a University of South Carolina.
b University of Oklahoma.
Multivariate Behav Res. 2019 Mar-Apr;54(2):224-245. doi: 10.1080/00273171.2018.1514484. Epub 2018 Dec 20.
In this study, we introduce an interval estimation approach based on Bayesian structural equation modeling to evaluate factorial invariance. For each tested parameter, the size of noninvariance with an uncertainty interval (i.e. highest density interval [HDI]) is assessed via Bayesian parameter estimation. By comparing the most credible values (i.e. 95% HDI) with a region of practical equivalence (ROPE), the Bayesian approach allows researchers to (1) support the null hypothesis of practical invariance, and (2) examine the practical importance of the noninvariant parameter. Compared to the traditional likelihood ratio test, simulation results suggested that the proposed Bayesian approach could offer additional insight into evaluating factorial invariance, thus, leading to more informative conclusions. We provide an empirical example to demonstrate the procedures necessary to implement the proposed method in applied research. The importance of and influences on the choice of an appropriate ROPE are discussed.
在这项研究中，我们介绍了一种基于贝叶斯结构方程建模的区间估计方法，以评估因子不变性。对于每个测试的参数，通过贝叶斯参数估计来评估非不变性的大小和不确定性区间（即最高密度区间[HDI]）。通过将最可信的值（即 95%HDI）与实际等效区间（ROPE）进行比较，贝叶斯方法允许研究人员：(1) 支持实际不变性的零假设；(2) 检验非不变参数的实际重要性。与传统的似然比检验相比，模拟结果表明，所提出的贝叶斯方法可以为评估因子不变性提供更多的见解，从而得出更有信息意义的结论。我们提供了一个实证示例，以演示在应用研究中实施所提出方法所需的步骤。讨论了适当的 ROPE 的选择的重要性和影响。
