GESIS Training Courses

Scientific Coordination

Marlene Mauk
Tel: +49 221 47694-579

Administrative Coordination

Angelika Ruf
Tel: +49 221 47694-162

Week 2: Bayesian Structural Equation Modelling

Prof. Dr. Rens van de Schoot, Asst. Prof. Dr. Milica Miocevic

Date: 18.03 - 22.03.2019 ics-file

Location: Cologne

About the lecturer - Prof. Dr. Rens van de Schoot

About the lecturer - Asst. Prof. Dr. Milica Miocevic

Course description

During this course students will be introduced to philosophical underpinnings of Bayesian statistics and will learn how to fit regression, mediation, CFA, and longitudinal growth models in the Bayesian framework. Students will learn the steps in conducting Bayesian analyses and will be able to understand articles that examine and apply Bayesian SEM. The course is highly interactive, and the afternoons will be dedicated to implementing and practicing the material using the participant's software of choice (Mplus or R in tandem with JAGS or stan). We highly recommend bringing your own data for Day 5 of the course; however, the instructors will have example data sets for participants who do not have their own data.
Participants will have the choice between using Mplus or R for the course exercises. Participants should bring their own laptops with the software of their choice installed. GESIS provides participants with workshop computers and R.
For a full length syllabus of this course, please click here.


Target group

Participants will find the course useful if they
  • Are interested in using Bayesian statistics in their own work.
  • Encounter convergence issues using classical methods.
  • Have small samples and/or access to prior information from previous studies.
  • Wish to understand new methodological developments that make use of Bayesian statistics and/or Markov Chain Monte Carlo (MCMC).

Learning objectives

By the end of the course participants will:
  • Know the differences between 'classical' and Bayesian statistics, and when to use to Bayesian analyses instead of classical statistics.
  • Know how to apply Bayesian SEM to analyze their own data.
  • Know how to apply the WAMBS-checklist (When to worry and how to Avoid the Misuse of Bayesian Statistics).
  • Critically evaluate applications of Bayesian methods in scientific studies.


Participants should have knowledge of regression analysis and basic SEM. No previous knowledge of Bayesian analysis is assumed. Participants should have a good grasp of the software package they plan to use (R or Mplus).


Recommended readings