Wei Junhuan, Cai Yan, Tu Dongbo
School of Psychology, Jiangxi normal university, Nanchang, China.
Appl Psychol Meas. 2023 Jun;47(4):259-274. doi: 10.1177/01466216231165302. Epub 2023 Mar 17.
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.
为了更深入地了解个体的反应过程和认知过程，本研究针对由多项选择题和开放式问题组成的混合格式题目提出了三种混合顺序项目反应模型（MS-IRM），这些题目强调顺序反应过程并按顺序计分。相对于现有的多分类模型，如等级反应模型（GRM）、广义部分计分模型（GPCM）或传统顺序拉施模型（SRM），所提出的模型针对每个任务采用了适当的处理函数来改进传统的多分类模型。进行了模拟研究以考察所提出模型的性能，结果表明，在参数恢复和模型拟合方面，所有提出的模型均优于SRM、GRM和GPCM。通过使用2007年国际数学和科学趋势研究（TIMSS）的真实数据，展示了MS-IRM与传统模型相比的应用示例。
