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http://dx.doi.org/10.25673/78585
Titel: | Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R : A Workbook |
Autor(en): | Hair, Joseph F. Hult, G. Tomas M. Ringle, Christian M. Sarstedt, Marko Danks, Nicholas P. Ray, Soumya |
Erscheinungsdatum: | 2021 |
Umfang: | 1 Online-Ressource(XIV, 197 p. 77 illus., 51 illus. in color.) |
Art: | Buch |
Sprache: | Englisch |
Herausgeber: | Springer International Publishing, Cham Imprint: Springer, Cham |
Serie/Report Nr.: | Classroom Companion: Business Springer eBook Collection |
URN: | urn:nbn:de:gbv:ma9:1-1981185920-805393 |
Schlagwörter: | Structural Equation Modeling RStudio SEMinR package |
Zusammenfassung: | 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-Publikation |
Nutzungslizenz: | (CC BY 4.0) Creative Commons Namensnennung 4.0 International |
Sponsor/Geldgeber: | OVGU-Publikationsfonds 2021 |
Enthalten in den Sammlungen: | Fakultät für Wirtschaftswissenschaft (OA) |
Dateien zu dieser Ressource:
Datei | Beschreibung | Größe | Format | |
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Hair et al._Partial least_2021.pdf | Zweitveröffentlichung | 12.44 MB | Adobe PDF | Öffnen/Anzeigen |