Experimental Methods in Management and Consumer Psychology

• 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

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.