Advanced Quantitative Methods (elective)

The course provides a broad overview of quantitative methods as they are commonly used in political science. 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 will examine some of the best-known and recent work in political behavior, and ask: How did the authors produce their results? Can we re-produce them? How plausible are the assumptions for causal inference? And how sensitive are the results to different methodological choices? The course will place particular emphasis on students’ ability to apply the methods introduced to real-world data from top-level political science articles.

The course revolves around two sections, as described below.

1. Statistical programming in R

The first classes 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. LaTeX should 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, and recent methods developed for estimating causal effects. The social sciences (particularly economics and political science) underwent a "credibility revolution" in the late 2000s, which places methods for estimating causal effects at the center of research designs. This is because theoretically driven empirical research often centers on causal questions. As a result, much of the course will emphasize how to think about causality, and the statistical techniques for causal inference.