Please enable JavaScript.
Coggle requires JavaScript to display documents.
Casual Research (experimentation) (Limitations of Exp (Time Consuming,…
Casual Research (experimentation)
Research
Exploratory
Descriptive
Causal
Experimentation
Most interesting questions in marketing (and life) are causal Qs
Does higher price - more $?
Sun bathing cause skin cancer?
Alot of confusion around causality
Focus of today
procedures involved in causal research
why these procedures are necessary
process of manipulating the independent variable/ measuring its effect on the DV, while controlling for the effect of everything else
you need to offer X at different values/ obtain the corresponding values of Y
Option 1: Cre8 sub grps using your sample/apply different treaments (X) to diff grps
Option 2: Apply different treatments (X) over time
Experimental Group: grp exposed to the manipulated IV
Control Group: exposed to status quo value of the IV
Main obstacle: Extraneous Variables (EV)
variables, other than IV, influence the DV
Examples: diff in purchasing habits btwn customers, seasonality
Common sources of EV
poorly designed experiment stimuli
treatment/control grp X comparable
external events
Selection Bias
Random assignment of testing units -- eliminate selection bias
Commonly used Experimental Designs
One-Group Pretest-Posttest Design
O1 X O2
grp of test unit measured twice
X control grp
treatment effect is computed as O2-O1
-validity of this conclusion is questionable since EV are largely uncontrolled
Static Group Design
EG: X O1
CG: O2
2 group experimental design
EG exposed to treatment/ CG X
measurement on both grps made only after treatment
test unit X assigned at random
treatment effect O1-O2
Posttest only Control Group Design
EG: R X 01
CG: R X 02
similar to SGD, test units are randomly assigned to EG/CG
treatment effect is obtained by TE=O1-O2
selection bias is eliminated by randomisation
Classification of Exp Design
No RANDOMISATION
Pre-Experimental
1 group Pretest-Posttest
Static Group
Quasi-Experimental
Time Series
Multiple Time Series
Repeated measurement
X random assignment
With RANDOMISATION
True Experimental
Posttest-Only Control Group
Statistical
Factorial Design
complex design test multiple IVs/ interactive effects
involve random assignment
Lab vs Field Exp
Lab Experiment
Takes place in Lab
Hypothetical scenarios
Research Participation
Field Experiment
Takes place in natural environment
Involves "real" decisions
A/B testing
Exp Design any good?
Internal Validity
manipulation of IV is real cause of obs effect
degree to which effects of extraneous variable are controlled
Factors that -- poor internal validity= different EV
poorly designed experimental stimuli
treatment/ control grps X randomly assigned
treatment/ control take place at diff time (external events)
External Validity
can effects found be generalised in reality?
often comes down to lab vs field exp
do target population/ test units match?
Limitations of Exp
Time Consuming
Expensive
Difficult to adminster
Competitors may deliberately contaminate the results of field exp