This course introduces graduate students to quantitative methods in political science. During the first half of the course, we will focus on linear regression models. The topics covered include discussions of the mathematical bases for such models, their estimation and interpretation, model assumptions and techniques for addressing violations of those assumptions, and topics related to model specification and functional forms. During the second half of the course, students will be introduced to the likelihood principle as a theory of inference, including models for binary and count data.

The main goals of this course are to develop sound critical judgment about quantitative studies of political problems, to interpret quantitative analyses in published work, to understand the logic of statistical inference and to recognize and understand basic regression models. It provides the skills necessary to conduct your own quantitative analyses and teaches how to do so using R. This class lays the foundation for Advanced Quantitative Methods, which will be taught in Spring 2022.