Tests for Comparing Dependent Correlations Revisited: A Monte Carlo Study.

Abstract

Abstract A Monte Carlo evaluation of 4 test statistics for comparing dependent zero-order correlations was conducted. In particular, the power and Type I error rates of Hotelling's t; Williams' t; Olkin's z; and Meng, Rosenthal, and Rubin's Z were evaluated for sample sizes of 20, 50, 100, and 300 under 3 different population distributions (normal, uniform, and exponential). For the power analyses, 3 different magnitudes of discrepancy or effect sizes between ρy, x1 , and ρy, x2 were examined (values of .1, .3, and .6). Likewise, for the Type I error rate analyses, 3 different magnitudes of the predictor-criterion correlations were evaluated (ρy, x1 = ρy, x2 = .1, .4, and .7). All of the analyses were conducted at 3 different levels of predictor intercorrelation (ρx1, x2 = .1, .3, and .6). The results indicated that the choice as to which test statistic is optimal, in terms of power and Type I error rate, depends not only on sample size and population distribution but also on (a) the predictor intercorrel...