Browsing by Author "Steinhaus, Stephen D."

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    Covariance and regression slope models for studying validity generalization
    (1986) Raju, Nambury S.; Fralicx, Rodney; Steinhaus, Stephen D.
    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.
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    A logistic regression model for personnel selection
    (1991) Raju, Nambury S.; Steinhaus, Stephen D.; Edwards, Jack E.; DeLessio, Juyne
    A two-parameter logistic regression model for personnel selection is proposed. In addition to presenting a theoretical basis for the model, a unified approach is provided for studying selection, validity generalization, employee classification, selection bias, and utility-based fair selection. The new model was tested with a large database (N = 84,808). Results show the logistic regression model to be valid and also quite robust with respect to direct and indirect range restriction on the predictor. Index terms: logistic regression, personnel selection, selection bias, utility-based fair selection, validity generalization.