The comparison of the scores obtained by Bayesian nonparametric model and classical test theory methods*

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dc.authoridYurtcu, Meltem/0000-0003-3303-5093
dc.authorwosidYurtcu, Meltem/ABH-6078-2020
dc.contributor.authorYurtcu, Meltem
dc.contributor.authorKelecioglu, Hulya
dc.contributor.authorBoone, Edward L.
dc.date.accessioned2024-08-04T20:50:24Z
dc.date.available2024-08-04T20:50:24Z
dc.date.issued2021
dc.departmentİnönü Üniversitesien_US
dc.description.abstractBayesian Nonparametric (BNP) modelling can be used to obtain more detailed information in test equating studies and to increase the accuracy of equating by accounting for covariates. In this study, two covariates are included in the equating under the Bayes nonparametric model, one is continuous, and the other is discrete. Scores equated with this model were obtained for a single group design for a small group in the study. The equated scores obtained with the model were compared with the mean and linear equating methods in the Classical Test Theory. Considering the equated scores obtained from three different methods, it was found that the equated scores obtained with the BNP model produced a distribution closer to the target test. Even the classical methods will give a good result with the smallest error when using a small sample, making equating studies valuable. The inclusion of the covariates in the model in the classical test equating process is based on some assumptions and cannot be achieved especially using small groups. The BNP model will be more beneficial than using frequentist methods, regardless of this limitation. Information about booklets and variables can be obtained from the distributors and equated scores that obtained with the BNP model. In this case, it makes it possible to compare sub-categories. This can be expressed as indicating the presence of differential item functioning (DIF). Therefore, the BNP model can be used actively in test equating studies, and it provides an opportunity to examine the characteristics of the individual participants at the same time. Thus, it allows test equating even in a small sample and offers the opportunity to reach a value closer to the scores in the target test.en_US
dc.identifier.doi10.1177/00368504211028371
dc.identifier.issn0036-8504
dc.identifier.issn2047-7163
dc.identifier.issue3en_US
dc.identifier.pmid34236901en_US
dc.identifier.scopus2-s2.0-85109368138en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1177/00368504211028371
dc.identifier.urihttps://hdl.handle.net/11616/100031
dc.identifier.volume104en_US
dc.identifier.wosWOS:000690903700001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherSage Publications Ltden_US
dc.relation.ispartofScience Progressen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectSingle group designen_US
dc.subjectBayesian nonparametric modelen_US
dc.subjecttest equatingen_US
dc.subjectcovariatesen_US
dc.subjectequated scoresen_US
dc.titleThe comparison of the scores obtained by Bayesian nonparametric model and classical test theory methods*en_US
dc.typeArticleen_US

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