- 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.
- Bayesian Statistics
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.
- Übung Deskriptivstatistik
An introduction to basic descriptive statistics using the R programming language
- Übung Inferenzstatistik
This is a subsequent course where the focus lies on how to use R for inferential
statistics (linear models, contrast analysis etc.)
- Statistics Toolbox
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.
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