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.

行为科学研究人员通常对纵向行为改变结果以及随着时间的推移影响这些结果变化的机制感兴趣。通常用于解决这些研究问题的统计模型不允许调查随时间动态变化的机制。然而，潜在变化评分模型允许随着时间的推移进行动态变化（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不具备的灵活性。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调解模型的各个方面与具有两个以上时间点的重复测量结果（以及调解变量和预测变量）的潜在变化评分模型相结合，在潜在变化评分框架中测试调解。我们还提供 Mplus 语法来完成这些分析和潜在变化评分中介（LCSM）模型的实际考虑。