rm assignment 01/02
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Assignment Set- 1
Q 1. Why should a manger know about research when the job
entails managing people, products, events, environments, and
the like?
Ans.:-Research simply means a search for facts answers to questions and solutions to
problems. It is a purposive investigation. It is an organized inquiry. It seeks to find
explanations to unexplained phenomenon to clarify the doubtful facts and to correct
the misconceived facts. Research is the organized and systematic inquiry or
investigation which provides information for solving a problem or finding answers to a
complex issue.
Research in business:Often, organization members want to know everything about their products, services,
programs, etc. Your research plans depend on what information you need to collect in
order to make major decisions about a product, service, program, etc. Research
provides the needed information that guides managers to make informed decisions to
successfully deal with problems.
The more focused you are about your resources, products, events and environmentswhat you want to gain by your research, the more effective and efficient you can be in
your research, the shorter the time it will take you and ultimately the less it will cost
you.
Managers role in research programs of a company:
Managing people is only a fraction of a manager's responsibility - they have to
manage the operations of the department, and often have responsibilities towards theprofitability of the organization. Knowledge of research can be very helpful for a good
manager.
Q 2. a. How do you evolve research design for exploratory
research? Briefly analyze.
Ans.:-
Research simply means a search for facts answers to questions and solutions to
problems. It is a purposive investigation. It is an organized inquiry. It seeks to find
explanations to unexplained phenomenon to clarify the doubtful facts and to correct
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the misconceived facts. Although any typology of research is inevitably arbitrary,
Research may be classified crudely according to its major intent or the methods.
It is also known as formulating research. It is preliminary study of an unfamiliar
problem about which the researcher has little or no knowledge. It is ill-structured and
much less focused on pre-determined objectives. It usually takes the form of a pilot
study. The purpose of this research may be to generate new ideas, or to increase the
researchers familiarity with the problem or to make a precise formulation of the
problem or to gather information for clarifying concepts or to determine whether it is
feasible to attempt the study. Katz conceptualizes two levels of exploratory studies.
At the first level is the discovery of the significant variable in the situations; at the
second, the discovery of relationships between variables.
b. Briefly explain Independent dependent and extraneous
variables in a research design.
Ans.:-
The research designer understandably cannot hold all his decisions in his head. Even
if he could, he would have difficulty in understanding how these are inter-related.
Therefore, he records his decisions on paper or record disc by using relevant symbols
or concepts. Such a symbolic construction may be called the research design or
model. A research design is a logical and systematic plan prepared for directing a
research study.
Dependent and Independent variables:
A magnitude that varies is known as a variable. The concept may assume different
quantitative values, like height, weight, income, etc. Qualitative variables are not
quantifiable in the strictest sense of objectivity. However, the qualitative phenomena
may also be quantified in terms of the presence or absence of the attribute
considered. Phenomena that assume different values quantitatively even in decimal
points are known as continuous variables. But, all variables need not be continuous.
Values that can be expressed only in integer values are called non-continuous
variables. In statistical term, they are also known as discrete variable. For example,
age is a continuous variable; whereas the number of children is a non-continuous
variable. When changes in one variable depends upon the changes in one or more
other variables, it is known as a dependent or endogenous variable, and the variables
that cause the changes in the dependent variable are known as the independent or
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explanatory or exogenous variables. For example, if demand depends upon price,
then demand is a dependent variable, while price is the independent variable.
And if, more variables determine demand, like income and prices of substitute
commodity, then demand also depends upon them in addition to the own price. Then,
demand is a dependent variable which is determined by the independent variables
like own price, income and price of substitute.
Extraneous variable:
The independent variables which are not directly related to the purpose of the study
but affect the dependent variable are known as extraneous variables. For instance,
assume that a researcher wants to test the hypothesis that there is relationship
between childrens school performance and their self-concepts, in which case the
latter is an independent variable and the former, the dependent variable. In this
context, intelligence may also influence the school performance. However, since it isnot directly related to the purpose of the study undertaken by the researcher, it would
be known as an extraneous variable. The influence caused by the extraneous variable
on the dependent variable is technically called as an experimental error. Therefore, a
research study should always be framed in such a manner that the dependent
variable completely influences the change in the independent variable and any other
extraneous variable or variables.
Q 3. A. Differentiate between Census survey and SampleSurvey.
Ans.:-
Census Survey Sample SurveyA census measures absolutely everyone
in the whole country. This obviously
means that a census survey is a much
bigger exercise in nature and
procedures
A part of the population is known as
sample
Census survey also is a very time
consuming exercise as information
needs to be collected from each and
every individual from the population.
On the other hand, sample survey is
easier as a representative sample is
taken from the population and the
results obtained are extrapolated to fit
the entire population.
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There are times and requirements
where governments have to indulge in
census survey even if it is time
consuming and very expensive as it
needs to formulate policies and welfare
programs for the population. For
example, when a government has to
count heads of the population
Sample surveys cannot count the
number of people in the country but
when government is planning on a
welfare program for cancer patients, it
can conduct a sample survey of some of
the cancer patients and then
extrapolate the results on the section of
the population that is undergoing
treatment for cancer.Census survey is more accurate. there is margin for error in sample
survey
b. Analyze multi-stage and sequential sampling.
Ans.:-
Multi-stage sampling:
In multi-stage sampling method, sampling is carried out in two or more stages. The
population is regarded as being composed of a number of second stage units and so
forth. That is, at each stage, a sampling unit is a cluster of the sampling units of the
subsequent stage. First, a sample of the first stage sampling units is drawn, then from
each of the selected first stage sampling unit, a sample of the second stage sampling
units is drawn. The procedure continues down to the final sampling units or population
elements. Appropriate random sampling method is adopted at each stage. It is
appropriate where the population is scattered over a wider geographical area and no
frame or list is available for sampling. It is also useful when a survey has to be made
within a limited time and cost budget. The major disadvantage is that the procedure
of estimating sampling error and cost advantage is complicated.
Sequential sampling:
Sequential sampling is a non-probability sampling technique wherein the researcher
picks a single or a group of subjects in a given time interval, conducts his study,
analyses the results then picks another group of subjects if needed and so on. This
sampling technique gives the researcher limitless chances of fine tuning his research
methods and gaining a vital insight into the study that he is currently pursuing. There
is very little effort in the part of the researcher when performing this sampling
technique. It is not expensive, not time consuming and not workforce extensive.
This sampling method is hardly representative of the entire population. Its only hope
of approaching representative ness is when the researcher chose to use a very large
sample size significant enough to represent a big fraction of the entire population.
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Due to the aforementioned disadvantages, results from this sampling technique
cannot be used to create conclusions and interpretations pertaining to the entire
population.
Q 4. List down various measures of central tendency and explain
the difference between them?
Ans.:-
Measures of Central Tendency:
The term central tendency refers to the "middle" value or perhaps a typical value of
the data, and is measured using the mean, median, or mode. Each of these
measures is calculated differently, and the one that is best to use depends upon the
situation.
Analysis of data involves understanding of the characteristics of the data. The
following are the important characteristics of a statistical data:
Central tendency
Dispersion
Skew ness
Kurtosis
In a data distribution, the individual items may have a tendency to come to a central
position or an average value. For instance, in a mark distribution, the individual
students may score marks between zero and hundred. In this distribution, many
students may score marks, which are near to the average marks, i.e. 50. Such a
tendency of the data to concentrate to the central position of the distribution is called
central tendency. Central tendency of the data is measured by statistical averages.
Averages are classified into two groups.
1. Mathematical averages
2. Positional averages Statistical Averages
Mathematical averages Positional averages
Arithmetic mean Median
Geometric mean Mode
Harmonic mean
Arithmetic mean, geometric mean and harmonic mean are mathematical averages.
Median and mode are positional averages. These statistical measures try to
understand how individual values in a distribution concentrate to a central value like
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average. If the values of distribution approximately come near to the average value,
we conclude that the distribution has central tendency.
Difference between Mean and Median:
Mean (Mathematical averages) Median (Positional averages)
When the sample size is large and doesnot include outliers, the mean score
usually provides a better measure of
central tendency.
The median may be a better indicator ofthe most typical value if a set of scores
has an outlier. An outlier is an extreme
value that differs greatly from other
values.The mean is the most commonly-used
measure of central tendency. When we
talk about an "average", we usually are
referring to the mean
The median often is used when there are
a few extreme values that could greatly
influence the mean and distort what
might be considered typical. The mean is simply the sum of the
values divided by the total number of
items in the set
The median is determined by sorting the
data set from lowest to highest values
and taking the data point in the middle
of the sequence
Q.5. Select any topic for research and explain how you will use bothsecondary and primary sources to gather the required information.
Ans.:-
For performing research on the literacy levels among families, the primary and
secondary sources of data can be used very effectively. More specifically the primary
sources of data collection is suggested in this regard. Because personal data or data
related to human beings consist of:
1. Demographic and socio-economic characteristics of individuals: Age, sex, race,social class, religion, marital status, education, occupation income, family size,
location of the household life style etc.
2. Behavioral variables: Attitudes, opinions, awareness, knowledge, practice,
intentions, etc.
3. Organizational data consist of data relating to an organizations origin,
ownership, objectives, resources, functions, performance and growth.
4.
Territorial data are related to geo-physical characteristics, resource endowment,population, occupational pattern infrastructure degree of development, etc. of
spatial divisions like villages, cities, talukas, districts, state and the nation.
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The data serve as the bases or raw materials for analysis. Without an analysis of
factual data, no specific inferences can be drawn on the questions under study.
Inferences based on imagination or guess work cannot provide correct answers to
research questions. The relevance, adequacy and reliability of data determine the
quality of the findings of a study.
Data form the basis for testing the hypothesis formulated in a study. Data also
provide the facts and figures required for constructing measurement scales and
tables, which are analyzed with statistical techniques. Inferences on the results of
statistical analysis and tests of significance provide the answers to research
questions. Thus, the scientific process of measurements, analysis, testing and
inferences depends on the availability of relevant data and their accuracy. Hence, theimportance of data for any research studies
The sources of data may be classified into:
a. Primary sources
b. Secondary sources.
Primary Sources of Data:
Primary sources are original sources from which the researcher directly collects data
that have not been previously collected e.g.., collection of data directly by the
researcher on brand awareness, brand preference, brand loyalty and other aspects of
consumer behaviour from as ample of consumers by interviewing them,. Primary data
are first-hand information collected through various methods such as observation,
interviewing, mailing etc.
Advantage of Primary Data:
It is original source of data
It is possible to capture the changes occurring in the course of time.
It flexible to the advantage of researcher.
Extensive research study is based of primary data
Disadvantage of Primary Data:
Primary data is expensive to obtain
It is time consuming
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It requires extensive research personnel who are skilled.
It is difficult to administer
Methods of Collecting Primary Data:
Primary data are directly collected by the researcher from their original sources. In
this case, the researcher can collect the required date precisely according to his
research needs, he can collect them when he wants them and in the form he needs
them. But the collection of primary data is costly and time consuming. Yet, for several
types of social science research required data are not available from secondary
sources and they have to be directly gathered from the primary sources.
In such cases where the available data are in appropriate, inadequate or obsolete,
primary data have to be gathered. They include: socioeconomic surveys, social
anthropological studies of rural communities and tribal communities, sociological
studies of social problems and social institutions. Marketing research, leadership
studies, opinion polls, attitudinal surveys, readership, radio listening and T.V. viewing
surveys, knowledge-awareness practice (KAP) studies, farm managements studies,
business management studies etc. There are various methods of data collection. A
Method is different from a Tool while a method refers to the way or mode of
gathering data, a tool is an instruments used for the method. For example, a schedule
is used for interviewing. The important methods are (a) observation, (b) interviewing,
(c)mail survey,(d)experimentation,(e) simulation and (f) projective technique. Each of
these methods is discussed in detail in the subsequent sections in the later chapters.
Secondary Sources of Data:
These are sources containing data which have been collected and compiled for
another purpose. The secondary sources consists of readily compendia and alreadycompiled statistical statements and reports whose data may be used by researchers
for their studies e.g., census reports , annual reports and financial statements of
companies, Statistical statement, Reports of Government Departments, Annual
reports of currency and finance published by the Reserve Bank of India, Statistical
statements relating to Co-operatives and Regional Banks, published by the NABARD,
Reports of the National sample survey Organization, Reports of trade associations,
publications of international organizations such as UNO, IMF, World Bank, ILO, WHO,etc., Trade and Financial journals newspapers etc.
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Secondary sources consist of not only published records and reports, but also
unpublished records. The latter category includes various records and registers
maintained by the firms and organizations, e.g., accounting and financial records,
personnel records, register of members, minutes of meetings, inventory records etc.
Features of Secondary Sources:
Though secondary sources are diverse and consist of all sorts of materials, they have
certain common characteristics. First, they are readymade and readily available, and
do not require the trouble of constructing tools and administering them
Second, they consist of data which a researcher has no original control over collection
and classification. Both the form and the content of secondary sources are shaped by
others. Clearly, this is a feature which can limit the research value of secondarysources. Finally, secondary sources are not limited in time and space. That is, the
researcher using them need not have been present when and where they were
gathered
Use of Secondary Data:The second data may be used in three ways by a researcher. First, some specific
information from secondary sources may be used for reference purpose. For example,
the general statistical information in the number of co-operative credit societies in the
country, their coverage of villages, their capital structure, volume of business etc.,
may be taken from published reports and quoted as background information in a
study on the evaluation of performance of cooperative credit societies in a selected
district/state.
Second, secondary data may be used as bench marks against which the findings of
research maybe tested, e.g., the findings of a local or regional survey may be
compared with the national averages; the performance indicators of a particular bank
may be tested against the corresponding indicators of the banking industry as a
whole; and so on.
Finally, secondary data may be used as the sole source of information for a research
project. Such studies as securities Market Behaviour, Financial Analysis of companies,
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Trade in credit allocation in commercial banks, sociological studies on crimes,
historical studies, and the like, depend primarily on secondary data. Year books,
statistical reports of government departments, report of public organizations of
Bureau of Public Enterprises, Censes Reports etc., and serve as major data sources for
such research studies
Advantages of Secondary Data:
Secondary sources have some advantages:
Secondary data, if available can be secured quickly and cheaply. Once their source of
documents and reports are located, collection of data is just matter of desk work.
Event he tediousness of copying the data from the source can now be avoided, thanks
to Xeroxing facilities.
Wider geographical area and longer reference period may be covered without much
cost. Thus, the use of secondary data extends the researchers space and time reach.
The use of secondary data broadens the data base from which scientific
generalizations can be made.
Environmental and cultural settings are required for the study.
The use of secondary data enables a researcher to verify the findings bases on
primary data. It readily meets the need for additional empirical support. The
researcher needs not wait the time when additional primary data can be collected.
Disadvantages of Secondary Data:
The use of a secondary data has its own limitations.
The most important limitation is the available data may not meet our
specific needs. The definitions adopted by those who collected those data
may be different; units of measure may not match; and time periods may
also be different.
The available data may not be as accurate as desired. To assess their
accuracy we need to know how the data were collected.
The secondary data are not up-to-date and become obsolete when they
appear in print, because of time lag in producing them. For example,
population census data are published two or three years later after
compilation and no new figures will be available for another ten years.
Finally, information about the whereabouts of sources may not be
available to all social scientists. Even if the location of the source is
known, the accessibility depends primarily on proximity. For example,
most of the unpublished official records and compilations are located in
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the capital city, and they are not within the easy reach of researchers
based in far off places.
Evaluation of Secondary Data:
When a researcher wants to use secondary data for his research, he should evaluate
them before deciding to use them.
1) Data Pertinence:
The first consideration in evaluation is to examine the pertinence of the available
secondary data to the research problem under study. The following questions should
be considered.
What are the definitions and classifications employed? Are they consistent?
What are the measurements of variables used? What is the degree to which they
conform to the requirements of our research?
On the basis of above consideration, the pertinence of the secondary data to the
research on hand should be determined, as a researcher who is imaginative and
flexible may be able to redefine his research problem so as to make use of otherwise
unusable available data.
2) Data Quality:
If the researcher is convinced about the available secondary data for his needs, the
next step is to examine the quality of the data. The quality of data refers to their
accuracy, reliability and completeness. The assurance and reliability of the available
secondary data depends on the organization which collected them and the purpose
for which they were collected. What is the authority and prestige of the organization?
Is it well recognized? Is it noted for reliability? It is capable of collecting reliable data?
Does it use trained and well qualified investigators? The answers to these questions
determine the degree of confidence we can have in the data and their accuracy. It is
important to go to the original source of the secondary data rather than to use an
immediate source which has quoted from the original. Then only, the researcher can
review the cautionary and other comments that were made in the original source.
3) Data Completeness:The completeness refers to the actual coverage of the published data. This depends
on the methodology and sampling design adopted by the original organization. Is the
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methodology sound? Is the sample size small or large? Is the sampling method
appropriate? Answers to these questions may indicate the appropriateness and
adequacy of the data for the problem under study. The question of possible bias
should also be examined. Whether the purpose for which the original organization
collected the data had a particular orientation? Has the study been made to promote
the organizations own interest? How the study was conducted? These are important
clues. The researcher must be on guard when the source does not report the
methodology and sampling design. Then it is not possible to determine the adequacy
of the secondary data for the researchers study.
Q 6. a. Explain the role of Graphs and Diagrams?
Ans.:-
In presenting the data of frequency distributions and statistical computations, it isoften desirable to use appropriate forms of graphic presentations. In additions to
tabular forms, graphic presentation involves use of graphics, charts and other pictorial
devices such as diagrams. These forms and devices reduce large masses of statistical
data to a form that can be quickly understood at the glance. The meaning of figures in
tabular form may be difficult for the mind to grasp or retain. Properly constructed
graphs and charts relieve the mind of burdensome details by portraying facts
concisely, logically and simply. They, by emphasizing new and significantrelationship, are also useful in discovering new facts and in developing hypothesis.
The device of graphic presentation is particularly useful when the prospective readers
are non-technical people or general public. It is useful to even technical people for
dramatizing certain points about data; for important points can be more effectively
captured in pictures than in tables. However, graphic forms are not substitutes for
tables, but are additional tools for the researcher to emphasize the research findings.
Graphic presentation must be planned with utmost care and diligence. Graphic forms
used should be simple, clear and accurate and also be appropriate to the data. In
planning this work, the following questions must be considered.
a. What is the purpose of the diagram?
b. What facts are to be emphasized?
c. What is the educational level of the audience?
d. How much time is available for the preparation of the diagram?
e. What kind of chart will portray the data most clearly and accurately?
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Role of Graphs:
Because graphs provide a compact, rhetorically powerful way of representing
research findings, recent theories of science have postulated their use as a
distinguishing feature of science. Studies have shown that the use of graphs in journal
articles correlates highly with the hardness of scientific fields, both across disciplines
and across subfields of psychology.
Role of Diagrams:
Recent technological advances have enabled the large-scale adoption of diagrams in
a diverse range of areas. Increasingly sophisticated visual representations are
emerging and, to enable effective communication, insight is required into how
diagrams are used and when they are appropriate for use. The pervasive, everydayuse of diagrams for communicating information and ideas serves to illustrate the
importance of providing a sound understanding of the role that diagrams can, and do,
play. Research in the field of diagrams aims to improve our understanding of the role
of diagrams, sketches and other visualizations in communication, computation,
cognition, creative thought, and problem solving. These concerns have triggered a
surge of interest in the study of diagrams.
The study of diagrammatic communication as a whole must be pursued as an
interdisciplinary endeavor. Diagrams attract a large number of researchers from
virtually all related fields, placing the conference as a major international event in the
area.
b. What are the Types and General rules for graphical representation of
data?Graphical representation is done of the data available. This is very important step of
statistical analysis. We will be discussing the organization of data. The word 'Data' is
plural for 'datum'; datum means facts. Statistically the term is used for numerical
facts such as measures of height, weight and scores on achievement and intelligence
tests.
Graphs and diagram leave a lasting impression on the mind and make intelligible and
easily understandable the salient features of the data. Forecasting also becomes
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easier with the help of graph. Thus it is of interest to study the graphical
representation of data.
The graphical representation of data is categorized as basic five types:
1) Bar graph
2) Pie graph
3) Line graph
4) Scatter plot
5) Histogram
Examples of graphical representation of data:
Let us see some examples of graphical representation of data.
1) Bar chart:
A Bar chart (or diagram) is a graphical representation of data using bars (rectangles
of same width).
It is one dimensional in which case only the height of the rectangle matters.
Solution: scale: Y axis 1 cm = 1000 years
2) Graphical Representation of Histogram:
year193
1
194
1
195
11961 1971 1981
population of aplace
6000
7600
8900
12000
13500
18000
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A histogram (or rectangular diagram or block diagram) is a graphical representation of
a frequency distribution in the form of rectangles one after the other with height
proportional to the frequencies.
It is two dimensional in which case the height as well as width of the
rectangle matters.
Que: Represent the following data by means of a Histogram:
Age( in years) 20-25 25-
30
30-
35
35-
40
40-
45
45-
50
50-
55Number of workers 3 4 5 6 5 4 3
3) Frequency Polygon of a Line Graph:
A frequency polygon can be constructed for a grouped frequency distribution, with
equal-interval, in two different ways:
Method I:
Represent the class-marks along the x-axis.
Represent the frequencies along y-axis. Join these points, in order, by straight lines.
The points at each end is joined to the immediate higher(or lower) class mark at
zero
Frequency so as to complete the polygon.
Method II:
Represent a histogram of the given data.
Join the mid points of the tops of the adjacent rectangles by straight lines.
The mid points at each end are joined to the immediate higher (or lower) at zero
frequency so as to complete the polygon.
The two classes, one at each end, are to be included.
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Construct a frequency polygon for the following data:
Monthly pocket
expenses of a student
0-5 5-10 10-15 15-20 20-25 25-30 30-35 35-40
Number of students 10 16 30 42 50 30 16 12
Solution: Here we have
Monthly pocket expenses of a
student(in $)class- marks
Number of
students
0-5 2.5 10
5-10 7.5 16
10-15 12.5 30
15-20 17.5 42
20-25 22.5 50
25-30 27.5 30
30-35 32.5 16
35-40 37.5 12
4) Cumulative Frequency Curve(ogive):
The Cumulative frequency curve for a grouped frequency distribution is obtained by
plotting the points and then joining them by a free-hand smooth curve.
This is also known as olive.
Method:
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Form the cumulative frequency table.
Mark the upper class limits along the x-axis.
Mark the cumulative frequencies along the y-axis.
Plot the points and join them by a free-hand smooth curve.
Draw a cumulative frequency curve for the following data:
Marks 0-4 4-8 8-12 12-16 16-20
Number of
students4 6 10 8 4
The cumulative frequency table is as follows:
Marks Number of students cumulative frequency
0-4 4 4
4-8 6 4+6=10
8-12 10 10+10=20
12-16 8 20+8=28
16-20 4 28+4=32
Total 32
Joining these points by a free-hand smooth curve, we have the following cumulative
frequency curve:
5) Pie-chart or Pie-graph:
It is drawn by first drawing a circle of a suitable radius and then dividing the angle of
360 degree at its centre in proportion to the figures given under various heads.
Solution:
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Survey recipients may have a variety of backgrounds so use simple language. For
example, "What is the frequency of your automotive travel to your parents' residents
in the last 30 days?" is better understood as, "About how many times in the last 30
days have you driven to your parent's home?"
Relax your grammar
Relax your grammatical standards if the questions sound too formal. For example, the
word "who" is appropriate in many instances when "whom" is technical correct.
Assure a common understanding
Write questions that everyone will understand in the same way. Don't assume that
everyone has the same understanding of the facts or a common basis of knowledge.
Identify even commonly used abbreviations to be certain that everyone understands.
Start with interesting questions
Start the survey with questions that are likely to sound interesting and attract the
respondents' attention. Save the questions that might be difficult or threatening for
later. Voicing questions in the third person can be less threatening than questions
voiced in the second question. For example, ask: "How do your colleagues feel about
management?" rather than "How do you feel about management?"
Don't write leading questions
Leading questions demand a specific response. For example: the question "Which day
of the month is best for the newly established company-wide monthly meeting?" leads
respondents to pick a date without first determining if they even want another
meeting.
Avoid double negatives
Respondents can easily be confused deciphering the meaning of a question that uses
two negative words.
Balance rating scales
When the question requires respondents to use a rating scale, mediate the scale so
that there is room for both extremes.
Don't make the list of choices too long
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If the list of answer categories is long and unfamiliar, it is difficult for respondents to
evaluate all of them. Keep the list of choices short.
Avoid difficult concepts
Some questions involve concepts that are difficult for many people to understand.
Avoid difficult recall questions
People's memories are increasingly unreliable as you ask them to recall events farther
and farther back in time. You will get far more accurate information from people if you
ask, "About how many times in the last month have you gone out and seen a movie in
a movie theater or drive-in?" rather than, "About how many times last year did you go
out and see a movie in a movie theater or drive-in?"
Use Closed-ended questions rather than Open-ended ones
Most questionnaires rely on questions with a fixed number of response categories
from which respondents select their answers. These are useful because the
respondents know clearly the purpose of the question and are limited to a set of
choices where one answer is right for them.
An open-ended question is a written response. For example: "If you do not want a
company picnic, please explain why". If there are an excessive number of writtenresponse questions, it reduces the quality and attention the respondents give to the
answers. However, Info Poll allows you to use a wide variety of other types of
questions.
Put your questions in a logic order
The issues raised in one question can influence how people think about subsequent
questions. It is good to ask a general question and then ask more specific questions.For example, you should avoid asking a series of questions about a free banking
service and then question about the most important factors in selecting a bank.
Pre-test your survey
t is better to identify a problem during the pretest than after you have published the
survey. Before sending a survey to a target audience, send it out as a test to a small
number of people. After they have completed the survey, brainstorm with them to see
if they had problems answering any questions. It would help if they explained what
the question meant to them and whether it was valid to the questionnaire or not.
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Naming your survey
Some people discard an electronic message based entirely on its subject or sender.
You should consider other titles that will pique the interest of the recipients. Here are
examples of survey names that might be successful in getting attention:
-Memo from the Chief Executive Officer
-Evaluation of Services of the Benefits Office
-Your Opinion about Financial Services
-Free T-shirt
-Win a Trip to Paris
-Please Respond By Friday
-Free Subscription
-Win a notebook computer
Cover memo or introduction
Once a recipient opens your survey, you may still need to motivate him or her to
complete it. The cover memo or introduction offers an excellent place to provide the
motivation. A good cover memo or introduction should be short and includes:
-Purpose of the survey
-Why it is important to hear from the correspondent
-What may be done with the results and what possible impacts may occur with theresults.
-Address identification
-Person to contact for questions about the survey.
-Due date for response
Q 2. What do you mean by primary data? What are the various methods of
collecting primary data?Ans.:-
Primary sources are original sources from which the researcher directly collects data
that have not been previously collected e.g.., collection of data directly by the
researcher on brand awareness, brand preference, brand loyalty and other aspects of
consumer behavior from a sample of consumers by interviewing them,. Primary data
are first hand information collected through various methods such as observation,
interviewing, mailing etc.
Methods of Collecting Primary Data
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Primary data are directly collected by the researcher from their original sources. In
this case, the researcher can collect the required date precisely according to his
research needs, he can collect them when he wants them and in the form he needs
them. But the collection of primary data is costly and time consuming. Yet, for several
types of social science research required data are not available from secondary
sources and they have to be directly gathered from the primary sources.
In such cases where the available data are inappropriate, inadequate or obsolete,
primary data have to be gathered. They include: socio economic surveys, social
anthropological studies of rural communities and tribal communities, sociological
studies of social problems and social institutions. Marketing research, leadership
studies, opinion polls, attitudinal surveys, readership, radio listening and T.V. viewing
surveys, knowledge-awareness practice (KAP) studies, farm managements studies,business management studies etc.
There are various methods of data collection. A Method is different from a Tool
while a method refers to the way or mode of gathering data, a tool is an instruments
used for the method. For example, a schedule is used for interviewing. The important
methods are
(a) observation, (b) interviewing, (c) mail survey, (d) experimentation,
(a) observation: Observation means viewing or seeing. Observation may be defined
as a systematic viewing of a specific phenomenon in its proper setting for the specific
purpose of gathering data for a particular study. Observation is classical method of
scientific study.
(b) Interviewing: Interviewing is one of the prominent methods of data collection. It
may be defined as a two way systematic conversation between an investigator and aninformant, initiated for obtaining information relevant to a specific study. It involves
not only conversation, but also learning from the respondents gesture, facial
expressions and pauses, and his environment. Interviewing requires face to face
contact or contact over telephone and calls for interviewing skills. It is done by using a
structured schedule or an unstructured guide.
(c) Mail survey: The mail survey is a data collection process for researchers.
Research practitioners should recognize that this is a viable means of collecting
specific market data.
(d) Experimentation: The popularity of experimentation in marketing research has
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much to do with the possibilities of establishing cause and effect. Experiments can be
configured in such a way as to allow the variable causing a particular effect to be
isolated. Other methods commonly used in marketing research, like surveys, provide
much more ambiguous findings. In fact, experimentation is the most scientific method
employed in marketing research.
Q 3.a. Analyze the case study and descriptive approach to research.
Ans.:-
Case study and Descriptive approach are two different aspects of any research
conducted in a given field. It is important to know that both these aspects differ in
terms of their study and presentation.
A case study though is conducted in several fields it is quite commonly seen in thefield of social science. It consists in a kind of deep investigation carried out in the
behavior of a single group or individual or event for that matter. As a matter of fact a
case study can be either descriptive or explanatory in character. Any single instance
or an event is taken for study and it will be investigated for months by adhering to a
protocol. Limited number of variables will be thoroughly examined too in the case of a
case study.
On the other hand descriptive approach involves more statistical study than
investigation. Descriptive approach is the foundation for conducting a survey
investigation. It involves the use of averages, frequencies and other statistical
calculations. The subject of mathematical statistics and probability play a vital role in
the descriptive approach of research study. In short it can be said that descriptive
approach deals with anything that can be counted and studied. This is the main
difference between a case study and descriptive approach.
A case study is more of a research strategy whereas descriptive approach is not
looked upon as a research strategy but as a part of research. Empirical inquiry is the
backbone of a case study whereas statistical calculation is the backbone of
descriptive approach. Case study contributes to qualitative research whereas
descriptive approach contributes to quantitative research. Both the aspects of
research should be conducted to bring out fruitful results to strengthen a given field.
These are the differences between case study and descriptive approach.
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b. Distinguish between research methods & research Methodology.
Ans.:-
Research Methods vs Research Methodology
Research Methods and Research Methodology are two terms that are often confused
as one and the same. Strictly speaking they are not so and they show differences
between them. One of the primary differences between them is that research
methods are the methods by which you conduct research into a subject or a topic. On
the other hand research methodology explains the methods by which you may
proceed with your research.
Research methods involve conduct of experiments, tests, surveys and the like. On the
other hand research methodology involves the learning of the various techniques that
can be used in the conduct of research and in the conduct of tests, experiments,surveys and critical studies. This is the technical difference between the two terms,
namely, research methods and research methodology.
In short it can be said that research methods aim at finding solutions to research
problems. On the other hand research methodology aims at the employment of the
correct procedures to find out solutions.
It is thus interesting to note that research methodology paves the way for research
methods to be conducted properly. Research methodology is the beginning whereas
research methods are the end of any scientific or non-scientific research.
Let us take for example a subject or a topic, namely, employment of figures of
speech in English literature. In this topic if we are to conduct research, then the
research methods that are involved are study of various works of the different poetsand the understanding of the employment of figures of speech in their works.
On the other hand research methodology pertaining to the topic mentioned above
involves the study about the tools of research, collation of various manuscripts related
to the topic, techniques involved in the critical edition of these manuscripts and the
like.
If the subject into which you conduct a research is a scientific subject or topic then the
research methods include experiments, tests, study of various other results of
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different experiments performed earlier in relation to the topic or the subject and the
like.
On the other hand research methodology pertaining to the scientific topic involves the
techniques regarding how to go about conducting the research, the tools of research,
advanced techniques that can be used in the conduct of the experiments and the like.
Any student or research candidate is supposed to be good at both research methods
and research methodology if he or she is to succeed in his or her attempt at
conducting research into a subject.
Q 4. Explain the important concepts in Research design?
Ans.:-
It is important to be familiar with the important concepts relating to research design.They are:
1. Dependent and Independent variables: A magnitude that varies is known as a
variable. The concept may assume different quantitative values, like height, weight,
income, etc. Qualitative variables are not quantifiable in the strictest sense of
objectivity. However, the qualitative phenomena may also be quantified in terms of
the presence or absence of the attribute considered. Phenomena that assume
different values quantitatively even in decimal points are known as continuousvariables. But, all variables need not be continuous. Values that can be expressed
only in integer values are called non-continuous variables. In statistical term, they are
also known as discrete variable. For example, age is a continuous variable; where as
the number of children is a non-continuous variable. When changes in one variable
depends upon the changes in one or more other variables, it is known as a dependent
or endogenous variable, and the variables that cause the changes in the dependent
variable are known as the independent or explanatory or exogenous variables. Forexample, if demand depends upon price, then demand is a dependent variable, while
price is the independent variable. And if, more variables determine demand, like
income and prices of substitute commodity, then demand also depends upon them in
addition to the own price. Then, demand is a dependent variable which is determined
by the independent variables like own price, income and price of substitute.
2. Extraneous variable:The independent variables which are not directly related to
the purpose of the study but affect the dependent variable are known as extraneous
variables. For instance, assume that a researcher wants to test the hypothesis that
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there is relationship between childrens school performance and their self-concepts, in
which case the latter is an independent variable and the former, the dependent
variable. In this context, intelligence may also influence the school performance.
However, since it is not directly related to the purpose of the study undertaken by the
researcher, it would be known as an extraneous variable. The influence caused by the
extraneous variable on the dependent variable is technically called as an
experimental error. Therefore, a research study should always be framed in such a
manner that the dependent variable completely influences the change in the
independent variable and any other extraneous variable or variables.
3. Control: One of the most important features of a good research design is to
minimize the effect of extraneous variable. Technically, the term control is used when
a researcher designs the study in such a manner that it minimizes the effects ofextraneous independent variables. The term control is used in experimental research
to reflect the restrain in experimental conditions.
4. Confounded relationship:The relationship between dependent and independent
variables is said to be confounded by an extraneous variable, when the dependent
variable is not free from its effects.
Research hypothesis: When a prediction or a hypothesized relationship is tested by
adopting scientific methods, it is known as research hypothesis. The research
hypothesis is a predictive statement which relates a dependent variable and an
independent variable. Generally, a research hypothesis must consist of at least one
dependent variable and one independent variable. Whereas, the relationships that are
assumed but not be tested are predictive statements that are not to be objectively
verified are not classified as research hypothesis.
Experimental and control groups: When a group is exposed to usual conditions in
an experimental hypothesis-testing research, it is known as control group. On the
other hand, when the group is exposed to certain new or special condition, it is known
as an experimental group. In the afore-mentioned example, the Group A can be called
a control group and the Group B an experimental one. If both the groups A and B are
exposed to some special feature, then both the groups may be called as experimental
groups. A research design may include only the experimental group or the both
experimental and control groups together.
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Treatments: Treatments are referred to the different conditions to which the
experimental and control groups are subject to. In the example considered, the two
treatments are the parents with regular earnings and those with no regular earnings.
Likewise, if a research study attempts to examine through an experiment regarding
the comparative impacts of three different types of fertilizers on the yield of rice crop,
then the three types of fertilizers would be treated as the three treatments.
Experiment: An experiment refers to the process of verifying the truth of a statistical
hypothesis relating to a given research problem. For instance, experiment may be
conducted to examine the yield of a certain new variety of rice crop developed.
Further, Experiments may be categorized into two types namely, absolute experiment
and comparative experiment. If a researcher wishes to determine the impact of achemical fertilizer on the yield of a particular variety of rice crop, then it is known as
absolute experiment. Meanwhile, if the researcher wishes to determine the impact of
chemical fertilizer as compared to the impact of bio-fertilizer, then the experiment is
known as a comparative experiment.
Experiment unit: Experimental units refer to the predetermined plots,
characteristics or the blocks, to which the different treatments are applied. It is worthmentioning here that such experimental units must be selected with great caution.
Q 5. What are the differences between observation and interviewing asmethods of data collection? Give two specific examples of situations where
either observation or interviewing would be more appropriate.
Ans.:-
Observation means viewing or seeing. Observation may be defined as a systematic
viewing of a specific phenomenon in its proper setting for the specific purpose of
gathering data for a particular study. Observation is classical method of scientific
study.
Observation as a method of data collection has certain characteristics.
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1. It is both a physical and a mental activity: The observing eye catches many
things that are present. But attention is focused on data that are pertinent to the
given study.
2. Observation is selective: A researcher does not observe anything and
everything, but selects the range of things to be observed on the basis of the nature,
scope and objectives of his study. For example, suppose a researcher desires to study
the causes of city road accidents and also formulated a tentative hypothesis that
accidents are caused by violation of traffic rules and over speeding. When he
observed the movements of vehicles on the road, many things are before his eyes;
the type, make, size and colour of the vehicles, the persons sitting in them, their hair
style, etc. All such things which are not relevant to his study are ignored and only over
speeding and traffic violations are keenly observed by him.
3. Observation is purposive and not casual: It is made for the specific purpose of
noting things relevant to the study. It captures the natural social context in which
persons behavior occur. It grasps the significant events and occurrences that affect
social relations of the participants.
4. Observation should be exact and be based on standardized tools of researchand such as observation schedule, social metric scale etc., and precision instruments,
if any.
Interviewing is one of the prominent methods of data collection. It may be defined as
a two way systematic conversation between an investigator and an informant,
initiated for obtaining information relevant to a specific study. It involves not only
conversation, but also learning from the respondents gesture, facial expressions andpauses, and his environment. Interviewing requires face to face contact or contact
over telephone and calls for interviewing skills. It is done by using a structured
schedule or an unstructured guide. Interviewing may be used either as a main method
or as a supplementary one in studies of persons. Interviewing is the only suitable
method for gathering information from illiterate or less educated respondents. It is
useful for collecting a wide range of data from factual demographic data to highly
personal and intimate information relating to a persons opinions, attitudes, values,
beliefs past experience and future intentions. When qualitative information is required
or probing is necessary to draw out fully, and then interviewing is required. Where the
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area covered for the survey is a compact, or when a sufficient number of qualified
interviewers are available, personal interview is feasible.
Interview is often superior to other data-gathering methods. People are usually more
willing to talk than to write. Once report is established, even confidential information
may be obtained. It permits probing into the context and reasons for answers to
questions. Interview can add flesh to statistical information. It enables the investigator
to grasp the behavioral context of the data furnished by the respondents.
Observation is suitable for a variety of research purposes. It may be used for studying
(a) The behavior of human beings in purchasing goods and services.: life style,
customs, and manner, interpersonal relations, group dynamics, crowd behavior,
leadership styles, managerial style, other behaviors and actions;(b) The behavior of other living creatures like birds, animals etc.
(c) Physical characteristics of inanimate things like stores, factories, residences etc.
(d) Flow of traffic and parking problems
(e) Movement of materials and products through a plant.
Q 6.Strictly speaking, would case studies be considered as scientific
research? Why or why not?Ans.:-
A carefully controlled case study would generally be considered research, even careful
research, but not generally scientific research.
One of the reasons is that the subjects are not randomly assigned to conditions. Many
case studies are post hoc.
Many case studies are dependent on subject reporting of experiences.
It is very difficult to do statistical analysis on case studies, which usually do not have
many subjects involved.
Earlier (in the early to mid 20th century) research" with an n of one" (meaning one
subject in the test) was common. Then social science got into numbers and
percentages and such, and the case study fell away as not useful enough to learn
from.
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However, especially in the "soft" sciences (psychology, sociology, anthropology,
ethologic, etc.) that have to do with people rather than formulas or numbers or the
like, case studies can be helpful and important, especially if they are well-constructed,
or longitudinal, or are in depth on their topic.
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