The document discusses various research designs and methods. It begins by explaining exploratory research design which is used to gain initial insights into a problem. Descriptive research designs such as cross-sectional and longitudinal studies are used to observe and describe behaviors without influencing them. Experimental research designs allow controlling variables to establish causal relationships. Various factors that influence the validity and types of experimental designs are also outlined.

1. Business Research Methods
Unit - II

2. I. Planning Process
• What – What we are trying to Do
• When – When we are going to do it
• How – How are planning to do it
• Where – Where will it be held
• By Whom – By whom it should be done

3. II. Identification of Problem
• There are two types of research problems, viz.,
– those which relate to states of nature and
– those which relate to relationships between variables.
• Initially the problem may be stated in a broad general
way and then the ambiguities, if any, relating to the
problem be resolved.
• Then, the feasibility of a particular solution has to be
considered before a working formulation of the
problem can be set up.
• The formulation of a general topic into a specific
research problem, thus, constitutes the first step in a
scientific enquiry.

4. II. Identification of Problem
• Essentially two steps are involved in formulating the research
problem, viz.,
– understanding the problem thoroughly,
– and rephrasing the same into meaningful terms from an
analytical point of view.
• The best way of understanding the problem is to discuss it
with one’s own colleagues or with those having some
expertise in the matter.
• In an academic institution the researcher can seek the help
from a guide.
• In private business units or in governmental organizations, the
problem is usually earmarked by the administrative agencies

5. II. Identification of Problem
• The researcher must at the same time
examine all available literature to get himself
acquainted with the selected problem.
• He may review two types of literature—
– the conceptual literature concerning the concepts and
theories,
– and the empirical literature consisting of studies made
earlier which are similar to the one proposed.

6. III. Process of Problem Identification
Statement of the problem in a general
way;
Understanding the nature of the
problem;
Surveying the available literature.
Developing the ideas through
discussions; and
Rephrasing the research problem into a
working proposition.

7. Illustration
• “Why is productivity in Japan so much higher than in
India”?
• “What factors were responsible for the higher labour
productivity of Japan’s manufacturing industries during
the decade 1971 to 1980 relative to India’s
manufacturing industries?”
• “To what extent did labour productivity in 1971 to
1980 in Japan exceed that of India in respect of 15
selected manufacturing industries? What factors were
responsible for the productivity differentials between
the two countries by industries?”

8. Qualitative Research Approaches
• Qualitative Research is primarily exploratory research.
• It is used to gain an understanding of underlying reasons, opinions,
and motivations.
• It provides insights into the problem or helps to develop ideas or
hypotheses for potential quantitative research.
• Qualitative Research is also used to uncover trends in thought and
opinions, and dive deeper into the problem.
• Qualitative data collection methods vary using unstructured or
semi-structured techniques.
• Some common methods include focus groups (group discussions),
individual interviews, and participation/observations.
• The sample size is typically small, and respondents are selected to
fulfill a given quota.

9. Quantitative research Approach.
• Quantitative Research is used to quantify the problem by way of
generating numerical data or data that can be transformed into
usable statistics.
• It is used to quantify attitudes, opinions, behaviors, and other
defined variables – and generalize results from a larger sample
population.
• Quantitative Research uses measurable data to formulate facts and
uncover patterns in research.
• Quantitative data collection methods are much more structured
than Qualitative data collection methods.
• Quantitative data collection methods include various forms of
surveys – online surveys, paper surveys, mobile surveys and kiosk
surveys, face-to-face interviews, telephone interviews,
longitudinal studies, website interceptors, online polls, and
systematic observations.

10. III. Hypothesis Formulation
• Working hypothesis is tentative assumption
made in order to draw out and test its logical
or empirical consequences.
• The role of the hypothesis is to guide the
researcher by delimiting the area of research
and to keep him on the right track.

11. III. Hypothesis Formulation
• How does one go about developing working hypotheses?
The answer is by using the following approach:
(a) Discussions with colleagues and experts about the problem, its
origin and the objectives in seeking a solution;
(b) Examination of data and records, if available, concerning the
problem for possible trends, peculiarities and other clues;
(c) Review of similar studies in the area or of the studies on
similar problems; and
(d) Exploratory personal investigation which involves original field
interviews on a limited scale with interested parties and
individuals with a view to secure greater insight into the
practical aspects of the problem.

12. III. Significance of Hypothesis
• It gives direction to the Research
• It assumes pre determined Result
• It helps in establishing relation between
different variable
• We Try to achieve the predetermined Result
• It creates clarity of the problem
• Resources can be arranged on the basis
Hypothesis.
• Helps in Designing Research

13. IV. Research Design - Conceptual
Framework
Research
Design
Exploratory Descriptive Experimental

14. Research Design
Research Design
Exploratory R.D. Conclusive R.D.
Descriptive R.D.
Cross-Sectional
R.D.
Single Cross-Sectional
Multiple Cross-
Sectional
Longitudinal R.D.
Causal R.D.

15. Exploratory Research Design

16. Various Methods of Research Design
I. Research design in case of exploratory research
studies
• The main 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.
– the survey of concerning literature;
– the experience survey and
– the analysis of ‘insight-stimulating’ examples

17. • Exploratory research is carried out for a
difficulty which has not been clearly defined.
It helps in figuring out the best research
design, data collection method and choice of
subjects.

18. Exploratory Research Design
• Initial research conducted to clarify and define the nature
of a problem
• Does not provide conclusive evidence
• Subsequent research expected.
• If not well defined:
• Exploratory Research is used to clarify/define a problem
• Case: Manager tells you “sales just aren’t what we
expected for this kite”
• Exploratory research is defined as the initial research into a
hypothetical or theoretical idea.
• This is where a researcher has an idea or has observed
something and seeks to understand more about it.

19. Qualitative techniques
• Projective Techniques
• Depth Interview
• Experience Survey
• Focus Groups
• Observation.

20. Projective Techniques
– Word association tests- wat 500 words.pdf
– Sentence completion tests - Projective TechniqueSentence
Completion.jpg
– Story completion tests - Projective Techniquestory-completion-
projective-technique-5-728.jpg
– Pictorial Techniques
• Thematic apperception test (T.A.T.)Projective Techniquethematic-
apperception-test-31-638.jpg
• Rosenzweig test(24 Cartoons)Projective TechniqueRosenwig Test.jpg
• Rorschach test (10 Inkblot) Projective TechniqueRorshach Test.png
• Holtzman Inkblot Test (HIT)(45 Inkblot Cards)Projective TechniqueHIT.jpg
• Tomkins-Horn picture arrangement test – (25 sets of three pictures )Projective
TechniqueTomkins Horn.jpg
– Quizzes, tests and examinations
– Sociometry

21. Depth Interview
• In-depth interviewing is a qualitative research
technique that involves conducting intensive
individual interviews with a small number of
respondents to explore their perspectives on a
particular idea, program, or situation.
– Face to face
– Emphasis on Body language
– Listen rather than talk
– Guided conversation
– Is conducted using a discussion guide
– Projective techniques can be incorporated into the
interview too.

22. Experience Survey
• An experience survey is a set of questions
designed to gauge the overall satisfaction of a
group of people who have shared a common
experience. Businesses commonly distribute
these surveys among customers, and
educational institutions often distribute
surveys among students.
• multiple-choice questions
• open-ended questions

23. Focus Groups
• Focus groups were originally called "focused
interviews" or "group depth interviews".
• A focus group could be defined as a group of
interacting individuals having some common
interest or characteristics, brought together by
a moderator, who uses the group and its
interaction as a way to gain information about
a specific or focused issue.

24. Observation
• Structured observation //Unstructured
observation
• Participant observation//non-participant
observation//disguised observation
• Controlled//uncontrolled observation

25. Descriptive Research Designs:
• Concept, types and uses.
• Concept of Cross-sectional and Longitudinal
Research

26. Descriptive Research Designs:
• Descriptive research design is a scientific method
which involves observing and describing the behavior
of a subject without influencing it in any way.
• Many scientific disciplines, especially social science
and psychology, use this method to obtain a general
overview of the subject.
• Give a detailed sketch or profile of the respondent
population being studied. This might require a
structured primary collation of the information to
understand the concerned population.

27. Descriptive Research Designs:
• Observational, defined as a method of
viewing and recording the participants
• Case study, defined as an in-depth study of an
individual or group of individuals
• Survey, defined as a brief interview or
discussion with an individual about a specific
topic.

28. Cross-Sectional Design
• Both the cross-sectional and the longitudinal
studies are observational studies. This means
that researchers record information about
their subjects without manipulating the study
environment.
• At Single time.
• Example: A Danish Ice-Cream firm wants to
find out how to target the Indian consumer to
indulge in high end ice creams.

29. Cross-Sectional Design
• Cohort Analysis: Multiple Sample groups at
different time intervals, is called cohort
studies.
• Example: in the 9/11 case, if we study and
compare the attitudes of middle-aged
Americans versus teenaged Americans
towards Asian-American, post the event. It
would be cohort analysis.

30. Longitudinal Design
• A longitudinal study, like a cross-sectional one,
is observational. So, once again, researchers
do not interfere with their subjects.
• In a longitudinal study, researchers conduct
several observations of the same subjects over
a period of time, sometimes lasting many
years.
• Example:

31. Experimental Design
• A blueprint of the procedure that enables the
researcher to maintain control over all factors
that may affect the result of an experiment.
• In doing this, the researcher attempts to
determine or predict what may occur.
• Experimental research is often used where there
is time priority in a causal relationship (cause
precedes effect), there is consistency in a causal
relationship (a cause will always lead to the same
effect), and the magnitude of the correlation is
great.

32. What do these studies tell you?
• Experimental research allows the researcher to
control the situation. In so doing, it allows
researchers to answer the question, “What
causes something to occur?”
• Permits the researcher to identify cause and
effect relationships.
• Experimental research designs support the ability
to limit alternative explanations and to infer
direct causal relationships in the study.
• Approach provides the highest level of evidence
for single studies.

33. What these studies don't tell you?
• The design is artificial, and results may not be
generalize well to the real world.
• The artificial settings of experiments may alter the
behaviors or responses of participants.
• Experimental designs can be costly if special
equipment or facilities are needed.
• Some research problems cannot be studied using an
experiment because of ethical or technical reasons.
• Difficult to apply ethnographic and other qualitative
methods to experimentally designed studies.

34. Experimental Design
• An experiment is generally used to infer a
causality. In experiment, a researcher actively
manipulates one or more causal variables and
measures their effects on dependent
variables.
• Causality:
• Sales Training  Workforce  Increased
Sales Performance.

35. Necessary conditions for Making
Causal Inferences
• Concomitant Variation: It is the extent to which a
cause X and effect Y occur together or vary together.
• Time order of occurrence of Variables: This
condition means that the causal variable must occur
prior to or simultaneously with the effect variable.
• Absence of other possible causal factors: The
objective of an experiment is to measure the
influence of the independent variable on a
dependent variable while keeping the effect of other
extraneous variables constant.

36. Concept Used in Experiments
• Independent Variable: explanatory variable -
treatment
• Test Units: Test Units are those entities on which
treatments are applied
• Dependent Variable: These variables measure the
effect of treatments(independent Variable) on the test
units.
• Experiment: To establish relationship between
independent and dependent variable.
• Extraneous Variable: Variables other than the
independent variables which influence the response of
test units to treatments.

37. Validity in Experimentation
• For conducting an experiment, it is essential
to specify:
– Treatments
– Test units to be used
– Dependent variables to be measured
– Procedure for dealing with the extraneous
Variables.

38. Validity in Experimentation
• To Draw valid conclusions about the effect of
treatments(independent variables) on the
dependent variables – Internal Validity.
• To make generalizations about the results to a
wider population – External Validity.

39. Definition and Symbols
• X – the exposure of a test group to an experimental
treatment whose effect is to be measured.
• O – The measurement or observation of the dependent
variable.
• R – The random assignment of test units or groups to
separate treatments.
• The movement from left to right indicates the time sequence of
events
• All symbols in one row indicate that the subject belongs to that
specific treatment group
• Vertical arrangement of the symbols means that these symbols
refers to the events or activities that occur simultaneously.

40. Definition and Symbols
• Example:
O1 X O2 O3
O1 = Measurement taken prior to applying
treatment X.
O2,O3 = measurement on the group taken after
the application of the treatment.

41. Definition and Symbols
• R O1 X O2
• R X O3
• The above scheme indicates that the two groups
of individuals were assigned at random (R) to
two treatment groups at the same time.
• First group received both a pretest (O1) and post
test (O2) measurement.
• The second group received the post-test
measurement (O3) at the same time as the first
group received the post – test treatment (O2).

42. Factors affecting Internal Validity
• History.
• Maturation.
• Testing – before presentation of the treatment
• Measuring Instrumentation – change in value of
money
• Statistical Regression – Extreme performances.
• Selection Bias – Selected on the judgement of
the researcher.
• Test Unit Mortality

43. Factors affecting External Validity
• Environmental Change
• Population Change
• Duration Problem
• Treatment change.

44. Classification of Experimental Design
Experimental
Design
Pre experimental
One Shot Case
Study
One Group Pre-
test – Post Test
Static Group
Quasi
Experimental
Time Series
Multiple Time
Series
True
Experimental
Pre-test-Post test
Control Group
Post-test-Only
control Group
Solomon Four
Group
Statistical
Completely
Randomised
Randomised
Blocks
Latin Square
Factorial

45. 1. Pre Experimental
• One shot case study:
X O
• This means that only one test group is subjected to the
treatment X and then a measurement on the
dependent variable is taken O.
• No random assignment
• Assignment is done as per the choice of the researcher.
• Example: study the impact of an extra ten day’s credit
period(X) on a credit card payment time(O)

46. 1. Pre Experimental
• One group pre-test-post-test design:
– Before and after without control group design.
O1 X O2
– The test units are subjected to the treatment X
and both pretreatment (O1 ) and post-treatment
measurement (O2) are taken.
– Factors affecting it – History, Maturation, Testing,
Instrumentation, Selection bias.

47. 1. Pre Experimental
• The economic condition might have changed during the
two periods.(History)
• The test units may mature over time.(Maturation)
• The Pre-test measurement on the test units may influence
the performance (testing)
• The prices of goods might have changed over time
(instrumentation)
• Test units might not have been selected at random
(Selection bias)
• Some test units might have left before the experiment was
complete. (mortality)
• Test unit non uniformity in performance. (regression)

48. 1. Pre Experimental
• Static Group Comparison:
– Group 1: X O1 (Experimental Group)
– Group 2: O2 (Control Group)
• Two groups are tested and not randomly selected
• First Group – Experimental Group is subjected to
treatment.
• Second Group – Control Group, is not subjected to the
treatment.
• Factors affecting the result – selection bias and mortality.
• Example: Domestic help – work life balance – BPO
employee.

49. 2. Quasi Experimental Design
• In this design the researcher can control when
measurements are taken and on whom they are
taken.
• However, this design lacks complete control of
scheduling of treatment and also lacks the ability
to randomize test units exposure to treatments.
• As the experimental control is lacking, the
possibility of getting confounded results is very
high.

50. 2. Quasi Experimental Design
• Time Series design:
• This involves a series of periodic measurements
on the dependent variable for a group of test
unit. The treatment X is then administered and
series of periodic measurements are again taken
to measure the effect of treatment.
• O1O2O3O4 X O5O6O7O8
• There is no randomization of treatment to the
test units.

51. 2. Quasi Experimental Design
• Time Series design:
• Because of the multiple observations in time
series design, the effect of maturation, main
testing effect, instrumentation and statistical
regression can be ruled out.
• Drawback of this experiment is the inability of
a researcher to control the effect of history.

52. Graph

53. 2. Quasi Experimental Design
• Multiple Time Series Design:
• One more group called the ‘control group’ is
added to the time series design. The design
may be diagrammed symbolically as:
• Experimental Group: O1O2O3O4 X O5O6O7O8
• Control Group: O1O2O3O4 O5O6O7O8
• Major drawback of this design is the
possibility of the interactive effect in
experiment group.

54. 3. True Experiment Designs
• In this researchers can randomly assign units
and treatments to an experimental group.
• Here the researcher is able to eliminate the
effect of extraneous variables from both the
experimental and control group.
• Randomization procedure allows the
researcher the use of statistical techniques for
analyzing the experimental results.

55. 3. True Experiment Designs
• Pre – Test – Post – Test Control Group:
• Experimental Group : R O1 X O2
• Control Group : R O3 O4
• Random Selection has taken place.
• Extraneous Variable act significantly.
• Only difference in the two groups is the effect of
treatment in the experimental group.
• A = O2 - O1 = Treatment + Extraneous Variable
• B = O4 - O3 = Extraneous Variable.
• A – B = (O2 - O1) – (O4 - O3)
• It is doubtful to generalize the result.
Extraneous Variable –
history, maturation, testing,
instrumentation, statistical
regression, selection bias
and test unit mortality.

56. 3. True Experiment Designs
• Post – Test – only control group design:
• Experimental Group: R X O1
• Control Group: R O2
• Random Selection
• O1 – O2 = (Treatment effect + Extraneous Factors)
– (Extraneous Factors) = Treatment Effect.
• Test unit mortality affects each group equally.
One can always justify these assumptions by
taking a large randomized sample. This design is
widely used in marketing research.

57. 3. True Experiment Designs
Solomon four-group design: This design is also called
four – group six-study design.
• This is also referred to as ‘ideal controlled experiment’.
As will be seen, this design helps the researcher to
remove the influence of extraneous variables and also
that of the interactive testing effect. This design is
symbolically presented as:
• Experiment Group1: R O1 X O2
• Control Group1: R O3 O4
• Experiment Group2: R X O5
• Control Group2 : R O6

58. 3. True Experiment Designs
Solomon four-group design:
• Experiment Group1
• O2 – O1 = Treatment effect + Extraneous
factors without interactive testing effect +
interactive testing effect ….(i)
• Control Group1
• O4 – O3 = Extraneous factors without
interactive testing effect. (no interactive
testing effect) ….(ii)

59. 3. True Experiment Designs
Solomon four-group design:
• Experimental Group 2:
• O5 – O1 = Treatment effect + extraneous factors without
interactive testing effect. …..(iii)
• O5 – O3 = Treatment effect + extraneous factors without
testing. …...(iv)
• As there was actually no pre-test measurement, the
interactive testing effect cannot occur here.
• Control Group2:
• O6 – O1 = Extraneous factors without testing effect. ….(v)
• O6 – O3 = Extraneous factors without testing effect. ….(vi)
• Would only give effect of extraneous factors.
*As the group was not subjected to any treatment, so no testing
effect or interactive effect.

60. 3. True Experiment Designs
• By taking the average of (v) and (vi) one gets:
• O2 –
(O1 + O3)
𝟐
= (Extraneous Factors without testing
effect.) …..(vii)
• By taking average of (iii) and (iv), one obtains
• O5 -
(O1 + O3)
𝟐
= (Treatment Effect + extraneous factors
without testing effect).(viii)
• By subtracting (vii) from (viii)
• O5 -
(O1 + O3)
𝟐
- O2 –
(O1 + O3)
𝟐
= O5 – O6
• = Treatment Effect

61. 3. True Experiment Designs
• By subtracting (viii) from (i)
• O2 – O1 - O5 -
(O1 + O3)
𝟐
= Interacting testing effect
• This design has helped not only in measuring the effect of
treatment, but also in obtaining magnitude of the
interactive testing effect and extraneous factors.
• It is a costly affair – so rarely used
• This design guarantees maximum internal validity.
• For business where cause and effect relationship is very
crucial for survival, this design is useful.

62. 4. Statistical Design
• Statistical design allow for statistical control and
analysis of external variables. The main
advantages of statistical design are the following:
– The effect of more than one level of independent
variable on the dependent variable can be
manipulated.
– The effect of more than one independent variable can
be examined
– The effect of specific extraneous variable can be
controlled.

63. 4. Statistical Design
• Completely Randomized Design:
• This design is used when a researcher is investigating
the effect of one independent variable on the
dependent variable.
• The independent variable is required to be measured
in nominal scale.
• Each of the categories of the independent variable is
considered as the treatment.
• The basic assumption of this design is that there are no
differences in the test units.
• There are no extraneous variables that could influence
the outcome.

64. 4. Statistical Design
• Example: Sales of a particular goods is influenced
by the price of that goods.
• Sales – Dependent variable.
• Price – Independent Variable.
• Let there be three level of price – high, medium
and low.
• We have to measure at which price level the sales
is highest.
• Average mean.
• Variance - ANOVA

65. 4. Statistical Design
• This design suffers from the main limitation that
it does not take into account the effect of
extraneous variables on the dependent variable.
• Different stores are the test unit where different
price level was implemented.
• The extraneous variable in the present example
may be size of the store, the competitor’s price
and price of the substitute product in question.
• The assumption of the effect of extraneous
variable may not be true.
• The design is very simple and inexpensive to
conduct.

66. 4. Statistical Design
• Randomized Block Design: In this randomized
block design it is possible to separate the
influence of one extraneous variable on a
particular dependent variable, thereby
providing a clear picture of the impact of
treatment on test units.
• Example: 12 stores of different sizes like small,
medium, large
• Price Level – Low, Medium and High.

67. 4. Statistical Design
Small Store 1 2 3 4 5 6 7 8 9 10 11 12
Low
Medium
High
Medium
Store
1 2 3 4 5 6 7 8 9 10 11 12
Low
Medium
High
Large Store 1 2 3 4 5 6 7 8 9 10 11 12
Low
Medium
High

68. 4. Statistical Design
• Latin Square Design:
• Researcher is interested in separating out the
influence of price(treatment) on Sales
• Extraneous Variable; Store size and type of
packaging.

69. 4. Statistical Design
• Latin Square Design:
Store Size
Packaging
1 2 3
1(Small) X1 X2 X3
2(Medium) X2 X3 X1
3(Large) X3 X! X2
Price -
X1= Low.
X2 = Medium.
X3 = High.

70. 4. Statistical Design
• Factorial Design
Store
Small(B1) Big(B2)
Low(A1)
Medium(A2)
Large(A3)