Bayesian Structural Equation Modeling: A More Flexible Representation of Substantive Theory
Bayesian Structural Equation Modeling: A More Flexible Representation of Substantive Theory
复制标题
DOI：
10.1037/a0026802
复制
发表时间：
2012-09-01
影响因子：
7
通讯作者：
Asparouhov, Tihomir
中科院分区：
文献类型：
--
作者：
Muthen, Bengt;Asparouhov, Tihomir
本文提出了一种利用贝叶斯分析进行因子分析和结构方程建模的新方法。该方法基于信息量小、方差小的先验信息，用近似零点代替精确零点的参数说明。有人认为，这会产生一种更好地反映实质性理论的分析。在将参数tu-e添加到常规模型以使得如果应用最大似然估计则获得未识别模型的应用中，所提出的贝叶斯方法特别有益。这种方法对于潜在变量建模的度量方面非常有用，例如验证性因子分析，以及结构方程建模的度量部分。研究了验证性因子分析中的交叉加载和残差相关两个应用领域。文中还给出了一个使用全结构方程模型的实例，说明了一种查找模型错误说明的有效方法。该方法包括3个要素：使用后验预测检验的模型检验、模型估计和模型修改。用Mplus软件对蒙特卡罗模拟和实际数据进行了分析。真实数据分析使用了Holzinger和Swineford(1939)的经典心智能力研究数据，英国一项调查的五大人格因素数据，以及1988年国家教育纵向研究的科学成就数据。
This article proposes a new approach to factor analysis and structural equation modeling using Bayesian analysis. The new approach replaces parameter specifications of exact zeros with approximate zeros based on informative, small-variance priors. It is argued that this produces an analysis that better reflects substantive theories. The proposed Bayesian approach is particularly beneficial in applications where parameters tu-e added to a conventional model such that a nonidentified model is obtained if maximum-likelihood estimation is applied. This approach is useful for measurement aspects of latent variable modeling, such as with confirmatory factor analysis, and the measurement part of structural equation modeling. Two application areas are studied, cross-loadings and residual correlations in confirmatory factor analysis. An example using a full structural equation model is also presented, showing an efficient way to find model misspecification. The approach encompasses 3 elements: model testing using posterior predictive checking, model estimation, and model modification. Monte Carlo simulations and real data are analyzed using Mplus. The real-data analyses use data from Holzinger and Swineford's (1939) classic mental abilities study, Big Five personality factor data from a British survey, and science achievement data from the National Educational Longitudinal Study of 1988.