Although finite mixture models have received considerable attention, particularly in the social and behavioral sciences, an alternative method for creating homogeneous groups, structural equation model trees (Brandmaier, von Oertzen, McArdle, & Lindenberger, 2013), is a recent development that has received much less application and consideration. It is our aim to compare and contrast these methods for uncovering sample heterogeneity. We illustrate the use of these methods with longitudinal reading achievement data collected as part of the Early Childhood Longitudinal Study–Kindergarten Cohort. We present the use of structural equation model trees as an alternative framework that does not assume the classes are latent and uses observed covariates to derive their structure. We consider these methods as complementary and discuss their respective strengths and limitations for creating homogeneous groups.

中文翻译：

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揭示样本异质性的方法比较：结构方程模型树和有限混合模型

尽管有限混合模型受到了相当大的关注，尤其是在社会和行为科学领域，但一种用于创建同构组、结构方程模型树的替代方法（Brandmaier、von Oertzen、McArdle 和 Lindenberger，2013 年）是最近的一项发展，已收到少得多的应用和考虑。我们的目标是比较和对比这些揭示样本异质性的方法。我们通过作为早期儿童纵向研究——幼儿园队列的一部分收集的纵向阅读成绩数据来说明这些方法的使用。我们提出使用结构方程模型树作为替代框架，该框架不假设类是潜在的，并使用观察到的协变量来推导它们的结构。