Students learn statistics to reason under uncertainty and avoid cognitive biases.
Show, don’t tell. Students expand their behavioral data science skills with (interactive) data visualizations in R—in collaboration with Abe Hofman.
Understanding follows measurement. Psychometrics provides the theory and techniques for psychological measurement. We’ll cover latent variable models, network models, and latent process models.
A modular masterclass series on programming in R, with a primary focus on the tidyverse—in collaboration with Simone Plak.
Students are introduced to the concept of a formal theoretical model, to aid in understanding latent cognitive and neural processes. Three leading formal modeling approaches are discussed (growth models, diffusion models, and catastrophe models), and these serve as case studies to understand the basic concepts of mathematical psychology—in collaboration with Leendert van Maanen, Han van der Maas.
Students refresh and improve their programming skills while independently developing software. After this course, students should be able to program large-scale simulations, implement new statistical methods, and program experiments—in collaboration with Jasper Wijnen, Raoul Grasman, Han van der Maas, Claire Stevenson.
A conceptual and practical introductory course on frequentist statistics in R—in collaboration with Abe Hofman, Han van der Maas.
Honours Bachelor course on state-of-the-art research methods with applications in Psychology. Several guest lecturers contribute to the course and topics include machine learning in cognitive psychology, network models for psychopathology, A/B testing in online learning, and N=1 timeseries in clinical psychology—in collaboration with Max van der Linden, Jolanda Kossakowski, Joost Zandvliet, Vera van der Molen.