Regression Discontinuity Design: Recent Developments and a Guide to Practice for Researchers in Higher Education

Abstract

In an educational research climate, where the understanding of causal relationships between predictors and outcome variables is of primary interest, however, randomized experiments are either unethical or unfeasible, researchers have been turning to research designs and statistical techniques that produce causal results in a quasiexperimental manner. One such quasiexperimental design is regression discontinuity (RD), which has been shown to require fewer assumptions than most other designs and the assumptions it does require are verified with nearly the same ease as a randomized experiment. We provide a user friendly guide to RD designs for the educational researcher. We begin by discussing terminology associated with both experimental and RD designs. Next, we describe the process of checking the assumptions associated with an RD design and use an empirical example to illustrate this process. We then expand on the empirical example to show how to obtain estimates of treatment effects in both “sharp” and “fuzzy” RD designs employing both parametric and nonparametric frameworks. Finally, we discuss new developments in RD research and some future applications in educational research. A technical appendix is also included with additional details on the selection of parameters in the estimation procedure and we include a URL link to statistical code to conduct RD analysis.