Handling of Missing Data with Growth Mixture Models

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dc.contributor.advisorHarring, Jeffrey Ren_US
dc.contributor.authorLee, Daniel Yangsupen_US
dc.contributor.departmentMeasurement, Statistics and Evaluationen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2019-06-21T05:37:46Z
dc.date.available2019-06-21T05:37:46Z
dc.date.issued2019en_US
dc.description.abstractThe recent growth of applications of growth mixture models for inference with longitudinal data has introduced a wide range of research dedicated to testing the different aspects of the model. One area of research that has not drawn much attention, however, is the performance of growth mixture models with missing data and when using the various methods for dealing with them. Missing data are usually an inconvenience that must be addressed in any data analysis scenario, and the use of growth mixture models is no less an exception to this. While the literature on various other aspects of growth mixture models has grown, not much research has been conducted on the consequences of mishandling missing data. Although the literature on missing data has generally accepted the use of modern missing data handling techniques, these techniques are not free of problems nor have they been comprehensively tested in the context of growth mixture models. The purpose of this dissertation is to incorporate the various missing data handling techniques on growth mixture models and, by using Monte Carlo simulation techniques, to provide guidance on specific conditions in which certain missing data handling methods will produce accurate and precise parameter estimates typically compromised when using simple, ad hoc, missing data handling approaches, or incorrect techniques.en_US
dc.identifierhttps://doi.org/10.13016/33uu-qmzi
dc.identifier.urihttp://hdl.handle.net/1903/22129
dc.language.isoenen_US
dc.subject.pqcontrolledQuantitative psychologyen_US
dc.subject.pqcontrolledEducational tests & measurementsen_US
dc.subject.pqcontrolledStatisticsen_US
dc.subject.pquncontrolledfinite mixture modelsen_US
dc.subject.pquncontrolledgrowth mixture modelsen_US
dc.subject.pquncontrolledmissing dataen_US
dc.subject.pquncontrolledmultiple imputationen_US
dc.subject.pquncontrolledsimulationen_US
dc.subject.pquncontrolledstructural equation modelingen_US
dc.titleHandling of Missing Data with Growth Mixture Modelsen_US
dc.typeDissertationen_US

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