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RANDOMIZATION-BASED INFERENCE ABOUT LATENT VARIABLES FROM COMPLEX SAMPLES: THE CASE OF TWO-STAGE SAMPLING

dc.contributor.advisorMislevy, Robert J.en_US
dc.contributor.authorLi, Tiandongen_US
dc.date.accessioned2012-07-06T11:09:25Z
dc.date.available2012-07-06T11:09:25Z
dc.date.issued2012en_US
dc.identifier.urihttp://hdl.handle.net/1903/12514
dc.description.abstractIn large-scale assessments, such as the National Assessment of Educational Progress (NAEP), plausible values based on Multiple Imputations (MI) have been used to estimate population characteristics for latent constructs under complex sample designs. Mislevy (1991) derived a closed-form analytic solution for a fixed-effect model in creating plausible values assuming a classical test theory model and a stratified student sample and proposed an analogous solution for a random-effects model to be applied with a two-stage student sample design. The research reported here extends the discussion of this random-effects model under the classical test theory framework. Under the simplified assumption of known population parameters, analytical solutions are provided for multiple imputations in the case of the classical test theory measurement model and two-stage sampling and their properties are verified in reconstructing population properties for the unobservable latent variables. With the more practical assumptions of unknown population and cluster means, this study empirically examines the reconstruction of population attributes. Next, properties of sample statistics are examined. Specifically, this research explores the impact of the variance components and sample sizes on the sampling variance of the MI-based estimate for the population mean. Findings include significant predictors and influential factors. Last, the relationships between the sampling variance of the estimate of the population mean based on the imputations and those based on observations of the true score and the observed score are discussed. The sampling variance based on the imputed score is expected to be the higher boundary of that based on the observed score, which is expected to be the higher boundary of that based on the true score.en_US
dc.titleRANDOMIZATION-BASED INFERENCE ABOUT LATENT VARIABLES FROM COMPLEX SAMPLES: THE CASE OF TWO-STAGE SAMPLINGen_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.pqcontrolledEducational tests & measurementsen_US
dc.subject.pqcontrolledQuantitative psychology and psychometricsen_US
dc.subject.pqcontrolledStatisticsen_US
dc.subject.pquncontrolledClassical test theoryen_US
dc.subject.pquncontrolledCluster sampleen_US
dc.subject.pquncontrolledLarge-scale assessmenten_US
dc.subject.pquncontrolledMultiple imputationen_US
dc.subject.pquncontrolledRandom-effects modelen_US
dc.subject.pquncontrolledRandomization-based inferenceen_US


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