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

            A Workbook

            Thumbnail
            Auteur
            Hair Jr., Joseph F.
            Hult, G. Tomas M.
            Ringle, Christian M.
            Sarstedt, Marko
            Danks, Nicholas P.
            Ray, Soumya
            Language
            English
            Afficher la notice complète
            Résumé
            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://doab-dev.siscern.org/handle/20.500.12854/197719
            Keywords
            Open Access; PLS-SEM) Using R; Workbook; Partial Least Squares Structural Equation Modeling; R Software Environment; thema EDItEUR::K Economics, Finance, Business and Management::KJ Business and Management::KJS Sales and marketing; thema EDItEUR::U Computing and Information Technology::UF Business applications::UFM Mathematical and statistical software; thema EDItEUR::K Economics, Finance, Business and Management::KC Economics::KCH Econometrics and economic statistics
            DOI
            10.1007/978-3-030-80519-7
            ISBN
            9783030805197
            Publisher
            Springer Nature
            Publisher website
            http://www.springernature.com/oabooks
            Publication date and place
            2021
            Grantor
            • Otto von Guericke University Magdeburg
            Imprint
            Springer International Publishing
            Series
            Classroom Companion: Business,
            Pages
            197
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            • logo EUEuropean Union
              This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871069.

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