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dc.contributor.advisorWood, Phillip K. (Phillip Karl)eng
dc.contributor.authorJahng, Seungmin, 1974-eng
dc.date.issued2008eng
dc.date.submitted2008 Falleng
dc.descriptionTitle from PDF of title page (University of Missouri--Columbia, viewed on November 18, 2010).eng
dc.descriptionThe entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.eng
dc.descriptionDissertation advisor: Dr. Phillip K. Wood.eng
dc.descriptionVita.eng
dc.descriptionPh. D. University of Missouri--Columbia 2008.eng
dc.description.abstractRecent developments in data collection methods in the behavioral and social sciences, such as Ecological Momentary Assessment (EMA) enables researchers to gather intensive longitudinal data (ILD) and to examine more detailed features of intraindividual variation of a variable(s) over time. Due to its high intensity of assessments within individuals, ILD often has different characteristics from traditional longitudinal data with a few measurement occasions and requires different assumptions of statistical models in use. In the present thesis, issues in the analysis of ILD and problems of current use of statistical models for the analysis of ILD are discussed and investigated. Specifically, the issue of heterogeneity of autocorrelation and variance across individuals in ILD is extensively studied for multilevel models (MLMs). In chapter 2, a brief introduction to multilevel models and issues in modeling residual covariance structure in MLMs are provided and discussed. In chapter 3, it is shown that bias in estimation of parameters in MLMs under homogeneity assumption is not ignorable when autocorrelation differs across individuals and its average is high. It is also shown that a transformation method, which multiplies variables in the model by the inverse of Cholesky factor of individual-specific error covariance, attenuates the bias for ILD. Chapter 4 reviews variance function models for heterogeneous variance and introduces a two-step MLM approach for modeling heterogeneous variance using squared residuals. A simulation study showed that the two-step MLM does not suffer from non-convergence and is applicable to ILD.eng
dc.description.bibrefIncludes bibliographical references (p. 70-79).eng
dc.format.extent80 pageseng
dc.identifier.oclc689998419eng
dc.identifier.urihttps://doi.org/10.32469/10355/9193eng
dc.identifier.urihttps://hdl.handle.net/10355/9193
dc.languageEnglisheng
dc.publisherUniversity of Missouri--Columbiaeng
dc.relation.ispartofcollectionUniversity of Missouri--Columbia. Graduate School. Theses and Dissertationseng
dc.rightsOpenAccess.eng
dc.rights.licenseThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License.
dc.subject.lcshSocial sciences -- Longitudinal studies -- Mathematical modelseng
dc.subject.lcshSocial sciences -- Statistical methods -- Mathematical modelseng
dc.subject.lcshLongitudinal method -- Statistics -- Mathematical modelseng
dc.subject.lcshAnalysis of covariance -- Mathematical modelseng
dc.subject.lcshMultilevel models (Statistics)eng
dc.titleMultilevel models for intensive longitudinal data with heterogeneous error structure : covariance transformation and variance function modelseng
dc.typeThesiseng
thesis.degree.disciplinePsychology (MU)eng
thesis.degree.grantorUniversity of Missouri--Columbiaeng
thesis.degree.levelDoctoraleng
thesis.degree.namePh. D.eng


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