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

TitleAn empirical Q‐matrix validation method for the sequential generalized DINA model
Authors
Keywordscognitive diagnosis
discrimination index
G-DINA
Q-matrix validation
sequential G-DINA
Issue Date2019
PublisherThe 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, 2019, Epub How to Cite?
AbstractAs 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. © 2019 The British Psychological Society
Persistent Identifierhttp://hdl.handle.net/10722/274088
ISSN
2019 Impact Factor: 2.388
2015 SCImago Journal Rankings: 2.380

 

DC FieldValueLanguage
dc.contributor.authorMa, W-
dc.contributor.authorde la Torre, J-
dc.date.accessioned2019-08-18T14:54:49Z-
dc.date.available2019-08-18T14:54:49Z-
dc.date.issued2019-
dc.identifier.citationBritish Journal of Mathematical and Statistical Psychology, 2019, Epub-
dc.identifier.issn0007-1102-
dc.identifier.urihttp://hdl.handle.net/10722/274088-
dc.description.abstractAs 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. © 2019 The British Psychological Society-
dc.languageeng-
dc.publisherThe British Psychological Society. The Journal's web site is located at http://www.bps.org.uk/publications/jMS_1.cfm-
dc.relation.ispartofBritish Journal of Mathematical and Statistical Psychology-
dc.rightsReproduced with permission from [journal name] © The British Psychological Society [year]-
dc.subjectcognitive diagnosis-
dc.subjectdiscrimination index-
dc.subjectG-DINA-
dc.subjectQ-matrix validation-
dc.subjectsequential G-DINA-
dc.titleAn empirical Q‐matrix validation method for the sequential generalized DINA model-
dc.typeArticle-
dc.identifier.emailde la Torre, J: jdltorre@hku.hk-
dc.identifier.authorityde la Torre, J=rp02159-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1111/bmsp.12156-
dc.identifier.pmid30723890-
dc.identifier.scopuseid_2-s2.0-85061024641-
dc.identifier.hkuros302283-
dc.publisher.placeUnited Kingdom-

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