Considerations about expected a posteriori estimation in adaptive testing: adaptive a priori, adaptive correction for bias, and adaptive integration interval.
In a computerized adaptive test, we would like to obtain an acceptable precision of the proficiency level estimate using an optimal number of items. Unfortunately, decreasing the number of items is accompanied by a certain degree of bias when the true proficiency level differs significantly from the a priori estimate. The authors suggest that it is possible to reduced the bias, and even the standard error of the estimate, by applying to each provisional estimation one or a combination of the following strategies: adaptive correction for bias proposed by Bock and Mislevy (1982), adaptive a priori estimate, and adaptive integration interval.
Raiche
Gilles
G
Faculty of Science in Education, Department of Education and Pedagogy, University of Quebec at Montreal, C. P. 8888, Succ. Centre-ville, Montreal, QC H3C 3P8, Canada. raiche.gilles@uqam.ca
Blais
Jean-Guy
JG
D015982:Bias; D003201:Computers; D003627:Data Interpretation, Statistical; D015233:Models, Statistical