ERIC Number: EJ1061227
Record Type: Journal
Publication Date: 2015-Jun
Abstractor: As Provided
Reference Count: 24
The Effects of Q-Matrix Design on Classification Accuracy in the Log-Linear Cognitive Diagnosis Model
Madison, Matthew J.; Bradshaw, Laine P.
Educational and Psychological Measurement, v75 n3 p491-511 Jun 2015
Diagnostic classification models are psychometric models that aim to classify examinees according to their mastery or non-mastery of specified latent characteristics. These models are well-suited for providing diagnostic feedback on educational assessments because of their practical efficiency and increased reliability when compared with other multidimensional measurement models. A priori specifications of which latent characteristics or "attributes" are measured by each item are a core element of the diagnostic assessment design. This item-attribute alignment, expressed in a Q-matrix, precedes and supports any inference resulting from the application of the diagnostic classification model. This study investigates the effects of Q-matrix design on classification accuracy for the log-linear cognitive diagnosis model. Results indicate that classification accuracy, reliability, and convergence rates improve when the Q-matrix contains isolated information from each measured attribute.
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
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