Research can be defined as a systematic investigation to establish facts and answer questions through gathering and interpreting data. The goal of research is to develop or contribute to generalizable knowledge. It involves formulating hypotheses, collecting and analyzing data, and drawing conclusions. Research design refers to the overall strategy used to integrate different components of a study to ensure the study is conducted properly. The key components of research design include sampling, observational techniques, statistical analysis, and operational procedures.

1. Cont…..
 The systematic and objective analysis and recording of
controlled observations that may lead to the
development of generalizations, principles or theories,
resulting in prediction and possibly ultimate control of
events.
 The process of systematically obtaining accurate
answers to significant and pertinent questions by the
use of the scientific method of gathering and
interpreting information.

2. Cont……
 Research can be defined as the search for knowledge
or any systematic investigation to establish facts.

3. Objectives of Research To gain familiarity with a phenomenon or to achieve
new insights into it.
 To portray accurately the characteristics of particular
individual, situation or a group.
 To determine the frequency with which it is associated
with something else.
 To test a hypothesis of a causal relationship between
variables.


4. Significance of Research
 The role of research in several fields of applied
economics, whether related to business or to the
economy.
 Research provides the basis for nearly all government
policies in our economic system
 In solving various operational and planning problem
of business and industry.
 In studying social relationships and in selecting
answers to various social problems.
 Research may mean the outlet for new ideas and
insights.


5. Scope of Research
 Extends knowledge
 Establishes generalizations and general laws
 Verifies and tests
 To make reliable predictions
 Analyse inter-relations
 Finding solutions
 Developing new tools, concepts and theories
 National development

6. RESEARCH PROCESS
Define
Research
Problem
Review
Concepts
And
theories
Review
Previous
Research
findings
Formulate
hypothesis
Design
Research
(Including
Sample
Design)
Collect
Data
(Execution)
Analyse
Data
(Test
Hypothesis
if any)
Interpret
and
report
FF
F
F F
FF
I
II
III IV V VI VII
F
FF
Feed Back
Feed Forward
Review the literature

7. Types of Research
Pure Research
 Pure research is undertaken for the sake of knowledge
without any intention to apply it in practice.
 It is undertaken out of intellectual curiosity
 It is not problem-oriented
 It aims at extension of knowledge.
 It is foundation to applied research
 Eg: Einstein’s contribution, Newton’s contribution etc

8. Contribution of Pure Research
 Offer solutions to many practical problems
 To find the critical factors
 Develops alternative solutions

9. Applied Research
 Applied research is carried on to find solution to a
real-life problems requiring an action or policy
decisions.
 It is problem-oriented and action-oriented.
 It seeks immediate and practical results
 Eg: Marketing Research, post purchase experience of
customers.

10. Contribution of Applied Research
 Contribute new facts
 Applied research can put theory to the test
 Aid in conceptual clarification
 Integrate previously existing theories.

11. Exploratory or Formulative
Research
 Exploratory research 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 just attempts to see what is there rather than to
predict the relationships that will be founded.

12. Purposes
 To generate new ideas
 To increase the researcher’s familiarity with the
problem
 To gather information for clarifying concepts
 To determine whether it is feasible to attempt the
study

13. Descriptive Research
 It is a fact-finding investigation with adequate
interpretation.
 It has focus on particular aspect or dimensions of the
problem
 It is designed to gather descriptive information and
provides information for formulating more
sophisticated studies
 Description of the state of affairs as it exists at present
 Eg: Frequency of shopping.

14. Analytical Research
 The researcher has to use facts or information already
available and analyze these to make a critical
evaluation of the material.

15. Quantitative and qualitative
research
 Quantitative research is based on the measurement of
quantity or amount. It is applicable to phenomena that
can be expressed in terms of quantity.
 Qualitative research is concerned with qualitative
phenomenon i.e. phenomena relating to or involving
quality. ( investigating the reasons for human
behavior)

16. RESEARCH PROBLEM
What is a research problem?
 The term ‘problem’ means a question or issue
to be examined.
 Research Problem refers to some difficulty
/need which a researcher experiences in the
context of either theoretical or practical
situation and wants to obtain a solution for
the same.

17. CRITERIA OF SELECTION
Internal / Personal criteria – Researcher’s Interest,
Researcher’s Competence, Researcher’s own
Resource: finance and time.
External Criteria or Factors – Researchability of the
problem, Importance and Urgency, Novelty of the
Problem, Feasibility, Facilities, Usefulness and
Social Relevance.

18. CRITERIA OF A GOOD RESEARCH
PROBLEM
 Clear and Unambiguous
 Verifiable
 Interesting
 Novel and Original
 Availability of Guidance

19. SOURCES OF PROBLEMS
 Reading
 Academic Experience
 Daily Experience
 Exposure to Field Situations
 Consultations
 Brainstorming
 Research
 Intuition

20. Steps in identifying research
problem
 Statement of the problem in a general way
 Understanding the nature of the problem
 Surveying the available literature
 Developing the ideas through discussions
 Rephrasing the research problem

21. 3. Literature review :
The literature review helps the researcher to develop a
good problem statement; it ensures that no important
variable is overlooked in the process of defining the
problem
Sometimes the investigator might spend considerable
time and effort in “discovering” something that has
already been thoroughly researched. A Literature
review would prevent such a waste of resources in
reinventing the wheel.
21

22. Conducting the literature review
1- Data sources
( you will need to use a combination of information
resources the precise combination of resources depend
on the nature and the objectives of your research project
) this combination come from information’s from text
books, journals, theses, conference proceedings,
unpublished manuscripts, reports, newspapers, the
internet.
22

23. 2- Searching for literature
In past go through several bibliographical indexes but
now by computer online systems (locating sources to
locate and printout the published information)
Internet online searching directories (subject, title,
geographical location, trade opportunities, industrial
plants, foreign traders, data bases)
23

24. 3- Evaluating the literature
Accessing the online system and searching for literature
in the area of interest will provide a comprehensive
bibliography on the subject.
24

25. 4- Documenting the literature review
Is important to convince the reader that
a) The researcher is knowledgeable about the problem
area and has done the preliminary homework that is
necessary to conduct the research
b) The theoretical framework will be structured on
work already done and will add to the solid
foundation of existing knowledge.
25

26. The process of building a theoretical
framework includes:
1. Introducing definitions of the concept or
variable in your model.
2. Developing a conceptual model that provides a
descriptive representation of your theory
3. Coming up with a theory that provides an
explanation for relationships between the
variable in your model.
26

27. Variables:
 A variable is anything that can take on differing or
vareing values.
 Examples of variables are: production units,
absentiesm and motivation.
27

28. Types of variables :
1- Dependent variable
The dependent variable is the variable of primary
interest to the researcher. Through the analysis of
the dependent variable is possible to find answers
or solutions to the problem
28

29. 2- Independent variable
The independent variable is generally conjectured
that an independent variable is one that influences
the dependent variable in either a positive or
negative way. That is, when the independent
variable is present, the dependent variable is also
present, and with each unit of increase in the
independent variable, there is an increase or
decrease in the dependent variable.
29

30. 3- Moderating variable
The moderating variable is the presence of a third
variable that modifies the relationship between the
independent and the dependent variables.
30

31. 4- Mediating variable
The mediating variable is one that surfaces between
the time the independent variable start operating
to influence the dependent variable and the time
their impact is felt on it. The Mediating variable
surfaces as a function of the independent variable
operating in any situation, and helps to
conceptualize and explain the influence of the
independent variable on the dependent variable.
31

32. Scientific Method
 A study is scientific when its data are subjected to a
logical analysis resulting in the development of a
theory whether those data are secured by experiment
or by statistics or by commonsense
 Scientific research is a systematic, controlled,
empirical and critical investigation of hypothetical
propositions about the presumed relations among
natural phenomena

33. Basic postulates or characteristics
of scientific methods
 It relies on empirical evidence
 It utilizes relevant concepts
 It is committed to only objective considerations
 It presupposes ethical neutrality
 It results into probabilistic predictions
 Its methodology is made known to all concerned for
critical scrutiny are for use in testing the
conclusions through replications
 It aims at formulating most general axioms or what
can be termed as scientific theories

34. Blocks in Scientific method
 Define the problem
 Establish hypothesis as to
causes/explanations/solutions of the problem
 Collect the data
 Analyze the data to test the hypothesis and draw
inferences

35. proposition
 A preposition is a declarative statement of a concept.
 A preposition is a narration of a concept, which
requires the same level of caution and precision that is
expected to scientific research.

36. Classification of prepositions
 Relational preposition:
 Declarative statement that serves the purpose for
identification of association between concept and
defining distinctive characteristics to a required level.
 Correlation Proposition states the co variation
between related concepts. The direction and degree
 of change are specific for each association.
 The Positively correlated Proposition states that if
one-concept changes, the other concept would also
 change in the same direction.

37.  The Negatively correlated Proposition states that if
one-concept changes, the other concept also changes
inversely or indirectly.
 The Sym metrical Relational Proposition states
that two concepts have a reversible relationship. i. c. if
a change occurs in one concept, then the other concept
will also change, and vice versa.
 Non-Symmetrical Relational Proposition states
that two concepts have a non-reversible relationship
i.e., if change occur in one concept then other concept
will change also but not vice versa.

38. Non-relational Propositions
 The non-relational proposition is a declarative
statement, which serves the purpose of identification of
concepts and defining the distinct characteristics of the
concept to the required level.

39. HYPOTHESIS
 A hypothesis is an assumption about relations
between variables.
 Hypothesis can be defined as a logically
conjectured relationship between two or more
variables expressed in the form of a testable
statement.

40. Characteristics of Hypothesis
 Clear and Precise
 Capable of being tested
 State relationship between variables
 Limited in scope and must be specific
 Consistent with most known facts
 Amenable to testing within reasonable time
 Explain the facts that give rise to the need for
explanation.

41. Statement of hypothesis : Formats
1- If-Then statement
To examine whether or not the conjectured
relationship or differences exist, this hypothesis
can be set either propositions or in the form of If-
Then statement.
Example:
IF the employees are more healthy, THEN they will
take sick leave less frequently.
41

42. 2- Directional and non directional
hypothesis
If, instating the relationship between two variables or
comparing two groups, terms such as positive, negative, more
then, less then, and the like are used, then these are:
Directional hypothesis because the direction of the
relationship between the variables ( positive – negative) is
indicated.
Example:
The greater the stress experienced in the job, the lower the
job satisfaction of employees.
Non directional hypothesis there is a significant relationship
between two variables, we may not be able to say whether the
relationship is positive or negative.
Example:
There is a relationship between age and job satisfaction.
42

43. 3- Null and alternate hypothesis
Null hypothesis may state that the correlation
between two variables is equal to zero.
The null statement is expressed in terms of there
being no relationship between two variables.
The alternate hypothesis, which is the opposite of
the null, is a statement expressing a relationship
between two variables.
43

44. Procedure for Hypothesis Testing
 Making a Formal Statement
 Selecting a significance level
 Deciding the distribution to use
 Selecting a random sample and computing an
appropriate value
 Calculation of the probability
 Comparing the probability

45. Research Design
 A research design is the arrangement of conditions for
collection and analysis of data in a manner that aims to
combine relevance to the research purpose with
economy in procedure.
 Research design is the conceptual structure within
which research is conducted; it constitutes the
blueprint for the collection, measurement and analysis
of data.

46. Components of Research Design
 Sampling Design which deals with the method of
selecting items to be observed for the given study
 Observational Design which relates to the conditions
under which the observations are to be made.
 Statistical Design which concerns with the question of
how many items are to be observed and how the
information and data gathered are to be analyzed.
 Operational Design which deals with the techniques by
which the procedures specified in the sampling, statistical
and observational designs can be carried out.

47. Nature for Research Design
 Facilitates the smooth sailing of the various research
operations.
 Stands for advance planning of the methods to be adopted
for the collecting the relevant data and techniques to be
used in their analysis.
 Research design has greater bearing on the reliability of the
results.
 Research design helps the researcher to organize his ideas
in a form whereby it will be possible for him to look flaws
and inadequacies.
 To provide a comprehensive review of the proposed study.

48. Features of Research Design
 Flexible
 Efficient
 Economical
 Appropriate
 Reliability

49. Types of Research Designs
 Research Design in case of Exploratory Research
Studies:
 Exploratory research studies are also termed as
formulative research studies.
 The purpose of such studies is that of formulating a
problem for more precise investigation or of
developing the working hypotheses from an
operational point of view.

50. Methods in explorative research
design
 Survey of concerning literature (Hypothesis stated by
earlier workers may be reviewed and their usefulness
be evaluated as a basis for further research)
 Experience survey (survey o people who have practical
experience with the problem to be studied)
 Analysis of insight-stimulating (intensive study of
selected instances of the phenomenon in which one is
interested)

51. Research design in case of
descriptive and diagnostic research
studies
 Descriptive research studies are those studies which
are concerned with describing the characteristics of
a particular individual, or of a group, whereas
diagnostic research studies determine the frequency
with which something occurs or its association
with something else.

52. Steps In Descriptive Research
Designs
 Formulating the objective of the study
 Designing the methods of data collection
 Selecting the sample
 Collecting the data
 Processing and analyzing the data
 Reporting the findings

53. Comparison
Exploratory Descriptive
Overall Research design Flexible design Rigid design
Sampling design Non-probability probability
Statistical design No pre-planned design Pre-planned design for
analysis
Observational design Unstructured instrument
for collection of data
Structured or well
thought out instrument
for collection of data
Operational design No fixed decisions about
the operation
Advanced decisions about
operational procedures

54. Experimental Research design
 Experimental research is designed to assess the effects
of particular variables on a phenomenon by keeping
the other variable constant or controlled.

55. Basic principles of experimental
design
 The principle of Replication
 The experiment should be repeated more than once . Thus
each treatment is applied in many experimental units
instead of one.
 The principle of Randomization provides protection,
when we conduct an experiment, against the effect of
extraneous factors by randomization.
 The principle of Local Control
 Under it the extraneous factors, the known source of
variability, is made to vary deliberately over a wide a range
as necessary and this needs to be done in such a way that
the variability it causes can be measured and hence
eliminated from experimental errors.

56. Types of Experimental designs
 Formal Experimental Designs
 Formal experimental designs offer more control and
use precise statistical procedure for analysis.
 Informal Experimental Designs
 Designs that normally use a less sophisticated form of
analysis based on differences in magnitudes.

57. Types of informal experimental
designs
 Before-and-after without control designs a single
test group or area is selected and the dependent
variable is measured before the introduction of the
treatment.
 After-only with control design: in this two groups or
areas are selected and the treatment is introduced into
the test area only.
 Before-and-after with control designs: In this this
design two areas are selected and the dependent
variable is measured in both the areas for an identical
time-period before the treatment.

58. Formal experimental designs
 Completely randomized design involves only two
principles i.e. the principle of replication and the
principle of randomization of experimental designs.
 Randomized block design involves principle of local
control can be applied along with the other two
principles of experimental designs.
 Latin square design : an experiment has to be made
through which the effects of five different varieties of
fertilizers on the yield of a certain crop and it to be
judged.

59. Cont…..
 Factorial designs: Factorial designs are used in
experiments where the effects of varying more than
one factor are to be determined.

60. Ethics and business research
Ethics in business research refers to a code of conduct or
expected societal norm of behavior while conducting
research.
60

61. What are Research Ethics?
 Ethics are norms or standards of behavior that guide
moral choices about our behavior and our
relationships with others
 The goal is to ensure that no one is harmed or suffers
adverse consequences from research activities

62. Ethical issues relating to the
respondents
 People are made to participate in a research project
without their knowledge or consent.
 Without informing the purpose of the research
 Incorrect information
 Expose participants to physical or mental stress
 Privacy issues

63. Primary data
 Primary data is directly collected by the researcher
from their original sources.
 Primary data are those which are collected afresh and
for the first time and thus happen to be original in
character.
 Primary data is originally collected for an
investigation.

64. Merits of primary data
 Degree of accuracy is quite high
 It does not required extra caution
 It depicts the data in great detail
 For some investigations secondary data are not
available

65. Demerits of primary data
 Collection of data requires a lot of time
 It requires lot of finance
 In some enquires it is not possible to collect primary
data
 It requires a lot of labour
 It requires a lot of skills

66. Methods and sources of primary
data
 Observation method
 A systematic viewing of a specific phenomenon in its
proper setting for the specific purpose of gathering
data for a particular study.
 Systematically planned and recorded and is subjected
to checks and controls on validity and reliability.

67. Characteristics of observational
method
 It is both a physical and mental activity.
 Observation is selective
 Observation is purposive and not casual
 It captures the natural social context in which person’s
behavior occurs
 Observation should be exact and be based on
standardized tools of research

68. Types of observations
 Participant observation
 Non-participant observation
 Direct observation
 Indirect observation
 Controlled observation
 Uncontrolled observation

69. Merits of Interview method
 More information and that too in greater depth can be
obtained
 Interviewer by his own skills can overcome the resistance
 There is greater flexibility
 Applied to recording verbal answers
 Personal information can be obtained easily
 Samples can be controlled more effectively
 Adopted to the ability or educational level of the person
 Supplementary information can be collected

70. Demerits
 Expensive method
 Supervision and control problem
 Difficult to approach respondents like executives
 More time consuming
 Over stimulate the respondents
 Difficulty in selecting field staff

71. Questionnaires
 Questionnaire consist of a number of questions
printed or typed in a definite order on a form and set
of forms.

72. merits
 Low cost
 Free from the bias of the interviewer
 Respondents have adequate time to give answers
 Respondents who are not easily approachable can also
be reached conveniently
 Large samples can be made

73. Demerits
 Low rate of returns of the duly filled in questionnaires
 Used only when respondents are educated and
cooperating
 Inbuilt flexibility
 Possibility of ambiguous replies
 Difficult to know willing respondents
 Likely to be the slowest of all

74. Constructing questionnaire
 General form
Structure and unstructured questionnaire
Question Sequence
Question formulation and wording
Questions should meet following standards
Easily understood
Simple
Convey only one thought at a time
Should be concrete
Should conform as much as possible to respondents what of
thinking

75.  Essentials of a good questionnaire
 Short and simple
 Logical sequence moving from simple to difficult
 Personal and intimate questions should be left to the end
 Questions may be dichotomous, multiple choice , open-
ended
 Control questions should be added to test reliability of
respondent
 Questions affecting sentiments of respondents should be
avoided
 Adequate space to answers

76. schedules
 This method of data collection is very much like the
collection of data through questionnaire with little
difference which lies in the facts that schedules
(proforma containing a set of questions) are being
filled in by the enumerators who are specially
appointed for the purpose.

77. Secondary data
 Secondary data on the other hand, are those which
have already been collected by someone else and
which have already been
 Secondary data sources ( it is that information’s
that already existed and the researcher has no role in
obtaining it but he read it and take what he need from
it )
 text books, journals, theses, conference proceedings,
unpublished manuscripts, reports, newspapers, the
internet.

78. Scaling
 Scaling describes the procedure of assigning to various
degrees of opinion, attitudes and other concepts.
 1) making a judgment about some characteristic of an
individual and then placing him directly on a scale
 2) constructing questionnaires in such a way that the
score of individual’s responses assigns him a place on a
scale.

79. Scale classification bases
 Subject orientation: scale may be designed to
measure characteristics of respondent who complete it
 Response form: we may classify the scales as
categorical and comparative (rating and ranking)
 Degree of subjectivity: scale data may be based on
whether we measure subjective personal preferences or
non-preference judgment
 Scale properties: nominal, ordinal, interval and ratio
scales
 Number of dimensions: scales can be classified as
unidimentional and multidimensional

80.  Scale construction techniques
 Arbitrary approach: it is presumed that such scales
measures the concepts for which they have been designed.
 Consensus approach: a panel of judges evaluate the items
chosen for inclusion in the instrument
 Item Analysis approach: number of individual items are
developed into a test which is given to a group of
respondents.
 Cumulative scales: chosen on the basis of their
conforming to some ranking of items
 Factor scales: constructed on the basis of inter correlation

81. Scaling techniques
 Rating Scales
 The rating scale involves qualitative description of a
limited number of aspects of a thing or of traits of a
person.
 i.e, we judge properties of objects without reference of
other similar objects.

82. Classification rating scale
 Graphic rating scale: under it various points are
usually put along the line to form a continuum and the
rater indicates his rating by simple marking a mark
 E.g: How do you like the product?
 Like very much ( )
 Like some what ( )
 Neutral ( )
 Dislike some what ( )
 Dislike very much ( )

83.  Itemized rating scale:
 Presents a series of statements from which a respondent
selects one as best reflecting his evaluation.
 E.g: To inquire as to how well does a worker get along with
his fellow workers
 He is almost always involved in some friction with a fellow
workers
 He is often at odds with one or more of his fellow workers
 He sometimes gets involved in friction
 He infrequently becomes involved in friction with others.

84. Merits
 More information and meaning to the rater
 Requires less time
 Used with large number of properties or variables
 Makes good judgment

85. Ranking Scales
 Under ranking scales we make relative judgments
against other similar objects.
 The respondents under this method directly compare
two or more objects and make choices among them.

86. Classification of ranking scales
 Method of paired comparisons
 Under it the respondent can express his attitude by
making a choice between two objects, say between a
new flavour of soft drink and an established brand of
drink.
 But when there are more than two stimuli to judge, the
number of judgments required in a paired comparison
is given by the formula
 N=n(n-1)/2 where N=number of judgments
 n=number of stimuli or objects to
be judged

87.  Method of rank order
 Under this method of comparative scaling the
respondents are asked to rank their choices.

88. validity
 Validity is the most critical criterion and indicates the
degree to which an instrument measures what it is
supported to measure.
 Validity is the extent to which differences found
with a measuring instrument reflect true
differences among those being tested.

89. Types of validity
 Content validity: the extent to which a measuring
instrument provides adequate coverage of the topic
under study.
 Criterion-related validity: relates to our ability to
predict some outcome or estimate the existence of
some current condition.
 Construct validity: a measure is said to possess
construct validity to the degree that it conforms to
predicted correlations with other theoretical
propositions

90. Reliability
 The test of reliability is another important test of
sound measurement.
 A measuring instrument is reliable if it provides
consistent results.


91. Two aspects of reliability
 Stability aspect is concerned with securing consistent
results with repeated measurements of the same
person and with the same instrument.
 Equivalence aspect considers how much error may
get introduced by different investigators or different
samples of the items being studied.

92. Ways to improve reliability
 By standardizing the conditions under which the
measurement takes place
 By carefully designed directions for measurement with
no variation from group to group

93. Pilot study is a small scale preliminary study
conducted in order to evaluate feasibility, time, cost,
adverse events, and effect size (Statistical variability)
in an attempt to predict an appropriate sample size
and improve upon the study design prior to
performance of a full scale research project.

94. A smaller version of a larger study that is conducted to prepare
for that study. A pilot study can involve pretesting a research tool,
like a new data collection method. It can also be used to test an
idea or hypothesis.
Pilot studies are used as ‘feasibility studies’, to ensure that the
ideas or methods behind a research idea are sound, as well as to
“work out the kinks” in a study protocol before launching a larger
study.
Definition:

95. 1.Pilot study is a small experiment designed to test logistics
2. Gather information prior to a large study
3. Improve the actual study’s quality and efficiency
4.Reveal deficiencies in the design of a proposed
experiment or procedure and these can then be addressed
before time
5. A good research strategy requires careful planning and
a pilot study will often be a part of this strategy
Objectives

96. 1.Carried out before large scale quantitative research in an attempt
to avoid time and money being wasted on an inadequately
designed project.
2.It is a potentially valuable insight and should anything be missing
in the pilot study, it can be added to the full scale (and more
expensive) experiment to improve the chances of a clear outcome.
3.Pilot experiments are used to sell a product and provide
quantitative proof that the system has potential to succeed on a full
scale basis.
Advantages:

97. 4.In social science, pilot studies can be referred to as small
scale studies that will help identify design issues before the
main research is done.
5.It permits preliminary testing of the hypothesis that leads to
testing more precise hypothesis in the main study. It may lead
to changing some hypothesis, dropping some or developing
new hypothesis.
6.It often provides the researcher with ideas, approaches, and
clues you may not have foreseen before conducting the pilot
study

98. 1.Pilot study is done on a smaller scale. Thus, actual results of the
study may vary from the results of pilot study.
2. Pilot studies, therefore, may not be appropriate for case studies
3. A pilot study is usually carried out on members of the relevant
population, but not on those who will form part of the final sample
4. A pilot study is normally small in comparison with the main
experiment and therefore, can provide only limited information on
the sources and magnitude of variation of response measures
Limitations:

99. Parametric test
 The parametric test is the hypothesis test which
provides generalisations for making statements about
the mean of the parent population.
 A statistical test in which specific assumptions are
made about the population parameter is known as the
parametric test.

100. Characteristics of parametric test
 Assumptions are made about the population parameter
 The test statistic is based on distribution
 It is assumed that the measurement of variables of interest
is done on interval or ratio level
 The measurement of central tendency in the parametric
test is mean
 There is complete information about the population.
 The applicability of parametric test is for variables only
 For measuring the degree of association between two
quantitative variables, pearson’s coefficient of correlation is
used in the parametric test

101. Non-parametric test
 The nonparametric test is defined as the hypothesis
test which is not based on underlying assumptions, i.e:
it does not require population’s distribution to be
denoted by specific parameters.
 The test is mainly based on differences in medians.


102. Characteristics of non-parametric
test
 A statistical test used in the case of non-metric
independent variables is called non parametric test.
 The test statistic is arbitrary in the case of non parametric
test
 The variables of interest are measured on nominal or
ordinal scale
 The measure of central tendency is median
 There is no information about the population
 Test applies to both variables and attributes
 Spearman’s rank correlation is used in the non parametric
test

103. Parametric tests
 Z-test
 t-test
 F-test

104. Z-test
 Z-test is based on the normal probability distribution
and is used for judging the significance of several
statistical measures, particularly the mean.
 The relevant test statistic(the value obtained from the
sample data that corresponds to the parameter under
investigation) z, is worked out and compared with its
probable value at a specified level of significance for
judging the significance of the measured concerned.

105. Applications
 Z-test is generally used for comparing the mean of a sample
to some hypothesised mean for the population in case of
large sample.
 For judging the significance of difference between means
of two independent samples in case of larger samples.
 For comparing the sample proposition to a theoretical
value of population proportion or for judging the difference
in proportions of two independent samples
 The test may be used for judging the significance of
median, mode, coefficient of correlation and several other
measures.

106. t-test
 t-test is based on t-distribution and is considered an
appropriate test for judgeing the significance of a
sample mean or for judging the significance of
difference between the means of two samples in case
of small samples when population variance is not
known.
 In case two samples are related we use paired t-test for
judging the significance of mean of difference between
the two related samples.

107. Applications
 Used for judging the significance of the coefficients of
sample and partial correlations.
 For accepting or rejecting the null hypothesis
 Applies only in case of small samples when population
variance is unknown.

108. F-test
 F-test is based on F-distribution and is used to
compare the variance of the two-independent samples.
 Applications:
 Used in context of analysis of variance (ANOVA) for
judging the significance of more than two sample
means at one and the same time.
 For judging the significance of multiple correlation
coefficients.
 For accepting or rejecting the null hypothesis.

109. Chi-square test
 A statistical method assessing the goodness of fit
between a set of observed values and those expected
theoretically.
 Statistical measure used in the context of sampling
analysis for comparing a variance to a theoretical
variance.

110. Applications
 Test the goodness of fit:
 Enables us to see how well does the assumed
theoretical distribution fit to the observed data.
 Test the significance of association between two
attributes
 Test the significance of population variance

111. Conditions for the application
 Observations recorded and used are collected on a
random basis
 All the items in the sample must be independent
 No group should contain very few items, say less than
10
 The overall number of items must also reasonably
large.
 The constrains must be linear.

112. Steps involved in applying chi-
square
 Calculate expected frequencies on the basis of given
hypothesis
 Obtain the difference between observed and expected
frequencies and find out squares
 Divide the quantity obtained
 Find summation of values
 ∑(Oij-Eij) square/Eij

113. Rank sum tests
 Wilcoxon-mann-whitney test (U-test)
 Kruskal-wallis test (H-test)

114. U-test
 This test is used to determine whether two
independent samples have been drawn from the same
population.
 It uses more information than sing test
 This test applies under very general conditions and
requires only that the populations sampled are
continuous.
 To perform this test we first rank the data jointly
taking them as belonging to a single sample in either
an increasing or decreasing order of magnitude.

115.  U= n1.n2+ n1(n1+1)/2 – R1
 n1,n2 are sample sizes
 R1=sum of ranks assigned to the values of first sample

116. The kruskal-wallis test (H-test)
 This test is conducted in a way similar to the U test
 This test used to test the null hypothesis that k
independent random samples come from identical
universes
 This test is applies to the one-way analysis of variance

117. Sign test
 It is based on the direction of the plus or minus signs
of the plus or minus signs of observations in a sample
and not on their numerical magnitudes.
 Types
 One sample sign test
 Two sample sign test

118. One sample sign test
 The one sample sign test is a very simple non-
parametric test applicable when we sample a
continuous symmetrical population in which case the
probability of getting a sample value less than mean is
½ and the probability of getting a sample value greater
than mean is also ½.
 To test the null hypothesis against an appropriate
alternative on the basis of a random sample of size n
we replace the value of each and every item of sample
with + sign if it is greater than null hypothesis and
with a – sign if it is less than null hypothesis

119. Two sample sign test or the sign
test for paired data
 Each pair of values can be replaced with + sign if the
first value of the first sample is greater than the first
value of second sample
 We take – sign if the first value of first sample is less
than the first value of second sample.

120. correlation
 If two quantities vary in such a way that movements in
one are accompanied by movements in the other, these
quantities are correlated. For example, there exist
some relationship between age of husband and age of
wife, price of commodity and amount demanded
 There are several methods of
determining the relationship between variables. Karl
Pearson’s coefficient of correlation (or simple
correlation) is the most widely used method of
measuring the degree of relationship between two
variables.

121. significance
 The study of correlation is of immense use in practical
life because of the following reasons:
 1) Most of the variables show some kind of relationship
 2) Once we know that two variables are closely related,
we can estimate the value of one variable given the
value of another with the help of regression analysis.
 3) Correlation analysis contributes to the
understanding of economic behavior eg : by studying
the effect of increase in income level or prices of the
commodities on amount of purchase

122. Types of correlation
 Correlation is described or classified in several
different ways. Three of the most important ways of
classifying correlation are:
 positive or negative
 Simple, partial and multiple
 linear and non-linear

123.  LINEAR AND NON-LINEAR(CURVILINEAR)
CORRELATION:
 The distinction between linear and non-linear
correlation based upon the contrary of the ratio of
change between the variables. If the amount of change
in one variable is exactly equal to the amount of
change in the other variable then the correlation is
said to be linear

124. REGRESSION
 Regression analysis is a mathematical measure of the
average relation ship between two or more variables.

 In regression analysis there are two types of variables.
The variables whose value is influenced or is to be
predicted is called DEPENDENT VARIABLE and the
variable which influences the values or is used for
prediction, is called INDEPENDENT VARIABLE.

125.  In regression analysis we have two regression lines

 Line of X on Y: to estimate the value of X at a given
value of Y
 and the equation is (X- )=bxy (Y- )

 Line of Yon X: to estimate the value of Y at a given
value of X
 and the equation is (Y- )=byx (X- )

126. Types
 Simple Regression Analysis: regression is the
determination of a statistical relationship between two
or more variables. In simple regression we have two
variables one variable is cause of the behavior of
another one.
 Multiple correlation and Regression: when there
are two or more than two independent variables the
analysis concerning relationship is known as multiple
correlation and the equation describing such
relationship as the multiple regression equation.


127. Multivariate analysis
 Multivariate analysis which is a collection of
methods for analyzing data in which a number of
observations are available for each object.
 Multivariate analysis techniques: all statistical
techniques which simultaneously analyze more than
two variables on a sample of observations can be
categorized techniques.

128. Characteristics and Applications
 Multivariate techniques are largely empirical and deal
with the reality
 They processes the ability to analyze complex data
 Help in various types of decision making
 Multivariate techniques transform a mass of
observations into a smaller number of composite
scores

129. classification
 Dependence methods
 Multiple regression
 Multiple discriminate analysis
 Multivariate analysis of variance
 Canonical analysis
 Interdependence methods
 Factor analysis
 Cluster analysis
 Multi dimensional scaling (MDS)
 Non-metric MDS
 Latent structure analysis

130.  Multiple regression: in multiple regression we form a
linear composite of explanatory variables in such way
that if has maximum correlation with a criterion
variables.
 Multiple discriminant analysis: in this resercher
may classify individuals or objects into one of two or
more mutually exclusive and exhaustive groups on the
basis of a set of independent variables.

131. Multivariate analysis of variance
 It is extension of bivariate analysis of variance in which
the ratio of among groups variance is calculated on a
set of variables instead of a single variable.
 Canonical correlation analysis: this technique was
first developed by Hotelling wherein an effort is made
to simultaneo usly predict a set of criterion
variables from their joint co-variance with a set of
explanatory variables.

132. Factor Analysis
 Basic terms in Factor Analysis
 Factor: A factor is underlying dimension that account
for several observed values. There can be one or more
factors, depending upon the nature of the study and
the number of variables involved in it.
 Factor-loadings: Factor-loadings are those values
which explain how closely the variables are related to
each one of the factors discovered.
 Communality: shows how much of each variable is
accounted for by the underlying factor taken together.

133.  Eigen value or latent root: when we take the sum of
squared values of factor loadings relating to a factor, then
sum is referred to as Eigen value or latent root. Eigen values
indicates the relative importance of each factor in
accounting for the particular set of variables being
analyzed.
 Total Sum of Squares: when Eigen values of all factors are
totaled, the resulting value is termed as the total sum of
squares. This value when divided by the number of
variables results in an index that shows how the particular
solution accounts for what the variables taken together
represents.

134.  Rotation: Rotation, in the context of factor analysis, is
something like staining a microscope slide. Though
different rotations give results that appear to be
entirely different but from a statistical point of view, all
results are taken as equal none superior or inferior to
others.
 Factor Scores: Factor scores represents the degree to
which each respondent gets high scores on the group
of items that load high on each factor. Factor scores
can help explain what the factors mean.

135. Important methods of Factor
Analysis
 The Centroid method
 The principle components methods
 The maximum likelihood method

136. Centroid method of factor analysis
 This method of factor analysis developed by
L.L.Thurstone. The centroid method tends to
maximize the sum of loadings, disregarding signs; it is
the method which extracts the largest sum of absolute
loadings for each factor inturn.
 It is defined by linear combinations in which all
weights are either +1.0 or-1.0

137. Merits
 This method is that it is relatively simple, can be easily
understood .
 Involves simple computations
 It becomes easy to understand the mechanics involved
in other methods of factor analysis.

138. Steps involved in this method
 This method starts with the computation of a matrix of
correlations.
 If the correlation matrix so obtained happens to be positive
manifold, the centroid method requires that the weights
for all variables be +1.0
 The first centroid factor is determined
 To obtain second centroid factor one must first obtain a
matrix of residual coefficients. For this purpose the
loadings for the two variables on the first centroid factor
are multiplied.
 For subsequent factors the same process outlined above is
repeated.

139. Principal components method of
Factor Analysis
 This method of factor analysis developed by
H.Hotelling
 To maximize the sum of squared loadings of each
factor extracted in turn.
 According to PC factor explains more variance than
would the loadings obtained from any other method of
factoring.
 The aim of the principal components method is the
construction out of a given set of variables X’s of new
variables P called principal components which are
linear combinations of the X

140. Maximum Likelihood(ML) method
of Factor Analysis
 The ML method consists in obtaining sets of factor
loadings successively in such a way that each in turn
explains as much as possible of the population
correlation matrix as estimated from the sample
correlation matrix.
 ML method is a statistical approach in which one
maximizes some relationship between the sample of
data and the population from which the sample was
drawn.

141. Cluster Analysis
 Cluster analysis consists of methods of classifying
variables into clusters.
 A cluster consists of variables that correlate highly
with one another and have comparatively low
correlations with variables in other clusters.
 The basic objective of cluster analysis is to determine
how many mutually and exhaustive groups or clusters
based on the similarities of profiles among entities
really exist in the population.

142. Steps in cluster analysis
 The First of all, if some variables have a negative sum of correlations in
the correlation matrix, one must reflect variables so as to obtain a
maximum sum of positive correlations for the matrix as a whole.
 The second step consists in finding out the highest correlation in the
correlation matrix and the two variables involved form the nucleus of the
first cluster.
 Then one looks for those variables that correlate highly with the said two
variables and includes them in the cluster. This is how the first cluster is
formed
 To obtain the nucleus of the second cluster we find two variables that
correlate highly but have low correlations with members of the first
cluster
 One proceeds on similar lines to search for a third cluster and so on


143. Multidimensional Scaling (MDS)
 MDS allows a researcher to measure an item in more
than one dimension at a time.
 The basic assumption is that people perceive a set of
objects as being more or less similar to one another on
a number of dimensions instead of only one.

144. significance
 It enables the researcher to study the perceptual
structure of a set of stimuli and the cognitive processes
underlying the development of this structure.
 MDS provides a mechanism for determining the truly
salient attributes without forcing the judge
 One can scale objects, individuals or both with a
minimum of information
 MDS analysis will reveal the most salient attributes
which happen to be primary determinants for making
a specific decision.

145. Discriminant Analysis
 Discriminant analysis is a classification problem,
where two or more groups or clusters or population are
a priori and one or more new observations are
classified into one of the known populations based on
the measured characteristics.

146. procedure
 Collect ground truth or training data
 Prior probabilities ( expected portion of the community
that belong to population)
 Use Bartlett’s test to determine if variance covariance
matrices are homogeneous for the two or more populations
involved.
 Estimate the parameters of the conditional probability
density functions
 Compute discriminant functions
 Use cross validation to estimate misclassification
probabilities
 Classify observations with unknown group membership

147. How to select a test
 Data plan
 Data distribution
 Dependent variable’s level of measurement
 Strength of association between variables
 Sample size

148. Report writing
 A research report is a formal statement of the research
process and its results.
 It narrates the problem studied, methods used for
studying it and the findings and conclusions of the
study.

149. Steps in writing report
 Logical analysis of the subject matters
 Preparation of the final outline
 Preparation of the rough draft
 Rewriting and polishing of the rough draft
 Preparation of the final bibliography
 Writing a final draft

150. Structure and components of
report
 Preliminary pages
 Title and date
 Table of contents
 Preface or foreword
 List of tables and illustrations
 Main text
 Introduction
 Statement of findings and recommendations
 Results
 Implications of the results
 Summary
 End matter
 Questionnaires, mathematical derivations, bibliography

151. Types of reports
 Technical report
 In the technical report the main emphasis is on
 1)the methods employed
 2) assumptions made in the course of the study
 3) The detailed presentation of the findings including
their limitations and supporting data


152. Outline of a technical report
 Summary of results
 Nature of the study
 Methods employed
 Data
 Analysis of data and presentation of findings
 Conclusions
 Bibliography
 Technical appendices
 Index

153. Popular reports
 A popular report is used if the research results have
policy implications.
 The popular report is one which gives emphasis on
simplicity and attractiveness

154. Outline of a popular report
 The findings and their implications
 Recommendations
 Objective of the study
 Methods employed
 Results
 Technical appendices

155. Mechanics of writing a research
report
 Size and physical design
 Procedure
 Layout
 Treatment of quotations
 The footnotes
 Purpose of footnote
 The identification of materials used in quotations in
the report
 Notice of materials not immediately necessary

156.  Footnotes are placed at the bottom of the page on which
reference quotation
 Footnotes should be numbered consecutively
 Footnotes are always typed in single space
 Document style
 Regarding the single-volume reference
 Regarding multivolumed references
 Regarding works arranged alphabetically
 Regarding periodicals reference
 Regarding anthologies and collections reference
 Regarding second-hand quotations reference

157.  Case of multiple authorship
 Punctuation and abbreviations in footnotes
 Use of statistics, charts and graphs
 The final draft
 Bibliography
 Preparation of the index

158. Oral presentation
 Merits
 It provides an opportunity for give-and-take decisions
which generally lead to a better understanding of the
findings and their implications
 It is effective when supplemented by various visual devices.
 Provides better chance to the audience for understanding
speaker context
 Presenter is able to acquire an instant feedback
 High level of understanding and transparency
 Flexibility to audience so that they can take an appropriate
decisions

159. Presentation planning
 Think about the goal of presentation
 Consider audience
 Select main points
 Find best supporting information
 Start with solid information
 Present research and work in the body of the
presentation
 Use linking statements to make points clear
 Make use of visuals and graphics on slides
 Conclude presentation

160. preparation
 Needs assessment
 Development of the presentation
 Development of supporting material
 Development of a check-list of requirements
 Preparation of physical space
 Mental preparation

161. Making effective presentation
 Think audience
 Communicate
 Prepare the little things
 Structure presentation
 Finding your voice
 Do not read
 Non-verbal communication
 Slide design
 practice

162. Visual aids
 A visual aid supplements word with pictures, charts,
graphs or other visual information
 Importance
 Visual aids support your ideas
 Improve audience comprehension
 Feel connected with audience
 Helps illustrate complex ideas
 Help people to recall content
 Making presentation interesting
 Enhance understanding and support

163. What is Communication?
 What does it mean to you?
 The process of communication is what allows us to
interact with other people; without it, we would be
unable to share knowledge or experiences with
anything outside of ourselves. Common forms of
communication include speaking, writing, gestures,
touch and broadcasting.
 Wikipedia definition

164. Effective Communication Skills
Effective
Communication skills
Eye contact & visible mouth
Body language
Silence
Checking
for understanding
Smiling face
Summarising
what has been said
Encouragement
to continue
Some questions

165. Barriers to Effective Communication
Barriers to
effective
communication
Language
NoiseTime
DistractionsOther people
Put downsToo many questions
Distance
Discomfort
with the topic
Disability
Lack of interest