A Fit Index to Assess Model Fit and Detect Omitted Terms in Nonlinear SEM
A Fit Index to Assess Model Fit and Detect Omitted Terms in Nonlinear SEM
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DOI：
10.1080/10705511.2016.1268923
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发表时间：
2017-05-01
影响因子：
6
通讯作者：
Schermelleh-Engel, Karin
中科院分区：
文献类型：
--
作者：
Gerhard, Carla;Buechner, Rebecca D.;Schermelleh-Engel, Karin
提出了一种新的描述性拟合测度——均方差拟合指数（HFI），通过分析模型结构部分残差的离散性来检测扫描电镜中遗漏的非线性项（二次项和相互作用项）。HFI被定义为SEM的描述性拟合优度指数。在蒙特卡罗研究中，研究了HFI的I型错误率和由于遗漏非线性项或非正态分布变量而检测异方差的能力。结果表明，当样本量足够大时，新方法在ⅰ类误差率和功率方面表现满意。研究了在什么条件下第一类错误率被夸大。非正态分布的误差项导致了高功率。非正态分布的预测因子对I型错误率没有影响。
A new descriptive fit measure, the Homoscedastic Fit Index (HFI), is proposed to detect omitted nonlinear terms (quadratic and interaction terms) in SEM by analyzing the dispersion of the residuals in the structural part of the model. The HFI is defined as a descriptive goodness-of-fit index for SEM. The Type I error rates of the HFI and the power to detect heteroscedasticity due to omitted nonlinear terms or nonnormally distributed variables are investigated in a Monte Carlo study. The results show that the new measure performs satisfactorily with regard to Type I error rates and power when sample size was sufficiently large. It is investigated under what conditions the Type I error rate was inflated. Nonnormally distributed error terms resulted in high power. Nonnormally distributed predictors had no influence on the Type I error rates.