Detecting Local Item Dependence in Polytomous Adaptive Data

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dc.contributor.advisorHarring, Jeffrey R.en_US
dc.contributor.advisorRupp, Andre A.en_US
dc.contributor.authorMislevy, Jessica Lynnen_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.accessioned2011-07-07T05:40:36Z
dc.date.available2011-07-07T05:40:36Z
dc.date.issued2011en_US
dc.description.abstractA rapidly expanding arena for item response theory (IRT) is in attitudinal and health-outcomes survey applications, often with polytomous items. In particular, there is interest in computer adaptive testing (CAT). Meeting model assumptions is necessary to realize the benefits of IRT in this setting, however. Although initial investigations of local item dependence (LID) have been studied both for polytomous items in fixed-form settings and for dichotomous items in CAT settings, there have been no publications applying LID detection methodology to polytomous items in CAT despite its central importance to these applications. The research documented herein investigates the extension of widely used methods of LID detection, Yen's Q<sub>3</sub> statistic and Pearson's Statistic X<super>2</super>, in this context, via a simulation study. The simulation design and results are contextualized throughout with a real item bank and data set of this type from the Patient-Reported Outcomes Measurement Information System (PROMIS).en_US
dc.identifier.urihttp://hdl.handle.net/1903/11676
dc.subject.pqcontrolledEducational Tests and Measurementsen_US
dc.subject.pqcontrolledQuantitative Psychology and Psychometricsen_US
dc.subject.pquncontrolledComputer Adaptive Testingen_US
dc.subject.pquncontrolledItem Response Theoryen_US
dc.subject.pquncontrolledLocal Dependenceen_US
dc.subject.pquncontrolledMissing Dataen_US
dc.subject.pquncontrolledPolytomous Itemsen_US
dc.titleDetecting Local Item Dependence in Polytomous Adaptive Dataen_US
dc.typeDissertationen_US

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