@article {57,
	title = {Optimal stratification of item pools in α-stratified computerized adaptive testing},
	journal = {Applied Psychological Measurement},
	volume = {27},
	number = {4},
	year = {2003},
	pages = {262-274},
	abstract = {A method based on 0-1 linear programming (LP) is presented to stratify an item pool optimally for use in α-stratified adaptive testing. Because the 0-1 LP model belongs to the subclass of models with a network flow structure, efficient solutions are possible. The method is applied to a previous item pool from the computerized adaptive testing (CAT) version of the Graduate Record Exams (GRE) Quantitative Test. The results indicate that the new method performs well in practical situations. It improves item exposure control, reduces the mean squared error in the θ estimates, and increases test reliability. (PsycINFO Database Record (c) 2005 APA ) (journal abstract)},
	keywords = {Adaptive Testing, Computer Assisted Testing, Item Content (Test), Item Response Theory, Mathematical Modeling, Test Construction computerized adaptive testing},
	author = {Chang, Hua-Hua and van der Linden, W. J.}
}
@article {397,
	title = {Some alternatives to Sympson-Hetter item-exposure control in computerized adaptive testing},
	journal = {Journal of Educational and Behavioral Statistics},
	volume = {28},
	number = {3},
	year = {2003},
	pages = {249-265},
	abstract = {TheHetter and Sympson (1997; 1985) method is a method of probabilistic item-exposure control in computerized adaptive testing. Setting its control parameters to admissible values requires an iterative process of computer simulations that has been found to be time consuming, particularly if the parameters have to be set conditional on a realistic set of values for the examinees{\textquoteright} ability parameter. Formal properties of the method are identified that help us explain why this iterative process can be slow and does not guarantee admissibility. In addition, some alternatives to the SH method are introduced. The behavior of these alternatives was estimated for an adaptive test from an item pool from the Law School Admission Test (LSAT). Two of the alternatives showed attractive behavior and converged smoothly to admissibility for all items in a relatively small number of iteration steps. },
	keywords = {Adaptive Testing, Computer Assisted Testing, Test Items computerized adaptive testing},
	author = {van der Linden, W. J.}
}
@article {407,
	title = {Using response times to detect aberrant responses in computerized adaptive testing},
	journal = {Psychometrika},
	volume = {68},
	number = {2},
	year = {2003},
	pages = {251-265},
	abstract = {A lognormal model for response times is used to check response times for aberrances in examinee behavior on computerized adaptive tests. Both classical procedures and Bayesian posterior predictive checks are presented. For a fixed examinee, responses and response times are independent; checks based on response times offer thus information independent of the results of checks on response patterns. Empirical examples of the use of classical and Bayesian checks for detecting two different types of aberrances in response times are presented. The detection rates for the Bayesian checks outperformed those for the classical checks, but at the cost of higher false-alarm rates. A guideline for the choice between the two types of checks is offered.},
	keywords = {Adaptive Testing, Behavior, Computer Assisted Testing, computerized adaptive testing, Models, person Fit, Prediction, Reaction Time},
	author = {van der Linden, W. J. and van Krimpen-Stoop, E. M. L. A.}
}
