在低可靠性设置中估计相关性,使用受约束的等级模型
Mahbod Mehrvarz1, Jeffrey N Rouder2
1Department of Cognitive Sciences, University of California, 92697, Irvine, CA, USA. mehrvarm@uci.edu.
Behavior research methods
|January 17, 2025
概括
层次模型在认知任务中的相关性估计提高了高达43%. 这种方法通过对贝叶斯系数模型施加约束,提高了可靠性在可靠性较低的实验环境中的可靠性.
科学领域:
- 认知心理学 认知心理学
- 心理测量 心理测量 心理测量
- 计算神经科学是一种神经科学.
背景情况:
- 研究认知中的个体差异依赖于分析实验任务之间的相关性.
- 实验任务的可靠性较低,由于效应小,试验差异较高,阻碍了准确的相关性估计.
研究的目的:
- 研究分层建模在提高认知实验中相关性估计的准确性方面的有效性.
- 开发用于嵌套实验数据的新贝叶斯等级因子模型.
主要方法:
- 利用贝叶斯的层次因素模型,在试验,条件,任务和个人层面上单独建模变化.
- 引入了跨任务的先前共变性上的约束,特别是一个低维系数结构和非负负载,形成一个正分列.
主要成果:
- 当实质性约束被施加时,等级模型在相关性估计中的误差降低了高达43%.
- 不受约束的先验结果导致最小的错误减少,突出了模型约束的重要性.
结论:
- 约束的贝叶斯层次因素模型为估计低可靠性认知任务的相关性提供了显著的改进.
- 对于认知领域来说,低维因子结构和非负载的假设是合理的,这使得这种方法对研究人员有价值.
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