一个模型可能不适合所有:使用基于模型的递归分区进行子组检测
Marjolein Fokkema1, Mirka Henninger2, Carolin Strobl3
1Leiden University, Room number 3B20, Wassenaarseweg 52, 2333 AK Leiden, The Netherlands.
Journal of school psychology
|April 3, 2025
概括
基于模型的递归分区 (MOB) 在参数模型中识别出具有不同效果的子组. 这种方法,用通用线性混合模型和项目响应理论来证明,有助于理解教育研究中的异质性.
科学领域:
- 统计 统计 统计 统计
- 教育测量教育的测量
- 心理测量 心理测量 心理测量
背景情况:
- 基于模型的递归分区 (MOB) 提供了一个灵活的框架来分析参数模型中的异质性.
- 在干预和教育研究等领域,检测和解释子组差异至关重要.
- 现有的方法可能无法完全捕捉混合效应或物品响应理论模型中的复杂异质性模式.
研究的目的:
- 引入一般的基于模型的递归分区 (MOB) 框架.
- 为了说明在教育研究中应用基于MOB的方法来检测和解释广义线性混合模型 (GLMM) 和物品响应理论 (IRT) 框架中的异质性.
- 为了证明GLMM树和Rasch树对于子组检测的实用性.
主要方法:
- 该研究使用基于模型的递归分区 (MOB) 作为一般框架.
- 应用特定的MOB扩展,GLMM树和Rash树来分析异质性.
- 基于差异物品功能 (DIF) 效果大小的Rasch树,采用了一个新的停止标准.
主要成果:
- 在混合效果模型中,GLMM树成功地识别了具有不同参数的子组,应用于纵向Head Start数据,以揭示性能增益变化.
- 在IRT模型中,Rasch树检测了在IRT模型中显示差异性项目功能 (DIF) 的子组,这突显了在组比较之前进行DIF分析的重要性.
- 新的停止标准有效地指导了基于DIF效应大小的子组检测.
结论:
- 在教育研究中,MOB提供了一种强大而通用的工具,用于发现和解释子组异质性.
- GLMM树和Rasch树是MOB的有效扩展,用于分析混合效应模型和IRT中的复杂数据结构.
- 集成基于DIF的停止标准增强了Rasch树的实际应用,以进行强大的子组分析.
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