Business research methods_mba_unit-2

BRM :Unit -2 :RESEARCH DESIGN
( As per revised SPPU MBA syllabus 2019)
Dr. Shriram S. Dawkhar
M. Sc.(B.I.), MBA(Mktg.), M. Com. (Bus. Admin),
Ph. D., FDPM (IIM Ahmadabad)
Sinhgad Institute of Management, Pune.
ssdawkhar@gmail.com
© Shriram Dawkhar, May- 2010
(Updated – April -2021)

Unit-2: Research Design Syllabus
• Research Design: Concept, Features of a robust research
design. Exploratory, Descriptive, Quasi Experimental,
Experimental research designs, Concept of Cause and Effect,
Difference between Correlation and causation.
• Types of Variables – Independent, Dependent, concomitant,
mediating, moderating, extraneous variables, Basic knowledge
of Treatment & Control group, Case study design. Cross-
sectional and Longitudinal designs, Qualitative and
Quantitative research approaches, Pros and Cons of various
designs, choice of a research design.
• Hypothesis: Definition, research Hypothesis, Statistical
hypothesis, Null hypothesis, Alternative Hypothesis, Directional
Hypothesis, Non-directional hypothesis. Qualities of a good
Hypothesis, Framing Null Hypothesis & Alternative Hypothesis.
Concept of Hypothesis Testing - Logic & Importance.

PREPARING RESEARCH
DESIGN
STEP-4

➢ The following order concerning various steps
provides a useful procedural guideline regarding
the research process.
1. Formulating the research problem
2. Literature survey (use of Library)
3. Formulation of hypothesis
4.Preparing the research design
5. Determining sample design
(Updated - Nov-2019)

6. Collecting the data.(The evidence collected by
research process)
7. Analysis of data
8. Hypothesis Testing
9. Generalization and interpretation &
10. Preparation of the report or presentation of the result.
(i.e. formal write up or conclusions.)

Meaning
• Research design is overall plan or scheme
prepared by researcher for executing the
research study.
• It is an important stage in the process of
conducting research as it facilitates systematic &
smooth conduct of the research project.
• Acts as a guide to researcher for step by step
study.
• It is a roadmap : you can see where you are,
where you want to be at the completion of your
journey and can determine the best route to take
to get destination.

What Is Research Design?
Blueprint
Plan
Guide
Framework

What Tools Are Used in
Designing Research?

MindWriter
Project Plan
in Gantt chart
format
What Tools Are Used in
Designing Research?

RESEARCH DESIGN : Definitions
Research Design is the “framework” or “blueprint” for
collecting the information needed for your project in the
best possible way (Malhotra et al., 2002).
Research Design is the conceptual structure within which
research is conducted; it constitutes the blueprint for the
collection, measurement and analysis of data.

Research Design
• The research design is the master plan
specifying the methods and procedures for
collecting and analyzing the needed
information.
• The function of research design is to provide
frame work for the collection of relevant
evidence with minimal expenditure of effort,
time and money.

FUNCTIONS OF RESEARCH DESIGN
According to Black and Champion (1976-77), the
three important functions of research design are:
❖It provides blueprint.
❖It limits boundaries of research activity.
❖It enables investigation to anticipate potential
problems.

FEATURES / CHARACTERISTICS OF A
GOOD (robust ) RESEARCH DESIGN
✓It should be flexible.
✓It should be appropriate.
✓It should be efficient.
✓It should be economical.
✓It should minimize bias.
✓It should maximize the reliability of data collected.
✓It should give the smallest experimental error.

FEATURES OF A RESEARCH DESIGN
✓ It should yield maximum information.
✓It should provide the opportunity for considering different
aspects of the problem.
✓It should provide the means of obtaining information.
✓It should be appropriate with respect to the availability and
skills of the researcher and his staff.
✓It should be related to :
▪ the objective of the problem,
▪ the nature of the problem being studied,
▪ the availability of time and money for the research
work.

Types of Research Design
• Three traditional categories of research design:
• Exploratory
• Descriptive
• Causal (Experimental)
• The choice of the most appropriate design
depends largely on the objectives of the research
and how much is known about the problem and
these objectives.

Preparing the Research Design
Basic Research Objectives and Research Design
Research Objective Appropriate Design
To gain background information, to define terms, to clarify Exploratory
problems and develop hypotheses, to establish
research priorities, to develop questions to be
answered
To describe and measure marketing phenomena at a point Descriptive
in time
To determine causality, test hypotheses, to make “if-then” Causal /
statements, to answer questions (Experimental)

Exploratory
Research
Causal
Research
Descriptive
Research
•Formulate problems more precisely
•Develop Hypotheses
•Establish priorities for research
•Eliminate impractical ideas
•Clarify concepts
•Literature search
•Experience survey
•Analysis of select cases
•Interviews
•Ethnographies
•Focus groups
•Describe segment characteristics
•Estimate proportion of people
who behave in a certain way
•Make specific predictions
•Longitudinal study
•Panels
•Sample Survey
•Provide evidence regarding causal
•Rule out all other explanations •Laboratory experiment
•Field experiment
Uses Types
Overview of Research Design

Research Design: 1) Exploratory Research
• Exploratory research design may be
defined as
• “ Flexible design which must provide
opportunity for considering different
aspect of the problems.”
• The name itself gives a vision of
exploring i.e. to discover new ideas, to
have insight into the problem and to
achieve clarity.

• Exploratory research is most commonly
unstructured, “informal” research that is
undertaken to gain background information
about the general nature of the research
problem.
• Exploratory research is usually conducted when
the researcher does not know much about the
problem and needs additional information or
desires new or more recent information.

• Exploratory research is used in a number of
situations: (Uses/Objectives of exploratory research)
❖Gain background information
❖Define concepts more clearly
❖Develop operational definitions
❖Clarify and formulate a more precise research
problem and hypotheses
❖Achieve new insights or ideas into a phenomenon
❖Establish research priorities

• A variety of methods are available to conduct exploratory
research:
• Secondary Data Analysis
• Experience Surveys
• Case Analysis
• Focus Groups
• Projective Techniques

• Secondary Data Analysis:
• The initial process of exploratory research to
look for availability of secondary data .
• The rationale behind searching secondary data
is that is does not make any sense to collect a
primary data on the subject, when data is
already available.
• It provides adequate information about the
problems in hand.
• Helps to identify the methods that have proved
successful or unsuccessful.

• Experience Surveys :
• Survey of people who have practical experience
with the problem to be studied.
• Usually conducted to gain additional
knowledge on a particular subject area from
the experts in that field.
• This surveys are conducted because a lot of
vital information about a research area will not
be freely available.
• When adequate secondary data is difficult to
obtain.

• Experience Surveys:
• What is being done?
• What has been tried in the past without success?
With success?
• What are the change producing elements ?
• What problem areas & barriers can be seen?
• What are the priority areas?

• Focus Groups:
• Every individual has different perceptive
values & ideas. If we gather all these ideas
together, a new insight or new idea will
emerge from them. It would be accepted
universally.
• Focus group is the group of people or
participants led by an expert monitor.
• Usually used in the a new product
concept, a programme for improving new
production process, an employee
motivation programme.

• Focus Groups:
• The group is usually homogeneous,
such as a group of students, an athletic
team, etc.
• This method can also be used to find
out what people think about a product
or an advertisement.

Focus Groups
Group discussion
6-10 participants
led by Moderator
90 minutes-2
hours

• Projective techniques:
• is the psychological testing of a group or
individual, through a thematic
appreciation test, role plays by showing
video clips, providing case studies etc.

• Projective techniques are designed to
encourage the participants to reveal in
detail deeply suppressed attitudes,
opinions, feelings, and experiences.
• The participant's responses provide
her or his perceptions, interpretations
and awareness of the total situation
and of the interplay of the actors in the
scenario.

Research Design: 1)Exploratory Research
• Exploratory Research:
• Focus on discovery of ideas & generally based
on secondary data.
• Typically little prior knowledge of the subject.
• E.g.: 1) What new products should be
developed?
2) How our service Can be improved?
3) What product appeal will be effective in
advertising?

• Descriptive research: “Research design In
Which the major emphasis is on determining
the frequency with which something occurs or
the extent to which two variables covary”
• Unlike exploratory studies, descriptive studies
comes under formal research, where the objectives
are clearly established.
• It tries to understand or determine the
characteristics associated with subject population
such as age, sex, occupation, educational level,
income etc.
Research Design: 2)Descriptive research

• Descriptive research provides answers to
the questions of:
– Who
– What
– Where
– When
– How
• We cannot answer the question Why?
conclusively
Research Design: 2)Descriptive research

When to use ?
• Descriptive research is an appropriate
choice when the research aim is to identify
characteristics, frequencies, trends,
correlations, and categories.

Research Design: Descriptive Research
• Descriptive Research:
• Well structured study.
• Researcher has no control.
• The purpose is to provide an accurate snapshot of
some aspect of market environment.
• E.g.: 1) How should a new product be distributed ?
(How do people now buy similar products ?)
2) What should be the target segment ?
(What kind of people now buy the products?)

Descriptive Research is designed to provide further
insight into the research problem by describing the
variables of interest.
The major purpose of descriptive research is the
description of the state of affairs as it exist at present.

✓Descriptive research studies are aimed at
describing or portraying the characteristics of a
particular individual, group or a situation.
e.g. users of a product with different age, sex, education, etc.
✓It is also concerned with specific predictions, with
narration of facts and characteristics concerning
individual, group or situation.
e.g. sales of a company's product in each of the next five
years
(Objectives)

✓The study offers the researcher a profile or
description of relevant aspects of the phenomenon.
✓To estimate the proportion of people in a specified
population who behave in a certain way
e.g.: shopping persons who buy from a particular shop.
✓To determine whether certain variables are associated
e.g.. income and usage of a product.
(Objectives)

Major Types of Descriptive Studies
Descriptive
Studies
Consumer
Perception
And Behavior
Studies
Image
Product Usage
Advertising
Pricing
Market Characteristic
Studies
Distribution
Competitive
Analysis
Market
Potential
Market
Share
Sales
Analysis
Sales Studies

3) Causal Research
(Experimental)

Research Design: Causal Research(Experimental)
• Research that attempts to explain the relationship
between two variables (if A causes B to occur).
• Causality may be thought of as understanding a
phenomenon in terms of conditional statements
of the form “If x, then y.”
• Causal relationships are typically determined by
the use of experiments, but other methods are
also used.
• The causal study is made to investigate the cause
and effect relationship between the variables.

EXPERIMENTAL RESEARCH DESIGN
Experimental research design is concerned with making
experiments to find out the cause-effect relationship of
variables under study.
The main purpose of exp. design is to test a causal
hypothesis. Causal hypothesis is a statement that states
the cause and effect relationship between two or more
variables.
Thus experimental research design is also known as
hypothesis-testing or causal research design.
The premise of the design is that something (an
independent variable) directly influences the behavior of
something else ( the dependent variable).

Experimental Research Design
• Before studying Experimental & Quasi
Experimental research design we should
understand some basics concepts related
to experiments.

Experimental Design
• An experiment is defined as manipulating (changing
values/situations) one or more independent variables to see
how the dependent variable(s) is/are affected, while also
controlling the affects of additional extraneous variables.
• Independent variables: those over which the researcher has
control and wishes to manipulate i.e. package size, ad copy,
price.
• Dependent variables: those over which the researcher has
little to no direct control, but has a strong interest in testing
i.e. sales, profit, market share.
• Extraneous variables: those that may effect a dependent
variable but are not independent variables / not under study.

Correlation and causation
• Correlation : A statistical relationship
between two variables.
• Correlation is a measure of the strength of
the relationship between two variables.

Correlation and causation
• Correlation: The degree of relationship between the
variables under consideration is measure through
the correlation analysis.
• The measure of correlation called the correlation
coefficient .
• The degree of relationship is expressed by
coefficient which range from correlation ( -1 ≤ r ≥ +1)
• The direction of change is indicated by a sign.
• The correlation analysis enable us to have an idea
about the degree & direction of the relationship
between the two variables under study.

Correlation and Causation
• Causation : is a relationship between an
event ( the cause) and second event ( the
effect), Where the second event is the
consequences of the first.
• Everyone is familiar with the general notion
of causality,
• the idea is that one thing leads to the
occurrence of another.
• Does Factor X cause factor Y to happen?
• E.g. Does strong motivation leads to / cause
effective teamwork.

Correlation Vs causation
• Correlation is a term in statistics that refers to the
degree of association between two random
variables. So the correlation between two data sets
is the amount to which they resemble one another.
• Causation is implying that A and B have a cause-
and-effect relationship with one another. You’re
saying A causes B.
• Firstly, causation means that two events appear at
the same time or one after the other.
• And secondly, it means these two variables not only
appear together, the existence of one causes the
other to manifest.

Types of Variables
• Dependent,
• Independent,
• Concomitant,
• Mediating,
• Moderating,
• Extraneous variables

variable
• A variable is a concept or abstract idea that can be
described in measurable terms.
• In research, this term refers to the measurable
characteristics , qualities, traits, or attributes of a
particular individual, object, or situation being
studied.
• Anything that can vary can be considered a
variable.
• For instance, age can be considered a variable because
age can take different values for different people or for the
same person at different times.
• Similarly, Income can be considered a variable because a
person's Income can be assigned a value.

Dependant vs Independent
Variable
Dependent Variable:
If a variable depends upon or is a
consequence of other variable, it is termed as
dependent variable.
Eg: If we say Height is depends upon Age,
Then Height is a Dependent Variable &
Age is an independent variable.

Dependant vs Independent
Variable
• Independent Variable:
The variable that is antecedent to the
dependent variable is called as an
independent variable.
E.g.: If Height depends upon the
individuals Gender, Then
Height is a Dependent Variable &
Gender is an independent variable.

Concomitant Variable
• A concomitant variable (sometimes called a
“covariate”) is a variable that is not of primary
interest in a study, but nonetheless may have
some interaction with the variable(s) of interest
being studied.
• E.g. 1) Researchers want to understand the
relationship between population density and ice
cream sales. However, a concomitant variable that
likely affects ice cream sales is weather.
• E.g. 2) Researchers want to know whether or not a
certain fertilizer leads to increased plant
growth. However, sunlight exposure and watering
frequency are both potential concomitant variables
that likely affect plant growth.

Moderating Variable
• A moderating or interaction variable is a
second independent variable that is
included because it is believed to have a
significant contributory or contingent effect
on the original IV–DV relationship.

Mediator Variable
• A mediating variable is a variable that links the
independent and the dependent variables, and
whose existence explains the relationship between
the other two variables.
• A mediating variable is also known as a mediator
variable or an intervening variable.

Mediator variable
A mediator variable is the variable that causes mediation in the
dependent and the independent variables

Types of Variables
Explanatory vs Extraneous Variable
The variables selected for analysis are called
explanatory variables and all other variables that
are not related to the purpose of the study but may
affect the dependant variable are extraneous.

A control group
• A control group is used as a baseline measure.
• The control group is identical to all other items or
subjects that you are examining with the exception
that it does not receive the treatment or the
experimental manipulation that the treatment group
receives.
• For example, when examining test tubes for
catalytic reactions of enzymes when added to a
specific substrate, the control test tube would be
identical to all other test tubes with the exception of
lacking the enzyme.

The treatment group
• The treatment group is the item or subject
that is manipulated.
• In our example, all other test tubes
containing enzyme would be part of the
treatment group.

So in conclusion………
• The treatment group consists of participants
who receive the experimental treatment
whose effect is being studied.
• The control group consists of participants
who do not receive the experimental
treatment being studied. Instead, they get a
placebo (a fake treatment; for example, a
sugar pill); a standard, non experimental
treatment (such as vitamin C, in the zinc
study); or no treatment at all, depending on
the situation.

A control group Vs Experimental /
Treatment group
A placebo is a pill, injection, or thing that appears to be a medical treatment, but
isn’t.
An example of a placebo would be a sugar pill that's used in a control group
during a clinical trials.

A control group Vs Experimental or
Treatment group

Experimental Research Designs
• Experimental designs are concerned with
examination of the effect of an independent
variable on dependent variable, where the
independent variable is manipulated through
treatment or intervention(s).
• According to Riely, experimental design is a
powerful design for testing hypotheses of
causal relationship among variables.
• Experimental research design is further classified
as TRUE EXPERIMENTAL DESIGN, QUASI
EXPERIMENTAL DESIGN & PRE
EXPERIMENTAL DESIGN

Experimental
Research Design
(Principles /
Characteristics)
Manipulation
( Independent Variables)
Randomization
Control
True experimental
designs consists of
three cardinal
feature:
RANDOMIZATION,
CONTROL &
MANIPULATION or
TRIAL

(Principles / Characteristics
• Manipulation refers to conscious control of the
independent variable by the researcher through
treatment or intervention to observe it’s effect on
the dependent variable.
• Control refers to the use of control group and
controlling the effects of extraneous variables on
the dependent variable in which the researcher is
interested.
• A comparison of the experimental group is made
with the control group to observe the effect of the
treatment or intervention

(Principles / Characteristics
• RANDOMIZATION
• Means that every subject has an equal
chance of being assigned to
experimental or control group.
• This is called random assignment of
subjects.
• The process involves random
assignment to different groups

TRUE EXPERIMENTAL DESIGNS
• In true experimental designs the researchers
have complete control over the extraneous
variables and can predict confidently that the
observed effect on the dependent variable is
only due to the manipulation of independent
variable.

TYPES OF
TRUE
EXPERIMENT
AL DESIGNS
1. POST TEST ONLY
DESIGN.
2. PRETEST-
POST-TEST-ONLY
DESIGN
3. SOLOMOM
FOUR GROUP
DESIGN
4. FACTORIAL
DESIGN
5.
RANDOMIZED
BLOCK DESIGN
6. CROSS OVER
DESIGN.
TYPES OF TRUE EXPERIMENTAL DESIGNS

1) POST TEST ONLY CONTROL DESIGN
• Is composed of two randomly assigned
group - experimental & control groups.
• Both the groups are not tested previous to
the introduction of an intervention.
• While treatment is implemented on the
experimental group only, post test
observations are made on both the
groups.

• This design is helpful in situations where
it is not possible to pre test the subjects.
• E.g., A study on educational
intervention related to contraception
among couples.

POST TEST ONLY CONTROL
DESIGN
RANDOM
ASSIGNMENT
EXP GROUP
CONTROL
GRP
TREATMENT POST TEST
POST TEST

2. PRETEST –POST TEST ONLY
DESIGN
RANDOM
ASSIGNMENT
EXP GROUP
CONTROL
GRP
TREATMENT
POST TEST
POST TEST
PRE
TEST
PRE
TEST

EXPERIMENTAL DESIGNS
One of the simplest experimental designs is the ONE
GROUP PRETEST-POSTTEST DESIGN--EXAMPLE?
One way to examine Efficacy of a Drug:
O1 X O2
Measure DRUG Measure
Patients’ Condition Experimental Patients’ Condition
(Pretest) Condition/ (Posttest)
intervention
◆ RESULT: Significant Improvement from O1 to O2
(i.e., sig. O2 - O1 difference)
◆ QUESTION: Did X (the drug) cause the
improvement? © Shriram Dawkhar, May- 2010

◆ CONTROL GROUP Simulates Absence of X
Pretest Post-Test Control Group Design--Suppose
random assignment (R) was used to control confounding
variables:
R Exp. Group O1E X O2E
R Ctrl Group O1C O2C
◆ RESULT: O2E > O1E & O2C Not> O1C
QUESTION: Did X cause the improvement in Exp.
Group?

3. SOLOMON FOUR GROUP
DESIGN
• There are two experimental and
two control group.(control group - I
& II) (Exp group- I &II).
• Initially the researcher randomly
assigns subjects to the four groups.

• Out of four groups, only exp grp I & control
grp I receives the pre test followed by the
treatment to the experimental grp I & II.
• Finally all the four groups receive post test,
where the effects of the dependent
variables of the study are observed and
comparison is made of the four groups to
assess the effect of independent variable
(experimental variable) on the dependent
variable.

• The solomon four group design is
considered to be most prestigious
experimental research design, because it
minimizes the threat to internal and
external validity.
• The test effectively presents the reactive
effects of the pre test.
• Any difference between the experimental
and control group can be more confidently
attributed to the experimental treatment.

• The disadvantage of this design is that it
requires a large sample and statistical
analysis, and therefore not commonly
used in health care researches.

SOLOMON FOUR GROUP
DESIGN
RANDOM
ASSIGNMENT
Exp grp I
Exp grp II
Cont grp I
Cont grp II
Pre test
Pre test
Treatment
Treatment
Post
test
Post
test
Post
test
Post
test

4.FACTORIAL DESIGN
• Here the researcher manipulates
two or more independent variables
simultaneously to observe their
effects on the dependent variables.
• This design is particularly useful
when there are more than two
independent variables to be tested.

• E.g., researcher wants to test the
efficacy of two different medication.
• The design facilitates the testing of
several hypotheses at a single time.
• Typically factorial design incorporates
2x2 or 2x3 factorial. (it can be any
combination)

• The first number (alpha - A) refers to the
independent variables or the types of
experimental treatments and the
second number (beta -B) refers to the
level or frequency of the treatment.

FACTORIAL
DESIGN
FREQUENCY OF
TREATMENT
PROTOCOLS OF
TREATMENT
PROTOCOLS OF
TREATMENT
ALPHA (I)
(DRUG I)
BETA (II)
(DRUG II)
4 hourly (B1) A1 B1 A2 B1

5. RANDOMIZED BLOCK
DESIGN
• Randomized block design is used when the
researcher desires to bring homogeneity
among selected groups.
• This is a simple method to reduce the
variability among the treatment groups by
a more homogenous combination of the
subjects through randomized block design.

• For example if the researcher wants to test
the efficacy of three different medications
in reducing hypertension, to ensure
homogeneity among subjects under
treatment, researcher randomly places the
subjects in homogenous groups (blocks).
• like patients with hypertension,
diabetic patients with hypertension and
hypertensive patients with heart diseases.

The design looks similar to that of
factorial design in structure, but
out of two factors one factor is
not experimentally manipulated.

RANDOMIZED BLOCK
DESIGN
TYPE OF
HYPERTENSIVE
DRUG
BLOCKS BLOCKS BLOCKS
PATIENT WITH
HYPERTENSION
(I)
DIABETIC
PATIENT WITH
HYPERTENSION
(II)
PATIENT WITH
HEART DISEASE
AND
HYPERTENSION
(III)
A A,1 A, II A, III
B B,1 B, II B, III
C C,1 C, II C, III

6.CROSS OVER DESIGN
• In cross over design the study
subjects are exposed to more than
one treatment.
• It is also known as “repeat measure
design”.

• This design is more efficient in establishing
the highest possible similarity among
subjects exposed to different conditions
where groups compared obviously have
equal distribution of characteristics.
• Some times this design is not effective
because, when subjects are exposed to two
different conditions, their responses of the
second condition may be influenced by
their experience in the first condition.

CROSS OVER DESIGN
GROUPS TREATMENT
PROTOCOL
TREATMENT
PROTOCOL
GROUP I TREATMENT I TREATMENT II
GROUP II TREATMENT II TREATMENT I

ADVANTAGES OF TRUE
EXPERIMENTAL DESIGN
• Most powerful design to establish the
causal relationship between
independent and dependent variable.
• Since the study is conducted under
controlled environment, it can yield a
greater degree of purity in observation.

ADVANTAGES OF TRUE
EXPERIMENTAL DESIGN
• The main advantage of experimental
research is the control over external
factors.
• Especially in social sciences, where
preselection and randomization of groups
is often difficult, they can be very useful in
generating results for general trends.

• Conditions that are not found in natural
setting can be created in experimental
setting in a short period of time that may
take years to naturally occur (therefore
very useful in genetic studies).
• Because the experiment is carried out in
experimental setting the problems of
real life situations and the personal
problems of the researcher is eliminated.

DISADVANTAGES OF TRUE
EXPERIMENTAL DESIGN
• Most often the results of experimental
designs cannot be replicated in studies
conducted on humans due to ethical
problems.
• Many of the human variables neither
have valid measurable criteria nor
instruments to measure them.

• In experimental studies conducted in
natural settings like a hospital or
community, it is not possible to impose
control over extraneous variables.
• Experiments are often more impractical
when the effect of independent variable
may require a lengthy period of time
before it can emerge as a response on
the criterion measures.

• It is very difficult to obtain permission from the
participants.
• Because the size of the sample is kept small
especially studies involving humans, the
representativeness of the findings of such study
is questionable.
• Though theoretically experimental designs can
yields a greater insights , yet practically many a
times they are not possible in human studies as
humans & their parameters are complex.

Examples of Experimental Studies in the Lab
• Kenstar home appliance company wishes
to test three campaign approaches.
• Marketers prepare three different T.V. ad
versions for participants to evaluate.
• Participants were invited (and paid) to view
a TV program at the researcher’s offices
with one of the ads inserted into the
program.
• Participants rated the ads for attitude and
recall

Examples of Causal Studies in the Field
• P&G wishes to test the germ killing
capabilities of two agents added to bar soap.
(The germs are thought to cause body odor.)
• Two groups of randomly selected people are
assigned to use one of the soap formulations
for two weeks.
• After the test period, participants rate the
deodorant effectiveness of the soap they
used on a scale along with other measures
of attitude and preference relative to their old
soaps

Following are the areas where
experiments are predominantly:
• Product design: which product a consumer
would buy most.
• Package design: which attractive package a
producer should design based on the available
literature.
• Pricing Policies: the price elasticity of demand
can be better tested with the help of
experiments. Experiments are often used for
measuring this aspect pricing.
• Promotion Policies: the promotional policies
have been very widely explored through
experimentation as compared with the other
areas of marketing.

Quasi Experimental Research
• The prefix quasi means “resembling.”
• Thus quasi-experimental research is research
that resembles experimental research but is
not true experimental research.
• Although the independent variable is
manipulated, participants are not randomly
assigned to conditions or orders of conditions
(Cook & Campbell, 1979)

Quasi-experimental research design
• Quasi-experimental research design involves the
manipulation of independent variable to observe
to effect on dependent variable, but it lacks at
least one of the two characteristics of the true
experimental design; randomization or a control
group.
• In other words, quasi-experimental designs have
an element of manipulation but lack at least one
of the other two properties that characterize true
experiments; randomization or a control group.

Quasi experimental designs
• Quasi experimental designs are generally
used to establish the causality (effect of
independent variable on dependent
variable) in situations where researchers
are not able to randomly assign the
subjects to groups for various reasons.

Quasi experimental designs
CHARACTERISTICS
• Manipulation of the independent variable
to observe the effects of the dependent
variables.
• It lacks at least one of the two other
essential characteristics of the true
experiment.

Important types of Quasi Experimental
design
1. NON RANDOMIZED CONTROL
GROUP DESIGN.
2. TIME SERIES DESIGN

NON RANDOMIZED CONTROL
DESIGN
• Is also known as “non equivalent control
group design”.
• This design is identical to the pre test- post
test group design, except there is no
random assignment of the study subjects
in experimental and control groups.

• In this design experimental and
control groups are selected without
randomization.
• Dependent variables are observed
in experimental as well as control
groups before the intervention.

• Later the experimental group receives
treatment, following which the post test
observation of dependent variable is
carried out for both the groups to assess
the effect of the intervention or
treatment on experimental group.

NON RANDOMIZED CONTROL
GROUP DESIGN
EXP GROUP
CONTROL
GROUP
PRE TEST
PRE TEST
TREATMENT POST TEST
POST TEST
For example,…. The effects of integrated care on quality of work in nursing homes

TIME SERIES
DESIGN
• Time series design is useful when the
researcher intends to measure the
effects of a treatment over a long period
of time.
• The researcher would to continue to
administer the treatment and measure
the effects a number of times during the
course of the experiment.

• In a single - subject research, in which
the researcher carries out an
experiment on an individual or on a
small number of individuals, ……..by
alternating between administering and
then withdrawing the treatment to
determine the effectiveness of the
intervention.

TIME SERIES
DESIGN
EXP GROUP O2
O1 O2 O3 O1 O3
TREATMENT

EXAMPLE
• Measurement of a learner’s
performance in a college on weekly
basis and then introducing a new
teaching technique. Then again
measuring on weekly basis.

ADVANTAGES
• Quasi experimental designs are more
frequently used because they are more
practical and feasible to conduct
research.
• Where the sample size is small, and
where randomization & availability of
control group is not possible, this design
is preferred.

• Quasi experimental design is more suitable
for real natural world setting than true
experimental designs.
• This design allows the researchers to
evaluate the impact of quasi independent
variables under naturally occurring
conditions.
• In some cases hypotheses are practically
answered through this design.

DISADVANTAGES
• In this design there is no control over
extraneous variables influencing the
dependent variable.
• The absence of a control group and
absence of control over the research
setting makes the result of this design less
reliable and weak for the establishment of
causal relationship between independent
& dependent variable.

Validity for Experiment
• internal validity: measure of accuracy of an
experiment; measures whether manipulation
of independent variables actually caused
effects.
• External validity: determines whether
cause-and-effect relationships found in
experiment can be generalized.

Preparing the Research Design
Test Marketing
• Test marketing is the phrase commonly used to
indicate an experiment, study, or test that is
conducted in a field setting.
• Two broad classes:
• To test the sales potential for a new product or
service
• To test variations in the marketing mix for a
product or service

Case Study Design
The aim of case
studies is the
precise
description or
reconstruction of
a case. (Ragin and
Becker 1992).

What is a case study?
• A case study is a research approach that is used to generate
an in-depth, multi-faceted understanding of a complex issue
in its real-life context.
• It is an established research design that is used extensively
in a wide variety of disciplines, particularly in the social
sciences. A case study can be defined in a variety of ways,
the central tenet being the need to explore an event or
phenomenon in depth and in its natural context.
• It is for this reason sometimes referred to as a "naturalistic"
design; this is in contrast to an "experimental" design (such
as a randomised controlled trial) in which the investigator
seeks to exert control over and manipulate the variable(s) of
interest

Author Definition
Stake "A case study is both the process of learning about the case
and the product of our learning" (p.237)
Yin "The all-encompassing feature of a case study is its intense
focus on a single phenomenon within its real-life
context...[Case studies are] research situations where the
number of variables of interest far outstrips the number of
datapoints" (Yin 1999 p. 1211, Yin 1994 p. 13)
"A case study is an empirical inquiry that
• Investigates a contemporary phenomenon in depth and
within its real-life context, especially when
• The boundaries between phenomenon and context are
not clearly evident." (Yin 2009 p18)
Miles and Huberman "...a phenomenon of some sort occurring in a bounded
context" (p. 25)
Green and Thorogood "In-depth study undertaken of one particular 'case', which
could be a site, individual or policy" (p. 284)
George and Bennett "...an instance of a class of events [where] the term class of
events refers to a phenomenon of scientific interest...that
the investigator chooses to study with the aim of developing
theory regarding causes of similarities or differences among
instances (cases) of that class of events" (p. 17)"

Case Studies:
• Purpose: to do an in depth study
• In brief: Background, current status and/or
environmental factors that interact for each group
(individual, institution or community)
• Characteristics of Case Studies:
• It gives very detailed information about individuals /
group / community
• It may give a detailed explanation of a complete
life cycle or part of it
• Number of cases studied may be small but the
number of variables studied are usually more in-
depth (e.g. if compared to a survey)
Shriram Dawkhar

What are case studies used for?
▪ According to Yin, case studies can be used to explain,
describe or explore events or phenomena in the everyday contexts
in which they occur.
▪ In contrast to experimental designs, which seek to test a specific
hypothesis through deliberately manipulating the environment
(like, for example, in a randomised controlled trial giving a new
drug to randomly selected individuals and then comparing
outcomes with controls), the case study approach lends itself well
to capturing information on more explanatory 'how', 'what' and
'why' questions, such as 'how is the intervention being
implemented and received on the ground?’.
▪ The case study approach can offer additional insights
into what gaps exist in its delivery or why one implementation
strategy might be chosen over another. This in turn can help
develop or refine theory.

– Cross sectional studies: they measure the
population at only one point of time.
– Longitudinal studies: they repeatedly measure the
same population over a period of time.
Classification of research studies

Sample
Surveyed at T1
Sample
Surveyed at
time T1
Same Sample
also Surveyed
at time T2
T1 T2
Cross Sectional
Design
Longitudinal Design
Time
Cross Sectional vs. Longitudinal
Designs

Cross-Sectional Studies
Most common and most familiar.
Uses a representative sample of elements from a
population, often a sample survey.
Characteristics of the elements are measured once, i.e. it
provides a snapshot of the variables under investigation.

Longitudinal Studies
 Involves panel, i.e. a fixed sample of elements or respondents,
which are repeatedly measured over time, i.e. it provides a movie
of the variables under investigation.
Panel members are relatively constant over time.
Continuous panel : A fixed sample of respondents who are
measured repeatedly over time with respect to the same
variables.
Discontinuous panel :A fixed sample of respondents who
are measured repeatedly over time, but on variables that
change from measurement to measurement

QUALITATIVE &
QUANTITATIVE RESEARCH

OVERALL FRAMEWORK
RESEARCH
DATA
SECONDARY
DATA
PRIMARY
DATA
QUALITATIVE
DATA
QUANTITATIVE
DATA

KEEP IN MIND THAT …
• Qualitative research
generally deals in
words, images, Audio,
Video, Music and the
subjective
• Quantitative research
generally deals in
numbers, logic and
the objective
Use quantitative research if you want to confirm or test something (a
theory or hypothesis) Use qualitative research if you want to understand
something (concepts, thoughts, experiences)

QUALITATIVE RESEARCH
• Qualitative research is a method of inquiry
employed in many different academic
disciplines, traditionally in the social sciences,
but also in market research and further
contexts.
• Qualitative researchers aim to gather an in-
depth understanding of human behavior and
the reasons that govern such behavior.

• The qualitative method investigates the
why and how of decision making, not just
what, where, when.
• Hence, smaller but focused samples are
more often used than large samples.
• Qualitative research assumes that people
have meaningful actions or experiences
that can be interpreted

POPULARITY COMES FROM:
*ECONOMICAL
*FLEXIBLE
*OPENS A DOOR TO “WHY, HOW”
*RICHNESS OF DATA
*BEST TO START WITH...

◼ Research using qualitative data in the form of
text and pictures, not numbers.
◼ Takes a holistic approach with a specific focus
and tells a richer story than quantitative
research
◼ Used to answer questions about the complex
nature of phenomena, often with the purpose
of describing and understanding the
phenomena
◼ Builds on researcher’s ability to interpret and
make sense of what he or she sees for
understanding any social phenomenon
What is Qualitative Research?

◼ Qualitative research usually starts by
questions like:
◼ How do people feel while living under
occupation?
◼ How can a teacher use principles from
behaviorist psychology to help a student
with Autism succeed in an elementary
school?
When To Choose A Qualitative Approach

KEY FEATURES of QUALITATIVE RESEARCH
1. Collection Primarily of Data
Qualitative methods emphasize observations about natural
behavior and artifacts that capture social life as it is
experienced by the participants rather than the numerical
representations of the categories predetermined by the
researcher.
2. Exploratory Research Question.
Qualitative researchers typically begin their projects seeking
to discover what people think and how they act, and why, in
some social setting.
3. Inductive Reasoning (Reasoning that moves from more specific
kinds of statement to more general ones)
Only after immersing themselves to many observations, do
qualitative researchers try to develop general principles to
account their observations.

4. A focus on Human Subjectivity.
Qualitative methods emphasize the meanings that
participants attach to events and that people give to
their lives.
5. Reflexive Research Design.
In the qualitative methods, the research design may
need to be reconsidered or modified in response to new
developments, or to changes in some other component
as research progresses.
6. Sensitivity to the Subjective Role of the Researcher.
Qualitative researchers should be sensitive to the role
they play in the process of data collection. “Researcher
as an instrument”

 Provide rich data – that is, in-depth descriptions of
individual experiences.
 Particularly useful for investigating complex and
sensitive issues.
 Explain phenomena – that is, go beyond mere
observation to understand what lies behind them
(eg. why do people become homeless?)
 Generate new ideas and theories to explain and
overcome problems.
 People are studied in their own environment,
which increases credibility.

 Can be very time-consuming and generate a
huge amount of data.
 Data analysis can be difficult because of the
amount of data and no clear strategy for
analysis.
 Interpretation of data may be subjective (but
reflexivity can help to minimize this)
 Trained moderator are essential for study.

Types of qualitative techniques
• 1) Projective Techniques
• 2) Depth Interview
• 3) Experience Survey
• 4) Focus Groups
• 5) Observations

QUALITATIVE RESEARCH :PROJECTIVE TECHNIQUES
Projective Techniques
Word Association Sentence Completion Tests
Cartoon Tests Role Playing
Third-Person Techniques
Picture Interpretation /Story
Telling
For more details : Qualitative and quantitative research (slideshare.net)

Situations where qualitative research is often used:
• New product idea generation and development
• Investigating current or potential product/service/brand
positioning and marketing strategy
• Studying reactions to advertising and public relations
campaigns, other marketing communications, graphic
identity/branding, package design, etc.
• Studying emotions and attitudes on societal and public
affairs issues
• Assessing the usability of websites or other interactive
products or services
• Understanding perceptions of a company, brand,
category and product

Quantitative Research Design
• Quantitative research is defined as a systematic
investigation of phenomena by gathering
quantifiable data and performing statistical,
mathematical, or computational techniques.
Quantitative
Research

• Quantitative research collects information
from existing and potential customers using
sampling methods and sending out online
surveys, online polls, questionnaires, etc., the
results of which can be depicted in the form of
numerical.
• An example of quantitative research is the survey conducted to
understand the amount of time a doctor takes to tend to a
patient when the patient walks into the hospital.
• The survey was conducted on some of the offices of one city to
study the number of hours spent by the employees in the
office

• Quantitative research examines relationships
between variables, which are measured
numerically and analysed using a wide range of
statistical and graphical techniques.
• Data are collected in a structured and standard
manner – important that questions are asked
and expressed clearly so that they are
interpreted in the same way by each participant.
(Saunders et al., 2012)

QUANTITATIVE RESEARCH
ADVANTAGES DISADVANTAGES
⚫Specific research
problem
⚫Clear independent and
dependent variable
⚫High level of reliability
⚫Minimum personal
judgement
⚫Limited outcomes due to
structured method
⚫Unability to control the
environment
⚫Expensive(large number
of respondents)

COMPARISON OF
QUALITATIVE-QUANTITATIVE
RESEARCH
CHARACTERISTICS QUALITATIVE QUANTITATIVE
Research
Objectives
Discovery of new
ideas,insights and
feelings
Validation of
facts,estimates,
relationships
Type Of
Research
Usually
exploratory
Descriptive and
causal
Type Of
Questions
Open-ended,semi-
structured,
unstructured,
probing
Mostly structured

COMPARISON OF
QUALITATIVE-QUANTITATIVE
RESEARCH contd.
CHARACTERISTICS QUALITATIVE QUANTITATIVE
Time Of
Execution
Short Time
Frames
Usually long time
frames
Sample Size Small Large
Type Of
Analyses
Subjective,
Interpretitive
Statistical,
Descriptive,causal
Researcher Skills Psychology,
Sociology,CB, Social
Psychology
Statistics, MR, DSS,
Decision Models
Representativeness Limited Good

HYPOTHESIS
➢ Hypothesis are tentative, intelligent guesses as to the
solution of the problem.
➢ Hypothesis is a specific statement of prediction. It
describes in concrete terms what you expect to happen
in the study.
➢ Hypothesis is an assumption about the population of the
study.
➢ It delimits the area of research and keeps the researcher
on the right track.

HYPOTHESIS
➢ A hypothesis is an assumption about relations
between variables.
➢ Hypothesis can be defined as a logically conjectured
(guessed) relationship between two or more variables
expressed in the form of a testable statement.
➢ Relationships are conjectured on the basis of the
network of associations established in the theoretical
framework formulated for the research study.

HYPOTHESIS
➢ Research Hypothesis is a predictive statement that
relates an independent variable to a dependant
variable.
Hypothesis must contain at least one independent
variable and one dependant variable.

Characteristics of a Good Hypothesis:
• Simple / Simplicity
• Specific (Not Vague)
• Conceptually Clear. (precise and clear)
• Testable
• Falsifiable (Must be able to reject the hypothesis with data)
• Relevant to Problem
• Related to available technique.
• Should state relationship between variables
• Reliable with most known facts
• Hypothesis must explain the facts that gave rise to the
need for explanation

Simple vs. Complex
• Simple hypothesis contains one predictor
and one outcome variable –
• E.g. A sedentary lifestyle is associate with
increased risk of diabetes.
• Complex - more than one predictor
variable.
• E. g. a sedentary lifestyle and alcohol
consumption are associated with an
increased risk of diabetes.

Specific vs. Vague
• Specific hypothesis leaves no confusion about
what the question is
• Can be long but it is clear about what is being
collected, what the variables are and it allowed the
types of statistics that are going to be done.
• Eg. –
– "Eating more makes people fat".
– "Eating more than 3 meals for 3 month, will
increase the weight of individual adult by 8
kilogram.

Null and Alternate Hypothesis
• Null Hypothesis: states there is no
association between independent and
dependent variables..
• Alternative Hypothesis: states that there is
an association between variables.

Examples
• Question: Will advertisement attract attention?
• Hypothesis: Advertisement attracts the
attention of customers.
• ( the most likely answer of your problem)
• E.g.1) Illiteracy is the cause of unemployment.
• 2) Children of poor parents are
unemployed.

PROBLEM (VS) HYPOTHESIS
➢ Hypothesis is an assumption, that can be tested
and can be proved to be right or wrong.
➢ A problem is a broad question which cannot be
directly tested. A problem can be scientifically
investigated after converting it into a form of
hypothesis.

Types of hypothesis
1. Research Hypothesis,
2. Statistical hypothesis,
3. Null hypothesis,
4. Alternative Hypothesis,
5. Directional Hypothesis,
6. Non-directional hypothesis.

Types of hypothesis: 1. Research Hypothesis
• A research hypothesis is a statement of an expected or
predicted relationship between two or more variables.
• When a prediction or a hypothesised relationship is to be
tested by scientific methods, it is termed as research
hypothesis.
• For example a researcher may hypothesize that prolonged
exposure to loud noise will increase systolic blood pressure.
• Example- 2) Employee's job satisfaction is positively related
to their commitment in the organization.

• The research hypothesis is a predictive
statement, capable of being tested by
scientific methods and that relates an
independent variable to a dependent
variable.
• Usually a research hypothesis must
contain, at least, one independent and
one dependent variable.
Types of hypothesis: 1. Research Hypothesis

• A statistical hypothesis is an assumption about a
population parameter. This assumption may or may
not be true.
• A statement about the parameters describing a
population (not a sample).
• For instance, the statement that a population mean is
equal to 10 is an example of a statistical hypothesis.
• A researcher might conduct a statistical
experiment to test the validity of this hypothesis.
• Statistical hypothesis is a hypothesis concerning the parameters or from of
the probability distribution for a designated population or populations, or,
more generally, of a probabilistic mechanism which is supposed to
generate the observations.
Types of hypothesis: 2. Statistical hypothesis

Types of hypothesis: 2. Statistical hypothesis
• A statistical hypothesis is a statement about
value of a population parameter (e.g., mean,
median, mode, variance, standard deviation,
proportion etc.
• Example: The average of Honda Shine
claimed by company is 60 Km. per liter.
• This can be tested whether the claim made by
company withstands or not.
• Hence, statistical hypothesis framed as,
H0: µ = 60
H1: µ ≠ 60

❖When a hypothesis is stated negatively, it is called
null hypothesis. It is a ‘no difference’, ‘no
relationship’ hypothesis.
❖ It states that, no difference exists between the
parameter and statistic being compared to or no
relationship exists between the variables being
compared.
It is usually represented as HO or H0 .
Example:
➢ H0: There is no relationship between a family’s
income and expenditure on recreation.
Types of hypothesis: 3. Null Hypothesis

• It is the hypothesis that describes the
researcher’s prediction that, there exist a
relationship between two variables or it is the
opposite of null hypothesis.
• It is represented as HA or H1.
Example:
HA: There is a positive relationship between
family’s income and expenditure on recreation.
Types of hypothesis: 4. Alternate Hypothesis

• .
Types of hypothesis: 5. Directional Hypothesis

• Directional hypothesis are those where one can
predict the direction (effect of one variable on the
other as 'Positive' or 'Negative’)
• DIRECTIONAL HYPOTHESIS: Specifies not
only the existence, but also the expected
direction of the relationship between variables.
• E.g. : There is a positive relationship between
years of teaching experience and job
satisfaction among teachers.
Types of hypothesis: 5. Directional Hypothesis

• Reflects the relationship between two or more
variables, but it does not specify the anticipated
direction and /or nature of relationship such as
positive or negative.
• It indicates the existence of relationship
between the variables.
• E.g: There is a relationship between years of
teaching experience and job satisfaction
among teachers.
Types of hypothesis: 6. Non-Directional Hypothesis

FORMS OF RELATIONSHIPS
NON-DIRECTIONAL
• There IS a relationship
between
• X & Y
• X….linked….Y
Vs DIRECTIONAL
• If X goes up, Y ….
• or
• As X increases, Y…
• X = Independent
• variable
• Y = Dependent variable

DIRECTIONAL HYPOTHESES-
“X” causes “Y” to change
• If X changes
• (increases
• decreases)
• then
• Y will ______
• (increase or
• decrease)
• a causal link

DIRECTION OF RELATIONSHIP
• If X increases, Y increases
A POSITIVE relationship
• If X increase, Y decreases
A NEGATIVE or INVERSE relationship
• As X changes, Y does NOT change...>
No Change...>NO RELATIONSHIP

Positive correlation
• When the values of
• TWO variables
• “go together”
• or
• values on X & Y
• change in SAME
• DIRECTION 0
10
20
30
40
50
60
70
Hr
work
Earnin
gs
CORRELATIONAL RELATIONSHIP
Directional Hypothesis: There is Positive relationship between
Working hours and earnings of the employees.

Negative Correlation
• When the values of
two variables
• CO-VARY
• in Opposite direction
• (as one goes up,
• the other goes down)
CORRELATIONAL RELATIONSHIP
Directional Hypothesis: There is Negative relationship between
Working hours and expenditure of the employees.

Additional types of Hypothesis
• ( Not present in syllabus)
• Descriptive Hypothesis
• Relational Hypothesis
• Causal Hypothesis

Descriptive Hypothesis
These are assumptions that describe the
characteristics (such as size, form or
distribution) of a variable. The variable may
be an object, person, organisation, situation
or event.
Examples:
➢“Public enterprises are more amenable for
centralized planning”.
TYPES OF HYPOTHESIS

 Relational Hypothesis [Explanatory Hypothesis]
These are assumptions that describe the
relationship between two variables. The
relationship suggested may be positive, negative or
causal relationship.
Examples:
➢ “Families with higher incomes spend more for
recreation”.
Causal Hypothesis state that the existence of or
change in one variable causes or leads to an effect
on another variable.
The first variable is called the independent variable
and the latter is the dependant variable.

The Logic of Hypothesis Testing
• In classical tests of significance, two kinds of
hypotheses are used.
• The null hypothesis (H0) is used for testing.
• It is a statement that no difference exists between the
parameter (a measure taken by a census of the
population or a prior measurement of a sample of the
population) and the statistic being compared to it (a
measure from a recently drawn sample of the
population).
• Analysts usually test to determine whether there has
been no change in the population of interest or
whether a real difference exists.

Understand Hypothesis Testing logic: Toyota Prius
• A nominee for the 2012 Car of the Year, and a winner in 2010,
the 2013 Toyota Prius made news by bringing out an array of
models, including the V offering the space of an SUV.
• The Prius is the outstanding hybrid gas-electric car and
inspires a cult-like devotion which has translated into
unprecedented satisfaction rates in user studies.
• Its most fuel- efficient model produces an EPA fuel economy
of 51 mpg city and 60 mpg highway.
• And its 2013 version made additional news for delivering the
convenience of plug-in charging from any standard household
outlet.

• Lets test/ verify the company's claim by
making and testing hypothesis.
• In the hybrid-vehicle example, the null
hypothesis states that the population
parameter of 60 mpg has not changed.
• A second, alternative hypothesis (HA) holds
that there has been a change in average mpg.
• The alternative hypothesis is the logical
opposite of the null hypothesis.
Understand Hypothesis Testing logic: Toyota Prius

• The hybrid-car example can be explored further to
show how these concepts are used to test for
significance:
• The null hypothesis (H0): There has been no
change from the 60 mpg average.
• The alternative hypothesis (HA) may take several
forms, depending on the objective of the
researchers.
• The HA may be of the “not the same” or the “greater
than” or “less than” form: The average mpg has
changed from 60.
• The average mpg has increased (decreased) from
60.
Example to understand logic: Toyota Prius

Null & Alternative hypothesis
• These types of alternative hypotheses correspond with
two-tailed and one-tailed tests.
• A two-tailed test, or nondirectional test, considers two
possibilities: the average could be more than 60 mpg,
or it could be less than 60.
• To test this hypothesis, the regions of rejection are
divided into two tails of the distribution.
• A one-tailed test, or directional test, places the entire
probability of an unlikely outcome into the tail specified
by the alternative hypothesis.
• In Exhibit 17-2, the first diagram represents a
nondirectional hypothesis, and the second is a
directional hypothesis of the “greater than” variety

Stating Null & Alternate hypothesis

Type I and Type II Errors
True State of Nature
We decide to
reject the
null hypothesis
We fail to
reject the
null hypothesis
The null
hypothesis is
true
The null
hypothesis is
false
Type I error
(rejecting a true
null hypothesis)

Type II error
( fails to reject a
false null
hypothesis) 
Correct
decision
Correct
decision
Decision

Hypothesis testing / Decision rule
• In testing these hypotheses, adopt this
decision rule: take no corrective action if the
analysis shows that one cannot reject the null
hypothesis.
• Note the language “cannot reject” rather than
“accept” the null hypothesis.
• It is argued that a null hypothesis can never be proved and
therefore cannot be “accepted.
• Statistical testing gives only a chance to (1) disprove (reject)
or (2) fail to reject the hypothesis.
“fail to reject the null.” or Reject (disprove) the null

Hypothesis Testing : Example
• A principal at a Sinhgad Spring Dale
School claims that the students in her
school are above average intelligence.
• A random sample of thirty students IQ
scores have been collected and found
a mean score of 112.5.
• Is there sufficient evidence to support the
principal’s claim?
• The mean population IQ is 100 with
a standard deviation of 15.

Hypothesis Testing
• Step 1: State the Null & Alternate hypothesis.
• Null Hypothesis : The accepted fact is that
the population mean is 100, so:
. H0: μ=100
• Alternate Hypothesis.
• The claim is that the students have above
average IQ scores, so:
• H1: μ > 100.

• Step 2: Choose the statistical test. :
• To test a hypothesis, one must choose an
appropriate statistical test.
• There are many tests from which to
choose, and there are at least four criteria
that can be used in choosing a test.
• The fact that we are looking for scores
“greater than” a certain point means
that this is a one-tailed test. ( Z test)
Hypothesis Testing

Draw a picture to help you
visualize the problem

• Step - 3: Select the desired level of
significance. (Alpha- α)
• The choice of the level of significance should
be made before we collect the data.
• The most common level is .05, although .01
is also widely used.
• Other levels such as .10, .025, or .001 are
sometimes chosen.
• Here we will select the alpha level 0.05
• (α =0.05). i.e. 5%.
Hypothesis Testing

Step -4 : Compute the calculated difference value.
(Test Statistics) :
After the data are collected, use the formula for the appropriate
significance test to obtain the calculated value. Although the
computation typically results from a software program, we illustrate
the procedures in this chapter to help you visualize what is being
done.
Find the test statistic using this formula:
For this set of data: z= (112.5 – 100) / (15/√30) = 4.56.
Hypothesis Testing

• Step -5 : Obtain the critical test /Table
value:
• After we compute the calculated t , 2 , or
other measure, we must look up the critical
value in the appropriate table for that
distribution (or it is provided with the
software calculation).
• The critical value is the criterion that
defines the region of rejection from the
region of acceptance of the null hypothesis.
Hypothesis Testing

Hypothesis Testing
Z table Value :
1.645
Calculated
Value: 4.56

• Step -6: Interpret the test.
• For most tests if the calculated value is
larger than the critical value, we reject
the null hypothesis and conclude that
the alternative hypothesis is supported
(although it is by no means proved).
• If the critical value is larger, we conclude
we have failed to reject the null.
Hypothesis Testing

• In our example :
(4.56 > 1.645),
Decision : As calculated value ( Observed) is greater than
Critical (Table value) we will Reject the null hypothesis.
Hence we can say that “ Students of Sinhgad Spring dale
school are above average IQ.’’ ( i.e. greater than 100)
Hypothesis Testing
For More examples of
hypothesis testing please Click
HERE

Out of syllabus
•Extra
information

 Discussions with colleagues and experts about the
problem, its origin and objectives in seeking a
solution.
 Examination of data and records for possible trends,
peculiarities.
 Review of similar studies.
 Exploratory personal investigation / Observation.
 Logical deduction from the existing theory.
 Continuity of research.
 Intuition and personal experience.
SOURCES OF HYPOTHESIS

FUNCTIONS OR ROLE OF HYPOTHESIS
 It gives a definite point to the investigation and
provides direction to the study.
 It determines the data needs.
 It specifies the sources of data.
 It suggests which type of research is likely to be more
appropriate.
 It determines the most appropriate technique of
analysis.
 It contributes to the development of theory.

Fig-Hypothesis Testing and the
Research Process

References
• 1) Business Research Methods, Donald Cooper & Pamela
Schindler, TMGH. ( Shared in the class group)
• 2. Business Research Methods, Alan Bryman & Emma Bell,
Oxford University Press
• 3) Research Methods: The Basics, Nicholas S. R. Walliman,
Nicholas Walliman, Routledge,
• 4) PPT - Correlation vs Causation PowerPoint Presentation,
free download - ID:832840 (slideserve.com)
• 5) Concomitant Variable: Definition & Examples – Statology
• 6) Hypothesis Testing - Statistics How To

Business research methods_mba_unit-2

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