在探索性因子分析中通过对外部变量进行回归来识别因子得分
1Faculty of Sociology, Kansai University, Suita, Osaka, Japan.
Psychometrika
|June 16, 2025
概括
本研究引入了新的基于回归的因子探索 (RFE) 和基于集群的因子探索 (CFE) 方法,以独特地确定因子分析 (FA) 模型中的因子得分,提高准确性和效率.
科学领域:
- 统计 统计 统计 统计
- 心理测量 心理测量 心理测量
- 数据分析 数据分析
背景情况:
- 因子分析 (FA) 模型的特点是因子得分不确定性.
- 现有的方法在因子得分和参数估计的独特确定方面扎.
研究的目的:
- 引入基于回归的因子探索 (RFE) 来确定独特的因子得分.
- 开发基于集群的因子探索 (CFE) 作为RFE的变体,以改善集群.
- 评估拟议方法的性能和效率.
主要方法:
- 使用调参数,RFE最大限度地减少了平衡FA和多变量回归的损失函数.
- CFE将RFE因因数分数聚类的处罚术语概括为.
- 模拟研究和真实数据示例用于评估.
主要成果:
- RFE 独特地同时确定因子得分和估计 FA 参数.
- 与现有方法相比,CFE在创建集群结构方面表现出更高的准确性.
- 提出的方法准确地从错误污染的数据中恢复参数矩阵,计算成本较低.
结论:
- RFE和CFE为FA的因子得分不确定性提供了新的解决方案.
- 这些方法提供了可解释的结果,并且与现有的因子评分估计技术相关.
- 这些程序提高了因子分析的准确性和计算效率.
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