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- Publisher Website: 10.1111/bmsp.12228
- Scopus: eid_2-s2.0-85096695890
- PMID: 33231301
- WOS: WOS:000591670900001
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Article: Balancing fit and parsimony to improve Q‐matrix validation
Title | Balancing fit and parsimony to improve Q‐matrix validation |
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Authors | |
Issue Date | 2021 |
Publisher | The British Psychological Society. The Journal's web site is located at http://www.bps.org.uk/publications/jMS_1.cfm |
Citation | British Journal of Mathematical and Statistical Psychology, 2021, v. 74 n. suppl. 1, p. 110-130 How to Cite? |
Abstract | The Q-matrix identifies the subset of attributes measured by each item in the cognitive diagnosis modelling framework. Usually constructed by domain experts, the Q-matrix might contain some misspecifications, disrupting classification accuracy. Empirical Q-matrix validation methods such as the general discrimination index (GDI) and Wald have shown promising results in addressing this problem. However, a cut-off point is used in both methods, which might be suboptimal. To address this limitation, the Hull method is proposed and evaluated in the present study. This method aims to find the optimal balance between fit and parsimony, and it is flexible enough to be used either with a measure of item discrimination (the proportion of variance accounted for, PVAF) or a coefficient of determination (pseudo-R2). Results from a simulation study showed that the Hull method consistently showed the best performance and shortest computation time, especially when used with the PVAF. The Wald method also performed very well overall, while the GDI method obtained poor results when the number of attributes was high. The absence of a cut-off point provides greater flexibility to the Hull method, and it places it as a comprehensive solution to the Q-matrix specification problem in applied settings. This proposal is illustrated using real data. |
Persistent Identifier | http://hdl.handle.net/10722/305927 |
ISSN | 2023 Impact Factor: 1.5 2023 SCImago Journal Rankings: 1.735 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Nájera, P | - |
dc.contributor.author | Sorrel, MA | - |
dc.contributor.author | de la Torre, J | - |
dc.contributor.author | Abad, FJ | - |
dc.date.accessioned | 2021-10-20T10:16:19Z | - |
dc.date.available | 2021-10-20T10:16:19Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | British Journal of Mathematical and Statistical Psychology, 2021, v. 74 n. suppl. 1, p. 110-130 | - |
dc.identifier.issn | 0007-1102 | - |
dc.identifier.uri | http://hdl.handle.net/10722/305927 | - |
dc.description.abstract | The Q-matrix identifies the subset of attributes measured by each item in the cognitive diagnosis modelling framework. Usually constructed by domain experts, the Q-matrix might contain some misspecifications, disrupting classification accuracy. Empirical Q-matrix validation methods such as the general discrimination index (GDI) and Wald have shown promising results in addressing this problem. However, a cut-off point is used in both methods, which might be suboptimal. To address this limitation, the Hull method is proposed and evaluated in the present study. This method aims to find the optimal balance between fit and parsimony, and it is flexible enough to be used either with a measure of item discrimination (the proportion of variance accounted for, PVAF) or a coefficient of determination (pseudo-R2). Results from a simulation study showed that the Hull method consistently showed the best performance and shortest computation time, especially when used with the PVAF. The Wald method also performed very well overall, while the GDI method obtained poor results when the number of attributes was high. The absence of a cut-off point provides greater flexibility to the Hull method, and it places it as a comprehensive solution to the Q-matrix specification problem in applied settings. This proposal is illustrated using real data. | - |
dc.language | eng | - |
dc.publisher | The British Psychological Society. The Journal's web site is located at http://www.bps.org.uk/publications/jMS_1.cfm | - |
dc.relation.ispartof | British Journal of Mathematical and Statistical Psychology | - |
dc.rights | Reproduced with permission from [journal name] © The British Psychological Society [year] | - |
dc.title | Balancing fit and parsimony to improve Q‐matrix validation | - |
dc.type | Article | - |
dc.identifier.email | de la Torre, J: j.delatorre@hku.hk | - |
dc.identifier.authority | de la Torre, J=rp02159 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1111/bmsp.12228 | - |
dc.identifier.pmid | 33231301 | - |
dc.identifier.scopus | eid_2-s2.0-85096695890 | - |
dc.identifier.hkuros | 328189 | - |
dc.identifier.volume | 74 | - |
dc.identifier.issue | suppl. 1 | - |
dc.identifier.spage | 110 | - |
dc.identifier.epage | 130 | - |
dc.identifier.isi | WOS:000591670900001 | - |
dc.publisher.place | United Kingdom | - |