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因子混合分析的模型与策略:以心理障碍潜在结构为例

Models and Strategies for Factor Mixture Analysis: An Example Concerning the Structure Underlying Psychological Disorders.

作者信息

Clark Shaunna L, Muthén Bengt, Kaprio Jaakko, D'Onofrio Brian M, Viken Richard, Rose Richard J

机构信息

Virginia Commonwealth University, Richmond.

出版信息

Struct Equ Modeling. 2013 Oct 1;20(4). doi: 10.1080/10705511.2013.824786.

Abstract

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. While 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 paper, the FMM will be explored by studying a real data example on conduct disorder. By exploring this example, this paper aims to explain the different formulations of the FMM, the various steps in building a FMM, as well as how to decide between a FMM and alternative models.

摘要

因子混合模型(FMM)使用分类和连续潜在变量的混合形式。FMM对于精神病理学的潜在结构来说是一个很好的模型,因为分类和连续潜在变量的使用使得该结构同时具有分类性和维度性。这很有用,因为诊断类别归属以及诊断类别内部和之间的严重程度范围可以同时进行建模。虽然FMM的概念化在文献中已有解释,但FMM的使用仍然不普遍。一个原因是,关于如何在实践中应用此类模型以及一旦获得拟合良好的模型应如何解释的研究很少。在本文中,将通过研究一个关于品行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的不同形式、构建FMM的各个步骤,以及如何在FMM和替代模型之间做出选择。

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