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Cited 2 time in webofscience Cited 2 time in scopus
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A Poor Person's Posterior Predictive Checking of Structural Equation Models

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dc.contributor.authorLee, Taehun-
dc.contributor.authorCai, Li-
dc.contributor.authorKuhfeld, Megan-
dc.date.available2019-07-02T13:02:02Z-
dc.date.issued2016-03-
dc.identifier.issn1070-5511-
dc.identifier.issn1532-8007-
dc.identifier.urihttps://scholarworks.bwise.kr/cau/handle/2019.sw.cau/26606-
dc.description.abstractPosterior predictive model checking (PPMC) is a Bayesian model checking method that compares the observed data to (plausible) future observations from the posterior predictive distribution. We propose an alternative to PPMC in the context of structural equation modeling, which we term the poor person's PPMC (PP-PPMC), for the situation wherein one cannot afford (or is unwilling) to draw samples from the full posterior. Using only by-products of likelihood-based estimation (maximum likelihood estimate and information matrix), the PP-PPMC offers a natural method to handle parameter uncertainty in model fit assessment. In particular, a coupling relationship between the classical p values from the model fit chi-square test and the predictive p values from the PP-PPMC method is carefully examined, suggesting that PP-PPMC might offer an alternative, principled approach for model fit assessment. We also illustrate the flexibility of the PP-PPMC approach by applying it to case-influence diagnostics.-
dc.format.extent15-
dc.language영어-
dc.language.isoENG-
dc.publisherROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD-
dc.titleA Poor Person's Posterior Predictive Checking of Structural Equation Models-
dc.typeArticle-
dc.identifier.doi10.1080/10705511.2015.1014041-
dc.identifier.bibliographicCitationSTRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL, v.23, no.2, pp 206 - 220-
dc.description.isOpenAccessN-
dc.identifier.wosid000377132200004-
dc.identifier.scopusid2-s2.0-84957428852-
dc.citation.endPage220-
dc.citation.number2-
dc.citation.startPage206-
dc.citation.titleSTRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL-
dc.citation.volume23-
dc.type.docTypeArticle-
dc.publisher.location영국-
dc.subject.keywordAuthorcase influence-
dc.subject.keywordAuthorposterior normality-
dc.subject.keywordAuthorposterior predictive model checking-
dc.subject.keywordPlusITEM RESPONSE THEORY-
dc.subject.keywordPlusCOMPOSITE NULL MODELS-
dc.subject.keywordPlusEXPLORATORY DATA-ANALYSIS-
dc.subject.keywordPlusP-VALUES-
dc.subject.keywordPlusCOVARIANCE-STRUCTURES-
dc.subject.keywordPlusBAYESIAN-APPROACH-
dc.subject.keywordPlusASYMPTOTIC-DISTRIBUTION-
dc.subject.keywordPlusGIBBS SAMPLER-
dc.subject.keywordPlusDISTRIBUTIONS-
dc.subject.keywordPlusALGORITHM-
dc.relation.journalResearchAreaMathematics-
dc.relation.journalResearchAreaMathematical Methods In Social Sciences-
dc.relation.journalWebOfScienceCategoryMathematics, Interdisciplinary Applications-
dc.relation.journalWebOfScienceCategorySocial Sciences, Mathematical Methods-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassssci-
dc.description.journalRegisteredClassscopus-
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