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dc.contributor.CRUESPUNIVERSIDADE DE ESTADUAL DE CAMPINASpt_BR
dc.identifier.isbn1573-1375pt
dc.typeArtigo de Periódicopt_BR
dc.titleBayesian Longitudinal Item Response Modeling With Restricted Covariance Pattern Structurespt_BR
dc.contributor.authorAzevedopt_BR
dc.contributor.authorCLN; Foxpt_BR
dc.contributor.authorJP; Andradept_BR
dc.contributor.authorDFpt_BR
unicamp.author.emailcnaber@ime.unicamp.brpt_BR
dc.subjectLongitudinal Item Response Theorypt_BR
dc.subjectCovariance Patternspt_BR
dc.subjectBayesian Inferencept_BR
dc.subjectMcmcpt_BR
dc.description.abstractEducational studies are often focused on growth in student performance and background variables that can explain developmental differences across examinees. To study educational progress, a flexible latent variable model is required to model individual differences in growth given longitudinal item response data, while accounting for time-heterogenous dependencies between measurements of student performance. Therefore, an item response theory model, to measure time-specific latent traits, is extended to model growth using the latent variable technology. Following Muthen (Learn Individ Differ 10: 73-101, 1998) and Azevedo et al. (Comput Stat Data Anal 56: 4399-4412, 2012b), among others, the mean structure of the model represents developmental change in student achievement. Restricted covariance pattern models are proposed to model the variance-covariance structure of the student achievements. The main advantage of the extension is its ability to describe and explain the type of time-heterogenous dependency between student achievements. An efficient MCMC algorithm is given that can handle identification rules and restricted parametric covariance structures. A reparameterization technique is used, where unrestricted model parameters are sampled and transformed to obtain MCMC samples under the implied restrictions. The study is motivated by a large-scale longitudinal research program of the Brazilian Federal government to improve the teaching quality and general structure of schools for primary education. It is shown that the growth in math achievement can be accurately measured when accounting for complex dependencies over grades using time-heterogenous covariances structures.en
dc.relation.ispartofStatistics and Computingpt_BR
dc.publisher.cityDORDRECHTpt_BR
dc.publisherSPRINGERpt_BR
dc.date.issued2016pt_BR
dc.identifier.citationStatistics And Computing. SPRINGER, n. 26, n. 1, p. 443 - 460.pt_BR
dc.language.isoEnglishpt_BR
dc.description.volume26pt_BR
dc.description.issuenumberpt_BR
dc.description.firstpage443pt_BR
dc.description.lastpage460pt_BR
dc.rightsfechadopt_BR
dc.sourceWOSpt_BR
dc.identifier.issn0960-3174pt_BR
dc.identifier.wosidWOS:000373347100030pt_BR
dc.identifier.doi10.1007/s11222-014-9518-5pt_BR
dc.identifier.urlhttp://link-springer-com.ez88.periodicos.capes.gov.br/article/10.1007%2Fs11222-014-9518-5pt_BR
dc.description.sponsorshipCNPq (Conselho Nacional de Desenvolvimento Cientifico e Tecnologico) from Brazilpt_BR
dc.description.sponsorship1Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)pt_BR
dc.date.available2016-12-06T18:30:51Z-
dc.date.accessioned2016-12-06T18:30:51Z-
dc.description.provenanceMade available in DSpace on 2016-12-06T18:30:51Z (GMT). No. of bitstreams: 1 000373347100030.pdf: 1263304 bytes, checksum: ebc73035174f4688af2c712b4654eed0 (MD5) Previous issue date: 2016en
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/320143-
dc.description.conferencelocationpt_BR
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