This workshop introduces the Bayesian multilevel model framework. Bayesian methods allow for an extremely flexible approach for estimating hierarchical models with a variety different types of dependent variables. Topics covered will be the hierarchical linear model, as well as a models with limited dependent variables, summarizing results, in and out of sample predictions, and measures of model fit. No prior knowledge of Bayesian modeling is required, but will be beneficial.

Instructor: Ryan Bakker, University of Georgia

Part of the Interuniversity Consortium for Political and Social Research (ICPSR) Summer Workshops at UC Berkeley.

Click here to register and obtain further information.

Questions? Contact Eva Seto at evaseto@berkeley.edu.