在结构方程建模中的案例影响的无模型测量
Fathima Jaffari1, Jennifer Koran2
1Department of Tests and Measurement, National Center for Assessment, Education and Training Evaluation Commission (ETEC), Riyadh, Saudi Arabia.
Frontiers in psychology
|February 21, 2024
概括
一个新的无模型测量方法,删除-一-共变余 (DOCR),在结构方程建模中表现优于传统方法. 然而,DOCR对小样本大小敏感,需要更大的数据集才能获得可靠的结果.
科学领域:
- 统计 统计 统计 统计
- 量化心理学 量化心理学
- 计量经济学 计量经济学
背景情况:
- 在结构方程建模 (SEM) 中常用的案例影响措施是基于模型的.
- 基于模型的测量容易受到模型错误规格的错误的影响.
- 要克服这些局限性,需要一个无模型的案例影响度.
研究的目的:
- 引入一种新的无模型病例影响量,即删除-一个-共变余量 (DOCR).
- 将DOCR的性能与已建立的测量方法 (如Mahalanobis距离 (MD) 和通用库克距离 (gCD)) 相比进行评估.
主要方法:
- 在不同的条件下模拟数据:样本大小,目标病例与非目标病例的比例,以及生成数据的模型类型.
- 对DOCR,MD和gCD在识别有影响力的病例方面的比较分析.
主要成果:
- 与MD和gCD相比,DOCR在模拟条件下在识别目标病例方面普遍表现出优异的表现.
- 在小样本大小的情况下,DOCR的表现不令人满意,这表明对样本大小限制的敏感性.
结论:
- 在SEM中,DOCR是一个有前途的无模型替代方案,用于SEM中的案例影响分析.
- 研究人员应谨慎使用DOCR,采用足够大的样本大小 (理想情况下不超过600),以确保可靠的结果.
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