@article {68,
	title = {The relationship between item exposure and test overlap in computerized adaptive testing},
	journal = {Journal of Educational Measurement},
	volume = {40},
	number = {2},
	year = {2003},
	pages = {129-145},
	abstract = {The purpose of this article is to present an analytical derivation for the mathematical form of an average between-test overlap index as a function of the item exposure index, for fixed-length computerized adaptive tests (CATs). This algebraic relationship is used to investigate the simultaneous control of item exposure at both the item and test levels. The results indicate that, in fixed-length CATs, control of the average between-test overlap is achieved via the mean and variance of the item exposure rates of the items that constitute the CAT item pool. The mean of the item exposure rates is easily manipulated. Control over the variance of the item exposure rates can be achieved via the maximum item exposure rate (r-sub(max)). Therefore, item exposure control methods which implement a specification of r-sub(max) (e.g., J. B. Sympson and R. D. Hetter, 1985) provide the most direct control at both the item and test levels. (PsycINFO Database Record (c) 2005 APA )},
	keywords = {(Statistical), Adaptive Testing, Computer Assisted Testing, Human Computer, Interaction computerized adaptive testing, Item Analysis, Item Analysis (Test), Test Items},
	author = {Chen, S-Y. and Ankemann, R. D. and Spray, J. A.}
}
@article {220,
	title = {The effect of model misspecification on classification decisions made using a computerized test},
	journal = {Journal of Educational Measurement},
	volume = {36},
	number = {1},
	year = {1999},
	note = {National Council on Measurement in Education, US},
	pages = {47-59},
	abstract = {Many computerized testing algorithms require the fitting of some item response theory (IRT) model to examinees{\textquoteright} responses to facilitate item selection, the determination of test stopping rules, and classification decisions. Some IRT models are thought to be particularly useful for small volume certification programs that wish to make the transition to computerized adaptive testing (CAT). The 1-parameter logistic model (1-PLM) is usually assumed to require a smaller sample size than the 3-parameter logistic model (3-PLM) for item parameter calibrations. This study examined the effects of model misspecification on the precision of the decisions made using the sequential probability ratio test. For this comparison, the 1-PLM was used to estimate item parameters, even though the items{\textquoteright} characteristics were represented by a 3-PLM. Results demonstrate that the 1-PLM produced considerably more decision errors under simulation conditions similar to a real testing environment, compared to the true model and to a fixed-form standard reference set of items. (PsycINFO Database Record (c) 2003 APA, all rights reserved).},
	keywords = {computerized adaptive testing},
	author = {Kalohn, J.C. and Spray, J. A.}
}
@article {366,
	title = {The effect of item parameter estimation error on decisions made using the sequential probability ratio test},
	number = {Research Report 87-1},
	year = {1987},
	institution = {DTIC Document},
	address = {Iowa City, IA. USA},
	keywords = {computerized adaptive testing, Sequential probability ratio test},
	author = {Spray, J. A. and Reckase, M. D.}
}
