@article {401,
	title = {Assembling a computerized adaptive testing item pool as a set of linear tests},
	journal = {Journal of Educational and Behavioral Statistics},
	volume = {31},
	number = {1},
	year = {2006},
	pages = {81-99},
	publisher = {Sage Publications: US},
	abstract = {Test-item writing efforts typically results in item pools with an undesirable correlational structure between the content attributes of the items and their statistical information. If such pools are used in computerized adaptive testing (CAT), the algorithm may be forced to select items with less than optimal information, that violate the content constraints, and/or have unfavorable exposure rates. Although at first sight somewhat counterintuitive, it is shown that if the CAT pool is assembled as a set of linear test forms, undesirable correlations can be broken down effectively. It is proposed to assemble such pools using a mixed integer programming model with constraints that guarantee that each test meets all content specifications and an objective function that requires them to have maximal information at a well-chosen set of ability values. An empirical example with a previous master pool from the Law School Admission Test (LSAT) yielded a CAT with nearly uniform bias and mean-squared error functions for the ability estimator and item-exposure rates that satisfied the target for all items in the pool. },
	keywords = {Algorithms, computerized adaptive testing, item pool, linear tests, mathematical models, statistics, Test Construction, Test Items},
	isbn = {1076-9986 (Print)},
	author = {van der Linden, W. J. and Ariel, A. and Veldkamp, B. P.}
}
@article {399,
	title = {Equating scores from adaptive to linear tests},
	journal = {Applied Psychological Measurement},
	volume = {30},
	number = {6},
	year = {2006},
	pages = {493-508},
	publisher = {Sage Publications: US},
	abstract = {Two local methods for observed-score equating are applied to the problem of equating an adaptive test to a linear test. In an empirical study, the methods were evaluated against a method based on the test characteristic function (TCF) of the linear test and traditional equipercentile equating applied to the ability estimates on the adaptive test for a population of test takers. The two local methods were generally best. Surprisingly, the TCF method performed slightly worse than the equipercentile method. Both methods showed strong bias and uniformly large inaccuracy, but the TCF method suffered from extra error due to the lower asymptote of the test characteristic function. It is argued that the worse performances of the two methods are a consequence of the fact that they use a single equating transformation for an entire population of test takers and therefore have to compromise between the individual score distributions. },
	keywords = {computerized adaptive testing, equipercentile equating, local equating, score reporting, test characteristic function},
	isbn = {0146-6216 (Print)},
	author = {van der Linden, W. J.}
}
