A Poor Person's Posterior Predictive Checking of Structural Equation Models
DC Field | Value | Language |
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dc.contributor.author | Lee, Taehun | - |
dc.contributor.author | Cai, Li | - |
dc.contributor.author | Kuhfeld, Megan | - |
dc.date.available | 2019-07-02T13:02:02Z | - |
dc.date.issued | 2016-03 | - |
dc.identifier.issn | 1070-5511 | - |
dc.identifier.issn | 1532-8007 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/26606 | - |
dc.description.abstract | Posterior 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.extent | 15 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD | - |
dc.title | A Poor Person's Posterior Predictive Checking of Structural Equation Models | - |
dc.type | Article | - |
dc.identifier.doi | 10.1080/10705511.2015.1014041 | - |
dc.identifier.bibliographicCitation | STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL, v.23, no.2, pp 206 - 220 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000377132200004 | - |
dc.identifier.scopusid | 2-s2.0-84957428852 | - |
dc.citation.endPage | 220 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 206 | - |
dc.citation.title | STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL | - |
dc.citation.volume | 23 | - |
dc.type.docType | Article | - |
dc.publisher.location | 영국 | - |
dc.subject.keywordAuthor | case influence | - |
dc.subject.keywordAuthor | posterior normality | - |
dc.subject.keywordAuthor | posterior predictive model checking | - |
dc.subject.keywordPlus | ITEM RESPONSE THEORY | - |
dc.subject.keywordPlus | COMPOSITE NULL MODELS | - |
dc.subject.keywordPlus | EXPLORATORY DATA-ANALYSIS | - |
dc.subject.keywordPlus | P-VALUES | - |
dc.subject.keywordPlus | COVARIANCE-STRUCTURES | - |
dc.subject.keywordPlus | BAYESIAN-APPROACH | - |
dc.subject.keywordPlus | ASYMPTOTIC-DISTRIBUTION | - |
dc.subject.keywordPlus | GIBBS SAMPLER | - |
dc.subject.keywordPlus | DISTRIBUTIONS | - |
dc.subject.keywordPlus | ALGORITHM | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalResearchArea | Mathematical Methods In Social Sciences | - |
dc.relation.journalWebOfScienceCategory | Mathematics, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Social Sciences, Mathematical Methods | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | ssci | - |
dc.description.journalRegisteredClass | scopus | - |
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