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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：
术语“多层次荟萃分析”不仅在应用研究中遇到，但在多层次资源中，将传统荟萃分析与多层次荟萃分析进行比较。在本教程中，我们认为“多水平荟萃分析”一词是多余的，因为所有的荟萃分析都可以表述为一种特殊的多水平模型。为了阐明荟萃分析的多层次性质，使用多层次方程提出了四个标准荟萃分析模型，并使用四个软件程序拟合了示例数据集：两个特定于荟萃分析（R和SPSS宏中的元）和两个特定于多层次建模（SAS和HLM中的PROC混合）。跨程序获得相同的参数估计，强调所有荟萃分析本质上都是多层次的。尽管结果相当，并非所有软件程序都是相同的，并且在提供的输出和可用的估计器中注意到了差异。本教程还将传统和多层次荟萃分析之间的文献区别描述为荟萃分析选择之间的差异，不是在元分析模型之间，并提供指导，以告知估计量的选择，显著性测试，主持人分析，和建模序列。注意到软件程序在这些决定方面允许灵活性的程度，metafor成为审查的最有利的计划。
