Department of Methodology and Statistics

The Department of Methodology and Statistics of the Faculty of Psychology and Neuroscience (FPN) works in close collaboration with the Department of Methodology and Statistics of the Faculty of Health Medicine and Life Sciences (FHML) under common leadership. Together we are a group of around 20 staff members from PhD student to Professor. We provide the statistics education in five different Bachelor programmes and various master and PhD courses in the FPN and FHML. We also give statistical advice and support in data analyses to researchers from both faculties. Initially our own statistical research focused on study design and methods of analysis for nested and longitudinal data, in particular on sample size calculations and mixed (multilevel) regression analysis. In the last years our scope has broadened to include methods for multilevel interrater agreement and reliability, multiple imputation methods for missing data, Bayesian statistics for high-dimensional data, and innovative methods for growth curve analysis and for detecting person-situation interactions.


The department offers statistical advice on study design (a.o. sample size) and data analysis to all researchers in the FPN, as well as support in data analysis and reporting. A single consult is offered for free. Repeated consults or support in data analysis and reporting are offered in exchange for co-authorship, sometimes a co-promotorship.

Members of the department have expert knowledge not just on standard statistical methods, but also on advanced methods such as mixed/multilevel regression, structural equations modeling (SEM), group based trajectory modeling, missing data handling, and Bayesian analyses. Software used includes SPSS, R, STATA, SAS, MLWin, MPLus and Lisrel for SEM, Matlab, JAGS and WinBUGS (Bayesian stats), and PASS (sample sizes).

For details, see the individual staff members’ pages

Researchers who consider statistical help in their data analyses, are invited and advised to consult us already in the phase of study design. In statistics, just like in health care, prevention is the better cure!