This course provides students with the conceptual and practical foundations required to design, implement, and analyze experiments that produce valid, transparent and reproducible findings. Covering the entire experimental research process, the course guides students from the formulation of theory-driven hypotheses to the selection of appropriate designs, manipulation of variables, and application of advanced statistical techniques.
Students will learn to ensure internal and external validity, control for confounding variables, design robust manipulations, and adopt best practices for transparency, preregistration and reproducibility.
By the end of the course, participants will be equipped to design high-quality experiments, analyze complex data structures, and communicate results in line with international academic standards.
5 ECTS
Prof. Ingrid Poncin
See the full course description here
CONTENT
Introduction to Experimental Research
Experimental logic and causal inference
Types of validity (internal, external, construct, statistical)
Sample size and power
Ethical and transparency principles (pre-registration)
From Theory to Hypotheses
How to formulate research questions
Main effects, moderators, mediators
Illustrative examples from management and behavioral research
Experimental Design and Manipulation
Between vs. within-subjects designs
Mixed designs and factorial structures
Manipulation checks and control conditions
Practical exercise: designing an experiment
Data Analysis – ANOVA and Regression
ANOVA (univariate, repeated measures)
Regression for moderation and mediation
PROCESS macro
Hands-on SPSS session
Advanced Analyses and Visualization
Conditional process analysis (moderated mediation)
Bootstrap methods for indirect effects
Data visualization standards and best practices
Writing results sections (APA-style or journal-specific)
Reporting guidelines for top-tier journals
Integration and Application
Meta-analysis and replication strategies
Pitching an experimental study
Discussion and critique of student proposals
EVALUATION METHODS
Assessment is based on continuous evaluation, including:
Group activities: critique and discussion of experimental research articles.
Individual assignments: design an experimental study, conduct data analysis (ANOVA/regression), and prepare a research report.
Oral presentation: pitch an experimental project.
Class participation.
Important note: By submitting an assignment for evaluation, students affirm that (i) it accurately reflects verified facts—particularly when generative AI resources are used, in which case these tools must be explicitly acknowledged—and (ii) all specific requirements of the assignment have been respected, including those related to transparency and documentation of the process.
Failure to meet any of these commitments, whether through intent or negligence, constitutes a breach of academic integrity and is considered academic misconduct.