On the bias of Huffcutt and Arthur's (1995) procedure for identifying outliers in the meta-analysis of correlations
On the bias of Huffcutt and Arthur's (1995) procedure for identifying outliers in the meta-analysis of correlations
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DOI：
10.1037//0021-9010.87.3.583
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发表时间：
2002-06-01
影响因子：
9.9
通讯作者：
Dunlap, WP
中科院分区：
文献类型：
--
作者：
Beal, DJ;Corey, DM;Dunlap, WP
本研究记录了A. I. Huffcutt & W. A.亚瑟（1995）的样本调整后的荟萃分析偏差（SAMD）统计，用于识别相关荟萃分析中的离群值，导致平均r不准确。蒙特卡罗模拟发现，使用SAMD导致过度识别小相对于大的相关性作为离群值。此外，发现这种过度识别小相关性的趋势随着群体相关性的大小增加而增加，并导致高估群体相关性的平均rs。元分析师的影响进行了讨论，并提供了2种可能的解决方案。
This study documents how the use of A. I. Huffcutt & W. A. Arthur's (1995) sample adjusted meta-analytic deviancy (SAMD) statistic for identifying outliers in correlational meta-analyses results in inaccuracies in mean r. Monte Carlo simulations found that use of the SAMD resulted in the overidentification of small relative to large correlations as outliers. Furthermore, this tendency to overidentify small correlations was found to increase as the magnitude of the Population correlation increased and resulted in mean rs that overestimated the population correlation. The implications for meta-analysts are discussed, and 2 possible solutions are offered.