Strategies for Improving Precision in Group-Randomized Experiments

Abstract

Interest has rapidly increased in studies that randomly assign classrooms or schools to interventions. When well implemented, such studies eliminate selection bias, providing strong evidence about the impact of the interventions. However, unless expected impacts are large, the number of units to be randomized needs to be quite large to achieve adequate statistical power, making these studies potentially quite expensive. This article considers when and to what extent matching or covariance adjustment can reduce the number of groups needed to achieve adequate power and when these approaches actually reduce power. The presentation is nontechnical.