The course provides an introduction in statistical methodology for life sciences and discusses a number of statistical techniques for practical data analysis. Concrete examples and case studies are used to apply the theory to practical situations.
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NOTE: this course is fully booked! New applicants will be placed on a waiting list.
This is a four-day course on how to study dynamics in intensive longitudinal data, such as ambulatory assessments (AA), experience sampling method (ESM) data, ecological momentary assessments (EMA), real time data capture, observational data or electronic daily diaries. We provide a tour of diverse modeling approaches for such data and the philosophies behind them, as well as practical experience with these modeling techniques using different software packages (including R and Mplus).
This course introduces all the essential ingredients needed to start Bayesian inference or model selection. We discuss specifying priors, obtaining the posterior, prior/posterior predictive checking, and sensitivity analyses. We also discuss evaluating hypotheses via the Bayes Factor, using information criteria and aggregating evidence from multiple studies. We propose strategies for reproducibility and reporting standards, outlining the WAMBS-checklist (when to Worry and how to Avoid the Misuse of Bayesian Statistics). We have prepared many exercises in R (brms, blavaan, rjags, rstan, rstanarm, bayesreg, restrictor, bain) to get hands-on experience.
This course will teach you the theoretical basics of multilevel modelling and some important methodological and statistical issues. You will also learn how to analyse multilevel data sets with the HLM, Mplus and R programmes, to interpret the output and to report the results. The benefits of multilevel analysis are discussed both in theory as with empirical examples. This course restricts to a quantitative (i.e. continuous) outcome variable. Categorical outcomes are part of the course Advanced Multilevel.
This three-day course will teach you advanced topics in multilevel modelling. It builds upon the contents of the other summer school course “Introduction to multilevel analysis”. It consists of three days with lectures in the morning and computer labs in the afternoon. After taking this course, you should be able to analyse more complex multilevel models and to interpret and report the results.