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. Hult, G. Tomas M. Ringle, Christian M. Sarstedt, Marko Danks, Nicholas P. Ray, Soumya |
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 |
Sponsor/Funder: | OVGU-Publikationsfonds 2021 |
Appears in Collections: | Fakultät für Wirtschaftswissenschaft (OA) |
Files in This Item:
File | Description | Size | Format | |
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Hair et al._Partial least_2021.pdf | Zweitveröffentlichung | 12.44 MB | Adobe PDF | View/Open |