@article {156,
	title = {Computerized adaptive testing with item cloning},
	journal = {Applied Psychological Measurement},
	volume = {27},
	number = {4},
	year = {2003},
	note = {References .Sage Publications, US},
	pages = {247-261},
	abstract = {(from the journal abstract) To increase the number of items available for adaptive testing and reduce the cost of item writing, the use of techniques of item cloning has been proposed. An important consequence of item cloning is possible variability between the item parameters. To deal with this variability, a multilevel item response (IRT) model is presented which allows for differences between the distributions of item parameters of families of item clones. A marginal maximum likelihood and a Bayesian procedure for estimating the hyperparameters are presented. In addition, an item-selection procedure for computerized adaptive testing with item cloning is presented which has the following two stages: First, a family of item clones is selected to be optimal at the estimate of the person parameter. Second, an item is randomly selected from the family for administration. Results from simulation studies based on an item pool from the Law School Admission Test (LSAT) illustrate the accuracy of these item pool calibration and adaptive testing procedures. (PsycINFO Database Record (c) 2003 APA, all rights reserved).},
	keywords = {computerized adaptive testing},
	author = {Glas, C. A. W. and van der Linden, W. J.}
}
@article {402,
	title = {Capitalization on item calibration error in adaptive testing},
	journal = {Applied Measurement in Education},
	volume = {13},
	number = {1},
	year = {2000},
	note = {References .Lawrence Erlbaum, US},
	pages = {35-53},
	abstract = {(from the journal abstract) In adaptive testing, item selection is sequentially optimized during the test. Because the optimization takes place over a pool of items calibrated with estimation error, capitalization on chance is likely to occur. How serious the consequences of this phenomenon are depends not only on the distribution of the estimation errors in the pool or the conditional ratio of the test length to the pool size given ability, but may also depend on the structure of the item selection criterion used. A simulation study demonstrated a dramatic impact of capitalization on estimation errors on ability estimation. Four different strategies to minimize the likelihood of capitalization on error in computerized adaptive testing are discussed.},
	keywords = {computerized adaptive testing},
	author = {van der Linden, W. J. and Glas, C. A. W.}
}
