  Regression discontinuity (RD) designs are popular quasi-experimental studies
in which treatment assignment depends on whether the value of a running
variable exceeds a cutoff. RD designs are increasingly popular in educational
applications due to the prevalence of cutoff-based interventions. In such
applications sample sizes can be relatively small or there may be sparsity
around the cutoff. We propose a metric, density inclusive study size (DISS),
that characterizes the size of an RD study better than overall sample size by
incorporating the density of the running variable. We show the usefulness of
this metric in a Monte Carlo simulation study that compares the operating
characteristics of popular nonparametric RD estimation methods in small
studies. We also apply the DISS metric and RD estimation methods to school
accountability data from the state of Indiana.
