Humphry Stephen
Graduate School of Education, The University of Western Australia, Crawley, WA 6009, Australia.
J Appl Meas. 2012;13(2):165-80.
The aim is to show that it is possible to parameterize discrimination for sets of items, rather than individual items, without destroying conditions for sufficiency in a form of the Rasch model. The form of the model is obtained by formalizing the relationship between discrimination and the unit of a metric. The raw score vector across item sets is the sufficient statistic for the person parameter. Simulation studies are used to show the implementation of conditional estimation solution equations based on the relevant form of the Rasch model. The model also applied to two numeracy tests attempted by a group of common persons in a large-scale testing program. The results show improved fit compared with the Rasch model in its standard form. They also show the units of the scales were more accurately equated. The paper discusses implications for applied measurement using Rasch models and contrasts the approach with the application of the two parameter logistic (2PL) model.
目的是表明，有可能对项目集而非单个项目的区分度进行参数化，同时又不会破坏拉施模型形式下的充分性条件。该模型的形式是通过形式化区分度与度量单位之间的关系而获得的。跨项目集的原始分数向量是人员参数的充分统计量。模拟研究用于展示基于拉施模型相关形式的条件估计求解方程的实现。该模型还应用于一组普通人在大规模测试项目中尝试的两项算术测试。结果表明，与标准形式的拉施模型相比，拟合度有所提高。结果还表明，量表的单位得到了更准确的等值。本文讨论了拉施模型在应用测量中的意义，并将该方法与双参数逻辑斯蒂（2PL）模型的应用进行了对比。
