@inbook {1910,
	title = {Computerized adaptive testing by mutual information and multiple imputations},
	year = {2009},
	note = {{PDF file, 179 KB}},
	address = {D. J. Weiss (Ed.), Proceedings of the 2009 GMAC Conference on Computerized Adaptive Testing.},
	abstract = {Over the years, most computerized adaptive testing (CAT) systems have used score estimation procedures from item response theory (IRT). IRT models have salutary properties for score estimation, error reporting, and next-item selection. However, some testing purposes favor scoring approaches outside IRT. Where a criterion metric is readily available and more relevant than the assessed construct, for example in the selection of job applicants, a predictive model might be appropriate (Scarborough \& Somers, 2006).  In these cases, neither IRT scoring nor a unidimensional assessment structure can be assumed. Yet, the primary benefit of CAT remains desirable: shorter assessments with minimal loss of accuracy due to unasked items. In such a case, it remains possible to create a CAT system that produces an estimated score from a subset of available items, recognizes differential item information given the emerging item response pattern, and optimizes the accuracy of the score estimated at every successive item. The method of multiple imputations (Rubin, 1987) can be used to simulate plausible scores given plausible response patterns to unasked items (Thissen-Roe, 2005). Mutual information can then be calculated in order to select an optimally informative next item (or set of items). Previously observed response patterns to two complete neural network-scored assessments were resampled according to MIMI CAT item selection. The reproduced CAT scores were compared to full-length assessment scores. Approximately 95\% accurate assignment of examinees to one of three score categories was achieved with a 70\%-80\% reduction in median test length. Several algorithmic factors influencing accuracy and computational performance were examined.},
	author = {Thissen-Roe, A.}
}
