NET UGC _Research Methodology

1. Explain social research, with its main features as objectives and different stages?
Answer: Research is a careful investigation or inquiry especially through search for new
facts in any branch of knowledge. According to Redman and Mary research is a
systematized effort to gain new knowledge. According to D.Sleshinger and M.Stemson
has defined research as the manipulation of things, concepts or symbols for the purpose
of generalizing to extend, correct or verify knowledge, heather that knowledge aids in
construction of theory or in the practice of an art. Social research is a scientific
undertaking which by means of logical and systematized techniques, aims to discover
new factory verify a test old facts, analyze their sequence interrelationship and casual
explanation which were derived within an appropriate theoretical frame of reference,
develop new scientific tools, concepts and theories which would facilities reliable and
valid study of human behavior. According to PV young social research the systematic
method of discovering news facts or verifying old facts, their sequences, inter-relationships,
casual explanation and the natural laws which governs them. Prof C.A
Mosr defines social research as “systematized investigation to give new knowledge about
social phenomena and surveys”. Rummel defined social research as it is devoted to a
“study of mankind in his social environment and is concerned with improving his
understanding of social orders, groups, institutes and ethics”. Mary Stevenson defined
social research as “social research is a systematic methods of exploring, analyzing and
conceptualizing social life in order to extend ,correct or verify knowledge, whether that
knowledge aid in the construction of a theory or in the practice of an art.
The characteristic features of social research:
Social research is scientific approach of adding to the knowledge about society and social
phenomenon. Knowledge to be meaningful should have a definite purpose and direction.
The growth of knowledge is closely linked to the methods and approaches used in
research investigation. Hence the social science research must be guided by certain laid
down objectives enumerated below
Development knowledge
The main object of any research is to add to the knowledge. As we have seen earlier,
research is a process to obtain knowledge. Similarly social research is an organized and
scientific effort to acquire further knowledge about the problem in question. Thus social
science helps us to obtain and add to the knowledge of social phenomena. This one of the
important objective of social research.
Scientific study of social research:
Social research is an attempt to acquire knowledge about the social phenomena. Man
being the part of a society, social research studies human being as an individual, human
behavior and collects data about various aspects of the social life of man and formulates
law in this regards. Once the law is formulated then the scientific study tries to establish
the interrelationship between these facts. Thus, the scientific study of social life is the
base of the sociological development which is considered as second best objective of
social research. Welfare of humanity: The ultimate objective of the social science study if
often and always to enhance the welfare of humanity. No scientific research makes only
for the sake of study. The welfare of humanity is the most common objective in social
science research.
Classification of facts:

2. According to Prov P.V.Young, social research aims to clarify facts. The classification of
facts plays important role in any scientific research. Social control and prediction: The
ultimate object of many research undertaking is to make it possible, to redirect the
behavior of particular type of individual under the specified conditions. In social research
we generally study of the social phenomena, event and factors that govern and guide
them. a) Social research deals with phenomena. It studies the human behavior. b) It
discovers new facts and verifies old facts. With the improvement in the technique and
changes in the phenomena the researcher has to study the. c) Casual relationship between
various human activities can also be studies in social research. For the sake of systematic
presentation, the process of research may be classifies under three stages
Primary stage
Secondary stage
Tertiary stage
The primary stage includes
Observation
Interest
Crystallization, identification and statement of a research problem
Formulation of hypothesis
Primary synopsis
Conceptual clarity
Documentation
Preparation of bibliography and
Research design
The secondary stage includes
Project planning
Project formulation
Questionnaire preparation
Investigation and data collection
Preparation of final synopsis
Compilation of data
Classification
Tabulation and presentation of data
Experimentation
Analysis
Testing of hypothesis and
Interpretation
The tertiary stage includes
Report writing
Observation, suggestions and conclusions.

3. Give the significance of social research also mention the different problems of social
research and how they are solved?
within the last 20 to 25 years, courses in methods of social research have come to occupy
an increasingly important role in sociological curricula. It likely that at present every
major university offers such courses. This is because growing significance of social
research and also growing job opportunities in this field. The market analysis, the public
opinion expert, the investigator of communication and propaganda all are growing facts
for governmental and business needs. Knowledge of social research is useful for
interpreting and weighing such reports. In this present age, social science are accruing a
scientific method of study for this method, research is an important factor. In the last two
or three decades, a social research has become an important subject of the curriculum of
sociology. In fact almost all the universities, where sociology is taught, social research is
a apart of the curriculum of the sociology. Social research has therefore, assumed greater
importance. Apart from thus, the social science research is essential for proper
understanding the society and proper collection and analysis of social facts. The social
research is an effective method. Research laboratory techniques are helping in finding
further knowledge, about the subject. Through research only it has been possible to make
progress and reach further. It is part of man’s nature. The importance saying goes,
necessity is the mother of invention and invention is the result if research. So long as
necessity exists the research shall be these social science and particularly sociology has
come occupy an importance place for us. In fact, research is an organized effort to
acquire new knowledge. It is based on the past experience and past knowledge. The
richer the past knowledge, greater the surely of the results. In science sociology is
assuming a scientific base, research has become a part of study, it is not an easy task to
predict social behavior because of human nature is ever changing. Problems of scientific
social research In fact social research deals with social phenomena which are quite
different than natural phenomena. Hence there are fundamental difference between
research in social science and that of physical or natural science. Let us study main
difficulities faced by the researcher in the application so scientific methods in social
research. Complexity of social data It is a well known that social science studies the
human behavior which depends on several factor such as physical, social,
temperamental ,psychological, geographical, biological social cultural etc. because of
these factors a researcher is generally confused. It is therefore said that because of this
complexity of social fata human beings cannot be put to scientific test.
Problems of concepts:
In social science research, one has to face number of problems among which of a)
Abstraction b) Faculty reasoning Plays major role in formulating and defining the
concepts and laws.
Problems in interpreting relationship between cause and effects: In social science
research, we generally find interdependent relationship between cause and effect. The
cause and effect are one and the same, for example, in underdevelopment countries, the
economics development cannot be accelerated due to lack of technical know how and
capital cannot be obtained due to underdevelopment of the country.
Dynamic nature of social phenomena

4. Man is a social animal and human society undergoes constant change. What is true today
may not be useful tomorrow. The techniques used in past may prove useless for present
ad future studies. On a account of this dynamic nature of social phenomena our task of
analyzing data becomes very much complicated and the interferences drawn may be
misleading. Problems of maintaining objectivity The problem of impartiality in part of
problem of objectivity. It is generally argued that the social scientific are less objective
than natural scientific because their own interest affected by the finding of their studies,
hence leading to prejudice and bias.
Unpredictability
Predictability is one of the most important characteristics of science. In case of physical
science, high degree of predictability is possible but it is not so in case of social data. but
this statement is also partially true, the social scientist can roughly estimate the behavior
of the group. Difficulty in the verification of the inferences: In social research, the events
of social science are non repetitive and the social science are ill-equipped with their tools
to verify inferences. Difficulty in the use of experimental method. In case of social
science research its product being a human being cannot be put to laboratory test. Even if
it is done, their responses wouldn’t be natural but subject to the awareness of the artificial
condition. Thus social scientist has to watch them in wide world. Difficulty in the use of
experimental method. In case of social science research, its product being a human being
cannot be put to lab test. Even if it is done, their responses wouldn’t natural but subject to
the awareness of the artificial condition. Thus the social scientist has to watch them in the
wide world. Incapability of being dealt through empirical method: An empirical method
cannot be applied in case of social science research as repeated experiment is not possible
,for example, the problem of unbiased sampling selection of data etc.
Problem of inter-disciplinary research
Social science being, inter-disciplinary one i.e related with, economics, political science
and sociology, we cannot draw water-tight compartments for each other social science.
Paucity of funds:
In case of social science research, we generally observed that small amount if finance is
made available to them, it is not sufficient to conduct research effectively.
Less resources:
Prof Mitchell has rightly pointed out that social science researcher require less resources
in comparing to physical science.
briefly explain the various primary stages of research process.
Research is a source which can be draw upon to make a substantial contribution to the
body of the knowledge; research should be followed by some sort of original
contribution. The primary stage includes
Observation:
Research start with observation, which leads to curiosity to learn more about what has
been observed. Observation can either be unaided visual observation or guided and
controlled observation. Sometimes a casual or associated observation leading to
substantial research and a great invention. Deliberate and guided observation can also
form the basis for research. While observation leads to research, research results in
elaborate observation and convulsions; or even further research observation can either be

5. subjective or objective. These are participant observation, on –participant observation,
controlled observation and non controlled observation.
Interest:
The observation of certain occurrences creates an interest and inquisitiveness in the mind
of the researcher to study it further. This is the basis of interest to study the subject matter
of observation. It may be self interest or group interest. The interest is the guiding force
behind any research.
Crystallization, Crystallization is the process of designing the definite form of Research
to be undertaken for the purpose of studying the subject matter. It is the formulation of
the research project, a defining its objectives, rationale, scope, methodology, limitations,
including financial commitments and sources. It is at this stage that the research project is
given a concrete shape and structure, forming a basis of further investigation.
Formulation of hypothesis
At this stage the hypothesis is formed on the basis of observation. Hypothesis is apart of
the scientific method, and has been dealt with in detail in the chapter on “scientific
method and hypothesis”
Primary synopsis
Synopsis is a summary /outline/brief of any subject. It is not a complete subject still
formalization of a subject/replica of a subject. It saves time. It will give an idea of time
required for presentation of the main subject. Once the subject is decided you can arrange
titles likes like main headings, paragraph heading-elaborate the paragraph with important
of main issues.
Conceptual clarity
Any researcher should have in-depth background knowledge of the topic of his study. He
can gain such basic knowledge only be an extensive reading of text books, specialized
books and publications on the topic in addition to articles and research papers published
in journals and periodicals, reports of the past studies, etc. he can also gain knowledge by
details discussion with the people concerned and by his own observation. However it is
imperative for a researcher to gain a deep knowledge form any reliable source prior to
actually plunging himself into a research, so theta he may have clear knowledge of the
concepts which would be of value to him in his task.
Documentation
The documentary sources are important sources of information for a researcher. A
document is anything in writing – a record, files or diaries, published or unpublished-which
can be extracted and used in research. It is very valuable source of information for
research either in management or in social science. it may comprises office files, business
and legal papers, biographies, official and unofficial records, letters, proceedings of any
courts ,committees, societies, assemblies and parliaments, enactments, constitution,
reports of surveys or research of commissions, official statistics, newspapers editorials,
special articles, company news, cases or company directors reports etc. documentation is
the process of collecting and extracting the documents which relevant research.
Documents may be classified into
1) Personal documents

6. 2) Company documents
3) Consultants report and published materials and
4) Public documents
Bibliography
At the end of any research report a bibliography is generally added. This is the list of
books publication, periodicals, journals, reports, etc which are used by researcher in the
connection with the study. It is a description of books, their authorship, editions,
publishers, year of publication, place of publication etc. in ordinary circumstance, a
researcher reads, and makes notes form, many books and publications at the primary
stage of researcher in order to gain conceptual clarity. He prepares a list of such
publications are reports then and there, which helps him in the course of his research.
Some mistakenly believe that a bibliography is merely a list of publication compiled at
the end of report writing like an appendix. On the contrary a bibliography contains and is
composed of the details of publications that the researcher has used in connection with
his study. These facilities any further reference to the matter either by the researcher
himself or anybody who goes through the researcher report.
what is questionnaire- mention its characteristics and illustrate a sample questionnaire for
any product you can choose
Answer: Questionnaire is a method used for collecting data; a set of written questions
which calls for responses on the part of the client; may be self-administered or group-administered.
Questionnaires are an inexpensive way to gather data from a potentially
large number of respondents. Often they are the only feasible way to reach a number of
reviewers large enough to allow statistically analysis of the results. A well-designed
questionnaire that is used effectively can gather information on both the overall
performance of the test system as well as information on specific components of the
system. If the questionnaire includes demographic questions on the participants, they can
be used to correlate performance and satisfaction with the test system among different
groups of users. It is important to remember that a questionnaire should be viewed as a
multi-stage process beginning with definition of the aspects to be examined and ending
with interpretation of the results. Every step needs to be designed carefully because the
final results are only as good as the weakest link in the questionnaire process. Although
questionnaires may be cheap to administer compared to other data collection methods,
they are every bit as expensive in terms of design time and interpretation. The steps
required to design and administer a questionnaire include:
1. Defining the Objectives of the survey
2. Determining the Sampling Group
3. Writing the Questionnaire
4. Administering the Questionnaire
5. Interpretation of the Results
This document will concentrate on how to formulate objectives and write the
questionnaire. Before these steps are examined in detail, it is good to consider what
questionnaires are good at measuring and when it is appropriate to use questionnaires.
What can questionnaires measure? Questionnaires are quite flexible in what they can

7. measure, however they are not equally suited to measuring all types of data. We can
classify data in two ways, Subjective vs. Objective and Quantitative vs. Qualitative.
When a questionnaire is administered, the researchers control over the environment will
be somewhat limited. This is why questionnaires are inexpensive to administer. This loss
of control means the validity of the results are more reliant on the honesty of the
respondent. Consequently, it is more difficult to claim complete objectivity with
questionnaire data then with results of a tightly controlled lab test. For example, if a
group of participants are asked on a questionnaire how long it took them to learn a
particular function on a piece of software, it is likely that they will be biased towards
themselves and answer, on average, with a lower than actual time. A more objective
usability test of the same function with a similar group of participants may return a
significantly higher learning time. More elaborate questionnaire design or administration
may provide slightly better objective data, but the cost of such a questionnaire can be
much higher and offset their economic advantage. In general, questionnaires are better
suited to gathering reliable subjective measures, such as user satisfaction, of the system
or interface in question. Questions may be designed to gather either qualitative or
quantitative data. By their very nature, quantitative questions are more exact then
qualitative. For example, the word "easy" and "difficult" can mean radically different
things to different people. Any question must be carefully crafted, but in particular
questions that assess a qualitative measure must be phrased to avoid ambiguity.
Qualitative questions may also require more thought on the part of the participant and
may cause them to become bored with the questionnaire sooner. In general, we can say
that questionnaires can measure both qualitative and quantitative data well, but that
qualitative questions require more care in design, administration, and interpretation.
When to use a questionnaire?
There is no all encompassing rule for when to use a questionnaire. The choice will be
made based on a variety of factors including the type of information to be gathered and
the available resources for the experiment. A questionnaire should be considered in the
following circumstances.
a. When resources and money are limited. A Questionnaire can be quite inexpensive to
administer. Although preparation may be costly, any data collection scheme will have
similar preparation expenses. The administration cost per person of a questionnaire can
be as low as postage and a few photocopies. Time is also an important resource that
questionnaires can maximize. If a questionnaire is self-administering, such as a e-mail
questionnaire, potentially several thousand people could respond in a few days. It would
be impossible to get a similar number of usability tests completed in the same short time.
b. When it is necessary to protect the privacy of the participants. Questionnaires are easy
to administer confidentially. Often confidentiality is the necessary to ensure participants
will respond honestly if at all. Examples of such cases would include studies that need to
ask embarrassing questions about private or personal behavior.
c. When corroborating other findings. In studies that have resources to pursue other data
collection strategies, questionnaires can be a useful confirmation tools. More costly
schemes may turn up interesting trends, but occasionally there will not be resources to
run these other tests on large enough participant groups to make the results statistically
significant. A follow-up large scale questionnaire may be necessary to corroborate these
earlier results

8. Characteristics of a Good Questionnaire
• Questions worded simply and clearly, not ambiguous or vague, must be objective
• Attractive in appearance (questions spaced out, and neatly arranged)
• Write a descriptive title for the questionnaire
• Write an introduction to the questionnaire
• Order questions in logical sequence
• Keep questionnaire uncluttered and easy to complete
• Delicate questions last (especially demographic questions)
• Design for easy tabulation
• Design to achieve objectives
• Define terms
• Avoid double negatives (I haven't no money)
• Avoid double barreled questions (this AND that)
• Avoid loaded questions ("Have you stopped beating your wife?")
Explain the various measure of central tendency?
In statistics, the general level, characteristic, or typical value that is representative of the
majority of cases. Among several accepted measures of central tendency employed in
data reduction, the most common are the arithmetic mean (simple average), the median,
and the mode. FOR EXAMPLE, one measure of central tendency of a group of high
school students is the average (mean) age of the students. Central tendency is a term used
in some fields of empirical research to refer to what statisticians sometimes call
"location". A "measure of central tendency" is either a location parameter or a statistic
used to estimate a location parameter. Examples include: #Arithmetic mean, the sum of
all data divided by the number of observations in the data set.#Median, the value that
separates the higher half from the lower half of the data set.#Mode, the most frequent
value in the data set. Measures of central tendency, or "location", attempt to quantify
what we mean when we think of as the "typical" or "average" score in a data set. The
concept is extremely important and we encounter it frequently in daily life. For example,
we often want to know before purchasing a car its average distance per litre of petrol. Or
before accepting a job, you might want to know what a typical salary is for people in that
position so you will know whether or not you are going to be paid what you are worth.
Or, if you are a smoker, you might often think about how many cigarettes you smoke "on
average" per day. Statistics geared toward measuring central tendency all focus on this
concept of "typical" or "average." As we will see, we often ask questions in
psychological science revolving around how groups differ from each other "on average".
Answers to such a question tell us a lot about the phenomenon or process we are studying
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 mm is
used for the mean of a population. The symbol MM is used for the mean of a sample. The
formula for mm is shown below: m=SXN m S X N where SX S X is the sum of all the
numbers in the numbers in the sample and NN is the number of numbers in the sample.
As an example, the mean of the numbers 1+2+3+6+8=205=4 1 2 3 6 8 20 5 4 regardless
of whether the numbers constitute the entire population or just a sample from the

9. population. The table, Number of touchdown passes, shows the number of touchdown
(TD) passes thrown by each of the 31 teams in the National Football League in the 2000
season. The mean number of touchdown passes thrown is 20.4516 as shown below.
m=SXN=63431=20.4516 m S X N 634 31 20.4516 Number of touchdown passes
37 33 33 32 29 28 28 23
22 22 22 21 21 21 20 20
19 19 18 18 18 18 16 15
14 14 14 12 12 9 6
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 number 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. Let's return to the made up example of the quiz
on which you made a three discussed previously in the module Introduction to Central
Tendency and shown in table 2. Three possible datasets for the 5-point make-up quiz
Student Dataset 1 Dataset 2 Dataset 3
You 3 3 3
John's 3 4 2
Maria's 3 4 2
Shareecia's 3 4 2
Luther's 3 5 1
For Dataset 1, the median is three, the same as your score. For Dataset 2, the median is 4.
Therefore, your score is below the median. This means you are in the lower half of the
class. Finally for Dataset 3, the median is 2. For this dataset, your score is above the
median and therefore in the upper half of the distribution. 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 22, 44,
77, 1212 is 4+72=5.5 4 7 2 5.5 . 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 (see discussion of continuous
variables). 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). Grouped
frequency distribution

10. Range Frequency
500-600 3
600-700 6
700-800 5
800-900 5
900-1000 0
1000-1100 1
Trimean
The trimean is computed by adding the 25th percentile plus twice the 50th percentile plus
the 75th percentile and dividing by four. What follows is an example of how to compute
the trimean. The 25th, 50th, and 75th percentile of the dataset "Example 1" are 51, 55,
and 63 respectively. Therefore, the trimean is computed as:
The trimean is almost as resistant to extreme scores as the median and is less subject to
sampling fluctuations than the arithmetic mean in extremely skewed distributions. It is
less efficient than the mean for normal distributions. . The trimean is a good measure of
central tendency and is probably not used as much as it should be.
Trimmed Mean
A trimmed mean is calculated by discarding a certain percentage of the lowest and the
highest scores and then computing the mean of the remaining scores. For example, a
mean trimmed 50% is computed by discarding the lower and higher 25% of the scores
and taking the mean of the remaining scores. The median is the mean trimmed 100% and
the arithmetic mean is the mean trimmed 0%. A trimmed mean is obviously less
susceptible to the effects of extreme scores than is the arithmetic mean. It is therefore less
susceptible to sampling fluctuation than the mean for extremely skewed distributions. It
is less efficient than the mean for normal distributions. Trimmed means are often used in
Olympic scoring to minimize the effects of extreme ratings possibly caused by biased
judges.
Which are various measure of dispersion, explain each of them?
Answer: In many ways, measures of central tendency are less useful in statistical analysis
than measures of dispersion of values around the central tendency The dispersion of
values within variables is especially important in social and political research because:
• Dispersion or "variation" in observations is what we seek to explain.
• Researchers want to know WHY some cases lie above average and others below
Average for a given variable:
o TURNOUT in voting: why do some states show higher rates than others?
o CRIMES in cities: why are there differences in crime rates?
o CIVIL STRIFE among countries: what accounts for differing amounts?
• Much of statistical explanation aims at explaining DIFFERENCES in observations --
also known as
o VARIATION, or the more technical term, VARIANCE
If everything were the same, we would have no need of statistics. But, people's heights,
ages, etc., do vary. We often need to measure the extent to which scores in a dataset

11. differ from each other. Such a measure is called the dispersion of a distribution Some
measure of dispersion are
1) Range The range is the simplest measure of dispersion. The range can be thought of in
two ways
. 1. As a quantity: the difference between the highest and lowest scores in a distribution.
"The range of scores on the exam was 32."
2. As an interval; the lowest and highest scores may be reported as the range. "The range
was 62 to 94," which would be written (62, 94).
The Range of a Distribution
Find the range in the following sets of data:
NUMBER OF BROTHERS AND SISTERS
{ 2, 3, 1, 1, 0, 5, 3, 1, 2, 7, 4, 0, 2, 1, 2,
1, 6, 3, 2, 0, 0, 7, 4, 2, 1, 1, 2, 1, 3, 5, 12,
4, 2, 0, 5, 3, 0, 2, 2, 1, 1, 8, 2, 1, 2 }
An outlier is an extreme score, i.e., an infrequently occurring score at either tail of the
distribution. Range is determined by the furthest outliers at either end of the distribution.
Range is of limited use as a measure of dispersion, because it reflects information about
extreme values but not necessarily about "typical" values. Only when the range is
"narrow" (meaning that there are no outliers) does it tell us about typical values in the
data.
2) Percentile range
Most students are familiar with the grading scale in which "C" is assigned to average
scores, "B" to above-average scores, and so forth. When grading exams "on a curve,"
instructors look to see how a particular score compares to the other scores. The letter
grade given to an exam score is determined not by its relationship to just the high and low
scores, but by its relative position among all the scores. Percentile describes the relative
location of points anywhere along the range of a distribution. A score that is at a certain
percentile falls even with or above that percent of scores. The median score of a
distribution is at the 50th percentile: It is the score at which 50% of other scores are
below (or equal) and 50% are above. Commonly used percentile measures are named in
terms of how they divide distributions. Quartiles divide scores into fourths, so that a score
falling in the first quartile lies within the lowest 25% of scores, while a score in the fourth
quartile is higher than at least 75% of the scores. Quartile Finder
The divisions you have just performed illustrate quartile scores. Two other percentile
scores commonly used to describe the dispersion in a distribution are decile and quintile
scores which divide cases into equal sized subsets of tenths (10%) and fifths (20%),
respectively. In theory, percentile scores divide a distribution into 100 equal sized groups.
In practice this may not be possible because the number of cases may be under 100. A
box plot is an effective visual representation of both central tendency and dispersion. It
simultaneously shows the 25th, 50th (median), and 75th percentile scores, along with the
minimum and maximum scores. The "box" of the box plot shows the middle or "most
typical" 50% of the values, while the "whiskers" of the box plot show the more extreme
values. The length of the whiskers indicate visually how extreme the outliers are. Below
is the box plot for the distribution you just separated into quartiles. The boundaries of the

12. box plot's "box" line up with the columns for the quartile scores on the histogram. The
box plot displays the median score and shows the range of the distribution as well.
By far the most commonly used measures of dispersion in the social sciences are
Variance and standard deviation.
Variance is the average squared difference of scores from the mean score of a
distribution. Standard deviation is the square root of the variance. In calculating the
variance of data points, we square the difference between each point and the mean
because if we summed the differences directly, the result would always be zero. For
example, suppose three friends work on campus and earn $5.50, $7.50, and $8 per hour,
respectively. The mean of these values is $(5.50 + 7.50 + 8)/3 = $7 per hour. If we
summed the differences of the mean from each wage, we would get (5.50-7) + (7.50-7) +
(8-7) = -1.50 + .50 + 1 = 0. Instead, we square the terms to obtain a variance equal to 2.25
+ .25 + 1 = 3.50. This figure is a measure of dispersion in the set of scores. The variance
is the minimum sum of squared differences of each score from any number. In other
words, if we used any number other than the mean as the value from which each score is
subtracted, the resulting sum of squared differences would be greater. (You can try it
yourself -- see if any number other than 7 can be plugged into the preceeding calculation
and yield a sum of squared differences less than 3.50.) The standard deviation is simply
the square root of the variance. In some sense, taking the square root of the variance
"undoes" the squaring of the differences that we did when we calculated the variance.
Variance and standard deviation of a population are designated by and , respectively.
Variance and standard deviation of a sample are designated by s2 and s, respectively.
4) Standard Deviation
The standard deviation ( or s) and variance ( or s2) are more complete measures of
dispersion which take into account every score in a distribution. The other measures of
dispersion we have discussed are based on considerably less information. However,
because variance relies on the squared differences of scores from the mean, a single
outlier has greater impact on the size of the variance than does a single score near the
mean. Some statisticians view this property as a shortcoming of variance as a measure of
dispersion, especially when there is reason to doubt the reliability of some of the extreme
scores. For example, a researcher might believe that a person who reports watching
television an average of 24 hours per day may have misunderstood the question. Just one
such extreme score might result in an appreciably larger standard deviation, especially if
the sample is small. Fortunately, since all scores are used in the calculation of variance,
the many non-extreme scores (those closer to the mean) will tend to offset the misleading
impact of any extreme scores. The standard deviation and variance are the most
commonly used measures of dispersion in the social sciences because: • Both take into
account the precise difference between each score and the mean. Consequently, these
measures are based on a maximum amount of information.
• The standard deviation is the baseline for defining the concept of standardized score or
"z-score".
• Variance in a set of scores on some dependent variable is a baseline for measuring the
correlation between two or more variables (the degree to which they are related).
Standardized Distribution Scores, or "Z-Scores"
Actual scores from a distribution are commonly known as a "raw scores." These are
expressed in terms of empirical units like dollars, years, tons, etc. We might say "The

13. Smith family's income is $29,418." To compare a raw score to the mean, we might say
something like "The mean household income in the U.S. is $2,232 above the Smith
family's income." This difference is an absolute deviation of 2,232 emirical units (dollars,
in this example) from the mean. When we are given an absolute deviation from the mean,
expressed in terms of empirical units, it is difficult to tell if the difference is "large" or
"small" compared to other members of the data set. In the above example, are there many
families that make less money than the Smith family, or only a few? We were not given
enough information to decide. We get more information about deviation from the mean
when we use the standard deviation measure presented earlier in this tutorial. Raw scores
expressed in empirical units can be converted to "standardized" scores, called z-scores.
The z-score is a measure of how many units of standard deviation the raw score is from
the mean. Thus, the z-score is a relative measure instead of an absolute measure. This is
because every individual in the dataset affects value for the standard deviation. Raw
scores are converted to standardized z-scores by the following equations: Population z-score
Sample z-score where is the population mean, is the sample mean, is the population
standard deviation, s is the sample standard deviation, and x is the raw score being
converted. For example, if the mean of a sample of I.Q. scores is 100 and the standard
deviation is 15, then an I.Q. of 128 would correspond to:
= (128 - 100) / 15 = 1.87
For the same distribution, a score of 90 would correspond to:
z = (90 - 100) / 15 = - 0.67
A positive z-score indicates that the corresponding raw score is above the mean. A
negative z-score represents a raw score that is below the mean. A raw score equal to the
mean has a z-score of zero (it is zero standard deviations away). Z-scores allow for
control across different units of measure. For example, an income that is 25,000 units
above the mean might sound very high for someone accustomed to thinking in terms of
U.S. dollars, but if the unit is much smaller (such as Italian Lires or Greek Drachmas), the
raw score might be only slightly above average. Z-scores provide a standardized
description of departures from the mean that control for differences in size of empirical
units. When a dataset conforms to a "normal" distribution, each z-score corresponds
exactly to known, specific percentile score. If a researcher can assume that a given
empirical distribution approximates the normal distribution, then he or she can assume
that the data's z-scores approximate the z-scores of the normal distribution as well. In this
case, z-scores can map the raw scores to their percentile scores in the data. As an
example, suppose the mean of a set of incomes is $60,200, the standard deviation is
$5,500, and the distribution of the data values approximates the normal distribution. Then
an income of $69,275 is calculated to have a z-score of 1.65. For a normal distribution, a
z-score of 1.65 always corresponds to the 95th percentile. Thus, we can assume that
$69,275 is the 95th percentile score in the empirical data, meaning that 95% of the scores
lie at or below $69,275. The normal distribution is a precisly defined, theoretical
distribution. Empirical distributions are not likely to conform perfectly to the normal
distribution. If the data distribution is unlike the normal distribution, then z-scores do not
translate to percentiles in the "normal" way. However, to the extent that an empirical
distribution approximates the normal distribution, z-scores do translate to percentiles in a
reliable way.

14. define hypothesis-what are the nature, scope and testing of hypothesis?
Answer: A tentative proposal made to explain certain observations or facts that requires
further investigation to be verified. A hypothesis is a formulation of a question that lends
itself to a prediction. This prediction can be verified or falsified. A question can only be
use as scientific hypothesis, if their is an experimental approach or observational study
that can be designed to check the outcome of a prediction.
Nature of hypothesis
N the various discussions of the hypothesis which have appeared in works on inductive
logic and in writings on scientific method, its structure and function have received
considerable attention, while its origin has been comparatively neglected. The hypothesis
has generally been treated as that part of scientific procedure which marks the stage
where a definite plan or method is proposed for dealing with new or unexplained facts. It
is regarded as an invention for the purpose of explaining the given, as a definite
conjecture which is to be tested by an appeal to experience to see whether deductions
made in accordance with it will be found true in fact. The function of the hypothesis is to
unify, to furnish a method of dealing with things, and its structure must be suitable to this
end. It must be so formed that it will be likely to prove valid, and writers have formulated
various rules to be followed in the formation of hypotheses. These rules state the main
requirements of a good hypothesis, and are intended to aid in a general way by pointing
out certain limits within which it must fall.
In respect to the origin of the hypothesis, writers have usually contented themselves with
pointing out the kind of situations in which hypotheses are likely to appear. But after this
has been done, after favorable external conditions have been given, the rest must be left
to "genius," for hypotheses arise as "happy guesses," for which no rule or law can be
given. In fact, the genius differs from the ordinary plodding mortal in just this ability to
form fruitful
Hypotheses in the midst of the same facts which to other less gifted individuals remain
only so many disconnected experiences. Hypothesis is to determine its nature a little
more precisely through an investigation of its rather obscure origin, and to call attention
to certain features of its function which have not generally been accorded their due
significance.
The scope hypothesis
We should be surprised that language is as complicated as it is. That is to say, there is no
reasonable doubt that a language with a context-free grammar, together with a transparent
inductive characterization of the semantics, would have all of the expressive power of
historically given natural languages, but none of the quirks or other puzzling features that
we actually find when we study them. This circumstance suggests that the relations
between apparent syntactic structure on the one hand and interpretation on the other ---
the “interface conditions,” in popular terminology --- should be seen through the
perspective of an underlying regularity of structure and interpretation that can be revealed
only through extended inquiry, taking into consideration especially comparative data.
Indeed, advances made especially during the past twenty-five years or so indicate that, at
least over a broad domain, structures either generated from what is (more or less)
apparent, or else underlying those apparent structures, display the kind of regularity in

15. their interface conditions that is familiar to us from the formalized languages. The
elements that I concentrate upon here are two: the triggering of relative scope (from the
interpretive point of view), and the distinction between those elements that contribute to
meaning through their contribution to reference and truth conditions, on the one hand,
and those that do so through the information that they provide about the intentional states
of the speaker or those the speaker is talking about, on the other. As will be seen, I will in
part support Jaakko Hintikka’s view that the latter distinction involves scope too, but in a
more derivative fashion than he has explicitly envisaged.
TESTING OF HYPOTHESIS
Hypothesis testing refers to the process of using statistical analysis to determine if the
observed differences between two or more samples are due to random chance (as stated
in the null hypothesis) or to true differences in the samples (as stated in the alternate
hypothesis). A null hypothesis (H0) is a stated assumption that there is no difference in
parameters (mean, variance, DPMO) for two or more populations. The alternate
hypothesis (Ha) is a statement that the observed difference or relationship between two
populations is real and not the result of chance or an error in sampling. Hypothesis testing
is the process of using a variety of statistical tools to analyze data and, ultimately, to fail
to reject or reject the null hypothesis. From a practical point of view, finding statistical
evidence that the null hypothesis is false allows you to reject the null hypothesis and
accept the alternate hypothesis. Hypothesis testing is the use of statistics to determine the
probability that a given hypothesis is true. The usual process of hypothesis testing
consists of four steps.
1. Formulate the null hypothesis (commonly, that the observations are the result of pure
chance) and the alternative hypothesis (commonly, that the observations show a real
effect combined with a component of chance variation).
2. Identify a test statistic that can be used to assess the truth of the null hypothesis.
3. Compute the P-value, which is the probability that a test statistic at least as significant
as the one observed would be obtained assuming that the null hypothesis were true. The
smaller the -value, the stronger the evidence against the null hypothesis.
4. Compare the -value to an acceptable significance value (sometimes called an alpha
value). If , that the observed effect is statistically significant, the null hypothesis is ruled
out, and the alternative hypothesis is valid.
Flow Diagram
1 Identify the null hypothesis H0 and the alternate hypothesis HA.
2 Choose ?. The value should be small, usually less than 10%. It is important to consider
the consequences of both types of errors.
3 Select the test statistic and determine its value from the sample data. This value is
called the observed value of the test statistic. Remember that a t statistic is usually

16. appropriate for a small number of samples; for larger number of samples, a z statistic can
work well if data are normally distributed.
4 Compare the observed value of the statistic to the critical value obtained for the
chosen ?.
5 Make a decision.
If the test statistic falls in the critical region:
Reject H0 in favour of HA. If the test statistic does not fall in the critical region:
Conclude that there is not enough evidence to reject H0.
Practical Example
A) One tailed Test
An aquaculture farm takes water from a stream and returns it after it has circulated
through the fish tanks. The owner thinks that, since the water circulates rather quickly
through the tanks, there is little organic matter in the effluent. To find out if this is true,
he takes some samples of the water at the intake and other samples downstream the
outlet, and tests for Biochemical Oxygen Demand (BOD). If BOD increases, it can be
said that the effluent contains more organic matter than the stream can handle. The data
for this problem are given in the following table:
Table 3. BOD in the stream
One tailed t-test :
Upstream Downstream
6.782 9.063
5.809 8.381
6.849 8.660
6.879 8.405
7.014 9.248
7.321 8.735
5.986 9.772
6.628 8.545
6.822 8.063
6.448 8.001
1. A is the set of samples taken at the intake; and B is the set of samples taken
downstream.
o H0: ?B < ?A
o HA: ?B > ?A
2. Choose an ?. Let us use 5% for this example.
3. The observed t value is calculated
4. The critical t value is obtained according to the degrees of freedom
The resulting t test values are shown in this table:

17. Table 4. t-Test : Two-Sample Assuming Equal Variances
Upstream Downstream
Mean 6.6539 8.6874
Variance 0.2124 0.2988
Observations 10 10
Pooled Variance 0.2556
Hypothesized Mean Difference 0
Degrees of freedom 18
t stat -8.9941
P(T
The numerical value of the calculated t statistic is higher than the critical t value. We
therefore reject H0 and conclude that the effluent is polluting the stream.
what is a case study method? Briefly explain assumption and major steps in case study
method.
Answer: Case study research excels at bringing us to an understanding of a complex issue
or object and can extend experience or add strength to what is already known through
previous research. Case studies emphasize detailed contextual analysis of a limited
number of events or conditions and their relationships. Researchers have used the case
study research method for many years across a variety of disciplines. Social scientists, in
particular, have made wide use of this qualitative research method to examine
contemporary real-life situations and provide the basis for the application of ideas and
extension of methods. Researcher Robert K. Yin defines the case study research method
as an empirical inquiry that investigates a contemporary phenomenon within its real-life
context; when the boundaries between phenomenon and context are not clearly evident;
and in which multiple sources of evidence are used (Yin, 1984, p. 23). Critics of the case
study method believe that the study of a small number of cases can offer no grounds for
establishing reliability or generality of findings. Others feel that the intense exposure to
study of the case biases the findings. Some dismiss case study research as useful only as
an exploratory tool. Yet researchers continue to use the case study research method with
success in carefully planned and crafted studies of real-life situations, issues, and
problems. Reports on case studies from many disciplines are widely available in the
literature. This paper explains how to use the case study method and then applies the
method to an example case study project designed to examine how one set of users, non-profit
organizations, make use of an electronic community network. The study examines
the issue of whether or not the electronic community network is beneficial in some way
to non-profit organizations and what those benefits might be. Many well-known case
study researchers such as Robert E. Stake, Helen Simons, and Robert K. Yin have written
about case study research and suggested techniques for organizing and conducting the
research successfully. This introduction to case study research draws upon their work and
proposes six steps that should be used: • Determine and define the research questions
• Select the cases and determine data gathering and analysis techniques
• Prepare to collect the data

18. • Collect data in the field
• Evaluate and analyze the data
• Prepare the report
Step 1. Determine and Define the Research Questions
The first step in case study research is to establish a firm research focus to which the
researcher can refer over the course of study of a complex phenomenon or object. The
researcher establishes the focus of the study by forming questions about the situation or
problem to be studied and determining a purpose for the study. The research object in a
case study is often a program, an entity, a person, or a group of people. Each object is
likely to be intricately connected to political, social, historical, and personal issues,
providing wide ranging possibilities for questions and adding complexity to the case
study. The researcher investigates the object of the case study in depth using a variety of
data gathering methods to produce evidence that leads to understanding of the case and
answers the research questions. Case study research generally answers one or more
questions which begin with "how" or "why." The questions are targeted to a limited
number of events or conditions and their inter-relationships. To assist in targeting and
formulating the questions, researchers conduct a literature review. This review establishes
what research has been previously conducted and leads to refined, insightful questions
about the problem. Careful definition of the questions at the start pinpoints where to look
for evidence and helps determine the methods of analysis to be used in the study. The
literature review, definition of the purpose of the case study, and early determination of
the potential audience for the final report guide how the study will be designed,
conducted, and publicly reported.
Step 2. Select the Cases and Determine Data Gathering and Analysis Techniques During
the design phase of case study research, the researcher determines what approaches to use
in selecting single or multiple real-life cases to examine in depth and which instruments
and data gathering approaches to use. When using multiple cases, each case is treated as a
single case. Each case?s conclusions can then be used as information contributing to the
whole study, but each case remains a single case. Exemplary case studies carefully select
cases and carefully examine the choices available from among many research tools
available in order to increase the validity of the study. Careful discrimination at the point
of selection also helps erect boundaries around the case. The researcher must determine
whether to study cases which are unique in some way or cases which are considered
typical and may also select cases to represent a variety of geographic regions, a variety of
size parameters, or other parameters. A useful step in the selection process is to
repeatedly refer back to the purpose of the study in order to focus attention on where to
look for cases and evidence that will satisfy the purpose of the study and answer the
research questions posed. Selecting multiple or single cases is a key element, but a case
study can include more than one unit of embedded analysis. For example, a case study
may involve study of a single industry and a firm participating in that industry. This type
of case study involves two levels of analysis and increases the complexity and amount of

19. data to be gathered and analyzed. A key strength of the case study method involves using
multiple sources and techniques in the data gathering process. The researcher determines
in advance what evidence to gather and what analysis techniques to use with the data to
answer the research questions. Data gathered is normally largely qualitative, but it may
also be quantitative. Tools to collect data can include surveys, interviews, documentation
review, observation, and even the collection of physical artifacts. The researcher must use
the designated data gathering tools systematically and properly in collecting the evidence.
Throughout the design phase, researchers must ensure that the study is well constructed
to ensure construct validity, internal validity, external validity, and reliability. Construct
validity requires the researcher to use the correct measures for the concepts being studied.
Internal validity (especially important with explanatory or causal studies) demonstrates
that certain conditions lead to other conditions and requires the use of multiple pieces of
evidence from multiple sources to uncover convergent lines of inquiry. The researcher
strives to establish a chain of evidence forward and backward. External validity reflects
whether or not findings are generalizable beyond the immediate case or cases; the more
variations in places, people, and procedures a case study can withstand and still yield the
same findings, the more external validity. Techniques such as cross-case examination and
within-case examination along with literature review helps ensure external validity.
Reliability refers to the stability, accuracy, and precision of measurement. Exemplary
case study design ensures that the procedures used are well documented and can be
repeated with the same results over and over again.
Step 3. Prepare to Collect the Data Because case study research generates a large amount
of data from multiple sources, systematic organization of the data is important to prevent
the researcher from becoming overwhelmed by the amount of data and to prevent the
researcher from losing sight of the original research purpose and questions. Advance
preparation assists in handling large amounts of data in a documented and systematic
fashion. Researchers prepare databases to assist with categorizing, sorting, storing, and
retrieving data for analysis. Exemplary case studies prepare good training programs for
investigators, establish clear protocols and procedures in advance of investigator field
work, and conduct a pilot study in advance of moving into the field in order to remove
obvious barriers and problems. The investigator training program covers the basic
concepts of the study, terminology, processes, and methods, and teaches investigators
how to properly apply the techniques being used in the study. The program also trains
investigators to understand how the gathering of data using multiple techniques
strengthens the study by providing opportunities for triangulation during the analysis
phase of the study. The program covers protocols for case study research, including time
deadlines, formats for narrative reporting and field notes, guidelines for collection of
documents, and guidelines for field procedures to be used. Investigators need to be good
listeners who can hear exactly the words being used by those interviewed. Qualifications
for investigators also include being able to ask good questions and interpret answers.
Good investigators review documents looking for facts, but also read between the lines
and pursue collaborative evidence elsewhere when that seems appropriate. Investigators
need to be flexible in real-life situations and not feel threatened by unexpected change,
missed appointments, or lack of office space. Investigators need to understand the
purpose of the study and grasp the issues and must be open to contrary findings.

20. Investigators must also be aware that they are going into the world of real human beings
who may be threatened or unsure of what the case study will bring. After investigators
are trained, the final advance preparation step is to select a pilot site and conduct a pilot
test using each data gathering method so that problematic areas can be uncovered and
corrected. Researchers need to anticipate key problems and events, identify key people,
prepare letters of introduction, establish rules for confidentiality, and actively seek
opportunities to revisit and revise the research design in order to address and add to the
original set of research questions.
4. Collect Data in the Field The researcher must collect and store multiple sources of
evidence comprehensively and systematically, in formats that can be referenced and
sorted so that converging lines of inquiry and patterns can be uncovered. Researchers
carefully observe the object of the case study and identify causal factors associated with
the observed phenomenon. Renegotiation of arrangements with the objects of the study or
addition of questions to interviews may be necessary as the study progresses. Case study
research is flexible, but when changes are made, they are documented systematically.
Exemplary case studies use field notes and databases to categorize and reference data so
that it is readily available for subsequent reinterpretation. Field notes record feelings and
intuitive hunches, pose questions, and document the work in progress. They record
testimonies, stories, and illustrations which can be used in later reports. They may warn
of impending bias because of the detailed exposure of the client to special attention, or
give an early signal that a pattern is emerging. They assist in determining whether or not
the inquiry needs to be reformulated or redefined based on what is being observed. Field
notes should be kept separate from the data being collected and stored for analysis.
Maintaining the relationship between the issue and the evidence is mandatory. The
researcher may enter some data into a database and physically store other data, but the
researcher documents, classifies, and cross-references all evidence so that it can be
efficiently recalled for sorting and examination over the course of the study.
Step 5. Evaluate and Analyze the Data The researcher examines raw data using many
interpretations in order to find linkages between the research object and the outcomes
with reference to the original research questions. Throughout the evaluation and analysis
process, the researcher remains open to new opportunities and insights. The case study
method, with its use of multiple data collection methods and analysis techniques,
provides researchers with opportunities to triangulate data in order to strengthen the
research findings and conclusions. The tactics used in analysis force researchers to move
beyond initial impressions to improve the likelihood of accurate and reliable findings.
Exemplary case studies will deliberately sort the data in many different ways to expose or
create new insights and will deliberately look for conflicting data to disconfirm the
analysis. Researchers categorize, tabulate, and recombine data to address the initial
propositions or purpose of the study, and conduct cross-checks of facts and discrepancies
in accounts. Focused, short, repeat interviews may be necessary to gather additional data
to verify key observations or check a fact. Specific techniques include placing
information into arrays, creating matrices of categories, creating flow charts or other
displays, and tabulating frequency of events. Researchers use the quantitative data that
has been collected to corroborate and support the qualitative data which is most useful for

21. understanding the rationale or theory underlying relationships. Another technique is to
use multiple investigators to gain the advantage provided when a variety of perspectives
and insights examine the data and the patterns. When the multiple observations converge,
confidence in the findings increases. Conflicting perceptions, on the other hand, cause the
researchers to pry more deeply. Another technique, the cross-case search for patterns,
keeps investigators from reaching premature conclusions by requiring that investigators
look at the data in many different ways. Cross-case analysis divides the data by type
across all cases investigated. One researcher then examines the data of that type
thoroughly. When a pattern from one data type is corroborated by the evidence from
another, the finding is stronger. When evidence conflicts, deeper probing of the
differences is necessary to identify the cause or source of conflict. In all cases, the
researcher treats the evidence fairly to produce analytic conclusions answering the
original "how" and "why" research questions.
Step 6. Prepare the report Exemplary case studies report the data in a way that transforms
a complex issue into one that can be understood, allowing the reader to question and
examine the study and reach an understanding independent of the researcher. The goal of
the written report is to portray a complex problem in a way that conveys a vicarious
experience to the reader. Case studies present data in very publicly accessible ways and
may lead the reader to apply the experience in his or her own real-life situation.
Researchers pay particular attention to displaying sufficient evidence to gain the reader?s
confidence that all avenues have been explored, clearly communicating the boundaries of
the case, and giving special attention to conflicting propositions. Techniques for
composing the report can include handling each case as a separate chapter or treating the
case as a chronological recounting. Some researchers report the case study as a story.
During the report preparation process, researchers critically examine the document
looking for ways the report is incomplete. The researcher uses representative audience
groups to review and comment on the draft document. Based on the comments, the
researcher rewrites and makes revisions. Some case study researchers suggest that the
document review audience include a journalist and some suggest that the documents
should be reviewed by the participants in the study.
Case studies are complex because they generally involve multiple sources of data, may
include multiple cases within a study, and produce large amounts of data for analysis.
Researchers from many disciplines use the case study method to build upon theory, to
produce new theory, to dispute or challenge theory, to explain a situation, to provide a
basis to apply solutions to situations, to explore, or to describe an object or phenomenon.
The advantages of the case study method are its applicability to real-life, contemporary,
human situations and its public accessibility through written reports. Case study results
relate directly to the common reader’s everyday experience and facilitate an
understanding of complex real-life situations.
ASSUMPTION OF CASE STUDY METHOD
The case study method is based on several assumptions. The importance assumptions are
explained below

22. Uniformity of human nature
The assumption of uniformity in the basic human nature in spite of the fact that human
behavior may vary according to situations. This assumption underlines the collection of
case data.
Nature history of the unit
The assumption of studying the natural history of the unit concerned. It gives the
background for the study
Comprehensive study
The assumption of comprehensive study of the unit concerned
Applicability
Psychologist has stated that some statement about human broadly apply to each
individual or to each member of a large group.
Homogeneity
According to cora dubois,an antraopologist, the case study is possible only because of
certain basic homogeneity or similarity in evidenced in the mankind.
Major steps of case study method:
I. Identify the case topic, setting, primary focus, and perspective.
II. Obtain relevant public background materials and knowledgeable informant insights.
III. Obtain access, approval, and clarify anonymity issues with key gatekeeper.
IV. Obtain relevant documents, minutes, reports and other appropriate materials.
V. Develop preliminary chronology of key events leading to controversy or decision and
identify key players and issues.
VI. Consider varied perspective and sources of information and pedagogical purpose of
the case.
VII. Develop interview protocol (key questions for various informants) and further
information to collect. This will evolve further.
VIII. Conduct interviews and collect other documents, information and materials.

23. IX. Develop case outline and style of presentation.
X. Draft case. Obtain comment and feedback from key gatekeeper (and other students).
Revise and finalize the case
Documentation:
The documentary sources are important sources of information for a researcher. A
document is anything in writing – a record, files or diaries, published or unpublished-which
can be extracted and used in research. It is very valuable source of information for
research either in management or in social science. it may comprises office files, business
and legal papers, biographies, official and unofficial records, letters, proceedings of any
courts ,committees, societies, assemblies and parliaments, enactments, constitution,
reports of surveys or research of commissions, official statistics, newspapers editorials,
special articles, company news, cases or company directors reports etc. documentation is
the process of collecting and extracting the documents which relevant research.
Documents may be classified into
1) Personal documents:
personal documents are those are written by or on behalf of individuals. They may
include autobiographical, biographies diaries memories letters observations and
inscriptions, which are primarily written for the use and satisfaction of individuals and
which can be utilized for research purposes. Personal documents play a very vital role in
research.
2) Company documents
3) Consultants report and published materials and 4) Public documents
b) sources and tabulations
It is the process of condensation of the data for convenience, in statistical processing,
presentation and interpretation of the information.
A good table is one which has the following requirements :
1. It should present the data clearly, highlighting important details.
2. It should save space but attractively designed.
3. The table number and title of the table should be given.+
4. Row and column headings must explain the figures therein.
5. Averages or percentages should be close to the data.

24. 6. Units of the measurement should be clearly stated along the titles or headings.
7. Abbreviations and symbols should be avoided as far as possible.
8. Sources of the data should be given at the bottom of the data.
9. In case irregularities creep in table or any feature is not sufficiently explained,
references and foot notes must be given.
10. The rounding of figures should be unbiased.
"Classified and arranged facts speak of themselves, and narrated they are as dead as
mutton" This quote is given by J.R. Hicks. The process of dividing the data into different
groups ( viz. classes) which are homogeneous within but heterogeneous between
themselves, is called a classification. It helps in understanding the salient features of the
data and also the comparison with similar data. For a final analysis it is the best friend of
a statistician.
c) Classification and tabulation
The data is classified in the following ways : 1. According to attributes or qualities this is
divided into two parts :
(A) Simple classification
(B) Multiple classification.
2. According to variable or quantity or classification according to class intervals. -
Qualitative Classification : When facts are grouped according to the qualities (attributes)
like religion, literacy, business etc., the classification is called as qualitative
classification.
(A) Simple Classification : It is also known as classification according to Dichotomy.
When data (facts) are divided into groups according to their qualities, the classification is
called as 'Simple Classification'. Qualities are denoted by capital letters (A, B, C, D ......)
while the absence of these qualities are denoted by lower case letters (a, b, c, d, .... etc.)
(B) Manifold or multiple classification : In this method data is classified using one or
more qualities. First, the data is divided into two groups (classes) using one of the
qualities. Then using the remaining qualities, the data is divided into different subgroups.
For example, the population of a country is classified using three attributes: sex, literacy
and business
Classification according to class intervals or variables : The data which is expressed in
numbers (quantitative data), is classified according to class-intervals. While forming
class-intervals one should bear in mind that each and every item must be covered. After
finding the least value of an item and the highest value of an item, classify these items

25. into different class-intervals. For example if in any data the age of 100 persons ranging
from 2 years to 47 years In deciding on the grouping of the data into classes, for the
purpose of reducing it to a manageable form, we observe that the number of classes
should not be too large. If it were so then the object of summarization would be defeated.
The number of classes should also not be too small because then we will miss a great deal
of detail available and get a distorted picture. As a rule one should have between 10 and
25 classes, the actual number depending on the total frequency. Further, classes should be
exhaustive; they should not be overlapping, so that no observed value falls in more than
one class. Apart from exceptions, all classes should have the same length.
f) Scope of managerial research:
Management Research (MR) is an international journal dedicated to advancing the
understanding of management in private and public sector organizations through
empirical investigation and theoretical analysis. MR attempts to provide an international
dialogue between researchers and thereby improve the understanding of the nature of
management in different settings and, consequently, achieve a reasonable transfer of
research results to management practice in several contexts. MR is especially dedicated
to foster the general advancement of management scholarship among iberoamerican
scholars and/or those academics interested in iberoamerican issues. Iberoamerica is
defined broadly to include all of Latin America, Latino populations in North America,
and Spain/Portugal. However, submissions are encouraged from all management scholars
regardless of ethnicity or national origin and manuscripts should not be limited to themes
dealing with iberoamerican populations. MR is a multidisciplinary outlet open to
contributions of high quality, from any perspective relevant to the field and from any
country. MR intends to become a supranational journal which gives special attention to
national and cultural similarities and differences world-wide. This is reflected by its
international editorial board and publisher and its sponsorship by the Iberoamerican
Academy of Management. MR is open to a variety of perspectives, including those that
seek to improve the effectiveness of, as well, as those critical of, management and
organizations. MR is receptive to research across a broad range of management topics
such as human resource management, organizational behavior, organization theory,
strategic management, corporate governance, and managerial economics. The
management and organization contributions present in MR articles can also be grounded
in the basic social disciplines of economics, psychology, or sociology. Articles can be
empirical, theoretical or measurement oriented. Conceptual articles should provide new
theoretical insights that can advance our understanding of management and
organizations. Empirical articles should have well-articulated and strong theoretical
foundations. All types of empirical methods -quantitative, qualitative or combinations-are
acceptable. MR encourages the interplay between theorizing the empirical research in
the belief that they should be mutually informative. MR is especially interested in new
data sources. That includes models that test new theory and expand our sample pools by
including alternative approaches to sampling and measurement and samples drawn from
non-traditional sources (e.g., from iberoamerican firms), and the examination of the
validity and reliability of such samples. MR publishes only original research as articles or
research notes. Manuscripts will be considered for publication with the understanding
that their contents are not under consideration for publication elsewhere. Prior

26. presentation at conference or concurrent consideration for presentation at a conference
does not disqualify a manuscript from consideration by MR.