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EFFECTS OF UNMODELED LATENT CLASSES ON MULTILEVEL GROWTH MIXTURE ESTIMATION IN VALUE-ADDED MODELING

dc.contributor.advisorHancock, Gregory Ren_US
dc.contributor.advisorMislevy, Robert Jen_US
dc.contributor.authorYumoto, Futoshien_US
dc.date.accessioned2011-10-08T05:49:41Z
dc.date.available2011-10-08T05:49:41Z
dc.date.issued2011en_US
dc.identifier.urihttp://hdl.handle.net/1903/11930
dc.description.abstractFairness is necessary to successful evaluation, whether the context is simple and concrete or complex and abstract. Fair evaluation must begin with careful data collection, with clear operationalization of variables whose relationship(s) will represent the outcome(s) of interest. In particular, articulating what it is in the data that needs to be modeled, as well as the relationships of interest, must be specified before conducting any research; these two features will inform both study design and data collection. Heterogeneity is a key characteristic of data that can complicate the data collection design, and especially analysis and interpretation, interfering with or influencing the perception of the relationship(s) that the data will be used to investigate or evaluate. However, planning for, and planning to account for, heterogeneities in data are also critical to the research process, to support valid interpretation of results from any statistical analysis. The multilevel growth mixture model is a new analytic method specifically developed to accommodate heterogeneity so as to minimize the effect of variability on precision in estimation and to reduce bias that may arise in hierarchical data. This is particularly important in the Value Added Model context - where decisions and evaluations about teaching effectiveness are made, because estimates could be contaminated, biased, or simply less precise when data are modeled inappropriately. This research will investigate the effects of un-accounted for heterogeneity at level 1 on the precision of level-2 estimates in multilevel data utilizing the multilevel growth mixture model and multilevel linear growth model.en_US
dc.titleEFFECTS OF UNMODELED LATENT CLASSES ON MULTILEVEL GROWTH MIXTURE ESTIMATION IN VALUE-ADDED MODELINGen_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.pquncontrolledBiasen_US
dc.subject.pquncontrolledLatent Classen_US
dc.subject.pquncontrolledMultilevel Growth Mixture Modelen_US
dc.subject.pquncontrolledValue-Added Modelen_US


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