Equivalence Tests For Repeated Measures

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dc.contributor.advisorCribbie, Robert A.
dc.creatorNg, Victoria Ka Yin
dc.date.accessioned2015-12-16T19:19:09Z
dc.date.available2015-12-16T19:19:09Z
dc.date.copyright2015-06-25
dc.date.issued2015-12-16
dc.date.updated2015-12-16T19:19:09Z
dc.degree.disciplinePsychology (Functional Area: Quantitative Methods)
dc.degree.levelMaster's
dc.degree.nameMA - Master of Arts
dc.description.abstractEquivalence tests from the null hypothesis significance testing framework are appropriate alternatives to difference tests for demonstrating lack of difference. For determining equivalence among more than two repeated measurements, recently developed equivalence tests include the omnibus Hotelling T2, the pairwise standardized test, the pairwise unstandardized test, and the two one-sided test for negligible trend. With Monte Carlo simulations, the current research evaluated Type I error rates and power rates for these equivalence tests to inform an applied data analytic strategy. Because results suggest that there is no one statistical test that is optimal across all situations, I compare the tests’ statistical behaviours to provide guidance in test selection. Specifically, test selection should be informed by the measurement level of the repeated outcome, correlation structure, and precision.
dc.identifier.urihttp://hdl.handle.net/10315/30669
dc.language.isoen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectQuantitative psychology
dc.subject.keywordsequivalence
dc.subject.keywordsnull hypothesis significance testing
dc.subject.keywordsrepeated measures
dc.subject.keywordsstability
dc.subject.keywordslack of change
dc.subject.keywordslack of difference
dc.subject.keywordsintersection-union test
dc.subject.keywordsTOST
dc.titleEquivalence Tests For Repeated Measures
dc.typeElectronic Thesis or Dissertation

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