联合建模与通用项目响应理论模型家族和响应时间模型:增强模型结构灵活性和数据适应性
Jing Lu1, Xue Wang2, Jiwei Zhang3
1School of Mathematics and Statistics, Key Laboratory of Applied Statistics of MOE, Key Laboratory of Big Data Analysis of Jilin Province, Northeast Normal University, Changchun, Jilin, China.
Behavior research methods
|November 3, 2025
概括
本研究介绍了一种联合层次模型,将项目响应理论 (IRT) 和响应时间 (RT) 模型结合起来. 新模型提高了潜在特征估计的准确性,并为分析受试者表现提供了更好的模型适应性.
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
- 统计建模 统计建模
背景情况:
- 传统的层次模型经常忽视响应时间 (RTs) 作为辅助数据.
- 整合RT可以提高物品响应理论 (IRT) 模型中隐性特征估计的准确性.
- 现有的模型可能无法完全捕捉受试者的速度和能力之间的复杂,非线性关系.
研究的目的:
- 提出一种新的联合等级模型,将IRT和日志正常RT模型整合起来.
- 通过结合RTs来提高潜在特征估计的准确性.
- 研究速度和能力之间的非线性关系,并使用灵活的链接函数优化IRT模型.
主要方法:
- 开发了一个联合的等级模型,将IRT和日志正常响应时间 (RT) 模型结合起来.
- 集成的概括的逻辑连接IRT与日志正常的随机二进制变速模型用于非线性关系.
- 探索了模型优化项目的相同和不同的链接函数.
- 利用贝叶斯模型比较来评估模型的合适性.
主要成果:
- 与传统模型相比,拟议的联合等级模型可以更准确地估计能力,项目难度和歧视参数.
- 贝叶斯模型比较表明,新的联合模型比现有的IRT和RT组合模型更适合.
- 该模型证明了有效性,特别是在显示对称和不对称链接函数的数据中.
结论:
- 联合层次模型在分析项目响应和响应时间方面取得了重大进展.
- 整合响应时间和灵活的链接功能可以提高心理测量估计的精度.
- 该方法通过对PISA 2015科学检查数据的全面分析来验证.
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