基于组件的结构方程建模的探索程序,用于通过同时旋转来模拟简单结构的结构方程
1Faculty of Sociology, Kansai University, Osaka, Japan. nyam@kansai-u.ac.jp.
Psychometrika
|December 12, 2023
概括
探索性通用结构化组件分析 (EGSCA) 为探索变量和组件之间的关系提供了一种新方法. 与现有的程序相比,这种先进的技术可以改进模型探索和参数恢复.
科学领域:
- 统计 统计 统计 统计
- 量化心理学 量化心理学
- 数据分析 数据分析
背景情况:
- 一般化结构化组件分析 (GSCA) 是一种确认方法,用于检查观察到的变量和组件之间的关系.
- 现有的探索性SEM方法缺乏GSCA的基于组件的方法.
- 需要GSCA的探索性版本来促进模型发现.
研究的目的:
- 提出和开发一个探索版本的一般化结构化组件分析 (EGSCA).
- 为EGSCA引入一种新型的旋转算法,该算法旨在同时简化参数矩阵.
- 评估EGSCA在恢复真实参数值方面的表现及其在模型探索中的有效性.
主要方法:
- 在GSCA中理论证明直角旋转的不确定性,作为EGSCA的理由.
- 关于EGSCA程序的开发和介绍.
- 实施EGSCA的专用旋转算法.
- 进行两项数值模拟研究以评估参数恢复.
- 应用EGSCA到两个现实世界的数据集.
主要成果:
- 通过使用拟议的旋转,EGSCA在模拟研究中成功恢复了真参数矩阵.
- 与现有的GSCA程序相比,EGSCA在模拟中表现出更高的性能.
- 对真实数据集的分析表明,EGSCA衍生模型是对以前提出的模型的改进.
结论:
- 在结构方程建模中,EGSCA为探索性分析提供了一种有效的方法.
- 拟议的旋转算法增强了EGSCA.内的参数矩阵的简化.
- 埃格斯卡在改进模型探索和揭示数据中的复杂关系方面显示出巨大的潜力.
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