GESIS Training Courses

Scientific Coordination

Marlene Mauk
Tel: +49 221 47694-579

Administrative Coordination

Angelika Ruf
Tel: +49 221 47694-162

Week 1: Introduction to Bayesian Models for the Social Sciences

Prof. Dr. Susumu Shikano, Dr. Taehee Kim

Date: 11.03 - 15.03.2019 ics-file

Location: Cologne

About the lecturer - Prof. Dr. Susumu Shikano

About the lecturer - Dr. Taehee Kim

Course description

Social scientists increasingly apply the Bayesian approach to diverse kinds of research topics. To motivate further political scientists to use this approach, this course provides participants the following three points: First, the course provides a conceptual background for Bayesian inference. Second, participants will be guided how to read the literature using Bayesian statistics and interpret the results. Third, this course introduces to a software for Bayesian analysis with political science examples. The course consists of lectures (morning) and lab sessions (afternoon). The lecture deals with relevant background knowledge as well as specific skills for Bayesian analysis. In lab sessions, these skills are applied to political and social science data. Hence, course participants also learn the basic knowledge of JAGS, which is needed to conduct Bayesian estimation.
The course uses R and JAGS. GESIS provides participants with workshop computers and all relevant software.
For a full length syllabus of this course, please click here.


Target group

Participants will find the course useful if they
  • Are estimating complex models whose parameters can hardly be identified by the maximum-likelihood procedure.
  • Are working on data with many missing values.
  • Are working on data with smaller number of observations
  • Are working on non-sampled data
  • Wish to integrate some prior knowledge into data analysis.

Learning objectives

By the end of the course participants will
  • Understand the basic concepts needed for Bayesian inference.
  • Be able to conduct Bayesian analysis using Markov-Chain-Monte-Carlo and present their results.
  • Be able to interpret the Bayesian analysis of the other researchers.


  • Prior knowledge of statistics including regression models with different types of dependent variables.
  • Knowledge about maximum likelihood estimation (MLE), in particular the participants should be able to distinguish likelihood.
  • Basic knowledge of R.


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