多向拉希模型
拉什模型
项目反应理论
计算机科学
过程(计算)
任务(项目管理)
混合模型
功能(生物学)
统计
机器学习
人工智能
计量经济学
心理测量学
数学
管理
进化生物学
经济
生物
操作系统
作者
Junhuan Wei,Yan Cai,Dongbo Tu
标识
DOI:10.1177/01466216231165302
摘要
To provide more insight into an individual's response process and cognitive process, this study proposed three mixed sequential item response models (MS-IRMs) for mixed-format items consisting of a mixture of a multiple-choice item and an open-ended item that emphasize a sequential response process and are scored sequentially. Relative to existing polytomous models such as the graded response model (GRM), generalized partial credit model (GPCM), or traditional sequential Rasch model (SRM), the proposed models employ an appropriate processing function for each task to improve conventional polytomous models. Simulation studies were carried out to investigate the performance of the proposed models, and the results indicated that all proposed models outperformed the SRM, GRM, and GPCM in terms of parameter recovery and model fit. An application illustration of the MS-IRMs in comparison with traditional models was demonstrated by using real data from TIMSS 2007.
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