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Testing for Differentially Functioning Indicators Using Mixtures of Confirmatory Factor Analysis Models

dc.contributor.advisorHancock, Gregory Ren_US
dc.contributor.authorMann, Heather Marieen_US
dc.date.accessioned2009-07-02T05:46:09Z
dc.date.available2009-07-02T05:46:09Z
dc.date.issued2009en_US
dc.identifier.urihttp://hdl.handle.net/1903/9170
dc.description.abstractHeterogeneity in measurement model parameters across known groups can be modeled and tested using multigroup confirmatory factor analysis (CFA). When it is not reasonable to assume that parameters are homogeneous for all observations in a manifest group, mixture CFA models are appropriate. Mixture CFA models can add theoretically important unmeasured characteristics to capture heterogeneity and have the potential to be used to test measurement invariance. The current study investigated the ability of mixture CFA models to identify differences in factor loadings across latent classes when there is no mean separation in both the latent and measured variables. Using simulated data from models with known parameters, parameter recovery, classification accuracy, and the power of the likelihood-ratio test were evaluated as impacted by model complexity, sample size, latent class proportions, magnitude of factor loading differences, percentage of noninvariant factor loadings, and pattern of noninvariant factor loadings. Results suggested that mixture CFA models may be a viable option for testing the invariance of measurement model parameters, but without impact and differences in measurement intercepts, larger sample sizes, more noninvariant factor loadings, and larger amounts of heterogeneity are needed to distinguish different latent classes and successfully estimate their parameters.en_US
dc.format.extent658900 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.titleTesting for Differentially Functioning Indicators Using Mixtures of Confirmatory Factor Analysis Modelsen_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.pqcontrolledPsychology, Psychometricsen_US
dc.subject.pqcontrolledStatisticsen_US
dc.subject.pqcontrolledEducation, Tests and Measurementsen_US
dc.subject.pquncontrolledheterogeneityen_US
dc.subject.pquncontrolledlatent classen_US
dc.subject.pquncontrolledmeasurement invarianceen_US
dc.subject.pquncontrolledmixture CFAen_US


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