Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R
A Workbook
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Beschreibung
This open-access guide offers a practical, step-by-step introduction to partial least squares structural equation modeling (PLS-SEM) using R and the SEMinR package. PLS-SEM is widely used for analyzing complex relationships between observed and latent variables, valued for its flexibility, predictive capabilities, and ability to handle complex models.
Now in its second edition, the book incorporates the latest research, enhanced model evaluation routines, and updated guidance on methodological choices and result interpretation. Each chapter combines a concise overview of key concepts, metrics, and rules of thumb with a detailed case study to support hands-on learning.
Designed for researchers and practitioners alike, this guide provides the essential tools and insights needed to apply PLS-SEM effectively using SEMinR.
This open-access guide offers a practical, step-by-step introduction to partial least squares structural equation modeling (PLS-SEM) using R and the SEMinR package. PLS-SEM is widely used for analyzing complex relationships between observed and latent variables, valued for its flexibility, predictive capabilities, and ability to handle complex models.
Now in its second edition, the book incorporates the latest research, enhanced model evaluation routines, and updated guidance on methodological choices and result interpretation. Each chapter combines a concise overview of key concepts, metrics, and rules of thumb with a detailed case study to support hands-on learning.
Designed for researchers and practitioners alike, this guide provides the essential tools and insights needed to apply PLS-SEM effectively using SEMinR.
This book is open access, which means that you have free and unlimited access Latest edition with concise guidelines on how to apply and interpret PLS-SEM results Includes an llustrative step-by-step application of PLS-SEM within the R software environment Draws on the highly user-friendly SEMinR package, co-developed by two of the co-authors
Autor*in
Joseph F. Hair Jr.
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