Experimental Methods in Management and Consumer Psychology

Participation Prerequisites

• Basic courses in statistics
• It is not necessary that you have already collected data for your dissertation thesis. The course is designed for doctoral students at any stage of the dissertation process.
• No prior experience with R is required; all necessary commands will be introduced in the course.

Course Content

The following topics are discussed:

• Randomization
• ANOVA
• 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

Intended Learning Outcomes and Competencies

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. 

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. 

Course paper:

• 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). Participants may submit their papers at any time following the seminar, but no later than August 24, 2026. Extensions of the deadline can be arranged if needed, and feedback is typically provided within two weeks after submission.

Optional idea exchange session:

On the final day of the course (i.e., session 3), students who would like early input on ideas for an experiment may, if they wish, briefly share their thoughts or questions in an informal group discussion. This activity is entirely optional – students are welcome to share their ideas or simply listen and contribute to the discussion. The goal is to provide a supportive and constructive environment in which participants can explore options for designing their experiment and receive friendly, encouraging feedback from one another.

Instruction Type

• Presence
• Online participation can be arranged only in exceptional cases when on-site attendance is not possible. Students in this situation should contact me directly (walter.herzog@whu.edu).

Form of Examination

Participation and Course Paper

Literature

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