a Department of Psychology , University of California.
b Human Development and Family Studies , Texas Tech University.
Multivariate Behav Res. 2018 May-Jun;53(3):375-402. doi: 10.1080/00273171.2018.1454822. Epub 2018 Apr 6.
Statistical mediation analysis can help to identify and explain the mechanisms behind psychological processes. Examining a set of variables for mediation effects is a ubiquitous process in the social sciences literature; however, despite evidence suggesting that cross-sectional data can misrepresent the mediation of longitudinal processes, cross-sectional analyses continue to be used in this manner. Alternative longitudinal mediation models, including those rooted in a structural equation modeling framework (cross-lagged panel, latent growth curve, and latent difference score models) are currently available and may provide a better representation of mediation processes for longitudinal data. The purpose of this paper is twofold: first, we provide a comparison of cross-sectional and longitudinal mediation models; second, we advocate using models to evaluate mediation effects that capture the temporal sequence of the process under study. Two separate empirical examples are presented to illustrate differences in the conclusions drawn from cross-sectional and longitudinal mediation analyses. Findings from these examples yielded substantial differences in interpretations between the cross-sectional and longitudinal mediation models considered here. Based on these observations, researchers should use caution when attempting to use cross-sectional data in place of longitudinal data for mediation analyses.
统计中介分析可以帮助确定和解释心理过程背后的机制。在社会科学文献中，检验一组变量的中介效应是一种普遍的做法；然而，尽管有证据表明横断面数据可能会歪曲纵向过程的中介作用，但横断面分析仍以这种方式继续使用。目前可提供替代的纵向中介模型，包括基于结构方程建模框架（交叉滞后面板、潜在增长曲线和潜在差异评分模型）的模型，这些模型可能更能代表纵向数据的中介过程。本文的目的有两个：首先，我们比较了横断面和纵向中介模型；其次，我们主张使用模型来评估中介效应，以捕捉研究过程的时间顺序。本文提出了两个独立的实证示例来说明从横断面和纵向中介分析中得出的结论之间的差异。这些示例的结果表明，这里考虑的横断面和纵向中介模型之间的解释存在很大差异。基于这些观察结果，研究人员在尝试使用横断面数据代替纵向数据进行中介分析时应谨慎行事。
