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Jing Huang; Yuxiao Zhang; Jason W. Morphew; Jayson M. Nissen; Ben Van Dusen; Hua Hua Chang – Journal of Educational Measurement, 2025
Online calibration estimates new item parameters alongside previously calibrated items, supporting efficient item replenishment. However, most existing online calibration procedures for Cognitive Diagnostic Computerized Adaptive Testing (CD-CAT) lack mechanisms to ensure content balance during live testing. This limitation can lead to uneven…
Descriptors: Adaptive Testing, Computer Assisted Testing, Cognitive Measurement, Test Items
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Ersen, Rabia Karatoprak; Lee, Won-Chan – Journal of Educational Measurement, 2023
The purpose of this study was to compare calibration and linking methods for placing pretest item parameter estimates on the item pool scale in a 1-3 computerized multistage adaptive testing design in terms of item parameter recovery. Two models were used: embedded-section, in which pretest items were administered within a separate module, and…
Descriptors: Pretesting, Test Items, Computer Assisted Testing, Adaptive Testing
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Yuan, Lu; Huang, Yingshi; Li, Shuhang; Chen, Ping – Journal of Educational Measurement, 2023
Online calibration is a key technology for item calibration in computerized adaptive testing (CAT) and has been widely used in various forms of CAT, including unidimensional CAT, multidimensional CAT (MCAT), CAT with polytomously scored items, and cognitive diagnostic CAT. However, as multidimensional and polytomous assessment data become more…
Descriptors: Computer Assisted Testing, Adaptive Testing, Computation, Test Items
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Lei, Pui-Wa; Chen, Shu-Ying; Yu, Lan – Journal of Educational Measurement, 2006
Mantel-Haenszel and SIBTEST, which have known difficulty in detecting non-unidirectional differential item functioning (DIF), have been adapted with some success for computerized adaptive testing (CAT). This study adapts logistic regression (LR) and the item-response-theory-likelihood-ratio test (IRT-LRT), capable of detecting both unidirectional…
Descriptors: Evaluation Methods, Test Bias, Computer Assisted Testing, Multiple Regression Analysis