Introduction to Applied Bayesian Modeling for the Social Sciences
- Ryan Bakker, University of Georgia
- Johannes Karreth, University at Albany, State University of New York
This course introduces the basic theoretical and applied principles of Bayesian statistical analysis in a manner geared toward students and researchers in the social sciences. The Bayesian paradigm is particularly useful for the type of data that social scientists encounter given its recognition of the mobility of population parameters, its ability to incorporate information from prior research, and its ability to update estimates as new data are observed. The course begins with a discussion of the strengths of the Bayesian approach for social science data and the philosophical differences between Bayesian and frequentist analyses. Next, the course covers the theoretical underpinnings of Bayesian modeling and provides a brief introduction to the primary estimation algorithms. The bulk of the course focuses on estimating and interpreting Bayesian models from an applied perspective. Participants are introduced to the Bayesian forms of the standard statistical models taught in regression and MLE courses (i.e., linear, logit/probit, Poisson, etc.). Additional topics include measurement models, model comparison, and an in-depth treatment of multilevel modeling. This course assumes a solid understanding of the linear model and matrix algebra and some exposure to models with limited dependent variables. The course relies mostly on R and WinBUGS/JAGS for estimation. Prior experience with R is preferred but not assumed; we offer lab sessions to familiarize participants with R, WinBUGS, and JAGS (no prior experience necessary).
Fees: Consult the fee structure.
Location: ICPSR -- Ann Arbor, MI
Date(s): June 22 - July 17
Time: 9:00 AM - 11:00 AM