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USING LATENT PROFILE MODELS AND UNSTRUCTURED GROWTH MIXTURE MODELS TO ASSESS THE NUMBER OF LATENT CLASSES IN GROWTH MIXTURE MODELING

dc.contributor.advisorHancock, Gregory R.en_US
dc.contributor.authorLiu, Minen_US
dc.date.accessioned2011-10-08T05:36:41Z
dc.date.available2011-10-08T05:36:41Z
dc.date.issued2011en_US
dc.identifier.urihttp://hdl.handle.net/1903/11885
dc.description.abstractGrowth mixture modeling has gained much attention in applied and methodological social science research recently, but the selection of the number of latent classes for such models remains a challenging issue. This problem becomes more serious when one of the key assumptions of this model, proper model-specification is violated. The current simulation study compared the performance of a linear growth mixture model in determining the correct number of latent classes against two less parametrically restricted options, a latent profile model and an unstructured growth mixture model. A variety of conditions were examined, both for properly and improperly specified models. Results indicate that prior to the application of linear growth mixture model, the unstructured growth mixture model is a promising way to identify the correct number of unobserved groups underlying the data by using most model fit indices across all the conditions investigated in this study.en_US
dc.titleUSING LATENT PROFILE MODELS AND UNSTRUCTURED GROWTH MIXTURE MODELS TO ASSESS THE NUMBER OF LATENT CLASSES IN GROWTH MIXTURE 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.pqcontrolledQuantitative psychology and psychometricsen_US
dc.subject.pqcontrolledStatisticsen_US
dc.subject.pqcontrolledEducational tests & measurementsen_US
dc.subject.pquncontrolledclass enumerationen_US
dc.subject.pquncontrolledgrowth mixture modelsen_US
dc.subject.pquncontrolledlatent class analysisen_US
dc.subject.pquncontrolledlatent profile modelsen_US
dc.subject.pquncontrolledmodel fit indicesen_US


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