Partial least squares structural equation modeling (PLS-SEM) using R : : a workbook / / Joseph F. Hair [et al.]
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�...
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| Superior document: | Classroom companion. |
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| Year of Publication: | 2021 |
| Language: | English |
| Series: | Classroom companion. Business
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| Physical Description: | 1 online resource (xiv, 197 pages) :; illustrations (some color) |
| Notes: | Description based upon print version of record. |
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| Summary: | 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 |
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| Hierarchical level: | Monograph |
| Statement of Responsibility: | Joseph F. Hair [et al.] |