Please use this identifier to cite or link to this item: http://dx.doi.org/10.25673/78585
Full metadata record
DC FieldValueLanguage
dc.contributor.authorHair, Joseph F.-
dc.contributor.authorHult, G. Tomas M.-
dc.contributor.authorRingle, Christian M.-
dc.contributor.authorSarstedt, Marko-
dc.contributor.authorDanks, Nicholas P.-
dc.contributor.authorRay, Soumya-
dc.date.accessioned2022-03-25T06:50:51Z-
dc.date.available2022-03-25T06:50:51Z-
dc.date.issued2021-
dc.identifier.urihttps://opendata.uni-halle.de//handle/1981185920/80539-
dc.identifier.urihttp://dx.doi.org/10.25673/78585-
dc.description.abstractPartial least squares structural equation modeling (PLS-SEM) has become a standard approach for analyzing complex inter-relationships between observed and latent variables. Researchers appreciate the many advantages of PLS-SEM such as the possibility to estimate very complex models and the method’s flexibility in terms of data requirements and measurement specification. This practical open access guide provides a step-by-step treatment of the major choices in analyzing PLS path models using R, a free software environment for statistical computing, which runs on Windows, macOS, and UNIX computer platforms. Adopting the R software’s SEMinR package, which brings a friendly syntax to creating and estimating structural equation models, each chapter offers a concise overview of relevant topics and metrics, followed by an in-depth description of a case study. Simple instructions give readers the “how-tos” of using SEMinR to obtain solutions and document their results. Rules of thumb in every chapter provide guidance on best practices in the application and interpretation of PLS-SEM.eng
dc.description.sponsorshipOVGU-Publikationsfonds 2021-
dc.format.extent1 Online-Ressource(XIV, 197 p. 77 illus., 51 illus. in color.)-
dc.language.isoeng-
dc.publisherSpringer International Publishing, Cham-
dc.publisherImprint: Springer, Cham-
dc.relation.ispartofseriesClassroom Companion: Business-
dc.relation.ispartofseriesSpringer eBook Collection-
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/-
dc.subjectStructural Equation Modelingeng
dc.subjectRStudioeng
dc.subjectSEMinR packageeng
dc.subject.ddc658.8-
dc.titlePartial Least Squares Structural Equation Modeling (PLS-SEM) Using R : A Workbookeng
dc.typeBook-
dc.identifier.urnurn:nbn:de:gbv:ma9:1-1981185920-805393-
dc.relation.issupplementedbyClassroom Companion: Business-
dc.relation.issupplementedbySpringer eBook Collection-
local.versionTypepublishedVersion-
local.subject.keywordsAn Introduction to Structural Equation Modeling -- Introduction to R and RStudio -- Introduction to SEMinR -- Evaluation of Reflective Measurement Models -- Evaluation of Formative Measurement Models -- Evaluation of the Structural Model -- Mediation Analysis -- Moderation Analysis.-
local.openaccesstrue-
dc.identifier.ppn1776461029-
local.publication.countryXA-CH-
cbs.sru.importDate2022-03-25T06:36:24Z-
local.accessrights.dnbfree-
Appears in Collections:Fakultät für Wirtschaftswissenschaft (OA)

Files in This Item:
File Description SizeFormat 
Hair et al._Partial least_2021.pdfZweitveröffentlichung12.44 MBAdobe PDFThumbnail
View/Open