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dc.contributor.advisorHancock, Gregory Ren_US
dc.contributor.advisorHarring, Jeffrey Ren_US
dc.contributor.authorSweet, Shauna Jayneen_US
dc.date.accessioned2019-02-01T06:41:54Z
dc.date.available2019-02-01T06:41:54Z
dc.date.issued2018en_US
dc.identifierhttps://doi.org/10.13016/qmfb-kx33
dc.identifier.urihttp://hdl.handle.net/1903/21658
dc.description.abstractThis paper presents a new conceptual framework and corresponding psychometric model designed for the pre-calibration of automatically generated items. This model utilizes a multi-level framework and a combination of crossed fixed and random effects to capture key components of the generative process, and is intended to be broadly applicable across research efforts and contexts. Unique among models proposed within the AIG literature, this model incorporates specific mean and variance parameters to support the direct assessment of the quality of the item generation process. The utility of this framework is demonstrated through an empirical analysis of response data collected from the online administration of automatically generated items intended to assess young students’ mathematics fluency. Limitations in the application of the proposed framework are explored through targeted simulation studies, and future directions for research are discussed.en_US
dc.language.isoenen_US
dc.titleA FRAMEWORK FOR THE PRE-CALIBRATION OF AUTOMATICALLY GENERATED ITEMSen_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.pqcontrolledStatisticsen_US
dc.subject.pqcontrolledPsychologyen_US
dc.subject.pquncontrolledAutomatic Item Generationen_US


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