Statistical Foundations of EITM

11–13 June 2025

The basic motivation of EITM is it to closely connect formal theoretical models and empirical statistical tests. This course presents flexible Bayesian methods that lend themselves to testing predictions from formal models and to producing meaningful quantities of interest along with their uncertainties. Bayesian analysis involves two key aspects – inference based on probability theory and estimation using stochastic simulation. The course will spend some time on the basic principles of both aspects and then apply them to some of the workhorse models of the social sciences: choice models and IRT models for ideal point estimation. We will also discuss practical issues of applied Bayesian analysis, such as MCMC convergence diagnostics as well as Bayesian model checking and parameter summary.

Richard Traunmüller is Professor of Political Science and Empirical Democracy Research in the School of Social Sciences at the University of Mannheim and scientific director of the German Internet Panel (GIP). Before coming to Mannheim, he held academic positions at Goethe University Frankfurt, the University of Essex, the University of Berne and the University of Konstanz.