一个稀疏的隐性类模型,包含响应时间
Siqi He1, Steven Andrew Culpepper1, Jeffrey A Douglas1
1University of Illinois at Urbana-Champaign, Champaign, Illinois, USA.
The British journal of mathematical and statistical psychology
|December 26, 2024
概括
这项研究将响应时间 (RT) 集成到稀疏隐性类模型 (SLCM) 中,用于诊断建模. 增强框架提供了一种灵活的方法,可以在评估中共同分析项目响应和RT.
科学领域:
- 心理测量 心理测量 心理测量
- 教育测量教育的测量
- 心理评估 心理评估
背景情况:
- 诊断模型 (DM) 在评估中对潜在属性的分类至关重要.
- 将响应时间 (RT) 与DM集成,可以更深入地了解解决问题的行为.
- 现有的稀疏隐性类模型 (SLCM) 主要集中在项目响应上,对RT数据的探索有限.
研究的目的:
- 通过整合响应时间 (RT) 数据来扩展稀疏隐性类模型 (SLCM) 框架.
- 开发一个更灵活的模型,共同分析项目响应和RT,放松条件独立性假设.
- 将新型模型应用于人格评估,特别是费舍尔气质库存,并探索其实用性.
主要方法:
- 扩展稀疏隐性类型模型 (SLCM) 包括响应时间 (RT) 数据.
- 在RT和潜伏属性之间放松条件独立性假设,取决于个体速度.
- 开发和实施Gibbs采样算法用于参数估计.
主要成果:
- 拟议的扩展SLCM框架有效地将响应时间 (RT) 数据与项目响应集成在一起.
- 对"费舍尔气质清单"的应用为使用DM与RT进行人格评估提供了新的见解.
- 蒙特卡洛模拟证实了拟议的Gibbs采样算法的准确性和效率,用于参数估计.
结论:
- 扩展的SLCM提供了一种灵活而强大的方法,用于在诊断评估中共同建模项目响应和响应时间.
- 这种方法为人格评估提供了一个新的视角,可以在教育和心理测量中具有价值.
- 经过验证的吉布斯抽样算法确保了对拟议模型的可靠参数估计.
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