A Comparison of CFA, ESEM, and BSEM in Test Structure Analysis
A Comparison of CFA, ESEM, and BSEM in Test Structure Analysis
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CFA、ESEM 和 BSEM 在测试结构分析中的比较
DOI：
10.1080/10705511.2018.1562928
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发表时间：
2019-09-03
影响因子：
6
通讯作者：
Hau, Kit-Tai
中科院分区：
文献类型：
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作者：
Xiao, Yue;Liu, Hongyun;Hau, Kit-Tai
在心理或其他工具中经常发现非目标因素的轻微交叉负荷。在验证性因子分析（CFA）中强迫它们归零会导致有偏见的估计和扭曲的结构。此外，还提出了探索性结构方程建模（ESEM）和贝叶斯结构方程建模（BSEM）。在这项研究中，我们使用具有交叉加载的模拟数据，比较了传统的独立聚类-确认因子分析（ICM-CFA）、非标准CFA、具有Geomin或target旋转的ESEM以及具有不同交叉加载先验（正确的；小方差或大方差先验，平均值为零）的bsem的性能。考虑因子数量、因子相关性大小、交叉加载均值和加载方差四个因素。结果显示ICM-CFA表现最差。在正确的先验条件下，ESEMs总体上优于CFAs，但优于BSEM。具有大或小方差先验的BSEM表现相似，而交叉加载的先验均值比先验方差更重要。
Minor cross-loadings on non-targeted factors are often found in psychological or other instruments. Forcing them to zero in confirmatory factor analyses (CFA) leads to biased estimates and distorted structures. Alternatively, exploratory structural equation modeling (ESEM) and Bayesian structural equation modeling (BSEM) have been proposed. In this research, we compared the performance of the traditional independent-clusters-confirmatory-factor-analysis (ICM-CFA), the nonstandard CFA, ESEM with the Geomin- or Target-rotations, and BSEMs with different cross-loading priors (correct; small- or large-variance priors with zero mean) using simulated data with cross-loadings. Four factors were considered: the number of factors, the size of factor correlations, the cross-loading mean, and the loading variance. Results indicated that ICM-CFA performed the worst. ESEMs were generally superior to CFAs but inferior to BSEM with correct priors that provided the precise estimation. BSEM with large- or small-variance priors performed similarly while the prior mean for cross-loadings was more important than the prior variance.