Courses

2018
  • Intro to Political Science Research Methods syllabus course materials
  • An undergraduate-level course that introduces students to modern quantitative social science with a focus on computing with R. This is a required course for students in our department. We cover quantitative approaches to causal inference, measurement, prediction, and inference, focusing on recent social science empirical examples.

2016
  • Quantitative Research Methodology syllabus course materials
  • This is the first course in a PhD-level quantitative methods sequence and provides an introduction to the tools used in basic quantitative social science research. The first four weeks of the course cover introductory univariate statistics, while the remainder of the course focuses on linear regression models. Furthermore, the principles learned in this course provide a foundation for the future study of more advanced topics in quantitative political methodology.

2015
  • Causal Inference syllabus course materials
  • This is the third course in a PhD-level quantitative methods sequence and introduces students to both the theory and the practice behind making these kinds of causal inferences. We will cover causal identification, potential outcomes, experiments, matching, regression, difference-in-differences, instrumental variables estimation, regression discontinuity designs, sensitivity analysis, dynamic causal inference, and more. The course draws upon examples from political science, economics, sociology, public health, and public policy. Taught with R.

2014
  • Topics in Quantitative Methods syllabus course materials
  • A topics class in statistical methods for PhD students. Topics covered includes Bayesian methods, causal inference, text analysis, item-response models, and others. Taught with a mix of lecture, discussion, and student presentations. Designed for second-year graduate students. Taught with Arthur Spirling.

2013
  • Causal Inference syllabus course materials
  • Substantive questions in empirical social science research are often causal. Does voter outreach increase turnout? Do political institutions affect economic development? Are job training programs effective? This graduate-level class will introduce students to both the theory and the practice behind making these kinds of causal inferences. We will cover causal identification, potential outcomes, experiments, matching, regression, difference-in-differences, instrumental variables estimation, regression discontinuity designs, sensitivity analysis, dynamic causal inference, and more. The course will draw upon examples from political science, economics, sociology, public health, and public policy. Taught with R, with a mix of lectures, discussion, and in-class computing.

  • Applied Data Analysis syllabus course materials
  • Data analysis has become a key part of many fields including politics, business, law, and public policy. This undergraduate course covers the fundamentals of data analysis, giving students the necessary statistical skills to understand and critically analyze contemporary political, legal, and policy puzzles. Lectures focus on the theory and practice of quantitative analysis and weekly lab sessions guide students through the particulars of statistical software. No prior knowledge of statistics or data analysis is required. Taught with R.