### Harvard University

*(Gov 50, Fall 2017)*

How can we measure racial discrimination in job hiring? What is the best way to predict election outcomes? What factors drive the onset of civil wars? Is it possible to determine what members of Congress are more or less liberal given their voting record? These are just a few of the numerous question that social scientists are tackling with quantitative data. Beyond academia, companies and non-profits have invested heavily in data science techniques to learn about their users, platforms, and programs. Data scientists at these institutions are essentially applied social scientists and employ many of the same techniques you will learn in this course. Taught with R.

*(Gov 2002, Graduate, Fall 2015)*

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 class 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.

*(Gov 2000, Graduate, Fall 2016)*

How can we detect voting irregularities? What causes individuals to vote? In what sense (if any) does democracy (or trade) reduce the probability of war? Quantitative political scientists address these questions and many others by using and developing statistical methods that are informed by theories in political science and the social sciences more generally. In this course, we provide 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.

*(Gov 2002, Graduate, Fall 2014 with Arthur Spirling)*

A topics class in statistical methods. 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.

### University of Rochester

*(PSC 504, Spring 2013)*

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 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.

*(PSC 200, Fall 2012)*

Data analysis has become a key part of many fields including politics, business, law, and public policy. This 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.