历史测量信息可用于改进结构方程模型中结构参数的估计,使用小样本
James Ohisei Uanhoro1, Olushola O Soyoye2
1University of North Texas, Denton, USA.
Educational and psychological measurement
|June 19, 2025
概括
在贝叶斯结构方程建模 (BSEM) 中将历史测量数据作为 priors 纳入,可以改善结构参数估计,特别是在心理学研究中常见的小相关性.
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:
- 小样本大小往往会阻碍结构方程模型 (SEM) 中准确的参数估计.
- 发表的因子分析结果提供了有价值的历史测量信息 (例如,负载,标准误差).
研究的目的:
- 调查将历史测量信息作为小样本在贝叶斯式SEM (BSEM) 中的信息先验的实用性.
- 为了提高结构参数的估计,特别是两个结构之间的相关性,当测量错误导致偏差时.
主要方法:
- 使用贝叶斯式SEM (BSEM) 来估计构造之间的相关性.
- 生成的数据模拟了由于测量错误导致的总分数的皮尔森相关性中的偏差.
- 纳入历史测量信息作为BSEM中的信息先验.
主要成果:
- 使用历史数据作为先验改进了相关性估计,特别是对于小的真相关性.
- 来自元分析估计的预测显示出高准确性和可接受的覆盖范围.
- 对所有参数的信息性较弱的先验对于较大的真相关性是最佳的.
结论:
- 在BSEM中利用历史测量信息可以显著提高在小样本中的结构参数估计.
- 这种方法提供了一种实际的解决方案,可以在处理测量错误和有限数据时提高模型的准确性.
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