Objective This article guides researchers through the process of specifying, troubleshooting, evaluating, and interpreting latent growth mixture models. Methods Latent growth mixture models are conducted with small example dataset of N = 117 pediatric patients using Mplus software. Results The example and data show how to select a solution, here a 3-class solution. We also present information on two methods for incorporating covariates into these models. Conclusions Many studies in pediatric psychology seek to understand how an outcome changes over time. Mixed models or latent growth models estimate a single average trajectory estimate and an overall estimate of the individual variability, but this may mask other patterns of change shared by some participants. Unexplored variation in longitudinal data means that researchers can miss critical information about the trajectories of subgroups of individuals that could have important clinical implications about how one assess, treats, and manages subsets of individuals. Latent growth mixture modeling is a method for uncovering subgroups (or “classes”) of individuals with shared trajectories that differ from the average trajectory.

中文翻译：

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使用潜在增长混合建模了解纵向数据的变化

目标 本文指导研究人员完成指定、故障排除、评估和解释潜在生长混合模型的过程。方法 使用 Mplus 软件对 N = 117 名儿科患者的小型示例数据集进行潜在生长混合模型。结果 示例和数据显示了如何选择解决方案，这里是 3 类解决方案。我们还介绍了将协变量合并到这些模型中的两种方法的信息。结论 许多儿科心理学研究试图了解结果如何随时间变化。混合模型或潜在增长模型估计单个平均轨迹估计和个体变异性的总体估计，但这可能掩盖了一些参与者共享的其他变化模式。纵向数据中未经探索的变异意味着研究人员可能会错过有关个体亚群轨迹的关键信息，这些信息可能对人们如何评估、治疗和管理个体亚群具有重要的临床意义。潜在增长混合建模是一种发现具有不同于平均轨迹的共享轨迹的个体的子组（或“类别”）的方法。