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Detecting Measurement Noninvariance with Continuous Indicators Using Three Different Statistical Methods under the Framework of Latent Variable Modeling
Structural Equation Modeling: A Multidisciplinary Journal ( IF 2.5 ) Pub Date : 2022-03-31 , DOI: 10.1080/10705511.2021.2021533
Mingcai Zhang 1 , Lihong Yang 2
Affiliation  

Abstract

Under the framework of Latent Variable Modelling, three statistical methods, namely, the free baseline method (FR), the Benjamini-Hochberg method (B–H), and the alignment method (AM), were applied to identify the noninvariant measurement parameters at the indicator level through a simulation study. Model noninvariance was manipulated through varying the locations, degrees and magnitudes of noninvariant parameters with different sample sizes. As a result, the B-H method was discovered to possess a higher power rate in detecting noninvariant parameters than the other two methods, and the AM method performed the best in controlling the type I error rates in the process of parameter estimation. Comparatively speaking, the FR method performed the worst in almost all situations due to the choice of a fixed noninvariant reference indicator. The suggestions on which method is better used for model noninvariance detection was discussed and elaborated.



中文翻译:

在潜变量建模框架下使用三种不同的统计方法检测连续指标的测量非不变性

摘要

在潜变量建模的框架下,应用自由基线法(FR)、Benjamini-Hochberg法(B-H)和对齐法(AM)三种统计方法来识别非不变测量参数。通过模拟研究的指标水平。通过改变具有不同样本大小的非不变参数的位置、程度和大小来操纵模型的非不变性。结果发现,BH方法在检测非不变参数方面比其他两种方法具有更高的功率率,而AM方法在参数估计过程中对I类错误率的控制效果最好。相对而言,由于选择了固定的非不变参考指标,FR 方法在几乎所有情况下的表现最差。

更新日期:2022-03-31
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