Covariance and regression slope models for studying validity generalization

Abstract

Two new models, the covariance and regression slope models, are proposed for assessing validity generalization. The new models are less restrictive in that they require only one hypothetical distribution (distribution of range restriction for the covariance model and distribution of predictor reliability for the regression slope model) for their implementation, in contrast to the correlation model which requires hypothetical distributions for criterion reliability, predictor reliability, and range restriction. The new models, however, are somewhat limited in their applicability since they both assume common metrics for predictors and criteria across validation studies. Several simulation (monte carlo) studies showed the new models to be quite accurate in estimating the mean and variance of population true covariances and regression slopes. The results also showed that the accuracy of the covariance, regression slope, and correlation models is affected by the degree to which hypothetical distributions of artifacts match their true distributions; the regression slope model appears to be slightly more robust than the other two models.