The factor mixture model (FMM) uses a hybrid of both categorical and continuous latent variables. The FMM is a good model for the underlying structure of psychopathology because the use of both categorical and continuous latent variables allows the structure to be simultaneously categorical and dimensional. This is useful because both diagnostic class membership and the range of severity within and across diagnostic classes can be modeled concurrently. Although the conceptualization of the FMM has been explained in the literature, the use of the FMM is still not prevalent. One reason is that there is little research about how such models should be applied in practice and, once a well-fitting model is obtained, how it should be interpreted. In this article, the FMM is explored by studying a real data example on conduct disorder. By exploring this example, this article aims to explain the different formulations of the FMM, the various steps in building a FMM, and how to decide between an FMM and alternative models.

中文翻译：

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因素混合分析的模型和策略：一个关于心理障碍潜在结构的例子


因子混合模型 (FMM) 使用分类和连续潜在变量的混合。 FMM 是精神病理学基础结构的一个很好的模型，因为分类和连续潜在变量的使用允许结构同时具有分类和维度。这很有用，因为诊断类别成员资格以及诊断类别内部和之间的严重性范围可以同时建模。尽管文献中已经解释了 FMM 的概念，但 FMM 的使用仍然不普遍。原因之一是，关于如何在实践中应用这些模型以及一旦获得了拟合良好的模型，应该如何解释它的研究很少。在本文中，通过研究行为障碍的真实数据示例来探索 FMM。通过探讨这个示例，本文旨在解释 FMM 的不同公式、构建 FMM 的各个步骤，以及如何在 FMM 和替代模型之间做出决定。