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Methods Courses

Methods Courses

Minor / Major:

  • Minor: 4 classes for a minor (typically 1 per semester for the first 2 years and then students take the minor exam): 685/7551, 686/7552, 786/MLE/7553 and then either an advanced course or 684/7780.
  • Major: 5 classes, where at least two are advanced beyond 786/MLE/7553 (so if students also take 684/7780, then 6 classes). These additional courses can and likely will be taken beyond the second year.

Course Descriptions


This course is designed for Political Science graduate students intending to do empirical research. It introduces students to methods for constructing simple empirical representations of social science theories and for rigorously testing those theories with data. We focus on four topics, beginning with the logics of empirical analysis; descriptive statistics and the basic linear model; probability and statistics; and statistical inference. The course will emphasize fundamental statistical concepts as well as their practical application and will draw examples from a range of substantive subfields in Political Science. Topics include random variables, basic hypothesis testing, BLUE, regression and assumptions. This course is designed for students with little or no formal training in statistics or in the analysis of social science data. There are no prerequisites, though it is assumed that students have the equivalent of college algebra and will have taken our department's summer math camp. Upon completion of this course, students will be able to read and critically evaluate empirical political science research; will have sufficient background and experience to formulate and test is simple empirical representation of social science theories; and will have a solid statistical background needed for more advanced methodological training. The course also provides work with statistical software, such as Stata or R.


The course covers all core elements of OLS regression: bivariate and multivariate regression analysis, interaction effects, hypothesis testing, and violations of OLS assumptions. The aim of the course is to explore the statistical background of OLS in combination with its empirical application. Topics include regression, hypothesis testing, functional form, diagnostics, heteroskedasticity, autocorrelation, endogeneity, introduction to dichotomous dependent variables and other MLE topics. The course also provides work with statistical software, such as Stata or R.