一致的因子得分回归:为未经纠正的因子得分回归提供更好的替代方案?
Jasper Bogaert1, Wen Wei Loh2, Yves Rosseel1
1Ghent University, Belgium.
Educational and psychological measurement
|January 9, 2026
概括
一致的因子得分回归 (cFSR) 为分析潜在变量关系提供了一个更简单,更准确的替代方法,而不是未经纠正的因子得分回归 (UFSR). 该方法提供了公正的估计和有效的推断,使其成为行为,教育和社会科学研究的理想选择.
科学领域:
- 行为科学 行为科学
- 教育科学教育科学教育科学
- 社会科学 社会科学 社会科学
背景情况:
- 结构方程建模 (SEM) 是潜在变量分析的标准.
- 使用共同的因子得分的未经纠正的因子得分回归 (UFSR) 可以导致偏差的估计和无效的推断.
- 在因子得分回归 (FSR) 中最近的进展旨在提高准确性.
研究的目的:
- 重新审视和评估一致因素得分回归 (cFSR).
- 将cFSR与其他FSR和SEM方法进行比较.
- 要突出cFSR在隐性变量分析方面的优势.
主要方法:
- 进行了广泛的模拟研究.
- 将cFSR与UFSR和其他FSR/SEM方法进行比较.
- 根据收率,偏差,效率和I型错误率来评估业绩.
主要成果:
- 与UFSR相比,cFSR表现出了优越的表现.
- cFSR保持了UFSR的概念简单性.
- cFSR提供了公正的估计和有效的推断.
结论:
- cFSR是行为,教育和社会科学研究人员推的UFSR替代方案.
- 研究人员应该采用cFSR而不是UFSR来进行更可靠的潜变量分析.
- cFSR提供了准确性和简单性的平衡,作为SEM的可行替代品.
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