%0 Journal Article %J Journal of Educational and Behavioral Statistics %D 2006 %T Assembling a computerized adaptive testing item pool as a set of linear tests %A van der Linden, W. J. %A Ariel, A. %A Veldkamp, B. P. %K Algorithms %K computerized adaptive testing %K item pool %K linear tests %K mathematical models %K statistics %K Test Construction %K Test Items %X 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. %B Journal of Educational and Behavioral Statistics %I Sage Publications: US %V 31 %P 81-99 %@ 1076-9986 (Print) %G eng %M 2007-08137-004 %0 Journal Article %J Journal of Educational Measurement %D 2005 %T A comparison of item-selection methods for adaptive tests with content constraints %A van der Linden, W. J. %K Adaptive Testing %K Algorithms %K content constraints %K item selection method %K shadow test approach %K spiraling method %K weighted deviations method %X In test assembly, a fundamental difference exists between algorithms that select a test sequentially or simultaneously. Sequential assembly allows us to optimize an objective function at the examinee's ability estimate, such as the test information function in computerized adaptive testing. But it leads to the non-trivial problem of how to realize a set of content constraints on the test—a problem more naturally solved by a simultaneous item-selection method. Three main item-selection methods in adaptive testing offer solutions to this dilemma. The spiraling method moves item selection across categories of items in the pool proportionally to the numbers needed from them. Item selection by the weighted-deviations method (WDM) and the shadow test approach (STA) is based on projections of the future consequences of selecting an item. These two methods differ in that the former calculates a projection of a weighted sum of the attributes of the eventual test and the latter a projection of the test itself. The pros and cons of these methods are analyzed. An empirical comparison between the WDM and STA was conducted for an adaptive version of the Law School Admission Test (LSAT), which showed equally good item-exposure rates but violations of some of the constraints and larger bias and inaccuracy of the ability estimator for the WDM. %B Journal of Educational Measurement %I Blackwell Publishing: United Kingdom %V 42 %P 283-302 %@ 0022-0655 (Print) %G eng %M 2005-10716-004