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Comparing Methods for Multilevel Moderated Mediation: A Decomposed-first Strategy

机译:比较多级调节调解的方法:一种分解 - 第一策略

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摘要

The purpose of this study is to propose a decomposed-first strategy for multilevel moderated mediation and to compare the performance of three moderated mediation approaches in multilevel structural equation modeling. The following approaches were compared in simulations to test coefficients that were decomposed level by level: orthogonal partitioning with centering within cluster, random coefficient prediction, and latent moderated structural equations. The manipulated conditions for the simulation analysis were the analysis method, the number of groups, group size, and intraclass correlation. The results showed that, for samples consisting of a large number of groups, a large average group size and a large intraclass correlation, LMS had the strongest performance. This study is meaningful in that it produces interpretable coefficients by applying a decomposed-first strategy in multilevel moderated mediation and extends a basic moderated mediation model to include more specific research questions in multilevel structural equation modeling.
机译:本研究的目的是提出用于多级调节调解的分解 - 第一策略,并比较三种中调解方法在多级结构方程模型中的性能。将以下方法进行了比较,以测试系数通过级别分解水平:正交分区,在簇内,随机系数预测和潜在潜在的结构方程中的居中。用于仿真分析的操纵条件是分析方法,组数,组大小和脑内相关性。结果表明,对于由大量组成的样品,较大的平均群体大小和大的颅内相关性,LMS具有最强的性能。该研究的有意义在于它通过在多级中调解中应用分解的第一策略来产生可解释的系数,并扩展基本的中调解模型,包括在多级结构方程模型中的更具体的研究问题。

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