在认知模型中评估参数估计的稳定性:在估计方法的多元宇宙中对多项处理树模型的元分析性审查
Henrik Singmann1, Daniel W Heck2, Marius Barth3
1Department of Experimental Psychology, University College London.
Psychological bulletin
|June 27, 2024
概括
数据分析的选择影响认知模型的结果. 对于多项处理树 (MPT) 模型的部分聚合方法在不同的统计框架中提供了最强大和最一致的参数估计.
科学领域:
- 认知心理学 认知心理学
- 计算神经科学是一种神经科学.
- 心理测量方法 心理测量方法
背景情况:
- 数据分析决策显著影响认知建模中的研究成果.
- 多项式处理树 (MPT) 模型被广泛用于分析心理学中的分类数据.
- 选择数据聚合和统计框架可以创建估计方法的"多元".
研究的目的:
- 在不同的数据聚合和统计框架下系统地检查MPT模型参数估计的稳定性.
- 量化流行MPT模型的各种估计方法之间的差异.
- 确定解释参数估计差异的调节者.
主要方法:
- 在164个已发表的数据集中,对来自13,956名参与者的数据进行了元分析合成.
- 从使用频率主义和贝叶斯框架的九个流行的MPT模型中分析参数估计.
- 完全聚合,不聚合和部分聚合数据聚合策略的比较.
主要成果:
- 估计方法之间的平均绝对差异很小 (<.04),但有些案例显示出实质性的差异 (高达.97).
- 偏差部分是由特定的MPT模型参数和估计不确定性解释的.
- 部分聚合方法始终显示出最少的分歧,这表明它们是可靠的违约.
结论:
- 部分聚合成为MPT模型估计的强大默认策略,最大限度地减少分歧.
- 通过仔细考虑数据分析选择,可以提高认知建模的透明度和稳定性.
- 该研究为评估方法论决策对认知模型参数的影响提供了一个框架.
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