This course is part of the second year of the Quantitative Methodology sequence in the Department of Political Science and builds on the first year sequence (PLSC 30500, 30600, 30700). It will introduce students to likelihood and Bayesian inference with a focus on multilevel/hierarchical regression models. The overarching framework of this class is model-based inference for description and prediction -- a complement to the design-based framework of PLSC 30600 Causal Inference. Students will learn both the theory behind Bayesian modeling as well as how to implement common estimators (e.g. Expectation-Maximization, Markov Chain Monte Carlo (MCMC)) in the R statistical programming language. Applied examples will be drawn from across the political science literature, with a particular emphasis on the analysis of large survey data (e.g. the American National Election Survey (ANES), the Cooperative Election Survey (CES), the European Social Survey (ESS)).
We use this repository to distribute course materials: slides, assignments, data, and lab materials.
To download a file: Locate the file in the repository. Then "right-click" on the "Raw" button and save to your computer.
However, we recommend setting up a Github account and cloning the repository on your local machine. Github Desktop provides a relatively easy-to-use GUI interface to interacting with Git.
Readings will be posted on the course's Canvas site: https://canvas.uchicago.edu/courses/54700 - You should also submit your assignments and final project there.