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dc.contributor.advisorMislevy, Robert Jen_US
dc.contributor.authorLevy, Royen_US
dc.date.accessioned2006-06-14T05:50:51Z
dc.date.available2006-06-14T05:50:51Z
dc.date.issued2006-04-26en_US
dc.identifier.urihttp://hdl.handle.net/1903/3483
dc.description.abstractIf data exhibit a dimensional structure more complex than what is assumed, key conditional independence assumptions of the hypothesized model do not hold. The current work pursues posterior predictive model checking, a flexible family of Bayesian model checking procedures, as a tool for criticizing models in light of inadequately modeled dimensional structure. Factors hypothesized to influence dimensionality and dimensionality assessment are couched in conditional covariance theory and conveyed via geometric representations of multidimensionality. These factors and their hypothesized effects motivate a simulation study that investigates posterior predictive model checking in the context of item response theory for dichotomous observables. A unidimensional model is fit to data that follow compensatory or conjunctive multidimensional item response models to assess the utility of conducting posterior predictive model checking. Discrepancy measures are formulated at the level of individual items and pairs of items. A second study draws from the results of the first study and investigates the model checking techniques in the context of multidimensional Bayesian networks with inhibitory effects. Key findings include support for the hypothesized effects of the manipulated factors with regard to their influence on dimensionality assessment and the superiority of certain discrepancy measures for conducting posterior predictive model checking on dimensionality assessment. The application of these techniques to models both familiar to assessment and those that have not yet become standard practice speaks to the generality of the procedures and its potentially broad applicability.en_US
dc.format.extent1055488 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.titlePosterior predictive model checking for multidimensionality in item response theory and Bayesian networksen_US
dc.typeDissertationen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.contributor.departmentMeasurement, Statistics and Evaluationen_US
dc.subject.pqcontrolledEducation, Tests and Measurementsen_US
dc.subject.pqcontrolledPsychology, Psychometricsen_US
dc.subject.pqcontrolledStatisticsen_US
dc.subject.pquncontrolledposterior predictive model checkingen_US
dc.subject.pquncontrolledmultidimensionalityen_US
dc.subject.pquncontrolleddimensionality assessmenten_US
dc.subject.pquncontrolledlocal independenceen_US
dc.subject.pquncontrolledBayesian inferenceen_US
dc.subject.pquncontrolleditem response theoryen_US


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