Patrick Shea is an assistant professor in the Department of Political Science at the University of Houston. His research interests are international relations, the political economy of conflict, and statistical inference. His research can be found in the Journal of Conflict Resolution, International Studies Quarterly, Economics & Politics, and Statistics, Politics and Policy among other journals. He received his PhD from Rutgers University.
This course is an introduction to generalized linear models (GLM’s). GLM’s encompass an incredibly flexible family of models, which can be estimated via maximum likelihood. We adapt the standard linear model to a broader class of outcome variables. We will consider how to perform regression analysis with the following types of outcome variables: continuous, counts, dichotomous outcomes, ordered categorical outcomes, unordered categorical outcomes, duration, and more. These models are widely used across the social sciences to gain empirical traction upon all sorts of questions. The biggest payoff from this course will likely come from the substantive work you can do by unleashing generalized linear models into social science questions – work which you cannot properly do with a simple linear model.
The main goal of this course is to help you make progress towards becoming a responsible and well informed user and consumer of GLM models – a required skill for virtually any empirically-minded social scientist. The first aspect of this class focuses on understanding the unified theoretical basis for the using GLM. Emphasis will be placed on building from standard linear models, extending the linear model to GLMs, and going beyond GLMs. The second aspect of the course is focused on using the statistical package R to model GLMs. R is a powerful and capable statistical computing tool. And it’s free. The skills attained in this course are those that social science disciplines expect of any self-declared data-oriented researcher.
The background required for the course is a good introduction to probability and statistical inference, at least one good linear regression course, and working knowledge of R, Stata, or some comparable statistical software. If you are taking this course, it would be ideal to be minimally comfortable with elementary calculus (at the level of Daniel Kleppner and Norman Ramsey, Quick Calculus. A Self-Teaching Guide, 2nd edition), elementary matrix algebra (at the level of Krishnan Namboodiri, Matrix Algebra: An Introduction), and the basics of probability theory/probability distributions (see Evans, Hastings and Peacock, Statistical Distributions).
Jeff Gill’s Essential Mathematics for Political and Social Research (Cambridge University Press) is another excellent resource. If you are not confident about your calculus, your probability, or to a lesser extent, your matrix algebra, go over these texts. I will assume that your math skills may be rusty, but you will get more out of this class if you sharpen your math and probability skills before the class starts.
Representative Background Reading
Gailmard, Sean. Statistical modeling and inference for social science. Cambridge University Press, 2014.
Faraway, Julian J. 2016. Extending the Linear Model with R: Generalized Linear, Mixed Effects, and Nonparametric Regression Models. Chapman & Hall/CRC. 2nd Edition
Background knowledge required
OLS = m
Maximum Likelihood = e
Stata = e
R = e
e = elementary, m = moderate, s = strong