Bayesian Analysis   

Prof. Karreth is such a dedicated lecturer. He was extremely helpful, willing to accommodate individual student's interests/needs, and even spending extra time before and after class to help those who needed additional assistance. — participant from Malaysia

This course provides participants with an applied introduction to Bayesian data analysis and inference. Bayesian methods have rapidly grown in the social sciences in recent years and have become a central tool for a wide variety of analytical methods. Within the Bayesian framework, sampling via Markov chain Monte Carlo (MCMC) methods allows researchers to find solutions to otherwise difficult or intractable statistical problems, and this course offers participants hands-on training in how to use these methods to assess the probability distributions of effect sizes, deal with incomplete data, estimate and incorporate uncertainty in measurement models, use prior information to refine model estimates and predictions, and more.

As this course presumes a basic background in statistics, participants without any prior statistical knowledge might want to consider taking another quantitative methods course instead.


This course was offered in 2018.


Johannes Karreth (picture), Ursinus College

Detailed Description

This course provides an applied introduction to Bayesian data analysis and inference. Bayesian methods have rapidly grown in the social sciences in recent years and have become a central tool for a wide variety of analytical methods, such as multilevel and measurement models, quantitative text analysis, and network analysis. The goal of the course is to enable participants to use Bayesian tools in their own research and to effectively communicate their Bayesian results to other social science scholars.

Covering both Bayesian theory and applications, the course explores the following topics:

  • Why use Bayesian inference?

  • Philosophical and theoretical foundations for Bayesian inference

  • The mechanics of MCMC tools and sampling

  • Building and estimating Bayesian linear and generalized linear models

  • Using MCMC output for post-estimation, incl. marginal effects and predicted probabilities

  • Bayesian approaches to measurement

  • Bayesian tools for model comparison

  • Model presentation and communication

  • Optimal solutions for work-flow and reproducibility

Upon completion of this course, participants will be able to:

  • Understand the origins and logic behind Bayesian inference

  • Use Bayesian methods for analyzing continuous and categorical outcomes in a regression framework

  • Use Bayesian methods for measurement models

  • Communicate Bayesian estimation results to practitioners and social science audiences

To allow participants to take full advantage of Bayesian data analysis in their own work, the course also teaches participants how to use the free and open-source software packages R and Stan. Practical examples and applied exercises form an integral part of the course.


The course presumes a working knowledge of the linear regression model. Familiarity with probability theory would also be helpful, but is not formally required. Participants without any prior knowledge of statistics should consider a more basic quantitative methods course.


Participants are expected to bring a WiFi-enabled laptop computer. Access to data, temporary licenses for the course software, and installation support will be provided by the Methods School.

Core Readings

Jackman, Simon. 2009. Bayesian Analysis for the Social Sciences. Chichester: John Wiley & Sons.

Suggested Readings

Gelman, Andrew, John B. Carlin, Hal S. Stern, and Donald B. Rubin. 2013. Bayesian Data Analysis. 3rd edition. Boca Raton, FL: Chapman & Hall.

Kruschke, John K. 2014. Doing Bayesian Data Analysis. A Tutorial with R, JAGS, and Stan. 2nd. London: Academic Press.

Monogan, James E. 2015. Political Analysis Using R. Cham: Springer.

Teetor, Paul. 2011. R Cookbook. Sebastopol, CA: O'Reilly Media.

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