ABSTRACT

Cross-loadings are common in multidimensional instruments; however, they cannot be appropriately addressed in conventional structural equation modeling (SEM) owing to the assumption of zero cross-loadings in standard confirmatory factor analysis (CFA). Although it has been proposed that exploratory structural equation modeling (ESEM) and Bayesian structural equation modeling (BSEM) can address this issue more flexibly, their performance in structural parameter estimation has not been adequately compared. This study uses simulated data to evaluate and compare SEM, ESEM, and BSEM in estimating structural models under different manipulation conditions (i.e., sample size, target loading, cross-loading, and path coefficient). The results demonstrated that the performances of these approaches were similar in the case of zero cross-loadings. SEM performed worse as cross-loadings increased, and the performance of BSEM significantly depended on the accuracy of the priors for cross-loadings. ESEM was inferior to BSEM with correctly specified prior means for cross-loadings in most evaluation measures and exhibits unstable performance in conditions with small target loadings. Recommended strategies for selecting an appropriate modeling approach are discussed based on our findings.

摘要

交叉加载在多维仪器中很常见；然而，由于在标准验证性因子分析 (CFA) 中假设零交叉载荷，它们无法在传统的结构方程模型 (SEM) 中得到适当的处理。尽管有人提出探索性结构方程建模 (ESEM) 和贝叶斯结构方程建模 (BSEM) 可以更灵活地解决这个问题，但它们在结构参数估计方面的性能尚未得到充分比较。本研究使用模拟数据来评估和比较 SEM、ESEM 和 BSEM 在估计不同操作条件下的结构模型（即样本大小、目标加载、交叉加载和路径系数）。结果表明，在零交叉加载的情况下，这些方法的性能相似。随着交叉加载的增加，SEM 的表现更差，BSEM 的性能在很大程度上取决于交叉加载先验的准确性。ESEM 不如 BSEM，在大多数评估措施中正确指定了交叉加载的先前方法，并且在目标加载较小的条件下表现出不稳定的性能。根据我们的研究结果讨论了选择适当建模方法的推荐策略。