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.
Raju, Nambury S, Fralicx, Rodney & Steinhaus, Stephen D. (1986). Covariance and regression slope models for studying validity generalization. Applied Psychological Measurement, 10, 195-211. doi:10.1177/014662168601000211
Raju, Nambury S.; Fralicx, Rodney; Steinhaus, Stephen D..
Covariance and regression slope models for studying validity generalization.
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