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Correcting for the Multiplicative and Additive Effects of Measurement Unreliability in Meta-Analysis of Correlations

机译：在相关性的荟萃分析中纠正测量不可靠性的乘法效应和加法效应

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摘要

As a powerful tool for synthesizing information from multiple studies, meta-analysis has gained high popularity in many disciplines. Conclusions stemming from meta-analyses are often used to direct theory development, calibrate sample size planning, and guide critical decision-making and policymaking. However, meta-analyses can be conflicted, misleading, and irreproducible. One of the reasons for meta-analyses to be misleading is the improper handling of measurement unreliability. We show that even when there is no publication bias, the current meta-analysis procedures would frequently detect nonexistent effects, and provide severely biased estimates and intervals with coverage rates far below the intended level. In this study, an effective approach to correcting for unreliability is proposed and evaluated via simulation studies. Its sensitivity to the violation of the homogeneous reliability and residual correlation assumption is also tested. The proposed method is illustrated using a real meta-analysis on the relationship between extroversion and subjective well-being. Substantial differences in meta-analytic results are observed between the proposed method and existing methods. Further, although not specifically designed for aggregating effect sizes with various measures, the proposed method can be used to fulfill the purpose. The study ends with discussions on the limitations and guidelines for implementing the proposed approach. Translational Abstract Measurement unreliability refers to the overall inconsistency of a measure. Statistically, it indicates the amount of error in the observed scores of a measure. Measurement unreliability can distort the meta-analytic results of correlation in a multiplicative way as well as in an additive way. The multiplicative effect of unreliability produces substantial downward biases in the meta-analytic correlations, whereas the additive effect can bring in both downward and upward biases. Both effects can lead to quantitatively false conclusions, for example, detecting nonexistent effects and providing intervals with coverage rates far below the intended level. Existing correction methods only consider the multiplicative effect of unreliability. In this study, we propose a new unreliability correction method that can take into account both the multiplicative and the additive effects of unreliability. The new correction method also provides a tool for researchers to aggregate correlations based on different measures. We illustrate the proposed method using a real meta-analysis on the relationship between extroversion and subjective well-being, and evaluate it using Monte Carlo simulation studies. Limitations and practical guidance are included. R markdown files including snippets of embedded R code, annotations, and results for all analyses are provided.
机译：作为一个综合信息的有力工具从多个研究,分析获得了在许多学科高人气。源于荟萃分析常用于直接理论发展,调整样本量规划和指导决策和关键决策。矛盾、误导性,不能复制的。荟萃分析的原因是有误导性的是测量的处理不当不可靠。发表偏倚,目前的荟萃分析程序经常检测不存在影响,并提供严重偏差估计并与覆盖率远低于间隔预期的水平。纠正方法的不可靠性提出了通过模拟研究和评估。它的灵敏度的违反均匀的可靠性和剩余的相关性假设测试。使用一个真正的荟萃分析外向性和主观之间的关系幸福。整合的结果是观察之间的该方法和现有的方法。虽然不是专门设计聚合效应的大小与不同的措施,该方法可用于满足目的。限制和实施指南建议的方法。测量不可靠性指的是整体不一致的措施。表明在观察到的错误成绩的措施。扭曲的整合结果吗乘法的方式以及相关一种添加剂的方式。不可靠性产生实质性的下降偏见的整合关系,而添加剂的效果可以将下降和向上的偏见。定量错误的结论,例如,并提供检测不存在影响与覆盖率远低于间隔预期的水平。考虑的乘法效应不可靠。不可靠性可以校正方法考虑到乘法和添加剂不可靠性的影响。校正方法还提供了一个工具研究人员总体相关性的基础上不同的措施。使用一个真正的荟萃分析的方法外向性和主观之间的关系幸福,使用蒙特卡罗和评估它仿真研究。包括指导。嵌入R代码片段、注释和对所有提供分析结果。

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