Maximum information stratification method for controlling item exposure in computerized adaptive testing.
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.
Barrada
Juan Ramón
JR
Facultad de Psicología, Universidad Autónoma de Madrid, Spain. juanra.barrada@uam.es
Mazuela
Paloma
P
Olea
Julio
J
D000465:Algorithms; D001185:Artificial Intelligence; D016009:Chi-Square Distribution; D006801:Humans; D016013:Likelihood Functions; D008838:Microcomputers; D011581:Psychological Tests; D012985:Software Design