Courses

This course is the fourth course in the graduate-level sequence on quantitative political methodology offered in the Department of Political Science. Students will learn a variety of advanced topics in quantitative methods for descriptive and causal inference, such as simulated-data experimentation, statistical graphics, experimental design, Bayesian inference, multilevel modeling, ideal-point and measurement-error models, and time/spatial/network models.  This course builds on the materials covered in POLS 4700, 4720, 4722, and 4724, or their equivalent courses (i.e., probability, statistics, linear regression, logistic regression, causal inference with observational and experimental data, and knowledge of the statistical computing environment R).