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

        A Workbook

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        Author(s)
        Hair Jr., Joseph F.
        Hult, G. Tomas M.
        Ringle, Christian M.
        Sarstedt, Marko
        Danks, Nicholas P.
        Ray, Soumya
        Language
        English
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        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://library.oapen.org/handle/20.500.12657/51463
        Keywords
        Open Access; PLS-SEM) Using R; Workbook; Partial Least Squares Structural Equation Modeling; R Software Environment
        DOI
        10.1007/978-3-030-80519-7
        ISBN
        9783030805197, 9783030805197
        Publisher
        Springer Nature
        Publisher website
        https://www.springernature.com/gp/products/books
        Publication date and place
        2021
        Grantor
        • Otto von Guericke University Magdeburg - [grantnumber unknown]
        Imprint
        Springer International Publishing
        Series
        Classroom Companion: Business,
        Classification
        Sales and marketing
        Mathematical and statistical software
        Econometrics and economic statistics
        Pages
        197
        Rights
        https://creativecommons.org/licenses/by/4.0/
        • Imported or submitted locally

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        License

        • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

        Credits

        • logo EU
        • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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