QM0034

ASSIGNMENTS- MBA Sem-III MB0034 – Research Methodology

ASSIGNMENTS- MBA

SEM-III

Subject code: MB0034

Subject Name: RESEARCH METHODOLOGY

Set 1& Set 2

Submitted By:

Mr. Mithesh Kumar

Reg. No. 520930668

948-000-9987

[email protected]

Mr. Mithesh Kumar Reg. No. 520930668 Page 1

mailto:[email protected]


SET 1

Q 1. Give examples of specific situations that would call for the following

types of research, explaining why – a) Exploratory research b)

Descriptive research c) Diagnostic research d) Evaluation research.

Exploratory Research

It is also known as formulative 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 researcher’s 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.”

Descriptive Research

It is a fact-finding investigation with adequate interpretation. It is the simplest

type of research. It is more specific than an exploratory research. It aims at identifying

the various characteristics of a community or institution or problem under study and

also aims at a classification of the range of elements comprising the subject matter of

study. It contributes to the development of a young science and useful in verifying

focal concepts through empirical observation. It can highlight important

methodological aspects of data collection and interpretation. The information obtained

may be useful for prediction about areas of social life outside the boundaries of the

research. They are valuable in providing facts needed for planning social action

program.

Diagnostic Research

It is similar to descriptive study but with a different focus. It is directed

towards discovering what is happening, why it is happening and what can be done

about. It aims at identifying the causes of a problem and the possible solutions for it.

It may also be concerned with discovering and testing whether certain variables are



associated. This type of research requires prior knowledge of the problem, its

thorough formulation, clear-cut definition of the given population, adequate methods

for collecting accurate information, precise measurement of variables, statistical

analysis and test of significance.

Evaluation Research

It is a type of applied research. It is made for assessing the effectiveness of

social or economic programmes implemented or for assessing the impact of

developmental projects on the development of the project area. It is thus directed to

assess or appraise the quality and quantity of an activity and its performance, and to

specify its attributes and conditions required for its success. It is concerned with

causal relationships and is more actively guided by hypothesis. It is concerned also

with change over time.

Q 2. In the context of hypothesis testing, briefly explain the difference between

a) Null and alternative hypothesis b) Type 1 and type 2 error c) Two

tailed and one tailed test .

Null and alternate hypothesis

In hypothesis testing, we must state the assumed or hypothesized value of the

population parameter before we begin sampling. The assumption we wish to test is

called the null hypothesis and is symbolized by “Ho”.

The term “Null hypothesis” arises from earlier agricultural and medical

applications of statistics. In order to test the effectiveness of a new fertilizer or drug,

the tested hypothesis (null hypothesis) was that it had no effect, that is, there was no

difference between treated and untreated samples. If we use a hypothesized value of a

population mean in a problem, we would represent it symbolically as µHo. This is read

– ‘The hypothesized value of the population mean.’

If our sample results fail to support the null hypothesis, we must conclude that

something else is true. Whenever we reject the hypothesis, the conclusion we do



accept is called the alternative hypothesis and is symbolized H1 (“H sub –one”).

Type 1 & Type 2 Error

There is no single standard or universal level of significance for testing

hypotheses. In some instances, a 5% level of significance is used. In the published

result of research papers, researchers often test hypothesis at the the 1 percent level of

significance. Hence, it is possible to test a hypothesis at any level of significance. But

remember that our choice of minimum standard for an acceptable probability, or the

significance level, is also the risk we assume of rejecting a null hypothesis when it is

true.

The higher the significance level we use for testing a hypothesis, the higher

the probability of rejecting a null hypothesis when it is true. 5% level of significance

implies we are ready to reject a true hypothesis in 5% of cases. If the significance

level is high then we would rarely accept the null hypothesis when it is not true but, at

the same time, often reject it when it is true.

When testing a hypothesis we come across four possible situations. The table

shows the four possible situations.

Possible situations when testing a hypothesis

Type II

error

Type I error

The combinations are:

1. If the hypothesis is true, and the test result accepts it, then we have made a

right decision.

2. If hypothesis is true, and the test result rejects it, then we have made a wrong


Hypothesis is

Test results says True False

Accept

Reject


decision (Type I error). It is also known as consumer’s Risk, denoted by ά

3. If hypothesis is false, and the test result accepts it, then we have made a wrong

decision (type II error). It is known as producer’s risk, denoted by β 1- P is

called power of the test.

4. Hypothesis is false, test result rejects it. – we have made a right decision.

Two tailed and one tailed test

A two tailed test of a hypothesis will reject the null hypothesis if the sample

mean is significantly higher than or lower than the hypothesized population mean.

Thus, in a two tailed test, there are two rejection regions.

A two- tailed test is appropriate when :

The null hypothesis is µ = µHo (where µHo is some specified

value)

The alternative hypothesis is µ ≠ µHo

However, there are situations in which a two tailed test is not appropriate and we must

use a one – tailed test.

In general, a left tailed (lower – tailed) test is used if the hypotheses are H o : µ

= µHo. in such a situation, it is sample evidence with the sample mean significantly

below the hypothesized population mean that leads us to reject the null hypothesis in

favour of the alternative hypothesis. Stated differently, the region is the lower tail (left

tail) of the distribution of the sample mean, and that is why we call this a lower tailed

test.

A left tailed test is one of two kinds of one –tailed tests. As you have probably

guessed by now, the other kind of one tailed test is a right tailed test (or an upper

tailed test). An upper tailed test is use when the hypotheses are Ho : µ > µHo. only

values of the sample mean that are significantly above the hypothesized population

mean will cause us to reject the null hypothesis in favour of the alternative hypothesis.

This is called an upper tailed test because the rejection region is in the upper tail of

the distribution of the sample mean.



Q 3. Explain the difference between a causal relationship and correlation,

with an example of each. What are the possible reasons for a correlation

between two variables?

Economic and business variables are related. For instance, demand and supply

of a commodity is related to its price. Demand for a commodity increases as a price

falls. Demand for a commodity decreases as its price rises. We say demand and price

are inversely related or negatively correlated. But sellers supply more of a commodity

when its price rises. Supply of a commodity decreases when its price falls. We say

supply and price are directly related or positively co-related. Thus correlation

indicates the relationship between two such variables in which changes in the value of

one variable is accompanies with a change in the value of other variable.

According to L.R. Connor, “If two or more quantities vary in sympathy so that

movements in the one tend to be accompanied by corresponding movements in the

other(s) they are said to be correlated.

W.I. king defined “Correlation means that between two series or groups of

data, there exists some causal connection”.

The definitions make it clear that the term correlation refers to the study of

relationship between two or more variables. Correlation is a statistical device, which

studies the relationship between two variables. If two variables are said to be

correlated, change in the value of one variable result in a corresponding change in the

value of other variable. Heights and weights of a group of people, age of husbands

and wives etc., are examples of bi-variant data that change together.

The term correlation is used in the sense of mutual dependence of two or more

variable, it is not always necessary that they have cause and effect relation. Even a

high degree of correlation between two variables does not necessarily indicate a cause

and effect relationship between them. Correlation between two variables can be due to

following reasons:



a) Cause & effect relationship : Heat & temperature are cause and effect variable.

Heat is the cause of temperature. Higher the heat, higher will be the

temperature.

b) Both the correlated variables are being affected by a third variable. For

instance, price of rice and price of sugar are affected by rainfall. Here there

may not be any cause and effect relation between price of rice and price of

sugar.

c) Related variable may be mutually affecting each other so that none of them is

either a cause or an effect. Demand may be result of price. There are cases

when price rise due to increased demand.

d) The correlation may be due to chance. For instance, a small sample may show

correlation between wages and productivity. That is, higher wage leading to

lower productivity. In real life it need not be true. Such correlation is due to

chance.

e) There might be a situation of nonsense or spurious correlation between two

variables. For instance, relationship between number of divorces and

television exports may be correlated. There cannot be any relationship

between divorce and exports of television.

Q 4. Briefly explain any two factors that affect the choice of a sampling t

echnique. What are the characteristics of a good sample.

A part of population is known as sample. The method consisting of the

selecting for study, a portion of the ‘universe’ with a view to draw conclusions about

the universe or population is known as sampling. A statistical sample ideally purports

to be a miniature model or replica of the collectivity or the population constituted of

all the items that the study should principally encompass, that is the items which

potentially hold promise of affording information relevant to the purpose of a given

research.

Following are the factors affect the choice of a sampling technique :

1. Purpose of the survey : The choice of a particular type of probability

sampling depends on the geographical area of the survey and the size and the



nature of the population under study.

2. Measurability : the application of statistical inference theory requires

computation of the sampling error from the sample itself. Probability samples

only allow such computation. Hence, where the research objective requires

statistical inference, the sample should be drawn by applying simple random

sampling method or stratified random sampling method, depending on

whether the population is homogenous or heterogeneous.

3. Degree of Precision : the desired level of precision as one of the criteria of

sampling method selection.

4. Information about population : Exploratory study with non-probability

sampling may be made to gain a better idea of population. After gaining

sufficient knowledge about the population through the exploratory study,

appropriate probability sampling design may be adopted.

Characteristics of a Good Sample

The characteristics of a good sample are described below :

Representativeness : a sample must be representative of the population.

Probability sampling technique yield representative sample.

Accuracy : accuracy is defined as the degree to which bias is absent from

the sample. An accurate sample is the one which exactly represents the

population.

Precision : the sample must yield precise estimate. Precision is measured

by standard error.

Size : a good sample must be adequate in size in order to be reliable.

Q 5. Select any topic for research and explain how you will use both secondary

and primary sources to gather the required information.

The data serves as a 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. 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 analysed with statistical techniques. Inferences on the

results of statistical analysis and tests of significance provide the answer to research

questions.

The sources of data may be classified into (a) primary sources and (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 preference. Primary data are first hand

information collected through various 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.

Disadvantages 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.

Primary data are directly collected by the researcher from their original

sources. The researcher can collect the required data 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. In such cases

where the available data are inappropriate, inadequate or obsolete, primary data have

to be gathered.

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. The important methods are :

(a) Observation, (b) interviewing, (c) mail survey, (d) experimentation, (e)

simulation and (f) projective technique.

Secondary Sources of Data

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

another purpose. The secondary sources consists of readily compendia and already

compiled statistical statements and reports whose data may be used by researchers for

their studies. 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.

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.

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.

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

of research may be tested.

Finally, secondary data may be used as the sole source of information for a

research project.

Q 6. Case Study: You are engaged to carry out a market survey on behalf of a

leading Newspaper that is keen to increase its circulation in Bangalore

City, in order to ascertain reader habits and interests. Develop a title for



the study, define the research problem and the objectives or questions to

be answered by the study.

Generally, there is a significant relationship between the race or ethnic group

and the language medium of the newspapers. Generally, Kannada newspapers are

mostly read by the kannadigas respondents, Tamil newspapers by the tamilians etc.

However, there is no significant relationship in the readership of English newspapers

whereby they are read by all the ethnic groups.

Title:

Reader’s habits and interests in Bangalore

Research Problem:

To ascertain the reader habits and interests and to increase news paper cir

culation in Bangalore City.

Objectives or questions to be answered:

1. Have you read an entire book in the last 12 months?

a. Yes.

b. No.

2. How much time do you spend reading web pages each day?

a. I don’t read web pages.

b. Less than two hours.

c. Two to four hours.

d. Five or more hours.

3. Where do you read? Check all that apply.

a. In school.

b. On the bus.

c. In a car or truck.

d. In bed.

e. At the computer.

f. In the bathroom.

g. In the kitchen or family room.



h. At the library.

4. Have you ever pretended that you read a book when you hadn’t?

a. Yes.

b. No.

5. Why do you usually read a book?

a. Because I think I should.

b. Because it was assigned to me.

c. Because I am interested in the topic or author.

d. I don’t read books.

6. Have you ever pretended that you read a web page when you hadn’t?

a. Yes.

b. No.

7. What is the last book that you read? If you haven’t read a book, write “Not

Applicable.”

8. Is being able to read is important?

a. Yes.

b. No.



SET 2

Q1. Why should a manger know about research when the job entails

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

The manager, while managing people, products, events, and environments will

invariably face problems, big and small, and will have to seek ways to find long

lasting effective solutions.

This can be achieved only through knowledge of research even if consultants

are engaged to solve problems.

The primary purpose for applied research (as opposed to basic research) is

discovering, interpreting, and the development of methods and systems for the

advancement of human knowledge on a wide variety of scientific matters of our world

and the universe. Research can use the scientific method, but need not do so. The goal

of the research process is to produce new knowledge, which takes three main forms

(although, as previously discussed, the boundaries between them may be fuzzy):

Exploratory research, which structures and identifies new problems Constructive

research, which develops solutions to a problem. Empirical research, which tests the

feasibility of a solution using empirical evidence.

Research can also fall into two distinct types:

1) Primary research

2) Secondary research

In social sciences and later in other disciplines, the following two research

methods can be applied, depending on the properties of the subject matter and on the

objective of the research:

Qualitative research

Quantitative research


http://www.smumba.tk/2011/04/mb0050-q1-why-should-manger-know-about.html

http://www.smumba.tk/2011/04/mb0050-q1-why-should-manger-know-about.html


Research is often conducted using the hourglass model Structure of Research.

The hourglass model starts with a broad spectrum for research, focusing in on the

required information through the methodology of the project (like the neck of the

hourglass), then expands the research in the form of discussion and results.

Research and development is nowadays of great importance in business as the level of

competition, production processes and methods are rapidly increasing. It is of special

importance in the field of marketing where companies keep an eagle eye on

competitors and customers in order to keep pace with modern trends and analyze the

needs, demands and desires of their customers. Unfortunately, research and

development are very difficult to manage, since the defining feature of research is that

the researchers do not know in advance exactly how to accomplish the desired result.

As a result, higher R&D spending does not guarantee "more creativity, higher profit

or a greater market share.

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

analyze.

The central purpose is to formulate hypotheses regarding potential problems

and opportunities present in the decision situation. The hypotheses can be tested at a

later phase with a conclusive research design (Leinhardt and Leinhardt, 1980).

Exploratory research design applies when the research objectives include the

following:

a. Identifying problems (threats or opportunities).

b. Developing a more precise formulation of a vaguely identified problem

(threat or opportunity).

c. Gaining perspective regarding the breath of variables operating in a

situation.

d. Establishing priorities regarding the potential significance of various

problems (threats or opportunities).

e. Gaining management and researcher perspective concerning the

character of the problem situation.

f. Identifying and formulating alternative courses of action; and.



g. Gathering information on the problems associated with doing

conclusive research.

h. Identification of problems (threats or opportunities) can be assisted

through the following:

i) Searching secondary sources

ii) Interviewing knowledgeable persons

iii) Compiling case histories.

Q 2 b. Briefly explain Independent, dependent and extraneous variables in a

research design.

Independent Variable:

A variable that you believe might influence your outcome measure. This might

be a variable that you control, like a treatment, or a variable not under your

control, like an exposure. It also might represent a demographic factor like age or

gender. Contrast this with the definition of the dependent variable. An

independent variable is a hypothesized cause or influence on a dependent

variable. One way to distinguish these variables is to ask yourself what you are

want to learn from this research. The dependent variable is a variable you are

trying to predict. Any variable that you are using to make those predictions is an

independent variable. A recently published research study examined the

relationship of dietary fat consumption and the development of ischemic

stroke in a cohort of 832 men who were free of cardiovascular disease at

baseline (1966-1969) and who were followed for a twenty year period. In this

study, the independent variables were:

Percentage of total fat in the diet,

Percentage of saturated fat, and

The percentage of monounsaturated fat.

Dependent variable:

In a research study, the variable that you believe might be influenced or

modified by some treatment or exposure. It may also represent the variable you

are trying to predict. Contrast this with the definition of an independent variable.



Sometimes the dependent variable is called the outcome variable. This definition

depends on the context of the study. In a study of prenatal care, the birthweight is

an outcome or dependent variable, but in neonatology, it is more likely to be an

independent variable. A recently published research study examined the

relationship of dietary fat consumption and the development of ischemic

stroke in a cohort of 832 men who were free of cardiovascular disease at

baseline (1966-1969) and who were followed for a twenty year period. In this

study, the dependent variable was:

Incidence of ischemic stroke.

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 eg, if a researcher wants to test the hypothesis that there

is relationship between children’s school performance and their self-concepts, in

which case the latter is an independent variable and the former is

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 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 ‘ Sample Survey’.

Difference between Census and Sampling

Practically every country in the world conducts censuses and sampling surveys

on a regular basis in order to get valuable data from and about their populations.

This data is used by the federal and state governments in making numerous

decisions with regard to various health care, housing, and educational issues,

among others. While both these two data-gathering methods essentially serve the



same purpose, they have a number of differences with regard to approach and

methodology, as well as scope. These two methods may also differ in terms of

the variance in the data gathered, as you will see later.

Scope

A census involves the gathering of information from every person in a

certain group. This may include information on age, sex and language among

others. A sample survey on the other hand commonly involves gathering data

from only a certain section of a particular group.

Sampling Variance

The main advantage of a census is a virtually zero sampling variance,

mainly because the data used is drawn from the whole population. In addition,

more precise detail can generally be gathered about smaller groups of the

population.

As for sampling, there is a possibility of sampling variance, since the

data used is drawn from only a small section of the population. This makes

sampling a much less accurate form of data collection than a census. In

addition, the sample may be too small to provide an accurate picture of the

population.

Cost And Timetable

A census can be quite expensive to conduct, particularly for large

populations. In most cases, they are also a lot more time-consuming than

sample surveys. Adding considerably to the timetable is the necessity of

gathering data from every single member of the population. The huge scope of

a census also makes it harder to maintain control of the quality of the data. For

instance, anyone who does not complete a census form will be visited by a

government representative who’s only job to is to gather census data.

A sample survey for its part costs quite a bit less than a census, since data is

gathered from a much smaller group of people. In addition, sample surveys

generally take a much shorter time to conduct, again given the smaller scope.

This also means reduced requirements for respondents, which in turn leads to

better data monitoring and quality control.



Summary

Census

Gathers information from every individual in a certain group

Since data from the entire population is used, there is no sampling variance

Provides detailed information about smaller groups

Can be quite costly, particularly for large populations, due to census tally

workers as well as hiring temporary census home visitors

Includes an uncomfortable visit from a government worker if the census is

not filled out on time

Sampling

Gathers information from only a section of the population

May have a significant degree of sample variance, since the data is derived

from only a small section of a population

May not provide enough information about smaller groups or smaller

geographical sections of a place

Costs much less than a census, since data is gathered from only a small section

of a group

Q 3. b. Analyze multi-stage and sequential sampling.

Multistage sampling

Multistage sampling is a complex form of cluster sampling. Using all the

sample elements in all the selected clusters may be prohibitively expensive or not

necessary. Under these circumstances, multistage cluster sampling becomes

useful. Instead of using all the elements contained in the selected clusters, the

researcher randomly selects elements from each cluster. Constructing the clusters

is the first stage. Deciding what elements within the cluster to use is the second

stage. The technique is used frequently when a complete list of all members of

the population does not exist and is inappropriate.


http://www.answers.com/topic/cluster-sampling


In some cases, several levels of cluster selection may be applied before the

final sample elements are reached. For example, household surveys conducted by

the Australian Bureau of Statistics begin by dividing metropolitan regions into

'collection districts', and selecting some of these collection districts (first stage).

The selected collection districts are then divided into blocks, and blocks are

chosen from within each selected collection district (second stage). Next,

dwellings are listed within each selected block, and some of these dwellings are

selected (third stage). This method means that it is not necessary to create a list of

every dwelling in the region, only for selected blocks. In remote areas, an

additional stage of clustering is used, in order to reduce travel requirements.[1]

Although cluster sampling and stratified sampling bear some superficial

similarities, they are substantially different. In stratified sampling, a random

sample is drawn from all the strata, where in cluster sampling only the selected

clusters are studied, either in single stage or multi stage.

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, analyzes 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.

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

difference between them?

Arithmetic Mean

The arithmetic mean is the most common measure of central tendency. It

simply the sum of the numbers divided by the number of numbers. The symbol

m is used for the mean of a population. The symbol M is used for the mean of a

sample. The formula for m is shown below:


http://www.experiment-resources.com/what-is-sampling.html

http://www.answers.com/topic/stratified-sampling-1

http://www.answers.com/topic/stratified-sampling-1

http://www.abs.gov.au/AUSSTATS/[email protected]/Latestproducts/25EBCE8C88824592CA25710E007321C3?opendocument

http://www.answers.com/topic/australian-bureau-of-statistics


Where ΣX is the sum of all the numbers in the numbers in the sample and N

is the number of numbers in the sample.

Although the arithmetic mean is not the only "mean" (there is also a geometic

mean), it is by far the most commonly used. Therefore, if the term "mean" is used

without specifying whether it is the arithmetic mean, the geometic mean, or some

other mean, it is assumed to refer to the arithmetic mean.

Median

The median is also a frequently used measure of central tendency. The

median is the midpoint of a distribution: the same numbers of scores are above

the median as below it. For the data in the table, Number of touchdown passes,

there are 31 scores. The 16th highest score (which equals 20) is the median

because there are 15 scores below the 16th score and 15 scores above the 16th

score. The median can also be thought of as the 50th percentile.

Computation of the Median: When there is an odd number of numbers, the

median is simply the middle number. For example, the median of 2, 4, and 7 is

4. When there is an even number of numbers, the median is the mean of the two

middle numbers. Thus, the median of the numbers

Mode

The mode is the most frequently occuring value.For the data in the table,

Number of touchdown passes, the mode is 18 since more teams (4) had 18

touchdown passes than any other number of touchdown passes. With continuous

data such as response time measured to many decimals, the frequency of each

value is one since no two scores will be exactly the same. Therefore the mode of

continuous data is normally computed from a grouped frequency distribution.

The Grouped frequency distribution table shows a grouped frequency

distribution for the target response time data. Since the interval with the highest

frequency is 600-700, the mode is the middle of that interval (650).



Q.5. Explain secondary and primary sources of data.

Primary Sources of Data

Primary sources are original sources from which the researcher directly

collects data that has not been previously collected, e.g., collection of data

directly by the researcher on brand awareness, brand preference, and brand

loyalty and other aspects of consumer behavior, from a sample of consumers by

interviewing them. Primary data is first hand information collected through

various methods such as surveys, experiments and observation, for the purposes

of the project immediately at hand.

The advantages of primary data are –

It is unique to a particular research study

It is recent information, unlike published information that is already

available

The disadvantages are –

It is expensive to collect, compared to gathering information from

available sources

Data collection is a time consuming process

It requires trained interviewers and investigators

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 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, (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 has been collected and compiled for

another purpose. Secondary sources may be internal sources, such as annual

reports, financial statements, sales reports, inventory records, minutes of

meetings and other information that is available within the firm, in the form of a

marketing information system. They may also be external sources, such as

government agencies (e.g. census reports, reports of government departments),

published sources (annual reports of currency and finance published by the

Reserve Bank of India, publications of international organizations such as the

UN, World Bank and International Monetary Fund, trade and financial journals,

etc.), trade associations (e.g. Chambers of Commerce) and commercial services

(outside suppliers of information).

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. Even the 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 researcher’s 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 need not wait the time when additional primary data can be

collected.

Disadvantages of Secondary Data

The use of a secondary data has its own limitations.

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 tow 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 the capital city,

and they are not within the easy reach of researchers based in far off places.

Q 6. What are the differences between observation and interviewing as

methods of data collection?

Observation vs. interviewing as Methods of Data Collection:

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

success or failure of the project is dependent upon the accuracy of the data. Use of



wrong methods of data collection or any inaccuracy in collecting data can have

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

There are many techniques of data collection along a continuum and

observation and interviewing are two of the popular methods on this continuum that

has quantitative methods at one end while qualitative methods at the other end.

Though there are many similarities in these two methods and they serve the

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

Observation:

Observation, as the name implies refers to situations where participants are

observed from a safe distance and their activities are recorded minutely. It is a

time consuming method of data collection as you may not get the desired

conditions that are required for your research and you may have to wait till

participants are in the situation you want them to be in. Classic examples of

observation are wild life researchers who wait for the animals of birds to be in a

natural habitat and behave in situations that they want to focus upon. As a

method of data collection, observation has limitations but produces accurate

results as participants are unaware of being closely inspected and behave

naturally.

Interviewing:

Interviewing is another great technique of data collection and it involves

asking questions to get direct answers. These interviews could be either one to

one, in the form of questionnaires, or the more recent form of asking opinions

through internet. However, there are limitations of interviewing as participants

may not come up with true or honest answers depending upon privacy level of

the questions. Though they try to be honest, there is an element of lie in answers

that can distort results of the project.

Though both observation and interviewing are great techniques of data

collection, they have their own strengths and weaknesses. It is important to keep

in mind which one of the two will produce desired results before finalizing.



Observation vs. Interviewing:

Observation Interviewing

Observation requires precise analysis

by the researcher and often produces

most accurate results although it is very

time consuming.

Interviewing is easier but suffers from

the fact that participants may not come

up with honest replies.


QM0034

Documents

twenty year

dietary fat

mb0034 research

test result

test result

grouped frequency

upper tailed

mithesh kumar