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我们可以从对象响应和响应时间的半参数因子分析中学到什么? 一个插图与PISA 2015数据

Yang Liu¹, Weimeng Wang²

¹Department of Human Development and Quantitative Methodology, University of Maryland, 3304R Benjamin Bldg, 3942 Campus Dr, College Park, MD, 20742, USA. yliu87@umd.edu.

|November 16, 2023

概括

结合项目响应和响应时间 (RT) 分析,提高了能力得分的精度. 一个半参数模型显示了能力和速度之间的强烈,非线性联系,提高了教育评估中的得分准确性.

科学领域:

教育测量教育的测量
心理测量心理测量心理测量
数据分析数据分析

背景情况:

传统的项目响应理论 (IRT) 模型仅使用响应数据来进行能力评分.
建议对反应和响应时间 (RT) 进行联合因子分析,以提高能力得分的精度.
通常使用简单的结构因子模型,但可能是限制性和不合适的.

研究的目的:

评估一个半参数简单结构模型,用于对项目响应和RT的联合分析.
当参数假设放松时,评估一个简单的因子结构的合适性.
为了研究潜伏能力和响应速度之间的关系.

主要方法:

对2015年国际学生评估计划 (PISA) 数学数据的分析.
应用一个半参数的简单结构因子模型.
在RT的测量模型中放松参数假设.

主要成果:

一个简单的因子结构在放松参数假设后实现了与数据的良好匹配.
潜伏能力和速度/缓慢之间的关联被发现是强烈的,但非线性.
半参数模型证明了提高能力得分精度的潜力.

结论:

半参数建模提供了一种灵活的方法,用于对响应和RT的联合因子分析.
放松限制性参数假设可以提高简单的因子结构的适应性.
对非线性能力-速度关系的准确建模对于精确的能力估计至关重要.

关键词:

在 PISA 测试中,这是一个bootstrap系统.这里是Copula copula.进行交叉验证.立方线是指立方线的线条.在因子分析的过程中,因素分析.项目响应理论是物品响应理论.当地的独立性当地的独立性适合模型适合模型适合被处罚的最大概率是最大的概率.响应时间响应时间

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