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Experimental Research - Full Summary

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Full summary for the Experimental Research exam (including table interpretations)

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  • March 20, 2024
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EXPERIMENTAL RESEARCH
WEEK 1

LECTURE 1

Types of marketing research




Why experimenting?

 In marketing (and other sciences) we seek to:
o Describe
o Predict
o Explain behaviour of (market-parties, employees, buyers).
 Break up the phenomena in variables and relations between those variables.

Conversion optimalisation

 A structured and systematic approach to improving the performance of a website.
 Informed by data insights & psychology.
 Taking the traffic there and making most of it.

Behavioural research

 Descriptive research
o Thoughts, feelings, ideas, behaviours.


1

,  Correlational research: identifying relationships between different observed variables: measuring
thoughts, feelings, behaviour.
o “People save more during the economic crisis”.
o “Smoking mothers have more often problem children.”
o Measures the association between 2 variables.
 Correlation of -1 (negative association) to 1 (positive association).




Problems with correlational research

 Direction – correlations are bidirectional, you don’t know what is causing what.
 Spurious correlations / confounds / omitted variable – there may be alternative explanations.

From description to prediction

 Description: careful observation.
 Correlation: relationship between observed variables.
 Experimental: testing causality, A  B?

Experimental research: settings

 Field experimentation.
o Real life setting.
o Mundane reality: natural behaviour / setting / treatment.
o Less control.
 Laboratory experimentation
o More control.
o Better able to manipulate variables.
o No natural setting.

Experimental research: Crux

 Experimentation: only type of research that (potentially) can demonstrate that a change in one
variable causes a predictable change in another variable.
 Most difficult: making sure that a change in Y was not caused by something else than X.



2

, Experimentation & Causation

 Needs to be correlation between 2 variables.
 Asymmetrical direction
 A (cause)  B (effect)
 Change in A is accompanied by a change in B.
 No alternative explanation for the change in B than the change in A (other possible causes are
controlled for).

Experimental research: essence

 Test specific hypotheses about relationship between cause and effect via controlled (laboratory)
conditions.
o The effect of an IV on a DV.
o Manipulating the IV.
o Measure effects on dependent variable.
o Control other influences (high internal but low external validity).

Importance of randomization

 Use all sorts of people with all sorts of characteristics.
 Potential confounds are better under control.
 So: change in Y can be attributed to X.

Problem identification

 Sources of research ideas
o Real life experiences
o Previous research and theory
 Conflicting findings.
 Boundary conditions (moderator)
 Find explanation for observed effects (mediator)
 Applying a theory to a consumer setting.

Good research questions should both

 Have real life relevance (implication for managers and consumers).
 Contribute to current knowledge (theoretical contribution).
 So, have a substantive and theoretical contribution.




LECTURE 2

Full empirical cycle

 Identifying interesting and relevant research question.
 Pinpoint conceptual variables
 Identifying the proper relations between them that fit your theorizing (=hypotheses).
 Develop a research design.
 Develop manipulations and measures for the IV’s, DV’s, mediators and moderators.

3

,  Collect data.
 Analyse and interpret the results.
 Write up study to submit to a journal.

Experimental research: main steps

 Theoretical framework
o Problem identification.
o Hypothesis formulation.
 Experimental design
o Manipulation of IVs.
o Measurement of DV.
o Control for confounds.
 Data analyses
o Get familiar with your data.
o Checks
o Conduct main analyses.
o Conduct follow up analyses.

Conceptual models

 All concepts measured in the experiment visualized, to show their expected relations.
 What outcomes are you interested in? (DV)
 Concepts you manipulated expecting systematic variance because of them. (IVs).
 Thus: conceptual models show interrelations between IVs and DVs.




 A moderating variable tells us when an effect will happen. A moderator becomes interesting when it
says something about the relationship between X and Y.




4

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