Course Description
The stuff you think you know — until you look at it more closely.
Most PhD students use regression models and ANOVA on a regular basis: linear models, logistic regression, mixed models, and the usual suspects. These methods are central to empirical research. At the same time, many of us apply them a bit on autopilot. Even experienced users occasionally miss small but important details, which can lead to interpretations that are… let’s say, less informed than what one would hope.
Topics will include:
- Thinking carefully about the link between your research question and the statistical procedure you choose
- Interpreting the relative importance of predictor variables (and why this is trickier than it looks)
- Type III tests errors: how to get a perfectly correct answer to the wrong question
- Why contrasts matter in ANOVA (even if you’ve been ignoring them for years)
- Why experiments are better than surveys… but only to an extent
- The relationship between ANOVA and regression, and when the distinction actually matters
- The nasty nitty-gritty of interaction effects
- Recognizing and avoiding common analytical pitfalls
- The importance of effect sizes (and why we will try to gently move beyond Cohen’s d)
- The curious conventions behind factorial ANOVA
The workshop combines conceptual discussion with hands-on exercises and real-world examples. The aim is not just to learn, but to reflect on how you currently use statistical models — and perhaps adjust a few habits along the way.
Prerequisites
This workshop is for PhD candidates who already know their way around regression and ANOVA, but suspect that there may be more going on under the hood. We will not reteach the basics. Instead, we will revisit core ideas, sharpen intuition, question some standard habits, and try to build a more solid statistical foundation for your research.
Reading Materials
Hand, D. J. (1994). Deconstructing statistical questions. Journal of the Royal Statistical Society: Series A (Statistics in Society), 157(3), 317-338. https://doi.org/10.2307/2983526
Haans, A. (2008). What does it mean to be average?: the miles per gallon versus gallons per mile paradox revisited. Practical Assessment, Research & Evaluation, 13(3), 1-5. https://doi.org/10.7275/w9rp-w968
Haans, A. (2018). Contrast analysis: A tutorial. Practical Assessment, Research & Evaluation, 23(9), 1-21. https://doi.org/10.7275/7dey-zd62
Rosnow, R. L., & Rosenthal, R. (1995). “Some things you learn aren’t so”: Cohen’s paradox, Asch’s paradigm, and the interpretation of interaction. Psychological Science, 6(1), 3-9. https://doi.org/10.1111/j.1467-9280.1995.tb00297.x
Capacity
This course has a maximum capacity of 35 participants.
Time and Location
This workshop will be held on-site only at Eindhoven University of Technology on March 27, 2026. Details will be provided to all attendees over email after registration for the workshop.
Workshops start from 9:30 to 16:30 with a lunch break from 12:30 to 13:30. Lunch will not be provided but can be purchased at the university canteen or the on-campus supermarket.
Registration
To register for this workshop, please complete the following form by March 16. Note that your registration will be considered finalized only after receiving a confirmation email. The registration link will remain open after this date if spots are still available.
Instructors
Prof. Dr. Chris Snijders
Chris Snijders is a Full Professor at Eindhoven University of Technology. His research examines when and why computer models outperform human experts, and why their real-world adoption remains limited. He also studies how life online reshapes interaction and learning, leveraging digital traces to improve education. Additional interests include formal modeling in the social sciences, technology-supported choice (recommending, persuasion, nudging), social networks, trust and cooperation, and decision-making under uncertainty.
Dr. Antal Haans
Antal Haans is Assistant Professor at Eindhoven University of Technology. His research centers on smart urban lighting—its user requirements and its impact on behavior, perception, and perceived safety. A second line of work explores advanced media technologies (e.g., immersive VR, mediated social touch) as instruments for psychological science.