A linear model is often used to find the effect of a binary treatment D on a noncontinuous outcome Y with covariates X. Particularly, a binary Y gives the popular "linear probability model (LPM)," but the linear model is untenable if X contains a continuous regressor. This raises the question: what kind of treatment effect does the…

Researchers face many choices when conducting large-scale multisite individually randomized control trials. One of the most common quantities of interest in multisite RCTs is the overall average effect. Even this quantity is non-trivial to define and estimate. The researcher can target the average effect across individuals or sites. Furthermore,…

Evaluators of education interventions are increasingly designing studies to detect impacts much smaller than the 0.20 standard deviations that Cohen characterized as "small." While the need to detect smaller impacts is based on compelling arguments that such impacts are substantively meaningful, the drive to detect smaller impacts may…

This article assesses the likely generalizability of educational treatment-effect estimates from regression discontinuity designs (RDDs) when treatment assignment is based on academic pretest scores. Our assessment uses data on outcome and pretest measures from six educational experiments, ranging from preschool through high school, to estimate…

This article assesses the likely generalizability of educational treatment-effect estimates from regression discontinuity designs (RDDs) when treatment assignment is based on academic pretest scores. Our assessment uses data on outcome and pretest measures from six educational experiments, ranging from preschool through high school, to estimate…

In basic regression discontinuity (RD) designs, causal inference is limited to the local area near a single cutoff. To strengthen the generality of the RD treatment estimate, a design with multiple cutoffs along the assignment variable continuum can be applied. The availability of multiple cutoffs allows estimation of a pooled average treatment…

In basic regression discontinuity (RD) designs, causal inference is limited to the local area near a single cutoff. To strengthen the generality of the RD treatment estimate, a design with multiple cutoffs along the assignment variable continuum can be applied. The availability of multiple cutoffs allows estimation of a pooled average treatment…

Evaluators of education interventions are increasingly designing studies to detect impacts much smaller than the 0.20 standard deviations that Cohen (1988) characterized as "small." While the need to detect smaller impacts is based on compelling arguments that such impacts are substantively meaningful, the drive to detect smaller impacts…

Randomized experiments are commonly used to evaluate the effectiveness of educational interventions. The goal of the present investigation is to develop small-sample corrections for multiple contrast hypothesis tests (i.e., F-tests) such as the omnibus test of meta-regression fit or a test for equality of three or more levels of a categorical…

The goal of this paper is to provide guidance for applied education researchers in using multi-level data to study the effects of interventions implemented at the school level. Two primary approaches are currently employed in observational studies of the effect of school-level interventions. One approach employs intact school matching: matching…

Randomized controlled trials (RCTs) and regression discontinuity (RD) studies both provide estimates of causal effects. A major difference between the two is that RD only estimates local average treatment effects (LATE) near the cutoff point of the forcing variable. This has been cited as a drawback to RD designs (Cook & Wong, 2008).…