Testing Dependent Correlations With Nonoverlapping Variables: A Monte Carlo Simulation
Testing Dependent Correlations With Nonoverlapping Variables: A Monte Carlo Simulation
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测试非重叠变量的相关性：蒙特卡洛模拟
DOI：
10.3200/jexe.71.1.53-70
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发表时间：
2004
期刊：
影响因子：
--
通讯作者：
K. May
中科院分区：
文献类型：
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作者：
N. C. Silver;J. Hittner;K. May
作者对4个检验统计量进行了蒙特卡罗模拟，以比较无共同变量的相关关系。对K. Pearson和L. N. G. Filon (1898) z， O. J. Dunn和V. a . Clark (1969) z， J. H. Steiger（1980）对Dunn和Clark z的原始修正，以及Steiger对Dunn和Clark z的修正，在3种不同的总体分布下，样本大小为10、20、50和100，使用反向转换平均z程序确定了经验I型错误率和功率估计。对于I型错误率分析，作者评估了3种不同程度的预测-标准相关性（ρ12 = ρ34 = 0.10）。30和0.70)。同样，对于功率分析，检验了无共同变量的相关性（ρ12和ρ34）之间3种不同程度的差异或效应大小（值）。10日。40和0.60)。所有的分析都是在三个不同水平的预测因子相互关系下进行的。结果表明，选择哪个检验统计量是最优的，就功率和第一类错误率而言，不仅取决于样本量和总体分布，还取决于(a)预测因子相互关系和(b)效应大小（功率）或预测-标准相关性的大小（第一类错误率）。在本研究中检查的条件下，当预测标准相关性为低到中等时，Pearson和Filon的z会夸大I型错误率。当预测标准相关性较低时，Dunn和Clark的z和2 Steiger程序具有相似但保守的I型错误率。此外，3种方法的功率估计相似。
The authors conducted a Monte Carlo simulation of 4 test statistics for comparing dependent correlations with no variables in common. Empirical Type I error rates and power estimates were determined for K. Pearson and L. N. G. Filon's (1898) z, O. J. Dunn and V. A. Clark's (1969) z, J. H. Steiger's (1980) original modification of Dunn and Clark's z, and Steiger's modification of Dunn and Clark's z using a backtransformed average z procedure for sample sizes of 10, 20, 50, and 100 under 3 different population distributions. For the Type I error rate analyses, the authors evaluated 3 different magnitudes of the predictor-criterion correlations (ρ12 = ρ34 = .10, .30, and .70). Likewise, for the power analyses, 3 different magnitudes of discrepancy or effect sizes between correlations with no variables in common (ρ12 and ρ34) were examined (values of .10, .40, and .60). All of the analyses were conducted at 3 different levels of predictor intercorrelation. 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 intercorrelations and (b) the effect size (for power) or the magnitude of the predictor-criterion correlations (for Type I error rate). For the conditions examined in the present study, Pearson and Filon's z had inflated Type I error rates when the predictor-criterion correlations were low to moderate. Dunn and Clark's z and the 2 Steiger procedures had similar, but conservative, Type I error rates when the predictor-criterion correlations were low. Moreover, the power estimates were similar among the 3 procedures.