The Multidimensional Generalized Graded Unfolding Model for Assessment of Change across Repeated Measures

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dc.contributor.advisorRoberts, James Sen_US
dc.contributor.advisorDayton, Chan Men_US
dc.contributor.authorCui, Weiweien_US
dc.contributor.departmentMeasurement, Statistics and Evaluationen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2008-06-20T05:36:25Z
dc.date.available2008-06-20T05:36:25Z
dc.date.issued2008-05-13en_US
dc.description.abstractA multidimensional extension of the generalized graded unfolding model for repeated measures (GGUM-RM) is introduced and applied to analyze attitude change across time using responses collected by a Thurstone or Likert questionnaire. The model conceptualizes the change across time as separate latent variables and provides direct estimates of both individual and group change while accounting for the dependency among latent variables. The parameters and hyperparameters of GGUM-RM are estimated by fully Bayesian estimation method via WinBUGS. The accuracy of the estimation procedure is demonstrated by a simulation study, and the application of the GGUM-RM is illustrated by the analysis of attitude change toward abortion among college students.en_US
dc.format.extent1721535 bytes
dc.format.extent44302 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/8151
dc.language.isoen_US
dc.subject.pqcontrolledEducation, Tests and Measurementsen_US
dc.subject.pqcontrolledPsychology, Psychometricsen_US
dc.subject.pqcontrolledEducation, Tests and Measurementsen_US
dc.subject.pquncontrolledmultidimensional IRT modelsen_US
dc.subject.pquncontrolledunfolding IRT modelsen_US
dc.subject.pquncontrolledchange estimateen_US
dc.subject.pquncontrolledrepeated measuresen_US
dc.subject.pquncontrolledBayesian estimationen_US
dc.titleThe Multidimensional Generalized Graded Unfolding Model for Assessment of Change across Repeated Measuresen_US
dc.typeDissertationen_US

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