Fixed effects vs. random effects meta-analysis models: Implications for cumulative research knowledge
Fixed effects vs. random effects meta-analysis models: Implications for cumulative research knowledge
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DOI：
10.1111/1468-2389.00156
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发表时间：
2000-12-01
影响因子：
2.2
通讯作者：
Schmidt, FL
中科院分区：
文献类型：
--
作者：
Hunter, JE;Schmidt, FL
社会科学的研究结论越来越多地基于元分析，使得元分析的准确性问题对累积知识基础的完整性至关重要。固定效应（FE）和随机效应（RE）荟萃分析模型已被广泛用于已发表的荟萃分析。这篇文章表明，FE模型通常表现出实质性的I型偏倚的显着性检验的平均效应量和调节变量（相互作用），而RE模型没有。同样，FE模型（而不是RE模型）产生的平均效应量的置信区间比其名义宽度窄，从而夸大了荟萃分析结果的精确度。本文分析表明，FE程序中的这些偏差足以在研究文献中积累知识的结论中产生严重扭曲。因此，我们建议在荟萃分析中常规使用RE方法，而不是FE方法。
Research conclusions in the social sciences are increasingly based on meta-analysis, making questions of the accuracy of meta-analysis critical to the integrity of the base of cumulative knowledge. Both fixed effects (FE) and random effects (RE) meta-analysis models have been used widely in published meta-analyses. This article shows that FE models typically manifest a substantial Type I bias in significance tests for mean effect sizes and for moderator variables (interactions), while RE models do not. Likewise, FE models, but not RE models, yield confidence intervals for mean effect sizes that are narrower than their nominal width, thereby overstating the degree of precision in meta-analysis findings. This article demonstrates analytically that these biases in FE procedures are large enough to create serious distortions in conclusions about cumulative knowledge in the research literature. We therefore recommend that RE methods routinely be employed in meta-analysis in preference to FE methods.