如何提高回归因子得分预测器,当个人有不同的因素负载时
André Beauducel1, Norbert Hilger1, Anneke C Weide1
1University of Bonn, Bonn, Germany.
Educational and psychological measurement
|August 19, 2025
概括
这项研究引入了一种新的异质回归因子得分预测器 (HRFS),通过计算因子负载的个体差异来提高预测准确性,在异质性很大时优于传统方法.
科学领域:
- 心理测量 心理测量 心理测量
- 统计建模 统计建模
背景情况:
- 传统的因子模型假设个体之间的因子负载均.
- 忽视因子负载的个体差异可能会降低因子得分预测器的准确性.
研究的目的:
- 提出一种异质回归因子得分预测器 (HRFS),比传统的回归因子得分预测器 (RFS) 提供更大的确定性.
- 引入估计单个负载的方法和评估负载异质性的二项式试验.
主要方法:
- 估计单个因子负载的估计.
- 使用个人负载估计计算HRFS的计算.
- 一个双项测试,以确定基于负载异质性的HRFS是否合适.
主要成果:
- 模拟研究表明,HRFS在具有显著负载异质性的群体中比RFS产生更高的确定性.
- 一个经验示例显示,使用HRFS,情绪稳定因子的确定性得到改善.
结论:
- 当单个因子负载不同时,HRFS提供了更明确的因子得分预测.
- 拟议的方法和测试提供了一种实际方法来提高因子分析结果.
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