Measurement error significantly biases interaction effects and distorts researchers' inferences regarding interactive hypotheses. This article focuses on the single-indicator case and shows how to accurately estimate group slope differences by disattenuating interaction effects with errors-in-variables (EIV) regression. New analytic findings were…

Alternative models for a large dataset were analyzed by asymptotically distribution-free, generalized least squares, maximum likelihood, and ordinary least squares estimation methods, comparing incremental and nonincremental fit indexes. Incremental methods were quite unstable across estimation methods. This phenomenon is explained. (SLD)