在PLS-SEM中对一些缺失的数据处理进行实证比较
Lateef Babatunde Amusa1,2, Twinomurinzi Hossana1
1Centre for Applied Data Science, College of Business and Economics, University of Johannesburg, Johannesburg, South Africa.
PloS one
|January 19, 2024
概括
回归归算优于部分最小平方结构方程建模 (PLS-SEM) 中处理缺失数据的其他方法. 这种技术可以提高模型参数恢复和经验分析中的估计精度.
科学领域:
- 社会科学 社会科学 社会科学
- 统计 统计 统计 统计
- 信息系统信息系统信息系统
背景情况:
- 部分最小平方结构方程建模 (PLS-SEM) 广泛用于理论开发和测试中的因果预测建模.
- 缺少数据是经验研究中普遍存在的问题,影响了PLS-SEM分析的可靠性.
- 当前的PLS-SEM实践显示出对列表式删除和平均归算的强烈偏好,经常忽视更强大的方法.
研究的目的:
- 调查PLS-SEM中未充分利用的缺失数据归算技术的有效性.
- 为了比较回归归算法和预期最大化 (EM) 算法与传统方法 (平均归算,列表式删除) 的归算.
- 用著名的社会科学模型来评估这些方法.
主要方法:
- 使用蒙特卡洛模拟来评估不同的归算策略.
- 这项研究利用了两种已建立的社会科学模型:欧洲客户满意度指数 (ECSI) 和技术接受和使用统一理论 (UTAUT).
- 基于模型参数的恢复和参数估计的精度来评估性能.
主要成果:
- 回归归算表现出与平均归算和列表式删除相比更好的表现.
- 这种超越表现在模型参数的准确恢复和估计的精度上都很明显.
- 与回归归算法相对的预期最大化 (EM) 算法的性能并没有明确地被详细描述为优越,但被调查.
结论:
- 推回归归算法作为解决PLS-SEM研究中缺失值的更有效策略.
- 广泛采用回归归算法可以在利用PLS-SEM的社会科学研究中获得更准确,更可靠的结果.
- 对先进的归算方法的进一步研究对于推进PLS-SEM的方法严谨性至关重要.
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