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Item response theory : a first approach

2017-07Conference object Open access
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dc.contributor.authorNunes, Sandra
dc.contributor.authorOliveira, Teresa
dc.contributor.authorOliveira, Amilcar
dc.date.accessioned2018-02-09T15:17:03Z
dc.date.available2018-07-31T00:30:20Z
dc.date.issued2017-07
dc.description.abstractThe Item Response Theory (IRT) has become one of the most popular scoring frameworks for measurement data, frequently used in computerized adaptive testing, cognitively diagnostic assessment and test equating. According to Andrade et al. (2000), IRT can be defined as a set of mathematical models (Item Response Models – IRM) constructed to represent the probability of an individual giving the right answer to an item of a particular test. The number of Item Responsible Models available to measurement analysis has increased considerably in the last fifteen years due to increasing computer power and due to a demand for accuracy and more meaningful inferences grounded in complex data. The developments in modeling with Item Response Theory were related with developments in estimation theory, most remarkably Bayesian estimation with Markov chain Monte Carlo algorithms (Patz & Junker, 1999). The popularity of Item Response Theory has also implied numerous overviews in books and journals, and many connections between IRT and other statistical estimation procedures, such as factor analysis and structural equation modeling, have been made repeatedly (Van der Lindem & Hambleton, 1997). As stated before the Item Response Theory covers a variety of measurement models, ranging from basic one-dimensional models for dichotomously and polytomously scored items and their multidimensional analogues to models that incorporate information about cognitive sub-processes which influence the overall item response process. The aim of this work is to introduce the main concepts associated with one-dimensional models of Item Response Theory, to specify the logistic models with one, two and three parameters, to discuss some properties of these models and to present the main estimation procedures.pt_PT
dc.description.sponsorshipResearch partially supported by National Funds through FCT – Fundação para a Ciência e Tecnologia, projects Pest-OE/MAT/UI0006/2013 (CEAUL) and UID/MAT/00297/2013 (CMA/NOVA.ID.FCT).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1063/1.4992683pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.2/7104
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherAIP Publishingpt_PT
dc.relation.publisherversionhttp://aip.scitation.org/doi/abs/10.1063/1.4992683pt_PT
dc.subjectTesting for educationpt_PT
dc.subjectStatistical analysispt_PT
dc.subjectArchivespt_PT
dc.subjectMean square error methodspt_PT
dc.subjectMonte Carlo methodspt_PT
dc.titleItem response theory : a first approachpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/5876/UID%2FMAT%2F00297%2F2013/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/5876/UID%2FMAT%2F00006%2F2013/PT
oaire.citation.conferencePlaceRodes, Gréciapt_PT
oaire.citation.startPage550002pt_PT
oaire.citation.titleAIP Conference Proceedingspt_PT
oaire.citation.volumeVol. 1863, Nº 1pt_PT
oaire.fundingStream5876
oaire.fundingStream5876
person.familyNameOliveira
person.familyNameOliveira
person.givenNameTeresa Azinheira
person.givenNameAmilcar
person.identifier1155497
person.identifier.ciencia-id8814-A54B-12DE
person.identifier.ciencia-id7110-61B4-B87F
person.identifier.orcid0000-0003-3283-9946
person.identifier.orcid0000-0001-5500-7742
person.identifier.ridJ-3077-2019
person.identifier.scopus-author-id54403540300
person.identifier.scopus-author-id55675222550
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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