MacKinnon David P, Valente Matthew J
Department of Psychology, Arizona State University, Tempe, Ariz., USA.
Ann Nutr Metab. 2014;65(2-3):198-204. doi: 10.1159/000362505. Epub 2014 Nov 18.
The purpose of this article is to outline multilevel structural equation modeling (MSEM) for mediation analysis of longitudinal data. The introduction of mediating variables can improve experimental and nonexperimental studies of child growth in several ways as discussed throughout this article. Single-mediator individual-level and multilevel mediation models illustrate several current issues in the estimation of mediation with longitudinal data. The strengths of incorporating structural equation modeling (SEM) with multilevel mediation modeling are described. SUMMARY AND KEY MESSAGES: Longitudinal mediation models are pervasive in many areas of research including child growth. Longitudinal mediation models are ideally modeled as repeated measurements clustered within individuals. Further, the combination of MSEM and SEM provides an ideal approach for several reasons, including the ability to assess effects at different levels of analysis, incorporation of measurement error and possible random effects that vary across individuals.
本文旨在概述用于纵向数据中介分析的多水平结构方程模型(MSEM)。如本文通篇所讨论的,中介变量的引入可以在多个方面改善对儿童成长的实验性和非实验性研究。单中介个体水平和多水平中介模型阐述了纵向数据中介估计中的几个当前问题。描述了将结构方程模型(SEM)与多水平中介模型相结合的优势。总结与关键信息:纵向中介模型在包括儿童成长在内的许多研究领域普遍存在。纵向中介模型理想的建模方式是将个体内的重复测量作为聚类。此外,MSEM和SEM的结合提供了一种理想的方法,原因有几个,包括能够在不同分析水平上评估效应、纳入测量误差以及个体间可能变化的随机效应。