Longitudinal models have become increasingly popular in recent years because of their power to test theoretically derived hypotheses by modeling within-person processes with repeated measures. Growth models constitute a flexible framework for modeling a range of complex trajectories across time in outcomes of interest, including non-linearities and time-varying covariates. However, these models can be expanded to include the effects of multiple growth processes at once on a single outcome. Here, I outline such an extension, showing how multiple growth processes can be modeled as a specific case of the general ability to include time-varying covariates in growth models. I show that this extension of growth models cannot be accomplished by statistical models alone, and that study design plays a crucial role in allowing for proper parameter recovery. I demonstrate these principles through simulations to mimic important theoretical conditions where modeling the effects of multiple growth processes can address developmental theory including, disaggregating the effects of age and practice or treatment in repeated assessments and modeling age- and puberty-related effects during adolescence. I compare how these models behave in two common longitudinal designs, cohort and accelerated, and how planned missingness in observations is key to parameter recovery. I conclude with directions for future substantive research using the method outlined here.

近年来，纵向模型变得越来越流行，因为它们可以通过重复测量对人内过程进行建模来检验理论推导出的假设。增长模型构成了一个灵活的框架，用于对感兴趣的结果中的一系列跨时间复杂轨迹进行建模，包括非线性和时变协变量。但是，这些模型可以扩展为同时包含多个增长过程对单个结果的影响。在这里，我概述了这样的扩展，展示了如何将多个增长过程建模为在增长模型中包含随时间变化的协变量的一般能力的特定案例。我表明，这种增长模型的扩展不能单独通过统计模型来完成，并且研究设计在允许适当的参数恢复方面起着至关重要的作用。我通过模拟来模拟重要的理论条件来证明这些原则，其中模拟多个生长过程的影响可以解决发展理论，包括在重复评估中分解年龄和实践或治疗的影响，以及模拟青春期与年龄和青春期相关的影响。我比较了这些模型在两种常见的纵向设计（队列和加速）中的表现，以及观察中的计划缺失如何成为参数恢复的关键。我总结了使用此处概述的方法进行未来实质性研究的方向。在重复评估中分解年龄和实践或治疗的影响，并对青春期与年龄和青春期相关的影响进行建模。我比较了这些模型在两种常见的纵向设计（队列和加速）中的表现，以及观察中的计划缺失如何成为参数恢复的关键。我总结了使用此处概述的方法进行未来实质性研究的方向。在重复评估中分解年龄和实践或治疗的影响，并对青春期与年龄和青春期相关的影响进行建模。我比较了这些模型在两种常见的纵向设计（队列和加速）中的表现，以及观察中的计划缺失如何成为参数恢复的关键。我总结了使用此处概述的方法进行未来实质性研究的方向。