ABSTRACT

Latent moderated structural equations (LMS) is a popular method in estimating latent interaction effects. Mplus has implemented a variant of LMS (LMS-cat) that uses categorical confirmatory factor analysis to handle ordered-categorical indicators. We conducted a simulation study to examine the performance of the LMS-cat in varying sample size, interaction effect size, missing data rate and scenario, as well as number and symmetry of item response category conditions. Results showed that the LMS-cat is an excellent method in estimating structural parameters (i.e., interaction effect and lower-order effects). However, it could produce highly biased measurement parameters (i.e., factor loadings, and item category thresholds). We illustrated the LMS-cat by testing the interaction effect between participation in teachers` support activities and that of teachers` counterfactual activities (meditation, meditative movement, vigorous physical exercise) on teachers` sense of self-efficacy.

摘要

潜在缓和结构方程 (LMS) 是估计潜在相互作用效应的流行方法。中号加已经实现了 LMS (LMS-cat) 的一个变体，它使用分类验证性因素分析来处理有序分类指标。我们进行了一项模拟研究，以检查 LMS-cat 在不同样本大小、交互效应大小、缺失数据率和场景以及项目响应类别条件的数量和对称性方面的性能。结果表明，LMS-cat 是一种极好的估计结构参数（即相互作用效应和低阶效应）的方法。然而，它可能会产生高度偏差的测量参数（即，因子负载和项目类别阈值）。我们通过测试参与教师支持活动与教师反事实活动（冥想、冥想运动、