Implications of empirical Bayes meta-analysis for test validation

Abstract

Empirical Bayes meta-analysis provides a useful framework for examining test validation. The fixed-effects case in which ρ has a single value corresponds to the inference that the situational specificity hypothesis can be rejected in a validity generalization study. A Bayesian analysis of such a case provides a simple and powerful test of ρ = 0; such a test has practical implications for significance testing in test validation. The random-effects case in which ς 2 ρ > 0 provides an explicit method with which to assess the relative importance of local validity studies and previous meta-analyses. Simulated data are used to illustrate both cases. Results of published meta-analyses are used to show that local validation becomes increasingly important as ς 2 ρ increases. The meaning of the term validity generalization is explored, and the problem of what can be inferred about test transportability in the random-effects case is described. (PsycInfo Database Record (c) 2025 APA, all rights reserved)