title: Maximum information stratification method for controlling item exposure in computerized adaptive testing.
abstract: The proposal for increasing the security in Computerized Adaptive Tests that has received most attention in recent years is the a-stratified method (AS - Chang and Ying, 1999): at the beginning of the test only items with low discrimination parameters ( a ) can be administered, with the values of the a parameters increasing as the test goes on. With this method, distribution of the exposure rates of the items is less skewed, while efficiency is maintained in trait-level estimation. The pseudo-guessing parameter ( c ), present in the three-parameter logistic model, is considered irrelevant, and is not used in the AS method. The Maximum Information Stratified (MIS) model incorporates the c parameter in the stratification of the bank and in the item-selection rule, improving accuracy by comparison with the AS, for item banks with a and b parameters correlated and uncorrelated. For both kinds of banks, the blocking b methods (Chang, Qian and Ying, 2001) improve the security of the item bank.
authors.affiliation: Facultad de Psicología, Universidad Autónoma de Madrid, Spain. juanra.barrada@uam.es
authors.forename: Juan Ramón
authors.initials: JR
authors.lastname: Barrada
authors.forename: Paloma
authors.initials: P
authors.lastname: Mazuela
authors.forename: Julio
authors.initials: J
authors.lastname: Olea
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016009:Chi-Square Distribution; D006801:Humans; D016013:Likelihood Functions; D008838:Microcomputers; D011581:Psychological Tests; D012985:Software Design