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 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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British Psychological Society
01.02.2020
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| ISSN: | 0007-1102, 2044-8317, 2044-8317 |
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| Abstract | 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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| AbstractList | 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. 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.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. |
| Author | Ma, Wenchao Torre, Jimmy |
| Author_xml | – sequence: 1 givenname: Wenchao orcidid: 0000-0002-6763-0707 surname: Ma fullname: Ma, Wenchao email: wenchao.ma@ua.edu organization: University of Alabama – sequence: 2 givenname: Jimmy surname: Torre fullname: Torre, Jimmy organization: University of Hong Kong |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30723890$$D View this record in MEDLINE/PubMed |
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| Keywords | sequential G-DINA Wald test cognitive diagnosis stepwise Trends in International Mathematics and Science Study Q-matrix validation G-DINA discrimination index |
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| SubjectTerms | 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 |
| Title | An empirical Q‐matrix validation method for the sequential generalized DINA model |
| URI | https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fbmsp.12156 https://www.ncbi.nlm.nih.gov/pubmed/30723890 https://www.proquest.com/docview/2345261115 https://www.proquest.com/docview/2229081357 |
| Volume | 73 |
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