New Article in the Journal of Computerized Adaptive Testing (JCAT)

JCAT  has published its Volume 12 Number 3 article

Estimating the Joint Item-Score Density Using an Unrestricted Latent Class Model: Advancing Flexibility in Computerized Adaptive Testing

Anastasios Psychogyiopoulos, Niels Smits, and L. Andries van der Ark

University of Amsterdam

Abstract

Computerized adaptive testing (CAT) reduces cognitive fatigue and response burden while maintaining measurement precision by administering items tailored to the respondent. However, the assumptions of item response theory models—commonly used in CAT—might be too stringent for some tests. This study investigated the bias and accuracy of a flexible CAT procedure, called LSCAT (for latent-class sum-score CAT). In the calibration phase, an unrestricted latent class model estimates the joint item-score density ( ) and the total-score density ( ); in the operational phase, the respondents’ expected total scores are estimated. The paper’s first study indicated that using the Bayesian information criterion (BIC) to determine the number of latent classes produced the most accurate estimates of  and . The second study showed that the unrestricted latent class model more accurately estimated  and  than the two-parameter logistic model, especially under a complex data-generating mechanism. As a proof of concept, the third study compared the precision of LSCAT and a traditional CAT procedure using the two-parameter logistic model with a single empirical dataset. The two CAT procedures were approximately equally precise. Although the two procedures had the same fixed efficiency, LSCAT was more efficient for the high- and low-scoring respondents, while traditional CAT was more efficient for respondents in the middle.

The complete article is available at https://jcatpub.net/index.php/jcat using the Current option.