🗓️ Session 11: Experimental data in R
This session focuses on the practical analysis of data obtained from experiments in R, and on the most common tasks associated with designing and evaluating them.
This way it complements the more theoretical lectures. More precisely, we focus on the following topics:
- Import and explore data from between- and within-subjects experimental designs
- Check the assumptions of t-tests and ANOVA (normality and homogeneity of variance)
- Compare two groups using independent and paired t-tests
- Conduct one-way ANOVA for multi-group comparisons and understand it as a special case of linear regression
- Run post-hoc tests (Tukey’s HSD) for pairwise comparisons
- Analyze factorial designs and interaction effects using two-way ANOVA
- Calculate and interpret effect sizes (Cohen’s d and \(\eta^2\))
- Plan adequate sample sizes through power analysis
- Create effective visualizations of experimental results
👨🏫 Lecture Slides
There are no slides for this lecture. The relevant information is given through the corresponding lecture and the mandatory readings.
📚 Mandatory Reading
- Experiments in R on Method and Inquiry (Gräbner-Radkowitsch 2026)
Further Reading
- TBD
✍️ Coursework
- TBD
References
Gräbner-Radkowitsch, C. (2026) “Experiments in R” C. Gräbner-Radkowitsch (ed.), Method and Inquiry, available at https://euf-methodology-hub.netlify.app/resources/experiments-in-r.