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


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).

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