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Exploring Approaches for Estimating Parameters in Cognitive Diagnosis Models with Small Sample Sizes

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dc.contributor.authorSorrel, Miguel A.
dc.contributor.authorEscudero, Scarlett
dc.contributor.authorNájera, Pablo
dc.contributor.authorVázquez Lira, Ramsés
dc.contributor.authorSchames Kreitchmann, Rodrigo
dc.date.accessioned2024-05-20T11:49:30Z
dc.date.available2024-05-20T11:49:30Z
dc.date.issued2023-04-27
dc.description.abstractCognitive diagnostic models (CDMs) are increasingly being used in various assessment contexts to identify cognitive processes and provide tailored feedback. However, the most commonly used estimation method for CDMs, marginal maximum likelihood estimation with Expectation–Maximization (MMLE-EM), can present difficulties when sample sizes are small. This study compares the results of different estimation methods for CDMs under varying sample sizes using simulated and empirical data. The methods compared include MMLE-EM, Bayes modal, Markov chain Monte Carlo, a non-parametric method, and a parsimonious parametric model such as Restricted DINA. We varied the sample size, and assessed the bias in the estimation of item parameters, the precision in attribute classification, the bias in the reliability estimate, and computational cost. The findings suggest that alternative estimation methods are preferred over MMLE-EM under low sample-size conditions, whereas comparable results are obtained under large sample-size conditions. Practitioners should consider using alternative estimation methods when working with small samples to obtain more accurate estimates of CDM parameters. This study aims to maximize the potential of CDMs by providing guidance on the estimation of the parameters.en
dc.description.versionversión publicada
dc.identifier.doihttps://doi.org/10.3390/psych5020023
dc.identifier.issn2624-8611
dc.identifier.urihttps://hdl.handle.net/20.500.14468/12600
dc.journal.issue2
dc.journal.titlePsych
dc.journal.volume5
dc.language.isoen
dc.publisherMDPI
dc.relation.centerFacultad de Psicología
dc.relation.departmentMetodología de las Ciencias del Comportamiento
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0
dc.subject.keywordscognitive diagnosis modeling
dc.subject.keywordsestimation
dc.subject.keywordssample size
dc.subject.keywordsMMLE-EM
dc.subject.keywordsBayesian
dc.titleExploring Approaches for Estimating Parameters in Cognitive Diagnosis Models with Small Sample Sizeses
dc.typejournal articleen
dc.typeartículoes
dspace.entity.typePublication
relation.isAuthorOfPublicationf5f17927-c033-42e0-867c-3a658141ef26
relation.isAuthorOfPublication.latestForDiscoveryf5f17927-c033-42e0-867c-3a658141ef26
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