Hilley Chanler D, O'Rourke Holly P
Arizona State University, USA.
Int J Behav Dev. 2022 Mar;46(2):125-141. doi: 10.1177/01650254211064352. Epub 2022 Jan 24.
Researchers in behavioral sciences are often interested in longitudinal behavior change outcomes and the mechanisms that influence changes in these outcomes over time. The statistical models that are typically implemented to address these research questions do not allow for investigation of mechanisms of dynamic change over time. However, latent change score models allow for dynamic change (not just linear or exponential change) over time and have flexibility in parameter constraints that other longitudinal models do not have. Developmental researchers also frequently utilize mediation analyses to investigate mechanisms of influence in longitudinal research implemented in path analytic or latent growth curve models. In this article, we provide three examples of how mediation can be tested in the latent change score framework by combining aspects of traditional mediation models with latent change score models of repeated measures outcomes (and mediators and predictors) with more than two timepoints. We also provide the Mplus syntax to complete these analyses and practical considerations of latent change score mediation (LCSM) models.
行为科学领域的研究人员通常对纵向行为变化结果以及随时间影响这些结果变化的机制感兴趣。为解决这些研究问题而通常采用的统计模型无法对随时间动态变化的机制进行研究。然而，潜在变化分数模型允许随时间进行动态变化（不仅仅是线性或指数变化），并且在参数约束方面具有其他纵向模型所没有的灵活性。发展研究人员还经常利用中介分析来研究在路径分析或潜在增长曲线模型中实施的纵向研究中的影响机制。在本文中，我们提供了三个示例，说明如何通过将传统中介模型的各个方面与具有两个以上时间点的重复测量结果（以及中介和预测变量）的潜在变化分数模型相结合，在潜在变化分数框架中检验中介作用。我们还提供了完成这些分析的Mplus语法以及潜在变化分数中介（LCSM）模型的实际注意事项。
