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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Veröffentlicht in:British journal of mathematical & statistical psychology Jg. 73; H. 1; S. 142 - 163
Hauptverfasser: Ma, Wenchao, Torre, Jimmy
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England British Psychological Society 01.02.2020
Schlagworte:
Algorithms
Cognition
Cognition Disorders - diagnosis
cognitive diagnosis
Computer Simulation
discrimination index
Empirical analysis
G‐DINA
Humans
Mathematical analysis
Matrix methods
Models, Statistical
Psychometrics - methods
Q‐matrix validation
sequential G‐DINA
stepwise
Task Performance and Analysis
Trends in International Mathematics and Science Study
Wald test
sequential G-DINA
Wald test
cognitive diagnosis
stepwise
Trends in International Mathematics and Science Study
Q-matrix validation
G-DINA
discrimination index
ISSN:0007-1102, 2044-8317, 2044-8317
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Zusammenfassung: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