Applied Quantitative Research Design

Undergraduate

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 will practice programming, statistics, and data visualizations used in exemplary quantitative social science articles. Harnessing AI/LLMs for coding will be explored. Formal section enrollment is not required but highly encouraged. Prerequisite: Any statistics or data science course that teaches ordinary least squares regression and p-values, such as S&DS 2300 or S&DS 2400 . Some past or concurrent experience with a programming language such as R is also presumed. Ph.D. students in political science can join without prerequisite. QR , SO 0 Course cr TTh 9am-10:15am