发表机构
Deakin University; Meta-Research Innovation Center at Stanford; Institute of Economic Studies, Faculty of Social Sciences, Charles University; Centre for Economic Policy Research（迪肯大学; 斯坦福大学元研究创新中心; 查理大学社会科学学院经济研究所; 欧洲经济政策研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过调查172项经济元分析，发现偏相关系数元分析中小样本偏误可忽略，但存在出版选择偏误；UWLS和HS估计量优于随机效应及Fisher z变换，为实践提供指导。
AI 中文摘要
近期研究表明，传统的偏相关系数（PCC）元分析存在偏误。模拟实验已证明，若干调整方法可将这些小样本偏误降至可忽略不计。尽管每年在多个学科中都会开展大量偏相关系数的元分析，但这些问题的实际重要性仍属未知。为解答此问题并为应用提供建议，我们调查了172项关于偏相关系数的经济元分析。我们发现，小样本偏误在实践中可忽略不计。然而，部分出版选择偏误仍然存在。尽管Fisher的z变换常被推荐，但相对于传统随机效应模型，它既未减少小样本偏误，也未减少出版选择偏误。在这些应用中，无约束加权最小二乘（UWLS）估计量和Hunter-Schmidt（HS）估计量所得到的平均偏相关系数估计值均小于使用或不使用Fisher z变换的随机效应模型，且可以说偏误更小。这些发现为任何对偏相关进行元分析的学科提供了实用指导。
英文摘要
Recent studies have demonstrated that conventional meta-analyses of partial correlation coefficients (PCC) are biased. Several adjustments have been shown in simulations to reduce these small-sample biases to negligibility. While many meta-analyses of partial correlation coefficients are conducted each year across several disciplines, the practical importance of these issues remains unknown. To address this question and to offer advice for applications, we survey 172 economic meta-analyses of PCCs. We find that small-sample biases are negligible in practice. However, some publication selection biases remain. Although Fisher's z transformations have often been recommended, they reduce neither small-sample nor publication selection biases relative to conventional random effects. Both the unrestricted weighted least squares (UWLS) and the Hunter-Schmidt (HS) estimators produce smaller, arguably less biased, estimates of the mean PCC in these applications than either random effects with or without Fisher's z transformations. These findings offer practical guidance for any discipline that meta-analyzes partial correlations.
Comments26 pages. Data and code: https://github.com/PetrCala/pcc-survey
