Reports of the Death of Regression-Discontinuity Analysis are Greatly Exaggerated

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Abstract
Stanley (1991) argues that both random measurement error in the pretest and treatment-effect interactions bias the estimate of the treatment effect when multiple regression is used to analyze the data from a regression-discontinuity design (RDD). Stanley also argues that these biases are so severe that they should cause researchers to consider using statistical procedures other than regression analysis. The authors of the present article disagree. Curvilinearity in the regression of the posttest on pretest scores can be difficult to model, can bias the regression analysis of data from the RDD if not modeled correctly, and therefore should cause researchers to consider alternatives to regression analysis. If the regression surfaces are linear, however, unbiased estimates can be obtained easily via regression analysis, whether or not either random measurement error in the pretest or treatment-effect interactions are present. Improving upon regression analysis is a worthy goal but requires understanding just what are and are not the weaknesses of the method. In addressing these issues, this article elucidates some of the general principles that underlie the use of multiple regression to analyze data from the RDD quasi-experiment.