A primer on partial least squares structural equation modeling (PLS-SEM) / Joe F. Hair, Jr., G. Tomas M. Hult, Christian M. Ringle, Marko Sarstedt.
Material type: TextPublication details: Los angels: Sage, 2022.Edition: Third editionDescription: xx, 363 pages: illustrations (black and white)ISBN:- 9781544396408
- 511.42 HAI.P 23
Item type | Current library | Home library | Call number | Status | Date due | Barcode | |
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Book | Dept. of Education General Stacks | Dept. of Education | 511.42 HAI.P (Browse shelf(Opens below)) | Available | EDU19621 |
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Includes bibliographical references (pages 327-351) and index.
An introduction to structural equation modeling -- Specifying the path model and examining data -- Path model estimation -- Assessing PLS-SEM results part I : evaluation of reflective measurement models -- Assessing PLS-SEM results part II : evaluation of the formative measurement models -- Assessing PLS-SEM results part III : evaluation of the structural model -- Mediator and moderator analysis -- Outlook on advanced methods.
"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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