Please use this identifier to cite or link to this item: http://dx.doi.org/10.25673/80397
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dc.contributor.authorSchlittgen, Rainer-
dc.contributor.authorSarstedt, Marko-
dc.contributor.authorRingle, Christian M.-
dc.date.accessioned2022-04-01T09:39:05Z-
dc.date.available2022-04-01T09:39:05Z-
dc.date.issued2020-
dc.date.submitted2020-
dc.identifier.urihttps://opendata.uni-halle.de//handle/1981185920/82351-
dc.identifier.urihttp://dx.doi.org/10.25673/80397-
dc.description.abstractExamining the efficacy of composite-based structural equation modeling (SEM) features prominently in research. However, studies analyzing the efficacy of corresponding estimators usually rely on factor model data. Thereby, they assess and analyze their performance on erroneous grounds (i.e., factor model data instead of composite model data). A potential reason for this malpractice lies in the lack of available composite model-based data generation procedures for prespecified model parameters in the structural model and the measurements models. Addressing this gap in research, we derive model formulations and present a composite model-based data generation approach. The findings will assist researchers in their composite-based SEM simulation studies.eng
dc.description.sponsorshipProjekt DEAL 2020-
dc.language.isoeng-
dc.relation.ispartofhttp://link.springer.com/journal/11634-
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/-
dc.subjectComposite modelseng
dc.subjectData generationeng
dc.subjectGeneralized structural component analysiseng
dc.subjectGSCAeng
dc.subjectPartial least squareseng
dc.subjectPLSeng
dc.subjectStructural equation modelingeng
dc.subjectSEMeng
dc.subject.ddc330-
dc.titleData generation for composite-based structural equation modeling methodseng
dc.typeArticle-
dc.identifier.urnurn:nbn:de:gbv:ma9:1-1981185920-823514-
local.versionTypepublishedVersion-
local.bibliographicCitation.journaltitleAdvances in data analysis and classification-
local.bibliographicCitation.volume14-
local.bibliographicCitation.issue4-
local.bibliographicCitation.pagestart747-
local.bibliographicCitation.pageend757-
local.bibliographicCitation.publishernameSpringer-
local.bibliographicCitation.publisherplaceBerlin-
local.bibliographicCitation.doi10.1007/s11634-020-00396-6-
local.openaccesstrue-
dc.identifier.ppn1793803471-
local.bibliographicCitation.year2020-
cbs.sru.importDate2022-04-01T09:33:49Z-
local.bibliographicCitationEnthalten in Advances in data analysis and classification - Berlin : Springer, 2007-
local.accessrights.dnbfree-
Appears in Collections:Fakultät für Wirtschaftswissenschaft (OA)

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