关于多项处理树模型的聚合不变性
Edgar Erdfelder1, Julian Quevedo Pütter2, Martin Schnuerch3
1Department of Psychology, School of Social Sciences, University of Mannheim, Room B 118, A5, 68159, Mannheim, Germany. erdfelder@uni-mannheim.de.
Behavior research methods
|October 14, 2024
概括
多项式处理树 (MPT) 模型可以在满足结构和实证聚合不变性的特定条件时可靠地分析聚合数据. 这确保了群体层面的MPT参数反映了个人的认知过程意义.
科学领域:
- 认知心理学 认知心理学
- 数学建模的数学建模
- 心理测量 心理测量 心理测量
背景情况:
- 多项式处理树 (MPT) 模型被广泛用于认知过程测量.
- 它们应用于聚合组数据,而不是个人数据是常见的,但引发了关于有效性的问题.
研究的目的:
- 确定MPT对汇总数据的分析有效的条件.
- 调查个人层面和总体层面的MPT模型之间的关系.
- 评估MPT分析的稳定性,当聚合不变性条件不完全满足时.
主要方法:
- 介绍MPT模型的结构和实证聚合不变性概念.
- 理论推导表明单个MPT模型在不变性下保持在总量水平.
- 模拟研究操纵样本大小,参数化和参数分布.
主要成果:
- 如果在结构和经验上聚合不变,MPT模型在个人层面上持有,在总体层面上也持有.
- 总的MPT参数相当于在不变性下单个参数的平均值.
- 如果符合先决条件,即使存在一些不变性违规,从总量数据中估计的MPT参数通常是可信的.
结论:
- 应用到聚合数据的MPT模型的有效性取决于结构和经验聚合不变性.
- 当这些条件满足时,聚合的MPT分析为认知过程提供了有意义的见解.
- 模拟结果支持MPT参数估计的总体可靠性从总体数据在广泛的条件下.
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