哪种方法提供更高的信号噪声比:结构方程建模或使用加权复合材料的回归分析?
Ke-Hai Yuan1,2, Yongfei Fang3
1University of Notre Dame, Notre Dame, Indiana, USA.
The British journal of mathematical and statistical psychology
|October 3, 2023
概括
使用加权复合材料的回归分析为具有测量误差的观测数据提供了比基于共差的结构方程建模 (CB-SEM) 更高的信号噪声比率. 这种方法提供了更高效的参数估计,挑战了对CB-SEM的传统偏好.
科学领域:
- 统计 统计 统计 统计
- 计量经济学 计量经济学
- 心理测量 心理测量 心理测量
- 社会科学研究方法 社会科学研究方法
背景情况:
- 观测数据经常包含测量错误,影响统计分析.
- 基于共差的结构方程建模 (CB-SEM) 可以建模测量误差,但可能不是最佳的预测.
- 使用加权复合物的回归分析通常用于预测,但可以产生具有错误预测因子的减弱系数.
研究的目的:
- 挑战传统观点,即CB-SEM在分析具有测量错误的观测数据方面优越.
- 证明通过加权复合材料的回归分析可以产生更高效的参数估计和更高的信号噪声比 (SNR).
- 为了在各种条件下比较最小平方 (LS) 回归与加权复合材料与CB-SEM的性能.
主要方法:
- 数学推导比较LS回归的SNR与加权复合材料和CB-SEM.
- 数字模拟用于评估不同数据条件下的性能.
- 经验数据分析以验证理论发现.
主要成果:
- 使用同等加权复合材料的LS回归产生了数学上比CB-SEM更大的SNR,当预测项是并行的,即使有正确的模型规范.
- 用加权复合物的LS回归在许多场景中表现与CB-SEM的正常最大概率相似或更好,包括多变量正常分布.
- 考虑到复合重量的采样错误,进一步提高了LS回归系数的效率.
结论:
- 通过加权复合材料进行回归分析,特别是使用LS方法,是分析具有测量误差的观测数据的高效方法.
- 与CB-SEM相比,这种方法在参数估计效率和SNR方面具有优势,与常见假设相反.
- 这些发现建议重新评估支持CB-SEM的所有观测数据分析的标准实践,特别是当预测是目标时.
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