The purpose of this study was to investigate and to quantify the tolerable error in item parameter estimates for different sets of items used in computer-based testing. The study examined items that were administered repeatedly to different examinee samples over time, examining items that were administered linearly in a fixed order each time they…

Performed a simulation study to demonstrate the dramatic impact of capitalization on estimation errors on ability estimation in adaptive testing. Discusses four different strategies to minimize the likelihood of capitalization in computerized adaptive testing. (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…

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…

In the process of item calibration for a computerized adaptive test (CAT), many well-established calibrating packages show weakness in the estimation of item parameters. This paper introduces an on-line calibration algorithm based on the convexity of likelihood functions. This package consists of: (1) an algorithm that estimates examinee ability…

This paper suggests several item selection criteria for adaptive testing that are all based on the use of the true posterior. Some of the ability estimators produced by these criteria are discussed and empirically criticized. (SLD)

Many applications of educational testing have a missing data aspect (MDA). This MDA is perhaps most pronounced in item banking, where each examinee responds to a different subtest of items from a large item pool and where both person and item parameter estimates are needed. The Rasch model is emphasized, and its non-parametric counterpart (the…

The average Kullback-Keibler (K-L) information index (H. Chang and Z. Ying, in press) is a newly proposed statistic in Computerized Adaptive Testing (CAT) item selection based on the global information function. The objectives of this study were to improve understanding of the K-L index with various parameters and to compare the performance of the…

The relationship between examinee ability and the accuracy of maximum likelihood item parameter estimation is explored in terms of the expected (Fisher) information. Information functions are used to find the optimum ability levels and maximum contributions to information for estimating item parameters in three commonly used logistic item response…

In a variable-length adaptive test with a stopping rule that relied on the asymptotic standard error of measurement of the examinee's estimated true score, M. S. Stocking (1987) discovered that it was sufficient to know the examinee's true score and the number of items administered to predict with some accuracy whether an examinee's true score was…

In adaptive testing, item selection is sequentially optimized during the test. Since the optimization takes place over a pool of items calibrated with estimation error, capitalization on these errors is likely to occur. How serious the consequences of this phenomenon are depends not only on the distribution of the estimation errors in the pool or…

Computerized adaptive tests (CATs) are efficient because of their optimal item selection procedures that target maximally informative items at each estimated ability level. However, operational administration of these optimal CATs results in a relatively small subset of items given to examinees too often, while another portion of the item pool is…

When examinees are allowed to review responses on an adaptive test, can they "cheat" the adaptive algorithm in order to take an easier test and improve their performance? Theoretically, deliberately answering items incorrectly will lower the examinee ability estimate and easy test items will be administered. If review is then allowed,…

A computerized adaptive test (CAT) administration usually requires a large supply of items with accurately estimated psychometric properties, such as item response theory (IRT) parameter estimates, to ensure the precision of examinee ability estimation. However, an estimated IRT model of a given item in any given pool does not always correctly…