The goal of the current study was to introduce a new stopping rule for computerized adaptive testing (CAT). The predicted standard error reduction (PSER) stopping rule uses the predictive posterior variance to determine the reduction in standard error that would result from the administration of additional items. The performance of the PSER was…

This study examined the performance of the maximum Fisher's information, the maximum posterior weighted information, and the minimum expected posterior variance methods for selecting items in a computerized adaptive testing system when the items were grouped in testlets. A simulation study compared the efficiency of ability estimation among the…

Polytomous item response theory models and the research that has been conducted to investigate a variety of possible operational procedures for polytomous model-based computerized adaptive testing (CAT) are reviewed, along with studies comparing polytomous CAT systems with competing item response theory models. (SLD)

A simulation study was conducted to investigate the application of expected a posteriori (EAP) trait estimation in computerized adaptive tests (CAT) based on the partial credit model and compare it with maximum likelihood estimation (MLE). Results show the conditions under which EAP and MLE provide relatively accurate estimation in CAT. (SLD)

General guidelines are developed to assist practitioners in devising operational computerized adaptive testing systems based on the graded response model. The effects of the following major variables were examined: item pool size; stepsize used along the trait continuum until maximum likelihood estimation could be calculated; and stopping rule…

Simulated data were used to investigate the impact of characteristics of threshold values (number, symmetry, and distance between adjacent threshold values) and delta values on the distribution of item information in the successive intervals Rasch model. Implications for computerized adaptive attitude measurement are discussed. (SLD)

Basic procedures for performing computerized adaptive testing based on the successive intervals (SI) Rasch model were evaluated. The SI model was applied to simulated and real attitude data sets. Item pools as small as 30 items performed well, and the model appeared practical for Likert-type data. (SLD)

Effects of the following variables on performance of computerized adaptive testing (CAT) procedures for the partial credit model (PCM) were studied: (1) stopping rule for terminating CAT; (2) item pool size; and (3) distribution of item difficulties. Implications of findings for CAT systems based on the PCM are discussed. (SLD)