Investigating population heterogeneity with factor mixture models
Investigating population heterogeneity with factor mixture models
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DOI：
10.1037/1082-989x.10.1.21
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发表时间：
2005-03-01
影响因子：
7
通讯作者：
Muthén, B
中科院分区：
文献类型：
--
作者：
Lubke, GH;Muthén, B
可能会或可能不会观察到群体异质性的来源。如果观察到异质性的来源（例如，性别），样本可以分成几组，数据可以用多组的方法进行分析。如果未观察到群体异质性的来源，则可以使用潜在类别模型分析数据。因子混合模型是潜在类和公共因子模型的组合，可用于探索未观察到的群体异质性。观察到的异质性来源可作为协变量纳入。纳入协变量的不同方式对应于不同的概念解释。这些都进行了详细讨论。因素混合建模的特点进行了描述，比较其他方法设计的数据源于异质性的人口。一步一步的分析数据的一个子集，从美国青年纵向调查说明了如何因素混合模型可以应用在一个探索性的方式在一个单一的时间点收集的数据。
Sources of population heterogeneity may or may not be observed. If the sources of heterogeneity are observed (e.g., gender), the sample can be split into groups and the data analyzed with methods for multiple groups. If the sources of population heterogeneity are unobserved, the data can be analyzed with latent class models. Factor mixture models are a combination of latent class and common factor models and can be used to explore unobserved population heterogeneity. Observed sources of heterogeneity can be included as covariates. The different ways to incorporate covariates correspond to different conceptual interpretations. These are discussed in detail. Characteristics of factor mixture modeling are described in comparison to other methods designed for data stemming from heterogeneous populations. A step-by-step analysis of a subset of data from the Longitudinal Survey of American Youth illustrates how factor mixture models can be applied in an exploratory fashion to data collected at a single time point.