Please use this identifier to cite or link to this item: http://dx.doi.org/10.25673/78585
Title: Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R : A Workbook
Author(s): Hair, Joseph F.Look up in the Integrated Authority File of the German National Library
Hult, G. Tomas M.Look up in the Integrated Authority File of the German National Library
Ringle, Christian M.Look up in the Integrated Authority File of the German National Library
Sarstedt, MarkoLook up in the Integrated Authority File of the German National Library
Danks, Nicholas P.
Ray, SoumyaLook up in the Integrated Authority File of the German National Library
Issue Date: 2021
Extent: 1 Online-Ressource(XIV, 197 p. 77 illus., 51 illus. in color.)
Type: Book
Language: English
Publisher: Springer International Publishing, Cham
Imprint: Springer, Cham
Series/Report no.: Classroom Companion: Business
Springer eBook Collection
URN: urn:nbn:de:gbv:ma9:1-1981185920-805393
Subjects: Structural Equation Modeling
RStudio
SEMinR package
Abstract: Partial 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.
URI: https://opendata.uni-halle.de//handle/1981185920/80539
http://dx.doi.org/10.25673/78585
Open Access: Open access publication
License: (CC BY 4.0) Creative Commons Attribution 4.0(CC BY 4.0) Creative Commons Attribution 4.0
Sponsor/Funder: OVGU-Publikationsfonds 2021
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