Addressing Self-Selection Bias in Quasi-Experimental Evaluations of Whole-School Reform

This article discusses potential sources of self-selection bias in quasi-experimental evaluations of whole-school reform models and considers how individual student-level data might be used to provide valid impact estimates. Although repeated pretreatment and posttreatment measures of student performance can provide unbiased estimates under relatively weak assumptions, such data are difficult to obtain. The article develops an instrumental variable strategy that can be used to improve on common value-added estimators when only posttreatment measures of performance are available. Using data from New York City, the author shows that the instrumental variable strategy can provide estimates of model impacts similar to those provided by a difference-in-differences estimator provided that appropriate instruments are used.