阿卡克信息准则
多向拉希模型
贝叶斯信息准则
班级(哲学)
贝叶斯概率
选型
计算机科学
集合(抽象数据类型)
项目反应理论
选择(遗传算法)
统计
数据挖掘
数学
人工智能
机器学习
心理测量学
程序设计语言
作者
Xuliang Gao,Wenchao Ma,Daxun Wang,Yan Cai,Dongbo Tu
标识
DOI:10.3102/1076998620951986
摘要
This article proposes a class of cognitive diagnosis models (CDMs) for polytomously scored items with different link functions. Many existing polytomous CDMs can be considered as special cases of the proposed class of polytomous CDMs. Simulation studies were carried out to investigate the feasibility of the proposed CDMs and the performance of several information criteria (Akaike’s information criterion [AIC], consistent Akaike’s information criterion [CAIC], and Bayesian information criterion [BIC]) in model selection. The results showed that the parameters of the proposed CDMs could be recovered adequately under varied conditions. In addition, CAIC and BIC had better performance in selecting the most appropriate model than AIC. Finally, a set of real data was analyzed to illustrate the application of the proposed CDMs.
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