This course is part of the specialization in Political Behavior and Applied Quantitative Methods, and provides a broad overview of quantitative methods as they are commonly used to study political behavior. Its primary focus is on methods for causal inference (experiments, instrumental variables, difference-in-differences, and regression discontinuity), and their application in the statistical software R.
The course is designed to interact closely with Political Behavior—the specialization’s substantive course. The course will examine some of the best-known recent work on political behavior, and ask: How did the authors produce their results? Can we re-produce them? And how sensitive are the results to other methodological strategies? The course will place particular emphasis on students’ ability to apply the methods introduced in the course to real-world data.
The course revolves around two sections, as described below.
1. Statistical programming in R
The first classes (classes 1-4) introduce the statistical programming language R, the most widely used statistical software among political scientists who use advanced quantitative methods. Like Python, R’s data science counterpart, R is also widely used in government and the private sector for data analysis. For applied academic, government, and private-sector research, knowing R (and Python) will likely be the most valuable technical tools that you learn from your time in university. Accordingly, you will use R in every class throughout the term. You will learn R in class, and through exercises on DataCamp, an intuitive platform for learning a variety of statistical programming languages and methods.
The first section of the course will also re-introduce linear regression analysis (OLS), which you should be familiar with from your undergraduate methods sequence. Finally, the course will introduce you to LaTeX, a type-setting system used by the majority of researchers who do advanced quantitative research in political science, and which will be very useful to you when you write your assignments and, later, your Master’s thesis.
2. Causal inference
The second section of the course will focus on causal inference (classes 5-12), and recent methods developed for estimating causal effects. The social sciences (particularly economics and political science) have recently undergone a “credibility revolution” that places methods for estimating causal effects at the center of research design. This is because theoretically driven empirical research often centers on questions of a causal nature. As a result, much of the course will emphasize how to think about causality, and the statistical techniques for causal inference.
This class is taught to two groups of students: one in the political behavior stream, and others as an elective. The material will be the exact same for both groups of students.
Classroom information and times will be posted when they are available.
Office hours & e-mail
Please feel welcome to send me a short e-mail if you would prefer to meet another day or time.
Two texts will be used in the course and can be purchased from Academic Books:
Mastering ‘Metrics: The Path from Cause to Effect
Princeton, NJ: Princeton University Press, 2014.
Joshua D. Angrist and Jörn-Steffen Pischke
Field Experiments: Design, Analysis, and Interpretation
New York, NY: W. W. Norton & Company, 2012.
Alan S. Gerber and Donald P. Green.
The course is graded using a portfolio exam that will consist of two mandatory assignments. The first assignment will be due mid-way through the course; the final assignment, after our final class.