01448nas a2200205 4500008004100000020004100041245016200082210006900244250001500313300001100328490000700339520065200346653002500998653001501023653003701038653002401075100001401099700001501113856011401128 2009 eng d a1529-7713 (Print)1529-7713 (Linking)00aConsiderations about expected a posteriori estimation in adaptive testing: adaptive a priori, adaptive correction for bias, and adaptive integration interval0 aConsiderations about expected a posteriori estimation in adaptiv a2009/07/01 a138-560 v103 aIn 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.10a*Bias (Epidemiology)10a*Computers10aData Interpretation, Statistical10aModels, Statistical1 aRaiche, G1 aBlais, J G uhttp://iacat.org/content/considerations-about-expected-posteriori-estimation-adaptive-testing-adaptive-priori