Performance of factor mixture models as a function of model size, covariate effects, and class-specific parameters
Performance of factor mixture models as a function of model size, covariate effects, and class-specific parameters
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DOI：
10.1207/s15328007sem1401_2
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发表时间：
2007-01-01
影响因子：
6
通讯作者：
Muthen, Bengt O.
中科院分区：
文献类型：
--
作者：
Lubke, Gitta;Muthen, Bengt O.
因子混合模型被设计用于分析从由不同潜在类组成的群体获得的多变量数据。一个共同的因素模型被假定为保持在每个潜在的类。因子混合建模涉及获得模型参数的估计值，并且还可以用于将受试者分配到其最可能的潜在类别。该模拟研究调查了模型性能的各个方面，例如参数覆盖率和正确的类成员分配，并重点关注协变量效应、模型大小以及类特定参数与类不变参数。当拟合真实模型时，即使对于本研究中研究的最小类别分离（2个类别之间0.5 SD），参数覆盖率对于大多数参数也是良好的。收敛速度也是如此。正确的类分配对于没有协变量的小类分离是不令人满意的，但随着分离、协变量效应或两者的增加而显著改善。模型性能不受此处研究的模型大小差异的影响。类特定的参数可能会改善模型性能的某些方面，但会对其他方面产生负面影响。
Factor mixture models are designed for the analysis of multivariate data obtained from a population consisting of distinct latent classes. A common factor model is assumed to hold within each of the latent classes. Factor mixture modeling involves obtaining estimates of the model parameters, and may also be used to assign subjects to their most likely latent class. This simulation study investigates aspects of model performance such as parameter coverage and correct class membership assignment and focuses on covariate effects, model size, and class-specific versus class-invariant parameters. When fitting true models, parameter coverage is good for most parameters even for the smallest class separation investigated in this study (0.5 SD between 2 classes). The same holds for convergence rates. Correct class assignment is unsatisfactory for the small class separation without covariates, but improves dramatically with increasing separation, covariate effects, or both. Model performance is not influenced by the differences in model size investigated here. Class-specific parameters may improve some aspects of model performance but negatively affect other aspects.