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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
Kumar.mithesh@gmail.com
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ASSIGNMENTS- MBA Sem-III MB0034 – Research Methodology
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
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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
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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
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Hypothesis is
Test results says True False
Accept
Reject
ASSIGNMENTS- MBA Sem-III MB0034 – Research Methodology
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.
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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:
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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
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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
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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
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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
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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.
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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.
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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
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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.
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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.
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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
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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.
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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.
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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:
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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).
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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,
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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.
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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
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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.
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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.
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