当前位置: X-MOL 学术Struct. Equ. Model. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Chi-square Difference Tests for Comparing Nested Models: An Evaluation with Non-normal Data
Structural Equation Modeling: A Multidisciplinary Journal ( IF 6 ) Pub Date : 2020-02-12 , DOI: 10.1080/10705511.2020.1717957
Goran Pavlov 1, 2 , Dexin Shi 1 , Alberto Maydeu-Olivares 1, 2
Affiliation  

ABSTRACT The relative fit of two nested models can be evaluated using a chi-square difference statistic. We evaluate the performance of five robust chi-square difference statistics in the context of confirmatory factor analysis with non-normal continuous outcomes. The mean and variance corrected difference statistics performed adequately across all conditions investigated. In contrast, the mean corrected difference statistics required larger samples for the p-values to be accurate. Sample size requirements for the mean corrected difference statistics increase as the degrees of freedom for difference testing increase. We recommend that the mean and variance corrected difference testing be used whenever possible. When performing mean corrected difference testing, we recommend that the expected information matrix is used (i.e., choice MLM), as the use of the observed information matrix (i.e., choice MLR) requires larger samples for p-values to be accurate. Supplementary materials for applied researchers to implement difference testing in their own research are provided.

中文翻译:

用于比较嵌套模型的卡方差异检验:非正态数据的评估

摘要 两个嵌套模型的相对拟合可以使用卡方差异统计量进行评估。我们在具有非正态连续结果的验证性因素分析的背景下评估了五个稳健的卡方差异统计量的性能。均值和方差校正的差异统计在所有调查的条件下都充分执行。相比之下,平均校正差异统计需要更大的样本才能使 p 值准确。随着差异检验的自由度增加,对平均校正差异统计量的样本大小要求也会增加。我们建议尽可能使用均值和方差校正的差异检验。在进行均值校正差异检验时,我们建议使用预期信息矩阵(即选择 MLM),因为使用观察到的信息矩阵(即选择 MLR)需要更大的样本才能使 p 值准确。为应用研究人员在自己的研究中进行差异检验提供了补充材料。
更新日期:2020-02-12
down
wechat
bug