An IRtree Model for Aberrant Response and Missing Data

In standardized tests, examinees are likely to engage in either one or more following test behaviors: solution behavior, rapid guessing behavior, cheating behavior, nonresponse behavior, etc. Examinees do not always response all items with solution behavior due to various reasons (such as time constraint or low motivation). Aside from solution behavior, rapid guessing, cheating or nonresponse behavior can result in aberrant responses and inaccurate estimates of examinees' ability or trait, as well as item parameters, thus undermining the validity and fairness of the test. To address this issue, this paper aims to propose an IRTree model to that simultaneously considers rapid guessing, cheating and nonresponse behaviors in order to model the various behaviors exhibited by examinees. The proposed model offers a notable improvement over previous studies, as it provides additional classifications for examinee behaviors at both item and examinee levels. Furthermore, it is the first model to separate and simultaneously model guessing and cheating. Two real data sets are utilized to demonstrate the reasonableness and superiority of the proposed model. Subsequently, two simulation studies based on these real data sets are conducted to validate, revealing that it provide more precise estimates of person and item parameters compared to existing models, and explored the boundary condition of model application.