A primer on partial least squares structural equation modeling (PLS-SEM) - Info and Reading Options
By Joseph F. Hair, G. Tomas M. Hult, Christian M. Ringle and Marko Sarstedt
"A primer on partial least squares structural equation modeling (PLS-SEM)" was published by SAGE in 2022 - Los Angeles and it has 363 pages.
“A primer on partial least squares structural equation modeling (PLS-SEM)” Metadata:
- Title: ➤ A primer on partial least squares structural equation modeling (PLS-SEM)
- Authors: Joseph F. HairG. Tomas M. HultChristian M. RingleMarko Sarstedt
- Number of Pages: 363
- Publisher: SAGE
- Publish Date: 2022
- Publish Location: Los Angeles
“A primer on partial least squares structural equation modeling (PLS-SEM)” Subjects and Themes:
- Subjects: Least squares - Structural equation modeling
Edition Identifiers:
- ISBN-13: 9781544396408
- All ISBNs: 9781544396408
AI-generated Review of “A primer on partial least squares structural equation modeling (PLS-SEM)”:
"A primer on partial least squares structural equation modeling (PLS-SEM)" Description:
Open Data:
"The third edition of A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) guides readers through learning and mastering the techniques of this approach in clear language. Authors Joseph H. Hair, Jr., G. Tomas M. Hult, Christian Ringle, and Marko Sarstedt use their years of conducting and teaching research to communicate the fundamentals of PLS-SEM with limited emphasis on equations and symbols, instead, explaining the details in straightforward language. A running case study on corporate reputation follows the different steps in this technique so readers can better understand the research applications. Learning objectives, review and critical thinking questions, and key terms help readers cement their knowledge. This edition has been thoroughly updated, featuring the latest version of the popular software package SmartPLS 3. New topics have been added throughout the text, including a thoroughly revised and extended chapter on mediation, recent research on the foundations of PLS-SEM, distinctions between PLS-SEM and CB-SEM, use with secondary data, model fit and comparison, information on control variables, sample size calculations, and more"--
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