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.

翻译：回归不连续设计是一种流行的准实验研究方法，其处理分配取决于运行变量值是否超过某个阈值。由于基于阈值的干预措施广泛存在，RD设计在教育领域中的应用日益增多。在此类应用中，样本量可能相对较小，或者阈值附近存在稀疏性。我们提出了一种名为“密度包含的研究规模”（DISS）的度量标准，该标准通过纳入运行变量的密度，能比总体样本量更准确地刻画RD研究的规模。我们通过蒙特卡洛模拟研究展示了该度量在小样本研究中比较常用非参数RD估计方法操作特性时的实用性。此外，我们将DISS度量及RD估计方法应用于印第安纳州的学校问责数据。