Workshops

I have held workshops in a broad range of topics in the area of statistics and computational modelling in Psychology. Specifically, I have held worksops on:

  1. Structural Equation Modelling in Psychology & Social Sciences (2-3 days)
  2. Introdcution to Bayesian Statistics in R with brms (1 - 3 days, depending on depth)
  3. Simulation Studies to challenge intuitions about statistics and psychological theories (2 - 3 days)
  4. Analyzing Data on the level of cognitive processes / Introduction to the R package: bmm (2 - 3 days)

If you are interested in including one of my workshops in your PhD or Graduate program, please contact me.

Lectures & Seminars

Beyond the worshops that are targeted towards advanced graduate students, PhDs, and early career PostDocs, I held courses in Undergraduate and Graduate Programs at Heidelberg University, University of Zurich, and University of Lucerne. These courses focussed on the following topics:

  • Introduction to Statistics
  • Introduction to Psychometrics
  • Current topics in Cognitive Psychology

Typically these courses are designed for weekly sessions of about 2 hours, with additional course work for students that needs to be completed in between the sessions.

Teaching philosophy

Knowledge and skills in quantitative methods and statistics are essential tools for psychological researchers and applied psychologists who must critically evaluate scientific findings. These skills are increasingly vital in today’s information-rich society. I aim to present psychological methods and statistics as the foundation of scientific reasoning and the basis for all theoretical and empirical work in psychology.

My teaching emphasizes that statistical methods are not abstract mathematical exercises but carry direct meaning for our scientific questions. To achieve this, I focus on three goals:

  1. Teach the core mathematical foundations (e.g., probability theory, sampling, Bayes’ theorem) underlying psychological methods.
  2. Explain the logic and application of frequentist and Bayesian analyses, their theoretical models, and interconnections.
  3. Connect statistical knowledge to software implementation (e.g., R, RStudio, MPlus), enabling students to apply and adapt analyses to their own research questions.

I also promote open and cumulative science by requiring students to document code and data for reproducibility, encouraging preregistration, and sharing materials and results on platforms such as the Open Science Framework.