Tippie College of Business | University of Iowa
MKTG3100 Marketing Research
Ch8. Conducting Causal
Research–Part 1
Instructor: Minjee Sun
Re: Research Design: Definition
What is Research Design?
• A framework or blueprint for conducting the
marketing research project
• It specifies the detail of the procedures
necessary for obtaining the information
needed to structure or solve marketing
research problems
Research Design: Definition
“Do we know what information we need to get?
If yes,
then let’s think about how we can get the information.”
Re: Marketing Research Process
• Problem Definition
• Data Collection
• Data Analysis
• Solutions offered
Research Design: Classification
Research Design
What
How
Exploratory
Conclusive
Research Design
Research Design
Explore
Descriptive
Causal
Research Design
Research Design
Describe
Establish cause and effect
Research Design: Classification
Exploratory
Research Design
Descriptive
Research Design
Causal
Research Design
Research conducted to gain ideas and
insights to better define the problem or
opportunity confronting a manager.
Research in which the major emphasis is
on describing characteristics of a group or
the extent to which variables are related.
Type of research in which the major
emphasis is on determining cause-andeffect relationships.
Research Design: Classification
Determining cause-and-effect
relationships
e.g., a criminal investigation (search for clues → Interview witness and suspects → Build
enough evidence to identify the culprit)
Objectives
1. Know how to establish “Causality”
2. Learn about experimental designs
3. Learn about Extraneous Variables
Causality
X is cause of Y
 X is a cause and Y is an effect
 “The occurrence of X causes occurrence of Y.”
“X
Y”
Ex.
X
hot weather
R&D investment
price change
Y
thirsty
high quality product
sales change
Necessary Conditions for Causality
X is cause of Y
3 Necessary Conditions
(1) Consistent Variation
(2) Time Order (of occurrence)
(3) Elimination of Other Explanations
Necessary Conditions for Causality
(1) Consistent Variation
(2) Time Order (of occurrence)
(3) Elimination of Other Explanations
X is cause of Y
(1) X and Y always occur or vary together.
(2) X should occur before or simultaneously with Y.
(3) The occurrence of Y is because of X only.
How to Check a Causality
Step 1: Identify X and Y in the hypothesis
E.g., H: “Our new advertising campaign will increase
consumers’ awareness for our brand.”
X = New advertising campaign
Y = (relationship: Increase in) Brand awareness
Step 2: Check whether the statement satisfies
all three conditions (w/ some data)
Step 3: Accept the causality between X and Y
if all three conditions are satisfied
How to Check a Causality: example
e.g., Pepsi Cola Promotion (Buy One & Get One free)
“This promotion will lead to increase in sales.”
Step 1:
X=
Y=
Step 2:
Checking: Is condition (1) satisfied?
Is condition (2) satisfied?
Is condition (3) satisfied?
How to Check a Causality: example
e.g., Pepsi Cola Promotion (Buy One & Get One free)
“This promotion will lead to increase in sales.”
Promotion(X)
No promotion
“Buy one get one free”
Sales(Y)
$ 500
$ 1,500
Season
Spring
Summer
?
Step 3:
Condition (3) is violated. Hence, we do not accept the
causality between the promotion and sales increase.
Objectives
1. Know how to establish “Causality”
2. Learn about experimental designs
3. Learn about Extraneous Variables
Concept of Experiment
Testing causality”
”
Scientific investigation in which an investigator manipulates
one or more independent variables and observes the degree
to which the dependent variables change.
Experiments as Causal Research
cause
effect
X causes Y
Causality
manipulate
Experiment
measure
X
O
independent
variable
dependent
variable
Experimental Design: symbols
Experiment
X
O
independent
variable
dependent
variable
X: eXposed (treatment)
O: Observed (measure)
Experimental Design: example
Causality
Price change affects consumers’ purchase intention
Experiment
Symbol
Independent var = price change
Dependent var = purchase intention
Test Units = a group of consumers
X
O
(by row)
Experimental Design
X
Ex.
O1
O2
Experimental Design: Classification
Experimental Design
Preexperimental
True Experimental
• One-Shot Case
Study
• Pretest-Posttest
Control Group
• One Group PretestPosttest Design
• Posttest-Only
Control Group
• Static Group
• Four-Group SixStudy Design
Quasi Experimental
• Time-Series
Experimental Design: Classification
[1] One-Shot Case Study
X
O
Treatment effect = ?
How about measuring DV twice, rather than once?
✓ [2] Have ‘O’ before and after ‘X’
How about using two groups, rather than everyone
being treated?
✓ [3] Having two rows that indicates two group.
Only one group will receive the treatment (X).
Experimental Design: Classification
[2] One Group Pretest-Posttest Design
O1
X
O2
Treatment effect = 𝑶𝟐 − 𝑶𝟏
Experimental Design: Classification
[3] Static Group
EG:
CG:
X1 O1
O2
Treatment effect = 𝑶𝟏 − 𝑶𝟐
cf. Posttest-Only Control Group
EG:
R
X1 O1
CG: R
O2
Experimental Design: Classification
[4] Pretest-Posttest Control Group Design
EG: R
CG: R
O1 X1
O3
O2
O4
Treatment effect = 𝑶𝟐 − 𝑶𝟏 − (𝑶𝟒 − 𝑶𝟑 )
Experimental Design: Classification
[4] Pretest-Posttest Control Group Design
EG: R
CG: R
O1 X1
O3
O2
O4
Treatment effect = 𝑶𝟐 − 𝑶𝟏 − (𝑶𝟒 − 𝑶𝟑 )
Treatment effect = 𝑶𝟐 − 𝑶𝟒 − (𝑶𝟏 − 𝑶𝟑 )
This design is also called as
‘Difference-in-Differences method’
Experimental Design: example
Run a lemonade stand fundraiser
Ha: Set a lemonade stand sign on the intersection increases the sales.
Season
August 25, 2010
(Tuesday)
EG
O1 $32
CG
O3 $19
Estimated causal
effect of a sign
August 28, 2010
(Saturday)
X1
Difference
O2 $60
$28
Difference
in sales for
one set the
stand sign
O4 $35
$16
Baseline
$12
True sign
effect on
sales
Experimental Design: Classification
[5] Quasi-experimental design
When true experimentation cannot be used
E.g., Time-series Design
O1 O2 O3 O4
O5 O6 O7 O8
X
Effect of a TV Promotion on Sales by quarter
$8,000
$6,000
$4,000
$2,000
$0
O1
O2
O3
O4
O5
O6
O7
O8
Experimental Design: Classification
[5] Quasi-experimental design
When true experimentation cannot be used
E.g., Multiple Time-series Design
O1 O2 O3 O4 X O5 O6 O7 O8
O9 O10 O11 O12
O13 O14 O15 O16
Effect of a TV Promotion on Sales by quarter
$8,000
$6,000
$4,000
$2,000
$0
O1
O2
O3
O4
O5
O6
O7
O8
Experimental Design: Classification
[5] Quasi-experimental design
When true experimentation cannot be used
E.g., Multiple Time-series Design
How can we measure the treatment effect?
Effect of a TV Promotion on Sales by quarter
$8,000
$6,000
$4,000
$2,000
$0
O1
O2
O3
O4
O5
O6
O7
O8
Objectives
1. Know how to establish “Causality”
2. Learn about experimental designs
3. Learn about Extraneous Variables
Extraneous Variable: definition
Independent
variable
(X)
Dependent
variable
(O)
Z
z1
z2
Any variable you are not investigating that
can potentially affect the dependent variable
e.g., in-class example: what Zs can we think of?
X: use of the special additive; O: mileage performance
Extraneous Variable
• Maturation: Changes in the test units (e.g.,
improvement of performance) over time
• Testing (main): Prior measurement (O1) effecting a
latter measurement (O2)
• History: Events external to the experiment which
occur between the first and second measurement
• Instrumentation: Changes in the measuring
instrument, observers, or scorers.
• Selection of Subjects: Non-random assignment of
subjects (e.g., EG vs. CG)
• Experimental Mortality: The loss of subjects (e.g.,
Completed vs. Uncompleted groups)
Experimental Design: example revisited
Run a lemonade stand fundraiser
Ha: Set a lemonade stand sign on the intersection increases the sales.
Season
August 25, 2010
(Tuesday)
EG
O1 $32
CG
O3 $19
Estimated causal
effect of a sign
August 28, 2010
(Saturday)
X1
Difference
O2 $60
$28
Difference
in sales for
one set the
stand sign
O4 $35
$16
Baseline
$12
True sign
effect on
sales
Controlled: Weekday vs. Weekend, more/less traffics in the location, etc.
Not controlled: selection bias, Interactive testing effect (O1 -> X), etc.
Extraneous Variable
What happens if there is(are) extraneous
variable(s) in our experiment ?
Extraneous Variable
[1] Revisit the “causality”
These extraneous variables creates problems
regarding the third necessary condition:
“no other possible cause”
X
NO!
Y
Z (Extraneous variable)
Extraneous Variable
[2] Revisit the “internal validity”
These extraneous variables hurt the internal
validity of an experiment.
X
NO!
Y
Z (Extraneous variable)
Experiment that you
think happened
What actually
happened in your
experiment
Extraneous Variable
So, how do we eliminate or prevent these
extraneous variables from playing in our
experiment ?
• Randomization
the “R” in True Experimental designs
• Matching
• Control
• …
Extraneous Variable
(FYI) More readings on Experimental Design
and Extraneous Variable:
[1] Threats to validity of Research Design
[2] Churchill, G. A. J. (1995). Marketing Research:
Methodological Foundations (Vol. 6).
[3] Malhotra, N., Hall, J., Shaw, M., & Oppenheim, P.
(2006). Marketing research: An applied orientation.
Pearson Education Australia.
Summary
1. Three necessary conditions for
establishing “Causality”
2. Experimental design: classifications
3. Extraneous Variables