Multidimensional computerized adaptive testing (MCAT) is able to provide a vector of ability estimates for each examinee, which could be used to provide a more informative profile of an examinee's performance. The current literature on MCAT focuses on the fixed-length tests, which can generate less accurate results for those examinees whose…

Criteria had been proposed for assessing the severity of possible test security violations for computerized tests with high-stakes outcomes. However, these criteria resulted from theoretical derivations that assumed uniformly randomized item selection. This study investigated potential damage caused by organized item theft in computerized adaptive…

Content balancing is an important issue in the design and implementation of computerized adaptive testing (CAT). Content-balancing techniques that have been applied in fixed content balancing, where the number of items from each content area is fixed, include constrained CAT (CCAT), the modified multinomial model (MMM), modified constrained CAT…

In addition to its precision superiority over nonadaptive tests, another known advantage of computerized adaptive tests (CATs) is that they can be offered on a continuous basis. This is advantageous to examinees in terms of flexibility of test scheduling, as well as advantageous to schools and other testing centers in terms of both space and…

Combined the methods of alpha-stratified adaptive testing and constrained adaptive testing with shadow tests. Outlines the advantages of this approach in reducing overexposure and underexposure of items in an item pool and illustrates these advantages with an example from the Law School Admission Test. (SLD)

Developed a method based on 0-1 linear programming to stratify an item pool optimally for use in alpha-stratified adaptive testing. Applied the method to a previous item pool from the computerized adaptive test of the Graduate Record Examinations. Results show the new method performs well in practical situations. (SLD)

Proposed a refinement, based on the stratification of items developed by D. Weiss (1973), of the computerized adaptive testing item selection procedure of H. Chang and Z. Ying (1999). Simulation studies using an item bank from the Graduate Record Examination show the benefits of the new procedure. (SLD)

Proposes a new multistage adaptive-testing procedure that factors the discrimination parameter (alpha) into the item-selection process. Simulation studies indicate that the new strategy results in tests that are well-balanced, with respect to item exposure, and efficient. (SLD)

Item exposure control, test-overlap minimization, and the efficient use of item pool are some of the important issues in computerized adaptive testing (CAT) designs. The overexposure of some items and high test-overlap rate may cause both item and test security problems. Previously these problems associated with the maximum information (Max-I)…

Studied the impact of using five different exposure control algorithms in two sizes of item pool calibrated using the generalized partial credit model. Simulation results show that the a-stratified design, in comparison to a no-exposure control condition, could be used to reduce item exposure and overlap and increase pool use, while degrading…

Compared five item selection rules with respect to the efficiency and precision of trait (theta) estimation at the early stages of computerized adaptive testing (CAT). The Fisher interval information, Fisher information with a posterior distribution, Kullback-Leibler information, and Kullback-Leibler information with a posterior distribution…

An item selection procedure for computerized adaptive testing based on average global information is proposed. Results from simulation studies comparing the approach with the usual maximum item information item selection indicate that the new method leads to improvement in terms of bias and mean squared error reduction under many circumstances.…