Diagnostic Classification Models for Ordinal Item Responses

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dc.contributor.authorLiu, Ren
dc.contributor.authorJiang, Zhehan
dc.contributor.otherUniversity of California Merced
dc.contributor.otherUniversity of Alabama Tuscaloosa
dc.date.accessioned2023-09-28T21:05:16Z
dc.date.available2023-09-28T21:05:16Z
dc.date.issued2018
dc.description.abstractThe purpose of this study is to develop and evaluate two diagnostic classification models (DCMs) for scoring ordinal item data. We first applied the proposed models to an operational dataset and compared their performance to an epitome of current polytomous DCMs in which the ordered data structure is ignored. Findings suggest that the much more parsimonious models that we proposed performed similarly to the current polytomous DCMs and offered useful item-level information in addition to option-level information. We then performed a small simulation study using the applied study condition and demonstrated that the proposed models can provide unbiased parameter estimates and correctly classify individuals. In practice, the proposed models can accommodate much smaller sample sizes than current polytomous DCMs and thus prove useful in many small-scale testing scenarios.en_US
dc.format.mediumelectronic
dc.format.mimetypeapplication/pdf
dc.identifier.citationLiu, R., & Jiang, Z. (2018). Diagnostic Classification Models for Ordinal Item Responses. In Frontiers in Psychology (Vol. 9). Frontiers Media SA. https://doi.org/10.3389/fpsyg.2018.02512
dc.identifier.doi10.3389/fpsyg.2018.02512
dc.identifier.orcidhttps://orcid.org/0000-0002-1376-9439
dc.identifier.orcidhttps://orcid.org/0000-0002-6708-4996
dc.identifier.urihttps://ir.ua.edu/handle/123456789/12005
dc.languageEnglish
dc.language.isoen_US
dc.publisherFrontiers
dc.rights.licenseAttribution 4.0 International (CC BY 4.0)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectdiagnostic classification model
dc.subjectordinal item responses
dc.subjectpartial credit model
dc.subjectrating scales
dc.subjectBayesian estimation
dc.subjectMarkov Chain Monte Carlo (MCMC)
dc.subjectBAYESIAN-ESTIMATION
dc.subjectRULE-SPACE
dc.subjectPARAMETERS
dc.subjectACCURACY
dc.subjectPsychology, Multidisciplinary
dc.titleDiagnostic Classification Models for Ordinal Item Responsesen_US
dc.typeArticle
dc.typetext

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