Microcredential ekomex Introduction to Bayesian Statistical Analysis

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Content 

This course introduces social science researchers to Bayesian modeling in R, focusing on Bayesian probability theory, model fitting, and interpretation of statistical results.

What Is This Course About?
Bayesian methods offer a powerful and flexible framework for quantitative data analysis, allowing researchers to incorporate prior knowledge and make direct probabilistic claims about their hypotheses. This course provides a practical and conceptually grounded introduction to conducting Bayesian statistical analyses. Students will i) gain an understanding of Bayesian inference, including the theoretical elements characterizing this framework, and ii) learn to apply Bayesian methods to analyze original datasets.

Learning Goals

  • Conceptualise the theoretical principles underpinning Bayesian inference.
  • Critically compare Bayesian and frequentist approaches to statistical inference.
  • Fit Bayesian models to real-world data using R.
  • Visualize, summarize, and report results from Bayesian models.
  • Apply Bayesian reasoning to a range of social science research questions.


Recommended Readings for the Course

  • Kruschke, J. K., & Liddell, T. M. (2018). Bayesian data analysis for newcomers. Psychonomic bulletin & review, 25(1), 155-177.
  • Johnson, A. A., Ott, M. Q., & Dogucu, M. (2022). Bayes rules!: An introduction to applied Bayesian modeling. Chapman and Hall/CRC.
  • McElreath, R. (2018). Statistical rethinking: A Bayesian course with examples in R and Stan. Chapman and Hall/CRC.


Assignments for the Course
Students will complete three collaborative in-class assignments, which will involve

  • fitting Bayesian models to novel dataset using the brms package in R,
  • interpreting and running tests of statistical equivalence on model results,
  • assessing the clarity and completeness of Bayesian reporting in academic articles.

While these assignments will not be graded, active participation in these sessions is mandatory to receive course credit.

Schedule

  • 09:00-10.30h - Lecture on daily topic
  • 10:30-11:00h - Break
  • 11:00-12:30h - Hands-on application of lecture materials
  • 12:30-13:30h - Lunch break
  • 13.30-14.30h - Group work assignments.


Who Is Your Instructor?
Scott Kunkel is a postdoctoral researcher in linguistics at Universität Konstanz. He uses experimental and quantitative methods to test questions relating to speech perception, language attitudes, and sociolinguistic variation. His research applies cutting-edge statistical techniques – including Bayesian modeling and mixed-effects regression – to rigorously analyze complex datasets and uncover patterns of individual and social variability in language. You can read more about Scott's research interests and areas of expertise here: https://sites.google.com/view/scottkunkel/home.

Bildungszeit (can be claimed by employees in Baden-Württemberg) 
Anforderungen des Bildungszeitgesetzes Baden-Württemberg sind erfüllt
Fee 
350 EUR / Early bird 270 EUR / Please note: you will gain access to our learning management system Moodle only after having paid your course fee
ECTS Credits 
1
Contact for Questions 
komex Office
Place 
Virtual Academy
Date 
23.02.2026 (All day) to 25.02.2026 (All day)
Duration 
3 study days
Requirements 
Although no prior knowledge of Bayesian statistics is assumed, some experience with statistical analyses - particularly mixed-effects modeling - will be helpful in this course. Additionally, a working knowledge of R (as provided, for example, in the Introduction to R KOMEX short course) will also be beneficial.