Statistical models are widely used in the social sciences for measurement, prediction, and hypothesis testing. While popular statistical software packages cover a growing number of pre-implemented model types, the diversification of substantive research domains and the increasing complexity of data structures drive persistently high demand for custom modeling solutions. Implementing such custom solutions requires that researchers build their own models and use them to obtain reliable estimates of quantities of substantive interest. Bayesian methods offer a powerful and versatile infrastructure for these tasks.

 

This workshop equips participants with the required skills and tools for their first attempts at custom Bayesian statistical modeling. Rather than running through a series of statistical models, it teaches the mechanics of hands-on statistical modeling: Following an advanced theoretical and applied recap of the building blocks of generalized linear models and core concepts of Bayesian inference, the course introduces participants to Stan, a platform for statistical modeling and Bayesian statistical inference. Participants will get an overview of the programming language, the R interface RStan, and the workflow for Bayesian model building, inference, and convergence diagnosis. Applied exercises allow participants to write and run various model types and to process the resulting estimates into publication-ready graphs.

Software requirements