Covariate adjustment can increase the precision of estimates by removing unexplained variance from the error in randomized experiments, although chance covariate imbalance tends to counteract the improvement in precision. The author develops an easy measure to examine chance covariate imbalance in randomization by standardizing the average…

In social policy evaluations, the multiple testing problem occurs due to the many hypothesis tests that are typically conducted across multiple outcomes and subgroups, which can lead to spurious impact findings. This article discusses a framework for addressing this problem that balances Types I and II errors. The framework involves specifying…

The use of multiple regression for analyzing data from the regression-discontinuity design (RDD) is examined, considering the effects of random measurement error in the pretest, treatment-effect interactions, and curvilinearity in the regression analysis of RDD. Three sets of conditions of increasing generality are reviewed. (SLD)

Design, analysis, and reporting considerations for the application of analysis of covariance (ANCOVA) techniques in educational settings are described. Numerous examples are drawn from the national follow through evaluation, and suggestions for improving reports using ANCOVA-type techniques are presented. (Author/BW)

A conceptual approach and a set of computer simulations are presented to demonstrate that random measurement error in the pretest does not bias the estimate of the treatment effect in the regression-discontinuity design. Focus is on the case of no interaction between pretest and treatment on posttest. (SLD)