The Merits of Using Longitudinal Mediation
The Merits of Using Longitudinal Mediation
复制标题
DOI：
10.1080/00461520.2016.1207175
复制
发表时间：
2016-07-01
影响因子：
8.8
通讯作者：
Jose, Paul E.
中科院分区：
文献类型：
--
作者：
Jose, Paul E.
文献中报道的许多中介分析都是基于并发或单次数据集。本文的两个首要主题是：并发中介的结果本质上是模糊的，研究人员将明智地进行纵向数据集的中介。这里包含的一个示例表明，使用纵向数据进行的纵向中介分析不一定支持重要的并发中介结果。举例介绍了4种基本的纵向中介类型：（a）解释随时间推移的平均组差异，（B）嵌入在实验设计中的中介分析，（c）集中的纵向中介（1个特定的变量随时间的顺序），（d）完整的纵向中介（所有可能的变量随时间的顺序）。获取纵向数据通常是资源密集型的，但如果研究人员希望获得清晰明确的中介结果，建议他们对此类数据集进行这些分析。
Many of the mediation analyses reported in the literature are based on concurrent or single-occasion data sets. The 2 overarching themes of the present article are: Results of concurrent mediations are inherently ambiguous, and researchers would be wise to conduct mediations on longitudinal data sets instead. An example included here demonstrates that a significant concurrent mediation result is not necessarily supported by a longitudinal mediation analysis conducted with longitudinal data. Examples are presented of 4 basic longitudinal mediation types: (a) explaining a mean group difference over time, (b) a mediation analysis embedded in an experimental design, (c) a focused longitudinal mediation (1 specific ordering of variables over time), (d) a complete longitudinal mediation (all possible orderings of variables over time). Obtaining longitudinal data is often resource intensive, but if researchers want to obtain clear and unambiguous mediation results, it is advised that they perform these analyses on such data sets.