Implications of empirical Bayes meta-analysis for test validation.
Empirical Bayes meta-analysis provides a useful framework for examining test validation. The fixed-effects case in which rho 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 rho = 0; such a test has practical implications for significance testing in test validation. The random-effects case in which sigma2rho > 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 sigma2rho 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.
Brannick
M T
MT
Psychology Department, University of South Florida, Tampa 33620-8200, USA. mbrannic@luna.cas.usf.edu
D001499:Bayes Theorem; D006801:Humans; D015201:Meta-Analysis as Topic; D011584:Psychology / Q000706:statistics & numerical data*; D015203:Reproducibility of Results; D012107:Research Design