结构方程建模
验证性因素分析
潜变量
贝叶斯概率
先验概率
潜变量模型
计量经济学
项目反应理论
计算机科学
因子分析
蒙特卡罗方法
差异(会计)
数学
统计
心理测量学
会计
业务
作者
Bengt Muthén,Tihomir Asparouhov
出处
期刊:Psychological Methods
[American Psychological Association]
日期:2012-09-01
卷期号:17 (3): 313-335
被引量:1139
摘要
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 are 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.
科研通智能强力驱动
Strongly Powered by AbleSci AI