The maximum priority index method for severely constrained item selection in computerized adaptive testing.
This paper introduces a new heuristic approach, the maximum priority index (MPI) method, for severely constrained item selection in computerized adaptive testing. Our simulation study shows that it is able to accommodate various non-statistical constraints simultaneously, such as content balancing, exposure control, answer key balancing, and so on. Compared with the weighted deviation modelling method, it leads to fewer constraint violations and better exposure control while maintaining the same level of measurement precision.
Department of Psychology, University of Notre Dame, Notre Dame, USA. ycheng6@uiuc.edu
Ying
Y
Cheng
Hua-Hua
HH
Chang
D001077:Aptitude Tests / Q000706:statistics & numerical data*; D003936:Diagnosis, Computer-Assisted / Q000706:statistics & numerical data*; D004521:Educational Measurement / Q000706:statistics & numerical data*; D006801:Humans; D008432:Mathematical Computing; D015233:Models, Statistical; D010556:Personality Tests / Q000706:statistics & numerical data*; D011594:Psychometrics / Q000706:statistics & numerical data*; D015203:Reproducibility of Results; D012984:Software