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

Volume 10 Number 3 July 2023

Ming Him Tai, Allison W. Cooperman, Joseph N. DeWeese, and David J. Weiss

ABSTRACT

Adaptive measurement of change (AMC) is a psychometric procedure to detect intra-individual change in trait levels across multiple testing occasions. However, in studying how AMC performs as a function of change, most previous studies did not specify change patterns systematically. Inspired by Cronbach and Gleser (1953), a quantitative framework was proposed that systematically decomposes a change pattern into three components: magnitude, scatter, and shape. Shape was further decomposed into direction and order. Using monte-carlo simulations, a series of analyses of variance were performed to investi-gate how each of these components affected the false positive rates (FPRs) and true positive rates (TPRs) for detecting true change, and a change recovery index (CRI).

You can view the complete abstract and download the article at https://jcatpub.net/index.php/jcat/issue/view/33