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A dominance variant under the Multi-Unidimensional Pairwise-Preference framework: Model formulation and Markov Chain Monte Carlo estimation

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dc.contributor.authorMorillo Cuadrado, Daniel Vicente
dc.contributor.authorLeenen, Iwin
dc.contributor.authorAbad, Francisco J.
dc.contributor.authorHontangas, Pedro
dc.contributor.authorTorre, Jimmy de la
dc.contributor.authorPonsoda, Vicente
dc.date.accessioned2024-12-09T07:48:55Z
dc.date.available2024-12-09T07:48:55Z
dc.date.issued2016-08-13
dc.descriptionLa versión registrada de este artículo, publicado por primera vez en Applied Psychological Measurement, 40(7), 500-516, está disponible en línea en el sitio web del editor: https://doi.org/10.1177/0146621616662226 The registered version of this article, first published in Applied Psychological Measurement, 40(7), 500-516, is available online at the publisher's website: https://doi.org/10.1177/0146621616662226
dc.description.abstractForced-choice questionnaires have been proposed as a way to control some response biases associated with traditional questionnaire formats (e.g., Likert-type scales). Whereas classical scoring methods have issues of ipsativity, item response theory (IRT) methods have been claimed to accurately account for the latent trait structure of these instruments. In this article, the authors propose the multi-unidimensional pairwise preference two-parameter logistic (MUPP-2PL) model, a variant within Stark, Chernyshenko, and Drasgow’s MUPP framework for items that are assumed to fit a dominance model. They also introduce a Markov Chain Monte Carlo (MCMC) procedure for estimating the model’s parameters. The authors present the results of a simulation study, which shows appropriate goodness of recovery in all studied conditions. A comparison of the newly proposed model with a Brown and Maydeu’s Thurstonian IRT model led us to the conclusion that both models are theoretically very similar and that the Bayesian estimation procedure of the MUPP-2PL may provide a slightly better recovery of the latent space correlations and a more reliable assessment of the latent trait estimation errors. An application of the model to a real data set shows convergence between the two estimation procedures. However, there is also evidence that the MCMC may be advantageous regarding the item parameters and the latent trait correlations.en
dc.description.versionversión publicada
dc.identifier.citationMorillo, D., Leenen, I., Abad, F. J., Hontangas, P., de la Torre, J., & Ponsoda, V. (2016). A Dominance Variant Under the Multi-Unidimensional Pairwise-Preference Framework: Model Formulation and Markov Chain Monte Carlo Estimation. Applied Psychological Measurement, 40(7), 500-516. https://doi.org/10.1177/0146621616662226
dc.identifier.doihttps://doi.org/10.1177/0146621616662226
dc.identifier.issn0146-6216
dc.identifier.urihttps://hdl.handle.net/20.500.14468/24740
dc.journal.issue7
dc.journal.titleApplied Psychological Measurement
dc.journal.volume40
dc.language.isoen
dc.page.final516
dc.page.initial500
dc.publisherSage
dc.relation.centerFacultades y escuelas::Facultad de Psicología
dc.relation.departmentMetodología de las Ciencias del Comportamiento
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject61 Psicología
dc.subject.keywordsforced-choice questionnairesen
dc.subject.keywordsipsative scoresen
dc.subject.keywordsBayesian estimationen
dc.subject.keywordsMCMCen
dc.subject.keywordsmultidimensional IRTen
dc.titleA dominance variant under the Multi-Unidimensional Pairwise-Preference framework: Model formulation and Markov Chain Monte Carlo estimationen
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
relation.isAuthorOfPublicationeaf86785-e049-4e6e-a3cc-0409c8954333
relation.isAuthorOfPublication.latestForDiscoveryeaf86785-e049-4e6e-a3cc-0409c8954333
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