CHAPTER
4
Causal Designs and
Marketing
Experiments
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The Quest for Causality
“Are there other possible factors that could have led to the
observed changes?”
 scientific concept of causality (“probabilistic
causation”): a number of determining conditions act
together to make an effect probable
 causality can only be inferred, never proven
 3 conditions for causal inferences:
concomitant variation – correlation that helps rule out:
• reverse causation
• omitted variables
• insufficient variation
2. time order of occurrence of variables – precedence
3. elimination of other possible causal factors
1.
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Experimentation
 experiment: the manipulation of one or more
independent variable in a planned fashion and
measurement of the effects on the dependent variable
 the fundamental research tool used to help identify causal
relationships
 objective is to measure the effect of explanatory
(independent) variables on a variable of interest, while
controlling for other variables
© 2013 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
Basic Definitions in Experimentation
 treatments – the independent variables that are
manipulated and whose effects are then measured
 test units – recipients of the treatments, whose response
is measured
 dependent variables – measures taken on the test units
 extraneous variables – all variables, other than the
treatments, that potentially affect the dependent variable
 experimental design – specification of treatments, test
units, dependent variables, and procedures for dealing
with extraneous variables
© 2013 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
Basic Definitions in Experimentation
(continued)
 validity in experimentation
 internal validity – minimum validity to make conclusion:
have effects of extraneous variables been accounted for?
 external validity – can the results be generalized?
 X-O-R syntax – standard convention of symbols used to
define experiments:
 X – exposure of test group to treatment
 O – observation or measurement of the dependent variable
 R – test units have been assigned at random to treatments
 movement from left to right indicates passage through time
 symbols in a horizontal row refer to a specific treatment
group
 symbols aligned vertically refer to the same moment in time
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Types of Extraneous Variables
 history effect – events concurrent with the experiment
that may affect the dependent variable
 maturation effect – effect of changes in the test units
over time
 testing effect – effect of taking a measure before
treatment
 main testing effect – effect of 1st observation on the 2nd
observation
 interactive testing effect – effect of pre-treatment
observation on the response to the treatment
© 2013 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
Types of Extraneous Variables
(continued)
 instrumentation – changes in the observers or
calibration of the measuring instrument
 statistical regression effect – effect of test units being
selected for treatment on the basis of an extreme pretreatment score
 selection bias – effect of treatment groups differing on
the dependent variable prior to the presentation of the
treatments
 test unit mortality – effect of test units withdrawing
from the experiment while it is in progress
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Three Pre-Experimental Designs
 One-Shot Case Study (X
O) – test units exposed to
treatment X, then measurement taken
 One-Group Pretest-Posttest Design (O1 X
O2) –
pretest measurement is added to the one-shot case study
design
 Static-Group Comparison – two treatment groups, one
exposed to treatment and control group that is not, both
observed only after treatment
 Experimental group:
 Control group:
X1
X2
O1
O2
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Three True Experimental Designs
 Pre-test Post-test Control Group Design – controls for
extraneous variables, but not interactive testing effect
 experimental group:
R
 control group:
R
 (O2 – O1) – (O4 – O3) = TE + IT
O1
O3
X1
O2
O4
 Solomon Four-Group Design – controls for extraneous
variables and interactive testing effect with 2nd control group
 Experimental group 1:
R
O1
 Control group 1:
R
O3
 Experimental group 2:
R
X
O2
O4
X
 Control group 2:
R
 TE = O5 - O6, but all results are used equally
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O5
O6
Three True Experimental Designs
(continued)
 Post-test-Only Control Group Design – last two groups
of the Solomon four-group design
 since there is no pre-test, the interactive testing effect
cannot occur
 Experimental group:
R
 Control group:
R
X
O1
O2
 O1 - O2 = TE
 true experimental designs control for extraneous
variables (EXT)
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Quasi-Experimentation
 control over data collection procedures
 no randomization of assignment of treatments
 little control over scheduling of the treatments
 designed to increase external validity
 much more susceptible to confounded results
 time-series experiment
 periodic measurement before and after treatment
 O1 O2 O3 O4
X
O5
O6 O7 O8
 weaknesses
• inability to control history effect
• possibility of an interactive testing effect from repeated measurements
© 2013 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
Quasi-Experimentation
(continued)
 multiple time-series design
 a time-series design with control group of test units
 experimental group:
 control group:
O O O
O O O
X
O
O
O O
O O
 problems:
• difficulty of selecting a well-matched control group
• possible interactive effect in the experimental group
 equivalent time-sample design
 experimental group acts as its own control, preferably when
the effect of the treatment is transient:
O
X1 O
X0 OOO X1
OO
X0 O
(X1 = treatment and X0 = absence of treatment)
 possible interactive testing effect
© 2013 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
Quasi-Experimentation
(continued)
 nonequivalent control group design
 both experimental and control groups are measured pre-test
and post-test, but the control group is not equivalent to the
test group
 experimental group: O1 X O2
 control group:
O3
O4
 control over who is exposed to the treatment, thus
improving the ability to control the effects of history,
maturation, main testing, instrumentation, selection, and
test unit mortality
 possible interactive testing effect
 possible regression effect if either group selected on the
basis of extreme scores
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Managerial Aspects of Experimentation
and Quasi Experimentation
 descriptive techniques (secondary data, observation,
surveys, panels, web log analysis and simulation)
measure correlation, not causation
 laboratory versus field environments
 laboratory provides better control of confounds, greater
internal validity
 field experiments provide greater external validity
 laboratory generally less expensive
 laboratory often requires less time
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Control of Invalidity
+ = factor controlled
? = possible concern
History
Maturation
Testing
Instrumentation
Regression
Selection
Mortality
Interactive testing effect
–
–
–
–
–
–
?
–
–
static group
comparison
– = weakness
one group
pretest
posttest
one shot
Pre-experimental designs
+
?
+
+
+
–
–
–
© 2013 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
Control of Invalidity
(continued)
– = weakness
+ = factor controlled
? = possible concern
History
Maturation
Testing
Instrumentation
Regression
Selection
Mortality
Interactive testing effect
+
+
+
+
+
+
+
–
Posttest-only
control group
Pretestposttest
control group
Solomon fourgroup
True experimental designs
+
+
+
+
+
+
+
+
© 2013 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
+
+
+
+
+
+
+
+
Control of Invalidity
(continued)
History
Maturation
Testing
Instrumentation
Regression
Selection
Mortality
Interactive testing effect
Nonequivalent
control group
? = possible concern
Equivalent
time sample
+ = factor controlled
Multiple time
series
– = weakness
Time series
Quasi-experimental designs
–
+
+
?
+
+
+
–
+
+
+
+
+
+
+
–
+
+
+
+
+
+
+
–
+
+
+
+
?
+
+
–
© 2013 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
Limitations of Experimentation
 can not always control effects of extraneous variables,
particularly in field experiments
 lack of cooperation in field can limit experimental
activity
 lack of knowledge about experimental procedures on the
part of marketing personnel
 can be costly, time-consuming and requires expert data
analysis
 responses can still be biased by experimenter
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Stages in Conducting an Experiment
1. State problem
2. Formulate hypothesis
3. Construct experimental design
4. Mock up data – will it meet information needs?
5. Check results can be analyzed by available statistical
procedures
6. Perform experiment
7. Apply statistical analysis procedures
8. Draw conclusions
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Four Design Procedures: an Overview
 determine statistical significance – is “signal” (measured
effect) greater than “noise” (sampling error)?
 completely randomized design (CRD)
 one nominally scaled independent variable
 treatments assigned randomly
 randomized block design (RBD) – combines test units
into blocks based on extraneous variable, reducing
sampling error
 each treatment must appear at least once in each block
 number of test units  number of treatments
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Four Design Procedures: an Overview
(continued)
 Latin square (LS) design – two extraneous variables
 number of categories of each variable to be controlled
exactly equals the number of treatments
 treatments assigned to cells randomly
 factorial design – for more than one independent variable
or interactive effects* between variables
 levels = independent variable categories
 treatments = combinations of levels
 allows separation of interaction and main effect through
statistical procedures
* relationship between independent and dependent variable is different for different
categories of another independent variable
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International Marketing Experiments
Hold equally well for domestic and for international
research:
 principles of causality
 structure of experimental design
 nature of marketplace quasi-experiments
© 2013 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.