Dr. Ulf Mertens
Adaptive Design Optimization
- When it comes to hypothesis testing, most effort is put into the phase after an experiment has been conducted. ADO however focuses on the moment the data are collected. When ADO is used for model comparison, it tries to find optimal stimuli on-the-fly. ADO is able to discriminate among models in a much faster way because there are no more wasted trials (trials where all models predict a similar outcome).
Linear mixed models
- Linear mixed models (LMMs) are a popular statistical tool to model hierarchical structures in the data. Especially in psychology, LMMs are often used for the analysis of repeated measurements. I am currently working on a comparison of some well-known methods for such designs including ANOVA, MANOVA and LMMs.
- I like the way of thinking in Bayesian Statistics. You have some prior knowledge about your parameter of interest, collect some data, and then update you prior belief. Also, I'm not a fan of the p-value is why I sympathise with the Bayesian approach.
Below you find a list of courses I am going to teach this semester (Wintersemester 2020/21).
- An introduction to basic descriptive statistics using the R programming language
- This is a subsequent course where the focus lies on how to use R for inferential statistics (linear models, contrast analysis etc.)
- In this class, we show how to tackle some common problems in empirical theses such as dealing with violations of assumptions, missing values, marginally significant results and so on.