Deciding on the number of classes in latent class analysis and growth mixture modeling:: A Monte Carlo simulation study
Deciding on the number of classes in latent class analysis and growth mixture modeling:: A Monte Carlo simulation study
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DOI：
10.1080/10705510701575396
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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：
Nylund, Karen L.;Asparoutiov, Tihomir;Muthen, Bengt O.
混合模型是一种广泛应用的数据分析技术，用于识别群体中未观察到的异质性。尽管混合模型在实践中很有用，但在混合模型的应用中存在一个未解决的问题，即没有一个普遍接受的统计指标来决定研究人群中的类数。本文介绍了一个模拟研究的结果，检查性能的可能性为基础的测试和传统上使用的信息准则（IC）用于确定混合建模中的类的数量。我们研究了3种类型的混合模型的这些测试和指数的性能：潜在类别分析（LCA），因子混合模型（FMA）和增长混合模型（GMM）。我们评估了测试和索引在三种不同样本量（n = 200，500，1,000）下正确识别类数的能力。虽然贝叶斯信息准则表现最好的IC，自举似然比检验被证明是一个非常一致的指标类在所有的模型考虑。
Mixture modeling is a widely applied data analysis technique used to identify unobserved heterogeneity in a population. Despite mixture models' usefulness in practice, one unresolved issue in the application of mixture models is that there is not one commonly accepted statistical indicator for deciding on the number of classes in a study population. This article presents the results of a simulation study that examines the performance of likelihood-based tests and the traditionally used Information Criterion (ICs) used for determining the number of classes in mixture modeling. We look at the performance of these tests and indexes for 3 types of mixture models: latent class analysis (LCA), a factor mixture model (FMA), and a growth mixture models (GMM). We evaluate the ability of the tests and indexes to correctly identify the number of classes at three different sample sizes (n = 200, 500, 1,000). Whereas the Bayesian Information Criterion performed the best of the ICs, the bootstrap likelihood ratio test proved to be a very consistent indicator of classes across all of the models considered.