ON THE ROBUSTNESS, BIAS, AND STABILITY OF STATISTICS FROM METAANALYSIS OF CORRELATION-COEFFICIENTS - SOME INITIAL MONTE-CARLO FINDINGS

Each of several Monte Carlo simulations generated 100 sets of observed study correlations based on normal, heteroscedastic, or slightly nonl inear bivariate distributions, with one population correlation coeffic ient and true variance of 0. A version of J. E. Hunter and F. L. Schmi dt's (1990b) meta-analysis was applied to each study set. Within simul ations, <(rho)over cap> was accurate on average. <(sigma)over cap>(2)( rho) was biased; one would correctly conclude more than half the time that no moderator effects existed. However, cases of variation in <(rh o)over cap> and especially in <(sigma)over cap>(2)(rho) indicated that results from individual meta-analyses could deviate substantially fro m what was found on average. Findings for these no-moderator cases off er applied psychologists some guidelines and cautions when drawing con clusions about true population correlations and true moderator effects (e.g., situational specificity, validity generalization) from meta-an alytic results.