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Comparing Methods for Multilevel Moderated Mediation: A Decomposed-first Strategy

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dc.contributor.authorKim, Soyoung-
dc.contributor.authorHong, Sehee-
dc.date.accessioned2021-08-30T15:04:21Z-
dc.date.available2021-08-30T15:04:21Z-
dc.date.created2021-06-19-
dc.date.issued2020-09-02-
dc.identifier.issn1070-5511-
dc.identifier.urihttps://scholar.korea.ac.kr/handle/2021.sw.korea/53195-
dc.description.abstractThe purpose of this study is to propose a decomposed-first strategy for multilevel moderated mediation and to compare the performance of three moderated mediation approaches in multilevel structural equation modeling. The following approaches were compared in simulations to test coefficients that were decomposed level by level: orthogonal partitioning with centering within cluster, random coefficient prediction, and latent moderated structural equations. The manipulated conditions for the simulation analysis were the analysis method, the number of groups, group size, and intraclass correlation. The results showed that, for samples consisting of a large number of groups, a large average group size and a large intraclass correlation, LMS had the strongest performance. This study is meaningful in that it produces interpretable coefficients by applying a decomposed-first strategy in multilevel moderated mediation and extends a basic moderated mediation model to include more specific research questions in multilevel structural equation modeling.-
dc.languageEnglish-
dc.language.isoen-
dc.publisherROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD-
dc.subjectSTRUCTURAL EQUATION MODELS-
dc.subjectMAXIMUM-LIKELIHOOD-ESTIMATION-
dc.subjectSAMPLE-SIZE-
dc.subjectSCHOOL-
dc.subjectLEVEL-
dc.subjectVARIABLES-
dc.subjectFRAMEWORK-
dc.subjectISSUES-
dc.subjectPOWER-
dc.titleComparing Methods for Multilevel Moderated Mediation: A Decomposed-first Strategy-
dc.typeArticle-
dc.contributor.affiliatedAuthorKim, Soyoung-
dc.contributor.affiliatedAuthorHong, Sehee-
dc.identifier.doi10.1080/10705511.2019.1683015-
dc.identifier.scopusid2-s2.0-85075064067-
dc.identifier.wosid000495197400001-
dc.identifier.bibliographicCitationSTRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL, v.27, no.5, pp.661 - 677-
dc.relation.isPartOfSTRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL-
dc.citation.titleSTRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL-
dc.citation.volume27-
dc.citation.number5-
dc.citation.startPage661-
dc.citation.endPage677-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassssci-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaMathematics-
dc.relation.journalResearchAreaMathematical Methods In Social Sciences-
dc.relation.journalWebOfScienceCategoryMathematics, Interdisciplinary Applications-
dc.relation.journalWebOfScienceCategorySocial Sciences, Mathematical Methods-
dc.subject.keywordPlusSTRUCTURAL EQUATION MODELS-
dc.subject.keywordPlusMAXIMUM-LIKELIHOOD-ESTIMATION-
dc.subject.keywordPlusSAMPLE-SIZE-
dc.subject.keywordPlusSCHOOL-
dc.subject.keywordPlusLEVEL-
dc.subject.keywordPlusVARIABLES-
dc.subject.keywordPlusFRAMEWORK-
dc.subject.keywordPlusISSUES-
dc.subject.keywordPlusPOWER-
dc.subject.keywordAuthorMultilevel moderated mediation-
dc.subject.keywordAuthordecomposed-first strategy-
dc.subject.keywordAuthorlatent moderated structural equations-
dc.subject.keywordAuthorrandom coefficient prediction-
dc.subject.keywordAuthororthogonal partitioning with centering within cluster-
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