Statistical Power for Regression Discontinuity Designs in Education Evaluations

This article examines theoretical and empirical issues related to the statistical power of impact estimates tinder clustered regression discontinuity (RD) designs. The theory is grounded in the causal inference and hierarchical linear modeling literature, and the empirical work focuses on common designs used in education research to test intervention effects on student test scores. The main conclusion is that three to four times larger samples are typically required tinder RD than experimental clustered designs to produce impacts with the same level of statistical precision. Thus, the viability of using RD designs for new impact evaluations of educational interventions may be limited and will depend on the point of treatment assignment, the availability of pretests, and key research questions.