This course aims to familiarize social science students with the concepts and tools that will enable them to critically evaluate quantitative academic and policy studies and to conduct their own quantitative research. It builds on students’ prior coursework, deepens their understanding of introductory concepts, and discusses a number of advanced topics, including maximum likelihood, bootstrapping and Monte Carlo methods, and model fit and regression diagnostics. Particular attention will be paid to techniques for causal inference such as regression discontinuity and instrumental variable estimation. The course will discuss social science applications throughout the semester, with examples in both R and Stata, and students will be asked to conduct their own applied analysis using either software package.
Angrist, Joshua D., and Jörn-Steffen Pischke. 2014. Mastering Metrics. Princeton University Press.
Gelman, Andrew, and Jennifer Hill. 2006. Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.