用单级模型对多层次数据进行多次归算:采用调整组平均值的完全有条件规范方法.
Simon Grund1, Oliver Lüdtke2,3, Alexander Robitzsch2,3
1University of Hamburg, Hamburg, Germany. simon.grund@uni-hamburg.de.
Behavior research methods
|March 2, 2026
概括
一种新的调整组平均值 (AGM) 方法用于多级多重归算 (MI) 有效地处理复杂设计中的缺失数据. 这种方法提供了可靠的结果,甚至在具有挑战性的应用中表现优于传统的多层MI.
科学领域:
- 统计 统计 统计 统计
- 多层次建模多层次建模
- 数据分析 数据分析
背景情况:
- 缺失的数据在多层设计中带来了重大挑战.
- 多层次多重归算 (MI) 是一种常见的技术,但传统方法可能不稳定,多层次结构不那么明显.
- 现有的方法在多层结构不是主要关注的实际应用中难以获得可靠性.
研究的目的:
- 引入一种新的全条件规范 (FCS) 方法,用于使用组平均 (GM) 或调整组平均 (AGM) 的多级MI.
- 在各种场景中评估基于FCS的多层MI方法 (包括GM和AGM) 的性能.
- 将这些新方法的有效性与传统的多层MI方法进行比较.
主要方法:
- 开发了一种针对多层MI的完全条件规范 (FCS) 方法.
- 组合单级归算方法与组平均值 (GM) 或调整组平均值 (AGM).
- 在平衡和不平衡的设计中进行了理论调查和多次模拟研究,变量数量不同.
主要成果:
- 调整后的小组平均值 (AGM) 方法在大多数调查的场景中表现出强的表现.
- 在具有挑战性的应用中,AGM方法的性能优于传统的多层MI方法.
- 群体平均 (GM) 方法并不总是提供可靠的结果.
结论:
- 调整组平均值 (AGM) 方法为处理多层设计中缺少的数据提供了强大而可靠的解决方案,特别是在复杂或不太明显的多层结构中.
- 这种基于FCS的方法为传统的多层MI提供了有价值的替代方案,增强了数据分析实践.
- 该研究提供了实际实施指导,并强调了研究环境中统计分析的含义.
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