ABSTRACT Recent developments allow for incorporating exploratory features into structural equation models (SEM). Two approaches, exploratory SEM (ESEM) and Bayesian SEM (BSEM), have been shown flexible of estimating complex SEM. This simulation study compared the performance of ESEM and BSEM for estimating structural regression models with ordinal indicators where cross-loadings were present in selected factors. Data were generated under conditions including various categorical data distributions, similarity of categorical distributions across indicators, and sample sizes. ESEM with Geomin rotation and BSEM with four small-variance normal priors on cross-loadings were used. Results indicated that ESEM may be prioritized over BSEM when sample sizes were large, distributions of ordinal indicators were symmetric or moderately asymmetric, and cross-loadings were non-ignorable. When sample sizes were relatively small, we recommend using one approach to complement the other. For BSEM, a sensitivity test is recommended to evaluate the impact of various prior choices on the estimation outcomes.

中文翻译：

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ESEM 和 BSEM 在具有序数指标的结构方程模型中的性能

摘要 最近的发展允许将探索性特征纳入结构方程模型 (SEM)。两种方法，探索性 SEM (ESEM) 和贝叶斯 SEM (BSEM)，已被证明可以灵活地估计复杂的 SEM。该模拟研究比较了 ESEM 和 BSEM 在用有序指标估计结构回归模型方面的性能，其中在选定的因素中存在交叉载荷。数据是在各种分类数据分布、跨指标分类分布的相似性和样本大小等条件下生成的。使用带有 Geomin 旋转的 ESEM 和带有四个小方差正态先验的 BSEM 对交叉加载使用。结果表明，当样本量较大、序数指标分布对称或中度不对称时，ESEM 可能优先于 BSEM，和交叉载荷是不可忽视的。当样本量相对较小时，我们建议使用一种方法来补充另一种方法。对于 BSEM，建议进行敏感性测试以评估各种先验选择对估计结果的影响。