Welcome...

 ...to the working unit Statistics and Psychological Methods at the University of Münster (AE Nestler). The members of our lab are interested in the advancement of statistical methods for the analysis of complex psychological data. Latest research projects cover topics such as combining the social relations model with structural equation models, advancing models to examine intra-individual variability or combining standard statistical approaches such as multilevel or structural equation models with machine learning methods (e.g., trees, boosting, ...). We are also authors or contributors to different R packages. In teaching, we provide a comprehensive B.Sc.- and M.Sc.-program in psychological methods, including lectures and courses on basic and advanced statistics.

If you are interested in writing a thesis in our lab, you can find further information here. We offer supervision of both bachelor and master theses covering both substantive and methodological research questions.

Latest News

2025-04-02

New paper in press by C. Bhomwik, M. Back, S. Nestler,& F.-W. Schrader in Social Psychology of Education: Appearing smart, confident and motivated: A lens model approach to judgment accuracy in an educational setting.

2025-03-14

We have received a new grant of the German Research Foundation (DFG) for the project „Mixed-effects location scale models for ordinal data”.

2025-03-01

New paper in press by E. Ulitzsch, W. Viechtbauer, ..., S. Nestler, & G.V. Eisele in Psychological Assessment: Investigating the effect of experience sampling study design on careless and insufficient effort responding identified with a screen-time-based mixture model.

2025-02-24

New paper in press by Y. Hilker, B. Forthmann, & P. Doebler in Psychology in Aesthetics, Creativity, and the Arts: Assessing the robustness of automated scoring of divergent thinking tasks with adversarial examples.

2025-02-06

New paper in press by K. Jansen, & S. Nestler in Multivariate Behavioral Research: Correcting for differences in measurement unreliability in meta-analysis of variances.