Many courses teach students how to analyze data, but few courses teach how to choose what data to analyze in the first place. And few courses teach how to interpret analytic results in a way that makes appropriately qualified claims about generality, causality and the potential effects of future interventions. This course emphasizes fundamental issues including sampling, causality, significance and effect size. We examine research designs that are meant to enhance causal inference, including randomized experiments, regression discontinuity and matching. Familiarity with regression will be assumed; familiarity with Stata will be helpful.
This is a required course for 2nd year LBJ School Ph.D. students. For LBJ School master’s students and non-LBJ Ph.D. students, the consent of the instructor should be obtained so you can register in the course. Contact Dr. von Hippel by email at paulvonhippel@austin.utexas.edu.