Revisiting the Model Size Effect in Structural Equation Modeling
DC Field | Value | Language |
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dc.contributor.author | Shi, Dexin | - |
dc.contributor.author | Lee, Taehun | - |
dc.contributor.author | Terry, Robert A. | - |
dc.date.available | 2019-01-22T14:19:56Z | - |
dc.date.issued | 2018-01 | - |
dc.identifier.issn | 1070-5511 | - |
dc.identifier.issn | 1532-8007 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/1460 | - |
dc.description.abstract | Fitting a large structural equation modeling (SEM) model with moderate to small sample sizes results in an inflated Type I error rate for the likelihood ratio test statistic under the chi-square reference distribution, known as the model size effect. In this article, we show that the number of observed variables (p) and the number of free parameters (q) have unique effects on the Type I error rate of the likelihood ratio test statistic. In addition, the effects of p and q cannot be fully explained using degrees of freedom (df). We also evaluated the performance of 4 correctional methods for the model size effect, including Bartlett's (1950), Swain's (1975), and Yuan's (2005) corrected statistics, and Yuan, Tian, and Yanagihara's (2015) empirically corrected statistic. We found that Yuan et al.' s (2015) empirically corrected statistic generally yields the best performance in controlling the Type I error rate when fitting large SEM models. | - |
dc.format.extent | 20 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD | - |
dc.title | Revisiting the Model Size Effect in Structural Equation Modeling | - |
dc.type | Article | - |
dc.identifier.doi | 10.1080/10705511.2017.1369088 | - |
dc.identifier.bibliographicCitation | STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL, v.25, no.1, pp 21 - 40 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000428719400002 | - |
dc.identifier.scopusid | 2-s2.0-85030148813 | - |
dc.citation.endPage | 40 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 21 | - |
dc.citation.title | STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL | - |
dc.citation.volume | 25 | - |
dc.identifier.url | https://www.researchgate.net/publication/273490423_Revisiting_the_Model_Size_Effect_in_Structural_Equation_Modeling_SEM | - |
dc.type.docType | Article | - |
dc.publisher.location | 미국 | - |
dc.subject.keywordAuthor | correctional methods | - |
dc.subject.keywordAuthor | likelihood ratio statistic | - |
dc.subject.keywordAuthor | model size effect | - |
dc.subject.keywordAuthor | structural equation modeling (SEM) | - |
dc.subject.keywordPlus | CONFIRMATORY FACTOR-ANALYSIS | - |
dc.subject.keywordPlus | FIT INDEXES | - |
dc.subject.keywordPlus | COVARIANCE-STRUCTURES | - |
dc.subject.keywordPlus | TEST STATISTICS | - |
dc.subject.keywordPlus | IMPROPER SOLUTIONS | - |
dc.subject.keywordPlus | SAMPLE-SIZE | - |
dc.subject.keywordPlus | NUMBER | - |
dc.subject.keywordPlus | NONNORMALITY | - |
dc.subject.keywordPlus | INDICATORS | - |
dc.subject.keywordPlus | ROBUSTNESS | - |
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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