根据加权分数物流模型及其模拟研究,估计和拟合非连续类别的多种物品得分
Xiaozhu Jian1, Buyun Dai2, Yeqi Qing3
1Department of Psychology, School of Public Policy and Management, School of Economics and Management, Nanchang University, Nanchang, China.
Applied psychological measurement
|February 2, 2026
概括
本研究介绍了加权得分后勤模型 (WSLM) 用于分析复杂的测试项目. 模拟显示WSLM准确地估计了项目参数,并在教育和心理测试中很好地适应数据.
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:
- 传统的物流模型在与非连续的,多种类型的项目分数作斗争.
- 精确分析复杂的评分在教育和心理评估中至关重要.
研究的目的:
- 介绍和评估物流模型的新扩展,加权得分物流模型 (WSLM).
- 评估WSLM在分析具有非连续分数的多种项目的表现.
主要方法:
- 开发了加权得分后勤模型 (WSLM),其中包含一个加权得分参数.
- 用于项目参数 (难度和歧视) 的边际最大概率估计.
- 进行了蒙特卡洛模拟研究,以评估WSLM的性能和模型数据的合适性.
主要成果:
- WSLM 证明了对项目参数恢复的低偏差和根平均平方误差 (RMSE).
- 合适统计数据 (Q1,Q4) 一般保持在临界值以下,表明可接受的模型数据合适.
- 该模型在各种模拟条件下被证明是稳健的.
结论:
- WSLM是一种可行的和强大的统计工具,用于分析具有复杂分数方案的多种项目.
- 结果支持WSLM在实际教育和心理评估环境中的适用性.
- 该模型为特定物品类型提供了与传统二分制物流模型相比的进步.
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