ABSTRACT

In the analysis of hierarchical data, multilevel structural equation modeling (multilevel SEM) has become the standard in the social sciences. To estimate these models, maximum likelihood (ML) approaches have been applied because they are the default in latent variable software. However, one drawback of ML is that it tends to suffer from estimation problems such as nonconvergence when the sample size is small to moderate, and the results that come from nonconverged solutions are useless in research practice. Nonconvergence is a particularly serious problem when more complex multilevel SEMs are estimated. Therefore, in this article, we show how factor score regression (FSR) can be used to obtain estimates of multilevel mediation, moderation, and nonlinear effects. We conducted two simulation studies to validate our approaches. Our findings were generally promising, which renders FSR an attractive alternative to ML.

摘要

在分层数据的分析中，多层次结构方程模型（multilevel SEM）已经成为社会科学的标准。为了估计这些模型，应用了最大似然 (ML) 方法，因为它们是潜在变量软件中的默认值。然而，ML 的一个缺点是当样本量小到​​中等时，它往往会遇到诸如不收敛等估计问题，并且来自非收敛解决方案的结果在研究实践中是无用的。当估计更复杂的多级 SEM 时，不收敛是一个特别严重的问题。因此，在本文中，我们展示了如何使用因子得分回归 (FSR) 来获得多级中介、调节和非线性效应的估计值。我们进行了两项模拟研究来验证我们的方法。