Adjusting Incremental Fit Indices for Nonnormality
Adjusting Incremental Fit Indices for Nonnormality
复制标题
DOI：
10.1080/00273171.2014.933697
复制
发表时间：
2014-01-01
影响因子：
3.8
通讯作者：
Savalei, Victoria
中科院分区：
文献类型：
--
作者：
Brosseau-Liard, Patricia E.;Savalei, Victoria
在结构方程建模中，通常使用各种指标来评估模型拟合。然而，从正常理论最大似然拟合函数得到的拟合指数受数据中存在的非正态性的影响。我们提出了一个非正态性校正2常用的增量拟合指数，比较拟合指数和Tucker-Lewis指数。该校正使用Satorra-Bentler标度常数来修改这些拟合指数的样本估计值，但不影响总体值。我们认为，这种类型的非正态校正是上级的校正，改变人口的拟合指数在一些软件程序中实现的值。在模拟研究中，我们证明了我们的校正在各种样本量，模型类型和错误指定类型中表现良好。
A variety of indices are commonly used to assess model fit in structural equation modeling. However, fit indices obtained from the normal theory maximum likelihood fit function are affected by the presence of nonnormality in the data. We present a nonnormality correction for 2 commonly used incremental fit indices, the comparative fit index and the Tucker-Lewis index. This correction uses the Satorra-Bentler scaling constant to modify the sample estimate of these fit indices but does not affect the population value. We argue that this type of nonnormality correction is superior to the correction that changes the population value of the fit index implemented in some software programs. In a simulation study, we demonstrate that our correction performs well across a variety of sample sizes, model types, and misspecification types.