When what is wrong seems right: A Monte Carlo simulation investigating the robustness of coefficient omega to model misspecification

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dc.contributor.advisorFlora, David B.
dc.contributor.authorBell, Stephanie Marie
dc.date.accessioned2021-11-15T15:35:42Z
dc.date.available2021-11-15T15:35:42Z
dc.date.copyright2021-08
dc.date.issued2021-11-15
dc.date.updated2021-11-15T15:35:42Z
dc.degree.disciplinePsychology (Functional Area: Quantitative Methods)
dc.degree.levelMaster's
dc.degree.nameMA - Master of Arts
dc.description.abstractCoefficient omega is a model-based reliability estimate that is unrestricted by assumptions of a unidimensional essentially tau equivalent model. Rather, omega can be adapted to suit the underlying factor structure of a given population. A Monte Carlo simulation was used to investigate the performance of unidimensional omega and omega-hierarchical under circumstances of model misspecification for high and low reliability measures and different scale lengths. In general, bias increased with the amount of unmodeled complexity (i.e. unspecified multidimensionality or error correlations). When models were misspecified, observed bias was higher when true population reliability was lower, and increased with scale length. Less variable estimates were observed when true reliability and sample size were higher.
dc.identifier.urihttp://hdl.handle.net/10315/38748
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectpsychometrics
dc.subject.keywordsreliability
dc.subject.keywordscoefficient omega
dc.subject.keywordsomega hierarchical
dc.subject.keywordsmodel-based reliability
dc.titleWhen what is wrong seems right: A Monte Carlo simulation investigating the robustness of coefficient omega to model misspecification
dc.typeElectronic Thesis or Dissertation

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