Change Scores as Dependent Variables in Regression Analysis

Abstract

Change scores have been widely criticized for their purported unreliability and for their sensitivity to regression toward the mean. These objections are shown to be unfounded under a plausible regression model for the nonequivalent control group design. This model leads to inferences that are intuitively correct, as judged by changes in means over time, while the conventional model leads to inferences that are intuitively false. Moreover, the conventional model implies that regression toward the mean within groups leads to regression toward the mean between groups, an implausible result for naturally occurring groups. Nevertheless, the conventional model may be more appropriate when there is a true causal effect of the pretest on the posttest, or when cases are assigned to groups on the basis of their pretest scores.