We start with basic examples from clinical studies in psychology and medicine and review their study design, including statistical tests and sample size planning. Step by step we learn how to extend the methodology to adjust for baseline performance, to analyse subgroups, to deal with missing data, to deal with multiple outcome variables, to analyse binary data, count data, and event times, to deal with clustered data (e.g. group therapy), and to compare natural (i.e., not randomized) groups. After the first part, students will be able to plan and analyse standard study designs in real world-settings, including basic pre-processing of data, import and export of different files, and standardized reporting. Most of the analyses will be done with R (but in cases where it can be done in SPSS, both will be demonstrated).

In the second part, we will widen our horizon and improve our skills by learning non-standard approaches to data analysis, e.g. permutation and bootstrap, and stochastic modelling. We will review the measurement axioms underlying quantitative methods in psychology (what makes an interval scale) and we will encounter qualitative structures for representation of knowledge, skills, misconceptions and symptoms. The choice of the last topics also depends on the specific interests of the participants.
Requirements/Examination

PhD students of all fields can participate in this seminar. The minimum requirement is that students are interested in planning, conducting and analysing research studies. Importantly, participants are asked to reflect on their use of methodology, their way to formulate research questions, and to conduct large projects. Requirements for passing the seminar are (a) attendance at minimum eight classes and (b) active participation, which will be evaluated based on classroom performance (including homework).