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

Christian M. Ringle; Marko Sarstedt; Joseph F. Hair; G. Tomas M. Hult; Nicholas P. Danks; Soumya Ray

doi:10.1007/978-3-030-80519-7

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

Моделирование структурных уравнений методом наименьших квадратов по частям (PLS-SEM) с использованием R

Christian M. Ringle, Marko Sarstedt, Joseph F. Hair, G. Tomas M. Hult, Nicholas P. Danks, Soumya Ray

2021-01-01

SCID: 54.1/krnrfgxv Discuss with AI

A Primer on Partial Least Squares Structural Equation ModelingHair, Hult, Ringle, & SarstedtPLS-SEMPartial Least Squares Structural Equation ModelingR

Abstract (AI)

This is an open-access step-by-step guide to analyzing PLS models using R, especially using the SEMinR package and with in-depth case studies.

Key Findings

The work provides a primer on Partial Least Squares Structural Equation Modeling (PLS-SEM).

Research Object

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

Research Subject

Guidance and primer on applying PLS-SEM in R, including implementation steps, usage guidance, and practical application of the PLS-SEM method

Publication Details

DOI

10.1007/978-3-030-80519-7

Publication Date

2021-01-01

Journal

Publisher

ISSN

Cited by

9641

Open access PDF

https://link.springer.com/content/pdf/10.1007%2…

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Author Information

Authors

Christian M. Ringle

Marko Sarstedt

Joseph F. Hair

G. Tomas M. Hult

Nicholas P. Danks

Soumya Ray

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References available in scid.ai2

A new criterion for assessing discriminant validity in variance-based structural equation modeling2014 R: A Language and Environment for Statistical Computing2000

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