Bayesian Item Response Theory Models with Flexible Generalized Logit Links
报 告 人:: 陈明辉
报告地点:: 数学与统计学院403室
报告时间:: 2017年10月25日星期一16:00-17:00

In educational and psychological research, the logit and probit links are often used to fit the binary item response data. The appropriateness and importance of the choice of  links within the item response theory (IRT) framework has not been investigated yet. In this paper, we present a family of  IRT models with generalized logit links, which include the traditional logistic and normal ogive models as special cases. This family of models are flexible enough not only to adjust the item characteristic curve tail probability by two shape parameters but also to allow us to fit the same link or different links to different items within the IRT model framework. In addition, the proposed models are implemented in the Stan software to sample from the posterior distributions. Using readily available Stan outputs, the four Bayesian model selection criteria are computed for guiding  the choice of the links within the IRT model framework. Finally, a new Bayesian ranking method is introduced to rank item difficulties according to the posterior probabilities. A detailed analysis of the real reading assessment data is carried out to illustrate the proposed methodology.

发 布 人:科研助理 发布时间: 2017-12-19
陈明辉,教授,博士生导师, 累积发表论文340余篇,在JASA、Annals of statistics、JRSSB统计顶级杂志发表论文11篇。2018年国际贝叶斯协会的主席候选人,《Statistics and Its Interface》《Bayesian Analysis》主编,《Journal of the American Statistical Association》、《Lifetime Data Analysis》、《Lifetime Data Analysis》副主编。