Jonathan Kropko is an assistant professor of political science at the University of Virginia where he teaches the graduate methods sequence in mathematics, regression, generalized linear models, time series and panel data, and measurement. He is the author of Mathematics for Social Scientists (2015, Sage), and his work has been published in Political Analysis, Biomatrika, and the British Journal of Political Science.
Course Content: Students will learn about statistical models for binary, ordinal, unordered categorical, count, and duration dependent variables. They will learn about the statistical theory that is used to build these models, how to run them in Stata or in R, and how to produce results that are elegant and understandable to a wide audience.
Course Objectives: In this course, students will learn how to design statistical models with purpose and creativity and to adapt them for their own particular theories and data. Generalized linear models (GLMs) combine probability models for particular variable types with the classic linear regression. If a researcher chooses a probability function that accurately reflects the distribution of the outcome variable, and designs a linear model that expresses the hypotheses implied by the theory, then the researcher can combine these models in a GLM that is perfectly tailored to fit the data and test the theory.
Course Prerequisites:Participants should be knowledgeable about linear regression models and should be able to accurately interpret a coefficient table. Participants should also know the basics for programming in Stata or in R. A solid background in some math, especially logarithms, summations, and derivatives, is useful but not strictly required.
Representative Background Reading
Eric C. C. Chang, Miriam A. Golden and Seth J. Hill. 2010. “Legislative Malfeasance and Political Accountability.” World Politics. 62(2): 177-220.
Gary King. 1998. Unifying Political Methodology: The Likelihood Theory of Statistical Inference. Ann Arbor, MI: University of Michigan Press.