REPEATED MEANS MODELING: AN ALTERNATIVE FOR REPEATED MEASURES DESIGNS

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dc.contributor.advisorHancock, Gregory R.en_US
dc.contributor.authorMao, Xiulinen_US
dc.contributor.departmentHuman Developmenten_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2018-09-12T05:32:54Z
dc.date.available2018-09-12T05:32:54Z
dc.date.issued2018en_US
dc.description.abstractThe study presents a series of alternative ANOVA-based methods which offered a remedy to the traditional F test to accommodate violations of normality and sphericity assumptions. Specially Robust Means Modeling (RMM), developed from structured means modeling (SMM), a branch of structural equation modeling (SEM), is introduced to circumvent the sphericity assumption while alleviating the violation of normality assumption. Maximum likelihood, Satorra-Bentler scaled chi-square, asymptotic distribution-free (ADF) methods and its corrections, as well as residual-based ADF methods (RES) and its corrections, are included in this RMM category. A Monte Carlo simulation is designed to evaluate Type I error robustness and power of the ANOVA-based methods and the proposed RMM methods under the fully crossed conditions including degree of non-sphericity, degree of non-normality, sample size, and the number of levels of the repeated measures. The study gains strong ground for RMM methods to be recommended over ANOVA-based methods under almost all conditions except when the model was complex (i.e. 8 levels) and sample size was small (15, 30).en_US
dc.identifierhttps://doi.org/10.13016/M2P55DM10
dc.identifier.urihttp://hdl.handle.net/1903/21202
dc.language.isoenen_US
dc.subject.pqcontrolledEducational tests & measurementsen_US
dc.subject.pquncontrolledANOVA-based methodsen_US
dc.subject.pquncontrolledrepeated measures designen_US
dc.subject.pquncontrolledrobust means modelingen_US
dc.titleREPEATED MEANS MODELING: AN ALTERNATIVE FOR REPEATED MEASURES DESIGNSen_US
dc.typeDissertationen_US

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