Fitting meta-analytic structural equation models with complex datasets.
Fitting meta-analytic structural equation models with complex datasets.
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DOI：
10.1002/jrsm.1199
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发表时间：
2016-06
影响因子：
9.8
通讯作者：
Lipsey MW
中科院分区：
文献类型：
--
作者：
Wilson SJ;Polanin JR;Lipsey MW
提出了用于大型复杂数据集的两阶段Meta分析结构方程建模的标准程序的第一阶段的修改。该修改解决了此类Meta分析中出现的两个常见问题：（a）提供相同结构的多个测量的主要研究和（B）表现出显著异质性的相关系数，其中一些模糊了感兴趣的结构之间的关系或破坏了细胞间相关性的可比性。该方法的一个组成部分是三水平随机效应模型，该模型能够合成具有相关系数的合并相关矩阵。另一个组成部分是Meta回归，可用于生成协变量调整的相关系数，以减少选定的不均匀分布的调节变量的影响。这些技术的非技术性介绍，沿着一个Meta分析数据集的程序说明。版权所有© 2016约翰威利父子有限公司.
A modification of the first stage of the standard procedure for two‐stage meta‐analytic structural equation modeling for use with large complex datasets is presented. This modification addresses two common problems that arise in such meta‐analyses: (a) primary studies that provide multiple measures of the same construct and (b) the correlation coefficients that exhibit substantial heterogeneity, some of which obscures the relationships between the constructs of interest or undermines the comparability of the correlations across the cells. One component of this approach is a three‐level random effects model capable of synthesizing a pooled correlation matrix with dependent correlation coefficients. Another component is a meta‐regression that can be used to generate covariate‐adjusted correlation coefficients that reduce the influence of selected unevenly distributed moderator variables. A non‐technical presentation of these techniques is given, along with an illustration of the procedures with a meta‐analytic dataset. Copyright © 2016 John Wiley & Sons, Ltd.