Structural Equation Models and the Regression Bias for Measuring Correlates of Change

Abstract

ANCOVA and regression both exhibit a directional bias when measuring correlates of change. This bias confounds the comparison of changes between naturally occurring groups with large pretest differences (ANCOVA), or for identifying predictors of change when the predictor is correlated with pretest (regression). This bias is described in some detail. A computer simulation study is presented, which shows that properly identified structural equation models are not susceptible to this bias. Neither gain scores (posttest minus pretest) nor structural equation models exhibit the “regression bias.” Other factors, such as skewness, that may confound measurement of change are also discussed.