Evaluating Equivalence Testing Methods for Measurement Invariance

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dc.contributor.advisorCribbie, Robert A.
dc.creatorCounsell, Alyssa Leigh
dc.date.accessioned2018-03-01T14:01:26Z
dc.date.available2018-03-01T14:01:26Z
dc.date.copyright2017-07-25
dc.date.issued2018-03-01
dc.date.updated2018-03-01T14:01:25Z
dc.degree.disciplinePsychology (Functional Area: Quantitative Methods)
dc.degree.levelDoctoral
dc.degree.namePhD - Doctor of Philosophy
dc.description.abstractEstablishing measurement invariance (MI) is important to validly make group comparisons on psychological constructs of interest. MI involves a multi-stage process of determining whether the factor structure and model parameters are similar across multiple groups. The statistical methods used by most researchers for testing MI is by conducting multiple group confirmatory factor analysis models, whereby a statistically nonsignificant results in a chi square difference test or a small change in goodness of fit indices (GOFs) such as CFI or RMSEA are used to conclude invariance. Yuan and Chan (2016) proposed replacing these approaches with an equivalence test analogue of the chi square difference test (EQ). While they outline the EQ approach for MI, they recommend using an adjusted RMSEA version (EQ-A) for increased power. The current study evaluated the Type I error and power rates of the EQ and EQ-A and compare their performance to using traditional chi square difference tests and GOFs. Results demonstrate that the EQ for nested models was the only procedure that maintains empirical error rates below the nominal level. Results also highlight that the EQ requires larger sample sizes or equivalence bounds based on larger than conventional RMSEA values like .05 to ensure adequate power rates at later MI stages. Because the EQ-A test did not maintain accurate error rates, I do not recommend Yuan and Chans proposed adjustment.
dc.identifier.urihttp://hdl.handle.net/10315/34338
dc.language.isoen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectEducational tests & measurements
dc.subject.keywordsEquivalence testing
dc.subject.keywordsMeasurement in-variance
dc.subject.keywordsGroup comparisons
dc.subject.keywordsConfirmatory factor analysis
dc.subject.keywordsStructural equation modeling
dc.subject.keywordsLatent variables
dc.subject.keywordsScale validation
dc.subject.keywordsFactor structure
dc.titleEvaluating Equivalence Testing Methods for Measurement Invariance
dc.typeElectronic Thesis or Dissertation

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