在集群数据中聚合较低水平预测者的实践:反思反射变量
Timothy R Konold1, Elizabeth A Sanders2
1School of Education and Human Development, University of Virginia.
Psychological methods
|August 7, 2025
概括
多级建模中的明显聚合可能会对形成变量的结果产生偏差. 这项研究表明,什么时候显式聚合适用于反射变量,避免潜变量建模问题.
科学领域:
- 心理测量 心理测量 心理测量
- 多层次建模多层次建模
- 统计方法 统计方法
背景情况:
- 多层次建模 (MLM) 通过对人和物品分数的明显聚合,可以偏向形成变量的回归系数.
- 隐性变量建模提供了解决方案,但也可能带来融合和识别挑战.
研究的目的:
- 调查多级建模中反射变量显式聚合的适当性.
- 确定明显聚合是L2反射变量潜变量建模的可行替代方案的条件.
主要方法:
- 使用基于人口公式的计算.
- 采用蒙特卡洛模拟来评估明显聚合的条件.
主要成果:
- 证明了明显聚合适用于L2反射变量的特定条件.
- 展示了明显聚合如何绕过与潜在变量模型相关的收和识别问题.
结论:
- 在某些条件下,明显聚合可以可靠地用于L2反射变量,简化分析.
- 当特定的理论或数据结构需要时,研究人员应该考虑隐性聚合.
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