Meta-analysts often ask a yes-or-no question: Is there an intervention effect or not? This traditional, all-or-nothing thinking stands in contrast with current best practice in meta-analysis, which calls for a heterogeneity-attuned approach (i.e., focused on the extent to which effects vary across procedures, participant groups, or contexts). This heterogeneity-attuned approach allows researchers to understand where effects are weaker or stronger and reveals mechanisms. The current article builds on a rare opportunity to compare two recent meta-analyses that examined the same literature (growth mindset interventions) but used different methods and reached different conclusions. One meta-analysis used a traditional approach (Macnamara and Burgoyne, in press), which aggregated effect sizes for each study before combining them and examined moderators one-by-one by splitting the data into small subgroups. The second meta-analysis (Burnette et al., in press) modeled the variation of effects within studies-across subgroups and outcomes-and applied modern, multi-level meta-regression methods. The former concluded that growth mindset effects are biased, but the latter yielded nuanced conclusions consistent with theoretical predictions. We explain why the practices followed by the latter meta-analysis were more in line with best practices for analyzing large and heterogeneous literatures. Further, an exploratory re-analysis of the data showed that applying the modern, heterogeneity-attuned methods from Burnette et al. (in press) to the dataset employed by Macnamara and Burgoyne (in press) confirmed Burnette et al.'s conclusions; namely, that there was a meaningful, significant effect of growth mindset in focal (at-risk) groups. This article concludes that heterogeneity-attuned meta-analysis is important both for advancing theory and for avoiding the boom-or-bust cycle that plagues too much of psychological science.

中文翻译：

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为什么增长心态和其他干预措施的荟萃分析应遵循检查异质性的最佳实践：Macnamara 和 Burgoyne (2023) 以及 Burnette 等人的评论。 （2023）。


元分析师经常问一个是或否的问题：是否存在干预效应？这种传统的、全有或全无的思维与目前荟萃分析的最佳实践形成鲜明对比，后者需要异质性协调的方法（即关注不同程序、参与者群体或环境之间的影响变化程度）。这种异质性调整方法使研究人员能够了解哪些效应较弱或较强，并揭示机制。本文基于一个难得的机会来比较最近的两项荟萃分析，这两项分析研究了相同的文献（成长心态干预措施），但使用了不同的方法并得出了不同的结论。一项荟萃分析使用了一种传统方法（Macnamara 和 Burgoyne，正在出版），该方法在合并每项研究之前汇总其效应大小，并通过将数据分成小的子组来逐一检查调节者。第二项荟萃分析（Burnette 等人，待出版）对研究中的效应变化（跨亚组和结果）进行了建模，并应用了现代、多层次的荟萃回归方法。前者得出的结论是成长心态效应存在偏差，但后者得出了与理论预测一致的细致入微的结论。我们解释了为什么后一种荟萃分析所遵循的实践更符合分析大型异构文献的最佳实践。此外，对数据的探索性重新分析表明，应用 Burnette 等人的现代异质性调整方法。 （正在出版中）Macnamara 和 Burgoyne 使用的数据集（正在出版中）证实了 Burnette 等人的结论；也就是说，成长心态对重点（高危）群体具有有意义的、显着的影响。 本文的结论是，异质性协调的荟萃分析对于推进理论和避免困扰心理科学的繁荣或萧条循环都很重要。