstanding 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.



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b) Types and General rules for graphical representation of data:

Graphical representation is done of the data available. This is very important step of statisticalanalysis. 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 ofheight, 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 easier with the helpof 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 graph2) Pie graph

3) Line graph

4) Scatter plot5) 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.

year 1931 1941 1951 1961 1971 1981

population of a

place

6000 7600 8900 12000 13500 18000

Solution: scale: Y axis 1 cm = 1000 years

2) Graphical Representation of Histogram:

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.



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

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

Age( inyears)

20-25

25-30

30-35

35-40

40-45

45-50

50-55

Number

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 zerofrequency so as to complete the polygon.

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

Construct a frequency polygon for the following data:

Monthly pocket expenses

of a student0-5 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:



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Here we have

Monthly pocket

expenses of astudent(in $)

class- marks Number of

students

0-5 2.5 105-10 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 thepoints and then joining them by a free-hand smooth curve.

This is also known as ogive.

Method:

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 ofstudents

4 6 10 8 4



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The cumulative frequency table is as follows:

Marks Number of

students

cumulative

frequency

0-4 4 4

4-8 6 4+6=108-12 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 frequencycurve:

5) Pie-chart or Pie-graph:

It is drawn by first drawing a circle of a suitable radius and then dividing the angle of 360degree at its centre in proportion to the figures given under various heads.

Solution:


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55640691 Assignment MB0050 Research Methodology Set 1