An empirical Q‐matrix validation method for the sequential generalized DINA model

As a core component of most cognitive diagnosis models, the Q‐matrix, or item and attribute association matrix, is typically developed by domain experts, and tends to be subjective. It is critical to validate the Q‐matrix empirically because a misspecified Q‐matrix could result in erroneous attribut...

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Bibliographic Details
Published in:British journal of mathematical & statistical psychology Vol. 73; no. 1; pp. 142 - 163
Main Authors: Ma, Wenchao, Torre, Jimmy
Format: Journal Article
Language:English
Published: England British Psychological Society 01.02.2020
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ISSN:0007-1102, 2044-8317, 2044-8317
Online Access:Get full text
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Summary:As a core component of most cognitive diagnosis models, the Q‐matrix, or item and attribute association matrix, is typically developed by domain experts, and tends to be subjective. It is critical to validate the Q‐matrix empirically because a misspecified Q‐matrix could result in erroneous attribute estimation. Most existing Q‐matrix validation procedures are developed for dichotomous responses. However, in this paper, we propose a method to empirically detect and correct the misspecifications in the Q‐matrix for graded response data based on the sequential generalized deterministic inputs, noisy ‘and’ gate (G‐DINA) model. The proposed Q‐matrix validation procedure is implemented in a stepwise manner based on the Wald test and an effect size measure. The feasibility of the proposed method is examined using simulation studies. Also, a set of data from the Trends in International Mathematics and Science Study (TIMSS) 2011 mathematics assessment is analysed for illustration.
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ISSN:0007-1102
2044-8317
2044-8317
DOI:10.1111/bmsp.12156