Applied Quantitative Research Design

Graduate / Professional

Research designs are strategies to obtain empirical answers to theoretical questions. This class trains students with the best practices for implementing and communicating rigorous quantitative social science research. We cover econometrics techniques of causal inference, prediction, and missing data. These include fixed effects, time series, instrumental variables, survey weighting, and shrinkage. This is a hands-on, application-oriented class. Students practice programming, statistics, and data visualizations used in exemplary quantitative social science articles. Formal section enrollment is not required but highly encouraged. Prerequisite: Statistics, econometrics, or data science courses that teach ordinary least squares regression and p-values, such as S&DS 2300 . Some past or concurrent experience with R/Stata/Python is also presumed. This class is designed for juniors, seniors, master’s students, and Ph.D. students. Ph.D. students in political science can join without a prerequisite. TTh 9am-10:15am