Two sampling-and-classification-based procedures were developed for automated test assembly: the Cell Only and the Cell and Cube methods. A simulation study based on a 540-item bank was conducted to compare the performance of the procedures with the performance of a mixed-integer programming (MIP) method for assembling multiple parallel test…

In this study we evaluated and compared three item selection procedures: the maximum Fisher information procedure (F), the a-stratified multistage computer adaptive testing (CAT) (STR), and a refined stratification procedure that allows more items to be selected from the high a strata and fewer items from the low a strata (USTR), along with…

a-stratification is a method that utilizes items with small discrimination (a) parameters early in an exam and those with higher a values when more is learned about the ability parameter. It can achieve much better item usage than the maximum information criterion (MIC). To make a-stratification more practical and more widely applicable, a method…

It is widely believed that item selection methods using the maximum information approach (MI) can maintain high efficiency in trait estimation by repeatedly choosing high discriminating (alpha) items. However, the consequence is that they lead to extremely skewed item exposure distribution in which items with high alpha values becoming overly…

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)

The methods of alpha-stratified adaptive testing and constrained adaptive testing with shadow tests are combined in this study. The advantages are twofold. First, application of the shadow test allows the researcher to implement any type of constraint on item selection in alpha-stratified adaptive testing. Second, the result yields a simple set of…

Information based item selection methods in computerized adaptive tests (CATs) tend to choose the item that provides maximum information at an examinee's estimated trait level. As a result, these methods can yield extremely skewed item exposure distributions in which items with high "a" values may be overexposed, while those with low…

The purpose of this study was to compare a proposed revised a-stratified, or alpha-stratified, USTR method of test item selection with the original alpha-stratified multistage computerized adaptive testing approach (STR) and the use of maximum Fisher information (FSH) with respect to test efficiency and item pool usage using simulated computerized…

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)

Test security has often been a problem in computerized adaptive testing (CAT) because the traditional wisdom of item selection overly exposes high discrimination items. The a-stratified (STR) design advocated by H. Chang and his collaborators, which uses items of less discrimination in earlier stages of testing, has been shown to be very…

In the a-stratified method, a popular and efficient item exposure control strategy proposed by H. Chang (H. Chang and Z. Ying, 1999; K. Hau and H. Chang, 2001) for computerized adaptive testing (CAT), the item pool and item selection process has usually been divided into four strata and the corresponding four stages. In a series of simulation…

Item selection methods in computerized adaptive testing (CAT) can yield extremely skewed item exposure distribution in which items with high "a" values may be over-exposed while those with low "a" values may never be selected. H. Chang and Z. Ying (1999) proposed the a-stratified design (ASTR) that attempts to equalize item…

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…

Demonstrates mathematically that if every item in an item pool has an equal possibility to be selected from the pool in a fixed-length computerized adaptive test, the number of overlapping items among an alpha randomly sampled examinees follows the hypergeometric distribution family for alpha greater than or equal to 1. (SLD)

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…