Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R

di Joseph F. Hair Jr. (Autore)

Christian M. Ringle (Autore)

Nicholas P. Danks (Autore)

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Springer, 2022

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 ofthumb in every chapter provide guidance on best practices in the application and interpretation of PLS-SEM.

• Autore: Joseph F. Hair Jr., Christian M. Ringle, Nicholas P. Danks
• Anno edizione: 2022
• Editore: Springer
• Formato: EPUB3 con Adobe DRM
• Compatibilità: Tutti i dispositivi (eccetto Kindle)
• Layout: Reflowable
• EAN: 9783030805197

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