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

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...

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Hlavní autoři: Hair Jr., Joseph F., Hult, G. Tomas M., Ringle, Christian M., Sarstedt, Marko, Danks, Nicholas P., Ray, Soumya
Médium: Online
Jazyk:angličtina
Vydáno: Springer Nature 2021
Témata:
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
On-line přístup:ONIX_20211115_9783030805197_9
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Shrnutí: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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