@article {32,
title = {Features of the sampling distribution of the ability estimate in computerized adaptive testing according to two stopping rules},
journal = {Journal of Applied Measurement},
volume = {11},
number = {4},
year = {2010},
note = {Blais, Jean-GuyRaiche, GillesUnited StatesJournal of applied measurementJ Appl Meas. 2010;11(4):424-31.},
pages = {424-31},
edition = {2010/12/18},
abstract = {Whether paper and pencil or computerized adaptive, tests are usually described by a set of rules managing how they are administered: which item will be first, which should follow any given item, when to administer the last one. This article focus on the latter and looks at the effect of two stopping rules on the estimated sampling distribution of the ability estimate in a CAT: the number of items administered and the a priori determined size of the standard error of the ability estimate.},
isbn = {1529-7713 (Print)1529-7713 (Linking)},
author = {Blais, J. G. and Raiche, G.}
}
@article {320,
title = {Considerations about expected a posteriori estimation in adaptive testing: adaptive a priori, adaptive correction for bias, and adaptive integration interval},
journal = {Journal of Applied Measurement},
volume = {10},
number = {2},
year = {2009},
note = {Raiche, GillesBlais, Jean-GuyUnited StatesJournal of applied measurementJ Appl Meas. 2009;10(2):138-56.},
pages = {138-56},
edition = {2009/07/01},
abstract = {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.},
keywords = {*Bias (Epidemiology), *Computers, Data Interpretation, Statistical, Models, Statistical},
isbn = {1529-7713 (Print)1529-7713 (Linking)},
author = {Raiche, G. and Blais, J. G.}
}
@inbook {1874,
title = {Adaptive estimators of trait level in adaptive testing: Some proposals},
year = {2007},
note = {{PDF file, 125 KB}},
address = {D. J. Weiss (Ed.), Proceedings of the 2007 GMAC Conference on Computerized Adaptive Testing.},
author = {Ra{\^\i}che, G. and Blais, J. G. and Magis, D.}
}
@conference {853,
title = {Historique et concepts propres au testing adaptatif [Adaptive testing: Historical accounts and concepts]},
booktitle = {Presented at the 69th Congress of the Acfas. Sherbrooke: Association canadienne fran{\c c}aise pour l{\textquoteright}avancement des sciences (Acfas). [In French]},
year = {2002},
author = {Blais, J. G.}
}
@conference {1100,
title = {Practical considerations about expected a posteriori estimation in adaptive testing: Adaptive a prior, adaptive corrections for bias, adaptive integration interval},
booktitle = {Paper presented at the annual meeting of the International Objective Measurement Workshops-XI},
year = {2002},
note = {{PDF file, 100 KB}},
address = {New Orleans, LA},
author = {Raiche, G. and Blais, J. G.}
}
@conference {1101,
title = {Some features of the estimated sampling distribution of the ability estimate in computerized adaptive testing according to two stopping rules},
booktitle = {Communication propos{\'e}e au 11e Biannual International objective measurement workshop. New-Orleans : International Objective Measurement Workshops.},
year = {2002},
author = {Ra{\^\i}che, G. and Blais, J. G.}
}