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元分析的多层次性质:教程、软件程序比较以及分析选择讨论。

On the Multilevel Nature of Meta-Analysis: A Tutorial, Comparison of Software Programs, and Discussion of Analytic Choices.

机构信息

a Center for Assessment and Research Studies , James Madison University.

b The College Board.

出版信息

Multivariate Behav Res. 2018 Jan-Feb;53(1):74-89. doi: 10.1080/00273171.2017.1365684. Epub 2017 Sep 27.

Abstract

The term "multilevel meta-analysis" is encountered not only in applied research studies, but in multilevel resources comparing traditional meta-analysis to multilevel meta-analysis. In this tutorial, we argue that the term "multilevel meta-analysis" is redundant since all meta-analysis can be formulated as a special kind of multilevel model. To clarify the multilevel nature of meta-analysis the four standard meta-analytic models are presented using multilevel equations and fit to an example data set using four software programs: two specific to meta-analysis (metafor in R and SPSS macros) and two specific to multilevel modeling (PROC MIXED in SAS and HLM). The same parameter estimates are obtained across programs underscoring that all meta-analyses are multilevel in nature. Despite the equivalent results, not all software programs are alike and differences are noted in the output provided and estimators available. This tutorial also recasts distinctions made in the literature between traditional and multilevel meta-analysis as differences between meta-analytic choices, not between meta-analytic models, and provides guidance to inform choices in estimators, significance tests, moderator analyses, and modeling sequence. The extent to which the software programs allow flexibility with respect to these decisions is noted, with metafor emerging as the most favorable program reviewed.

摘要

“多级元分析”一词不仅出现在应用研究中,也出现在将传统元分析与多级元分析进行比较的多级资源中。在本教程中,我们认为“多级元分析”一词是多余的,因为所有的元分析都可以被表述为一种特殊的多级模型。为了阐明元分析的多级性质,使用多级方程呈现了四个标准的元分析模型,并使用四个软件程序(R 中的 metafor 和 SPSS 宏,以及 SAS 中的 PROC MIXED 和 HLM)拟合示例数据集:两个专门用于元分析,两个专门用于多级建模。所有程序都得到了相同的参数估计,这强调了所有元分析本质上都是多级的。尽管结果相同,但并非所有软件程序都相同,并且在提供的输出和可用的估计器方面存在差异。本教程还将文献中区分传统元分析和多级元分析的观点重新表述为元分析选择之间的差异,而不是元分析模型之间的差异,并提供有关估计器、显著性检验、调节分析和建模顺序选择的指导。还注意到了这些决策在软件程序中的灵活性程度,metafor 被认为是最有利的程序。

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