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Estimating Classification Consistency and Accuracy for Cognitive Diagnostic Assessment
Author(s) -
Cui Ying,
Gierl Mark J.,
Chang HuaHua
Publication year - 2012
Publication title -
journal of educational measurement
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.917
H-Index - 47
eISSN - 1745-3984
pISSN - 0022-0655
DOI - 10.1111/j.1745-3984.2011.00158.x
Subject(s) - consistency (knowledge bases) , reliability (semiconductor) , computation , subtraction , statistical inference , fraction (chemistry) , inference , sampling (signal processing) , computer science , statistics , cognitive test , artificial intelligence , cognition , mathematics , data mining , pattern recognition (psychology) , algorithm , psychology , arithmetic , neuroscience , power (physics) , physics , chemistry , organic chemistry , filter (signal processing) , quantum mechanics , computer vision
This article introduces procedures for the computation and asymptotic statistical inference for classification consistency and accuracy indices specifically designed for cognitive diagnostic assessments. The new classification indices can be used as important indicators of the reliability and validity of classification results produced by cognitive diagnostic assessments. For tests with known or previously calibrated item parameters, the sampling distributions of the two new indices are shown to be asymptotically normal. To illustrate the computations of the new indices, we apply them to the real diagnostic data from a fraction subtraction test (Tatsuoka). We also use simulated data to evaluate their performances and distributional properties.