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dc.contributor.authorKaradavut, Tugba
dc.date.accessioned2016-10-15T04:30:18Z
dc.date.available2016-10-15T04:30:18Z
dc.date.issued2016-05
dc.identifier.otherkaradavut_tugba_201605_ms
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/karadavut_tugba_201605_ms
dc.identifier.urihttp://hdl.handle.net/10724/36201
dc.description.abstractData sampling methods are promising for analysis of large-scale data sets to reduce computing time and resources. These methods include uniform (random), and leverage-based sampling methods with a recent one called shrinkage leverage-based method. In this study, we compared data sampling methods for accuracy of item parameter estimates in IRT models. In addition, we introduced a new method of sampling, adjusted shrinkage leverage-based (Adj-SLEV) method. We analyzed two samples from PISA 2012 mathematics data set that were normally and non-normally distributed. Random sampling provided the most accurate Rasch item parameter estimates. The method with the highest accuracy varied depending on the type of item parameter for 2-pl and 3-pl models, if each parameter was evaluated individually. Adj-SLEV did not necessarily provide the highest accuracy for each type of item parameter individually, however, consistently provided a good trade-off when all parameters in a model were evaluated together.
dc.languageeng
dc.publisheruga
dc.rightsOn Campus Only Until 2018-05-01
dc.subjectItem response theory, data sampling, PISA 2012 mathematics literacy test
dc.titleComparison of data sampling methods on IRT parameter estimation
dc.typeThesis
dc.description.degreeMS
dc.description.departmentStatistics
dc.description.majorStatistics
dc.description.advisorPing Ma
dc.description.committeePing Ma
dc.description.committeeJaxk Reeves
dc.description.committeeAllan Cohen


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