因子混合模型:潜在类别分析与因子分析的整合

Factor Mixture Model: An Integration of Latent Class Analysis and Factor Analysis

因子混合模型(FMM)是考虑了群体潜在异质性后的因子分析模型,它将潜在类别分析(LCA)与传统的因子分析(FA)整合在同一框架内,既保留了两种分析技术的优点,同时又展现出独特优势。FMM的应用主要包括描述变量的潜在结构、对被试进行分组以及探测社会称许偏差等。我们建议分别采用FA、LCA与FMM三种模型拟合数据,参考拟合指数和模型可解释性选择最优模型。总结了FMM的分析步骤以及软件使用,并用于探讨大学生社会面子意识的测量模型。未来研究应关注FMM分析过程的简化,继续深化对拟合指数等方面的探讨。

Factor Mixture Model （FMM） is a factor analysis model in which the latent population heterogeneity is considered. Combined with latent class analysis （LCA） and traditional factor analysis （FA）, the FMM model consistently preserves the advantages of these two statistical methods, and has some unique features as well. Present empirical applications of FMM include the description of latent structure of variables, classification of subjects, and detection of social desirability bias. We suggest to fit data with FA, LCA and FMM respectively, and to choose an optimal model according to the fit indexes and practical implications. By applying FMM to build the measurement model of consciousness of social face, we illustrate the analysis steps and software operation procedures. Future research efforts are needed for some issues on FMM, such as the simplification of analytical process and the selection of fit index.