A Quasi-Likelihood Approach to Assess Model Fit in Quadratic and Interaction SEM
A Quasi-Likelihood Approach to Assess Model Fit in Quadratic and Interaction SEM
复制标题
DOI：
10.1080/00273171.2019.1689349
复制
发表时间：
2019-12
影响因子：
3.8
通讯作者：
Rebecca D. Büchner;Andreas G. Klein
中科院分区：
文献类型：
--
作者：
Rebecca D. Büchner;Andreas G. Klein
摘要：为了评估线性结构方程建模 (SEM) 中的模型拟合度，已经开发了几种使用无约束均值和协方差结构的拟合度量，但不能轻易应用于具有二次效应和交互效应的 SEM。在本文中，我们提出了新颖的准似然比检验（Q-LRT）来评估非线性 SEM 模型的全局拟合。 Q-LRT 基于准最大似然法的简化，用于模型参数的估计。一项有关男性衰老的研究中的数据证明了 Q-LRT 的实证应用。蒙特卡洛研究的结果表明，当样本量足够大时，Q-LRT 的性能可靠。此外，模拟表明 Q-LRT 对于适度倾斜的潜在外生变量具有鲁棒性。
Abstract For the assessment of model fit in linear structural equation modeling (SEM), several fit measures have been developed that use an unconstrained mean and covariance structure, but cannot be readily applied to SEM with quadratic and interaction effects. In this article, we propose the novel quasi-likelihood ratio test (Q-LRT) to evaluate global fit of nonlinear SEM models. The Q-LRT is based on a simplification of the quasi-maximum likelihood method for the estimation of model parameters. An empirical application of the Q-LRT is demonstrated for data in a study about aging in men. Results from a Monte Carlo study show that the Q-LRT performs reliably when sample size is sufficiently large. Also, simulations suggest robustness of Q-LRT for moderately skewed latent exogenous variables.