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Experimental Methods in Management and Consumer Psychology

Course code
Course type
Doctoral Program Lecture
Weekly Hours
FS 2024
Prof. Dr. Walter Herzog
Please note that exchange students obtain a higher number of credits in the BSc-program at WHU than listed here. For further information please contact directly the International Relations Office.
The following topics are discussed:
  • Randomization
  • Main and interaction effects
  • Contrast analysis
  • Continuous moderators, spotlight, and floodlight analysis
  • (Instructional) manipulation checks
  • Confounding checks
  • Comprehension checks
  • Demand artifacts and suspicion probes
  • Experimental realism
  • Power analysis
  • Process hypotheses
  • Mediation analysis 
  • Pilot studies, supplementary studies, and intuition checks
  • Creating a series of experiments
  • Variable selection and theoretical coherence
Date Time
Wednesday, 22.05.2024 08:00 - 18:00
Thursday, 23.05.2024 08:00 - 18:00
Friday, 24.05.2024 08:00 - 18:00
The experimental method is one of the most powerful tools for testing psychological theories and it is widely regarded as the 'gold standard' for establishing causal effects. A comprehensive understanding of the method is therefore essential for doctoral students with an interest in behavioral phenomena. The present course offers an introduction to the experimental method and illustrates how experiments can be used in the context of management and consumer psychology. Importantly, the course emphasizes practical challenges that emerge when designing experiments and issues that are frequently raised in academic review processes. Overall, the course enables students to craft experimental studies that meet current academic standards.


Course objectives and learning goals:

  • Typically, experimental data are analyzed using analysis of variance (ANOVA). In the first part of the course, participants will learn the basics of this statistical procedure and how to implement it in the ‘R’ software package. Furthermore, students will learn how to conduct contrast analyses and deal with continuous moderators (e.g., traits) in experimental designs. A set of exercises is provided to ensure that all participants can effectively apply the discussed methods in R.
  • The second part of the course focuses on practical challenges that arise when designing experiments. Specifically, participants will learn how to minimize demand effects, conduct (instructional) manipulation and confounding checks, report tests of experimental realism as well as measurement reliability, and determine sample sizes via power analysis. We will discuss how these methods and quality checks are implemented in leading behavioral journals.
  • In the third part of the course, we will explore various approaches for testing process hypotheses, such as mediation analysis. Again, we will focus on how these methods are implemented in leading journals and discuss practical challenges that emerge when conducting process analyses.
  • Finally, participants will be asked to submit a short course paper (<10 pages, double-spaced). Depending on the stage of their dissertation project, participants can choose among the following options:
    • Option 1: Design a new experiment and submit a brief write-up of the analysis plan.
    • Option 2: Document the results of an already existing experiment.
      For both options, the write-up should contain the research hypothesis, a description of the manipulation(s), measures, quality checks, and, if applicable, process tests. Participants will receive comprehensive and constructive feedback on their papers (e.g., via Teams or a written review). The deadline for submitting papers is Monday, August 5, 2024.
Optional Readings (Examples): Krishna, A. (2016). A clearer spotlight on spotlight: Understanding, conducting and reporting. Journal of Consumer Psychology, 26 (3), 315–324. https://doi.org/10.1016/j.jcps.2016.04.001 Morales, A. C., Amir, O., & Lee, L. (2017).  Keeping it real in experimental research—Understanding when, where, and how to enhance realism and measure consumer behavior. Journal of Consumer Research, 44 (2), 465–476.https://doi.org/10.1093/jcr/ucx048 Oppenheimer, D. M., Meyvis, T., & Davidenko, N. (2009). Instructional manipulation checks: Detecting satisficing to increase statistical power. Journal of Experimental Social Psychology, 45 (4), 867–872.https://doi.org/10.1016/j.jesp.2009.03.009 Orne, M. T. (1962). On the social psychology of the psychological experiment: With particular reference to demand characteristics and their implications. American Psychologist, 17 (11), 776–783.https://doi.org/10.1037/h0043424 Perdue, B. C., & Summers, J. O. (1986). Checking the success of manipulations in marketing experiments. Journal of Marketing Research, 23 (4), 317–326. https://doi.org/10.2307/3151807 Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66 (5), 688–701. https://doi.org/10.1037/h0037350 Spencer, S. J., Zanna, M. P., & Fong, G. T. (2005). Establishing a causal chain: Why experiments are often more effective than mediational analyses in examining psychological processes. Journal of Personality and Social Psychology, 89 (6), 845–851. https://doi.org/10.1037/0022-3514.89.6.845 Spiller, S. A., Fitzsimons, G. J., Lynch, J. G., Jr., & McClelland, G. H. (2013). Spotlights, floodlights, and the magic number zero: Simple effects tests in moderated regression. Journal of Marketing Research, 50 (2), 277–288. https://doi.org/10.1509/jmr.12.0420
See course content/structure
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