New Article in the Journal of Computerized Adaptive Testing (JCAT)
How Do Trait Change Patterns Affect the Performance of Adaptive Measurement of Change?
Ming Him Tai, Allison W. Cooperman, Joseph N. DeWeese, and David J. Weiss
Partial 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
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