The integration of continuous and discrete latent variable models: Potential problems and promising opportunities
The integration of continuous and discrete latent variable models: Potential problems and promising opportunities
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DOI：
10.1037/1082-989x.9.1.3
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发表时间：
2004-03-01
影响因子：
7
通讯作者：
Curran, PJ
中科院分区：
文献类型：
--
作者：
Bauer, DJ;Curran, PJ
结构方程混合建模(SEMM)融合了连续和离散的潜变量模型。借鉴前人对连续和离散隐变量模型之间关系的研究，确定了可能导致SEMM中虚假潜在类估计的3个条件：结构模型的错误指定、非正态连续度量以及观测变量和/或潜在变量之间的非线性关系。当语义模型分析的目标是识别潜在类别时，这些条件应该被视为替代假设，结果应该被谨慎地解释。然而，随着在实践中对SEMM的估计有了更多的了解，研究人员可以利用该模型的灵活性来更全面地理解正在研究的现象。
Structural equation mixture modeling (SEMM) integrates continuous and discrete latent variable models. Drawing on prior research on the relationships between continuous and discrete latent variable models, the authors identify 3 conditions that may lead to the estimation of spurious latent classes in SEMM: misspecification of the structural model, nonnormal continuous measures, and nonlinear relationships among observed and/or latent variables. When the objective of a SEMM analysis is the identification of latent classes, these conditions should be considered as alternative hypotheses and results should be interpreted cautiously. However, armed with greater knowledge about the estimation of SEMMs in practice, researchers can exploit the flexibility of the model to gain a fuller understanding of the phenomenon under study.