title: Comparing dependent correlations.
abstract: In a recent article in The Journal of General Psychology, J. B. Hittner, K. May, and N. C. Silver (2003) described their investigation of several methods for comparing dependent correlations and found that all can be unsatisfactory, in terms of Type I errors, even with a sample size of 300. More precisely, when researchers test at the .05 level, the actual Type I error probability can exceed .10. The authors of this article extended J. B. Hittner et al.'s research by considering a variety of alternative methods. They found 3 that avoid inflating the Type I error rate above the nominal level. However, a Monte Carlo simulation demonstrated that when the underlying distribution of scores violated the assumption of normality, 2 of these methods had relatively low power and had actual Type I error rates well below the nominal level. The authors report comparisons with E. J. Williams' (1959) method.
authors.affiliation: Department of Psychology, University of Southern California, Los Angeles, CA 90089-1061, USA. rwilcox@usc.edu
authors.forename: Rand R
authors.initials: RR
authors.lastname: Wilcox
authors.forename: Tian
authors.initials: T
authors.lastname: Tian
mesh_terms: D015982:Bias; D003627:Data Interpretation, Statistical; D006801:Humans; D009010:Monte Carlo Method; D011589:Psychology, Experimental / Q000592:standards*; D015203:Reproducibility of Results; D012107:Research Design