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A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM)
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A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM)

Third Edition


July 2021 | 384 pages | SAGE Publications, Inc
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 in straightforward language to explain the details of this method, with limited emphasis on equations and symbols. 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, detailed descriptions of research summarizing the advantages as well as limitations of PLS-SEM, and extended coverage of advanced concepts and methods, such as out-of-sample versus in-sample prediction metrics, higher-order constructs, multigroup analysis, necessary condition analysis, and endogeneity

 
Preface
 
About the Authors
 
Chapter 1. An Introduction to Structural Equation Modeling
Chapter Preview

 
What Is Structural Equation Modeling?

 
Considerations in Using Structural Equation Modeling

 
Principles of Structural Equation Modeling

 
PLS-SEM, CB-SEM, and Regressions Based on Sum Scores

 
Considerations When Applying PLS-SEM

 
Guidelines for Choosing Between PLS-SEM and CB-SEM

 
Organization of Remaining Chapters

 
Summary

 
Review Questions

 
Critical Thinking Questions

 
Key Terms

 
Suggested Readings

 
 
Chapter 2. Specifying the Path Model and Examining Data
Chapter Preview

 
Stage 1: Specifying the Structural Model

 
Stage 2: Specifying the Measurement Models

 
Stage 3: Data Collection and Examination

 
Case Study Illustration—Specifying the PLS-SEM Model

 
Summary

 
Review Questions

 
Critical Thinking Questions

 
Key Terms

 
Suggested Readings

 
 
Chapter 3. Path Model Estimation
Chapter Preview

 
Stage 4: Model Estimation and the PLS-SEM Algorithm

 
Case Study Illustration—PLS Path Model Estimation (Stage 4)

 
Summary

 
Review Questions

 
Critical Thinking Questions

 
Key Terms

 
Suggested Readings

 
 
Chapter 4. Assessing PLS-SEM Results—Part I: Evaluation of the Reflective Measurement Models
Chapter Preview

 
Overview of Stage 5: Evaluation of Measurement Models

 
Stage 5a: Assessing Results of Reflective Measurement Models

 
Case Study Illustration—Evaluation of the Reflective Measurement Models (Stage 5a)

 
Summary

 
Review Questions

 
Critical Thinking Questions

 
Key Terms

 
Suggested Readings

 
 
Chapter 5. Assessing PLS-SEM Results—Part II: Evaluation of the Formative Measurement Models
Chapter Preview

 
Stage 5b: Assessing Results of Formative Measurement Models

 
Case Study Illustration—Evaluation of the Formative Measurement Models (Stage 5b)

 
Summary

 
Review Questions

 
Critical Thinking Questions

 
Key Terms

 
Suggested Readings

 
 
Chapter 6. Assessing PLS-SEM Results—Part III: Evaluation of the Structural Model
Chapter Preview

 
Stage 6: Structural Model Results Evaluation

 
Case Study Illustration—Evaluation of the Structural Model (Stage 6)

 
Summary

 
Review Questions

 
Critical Thinking Questions

 
Key Terms

 
Suggested Readings

 
 
Chapter 7. Mediator and Moderator Analysis
Chapter Preview

 
Mediation

 
Moderation

 
Case Study Illustration—Moderation

 
Summary

 
Review Questions

 
Critical Thinking Questions

 
Key Terms

 
Suggested Readings

 
 
Chapter 8. Outlook on Advanced Methods
Chapter Preview

 
Importance-Performance Map Analysis

 
Necessary Condition Analysis

 
Higher-Order Constructs

 
Confirmatory Tetrad Analysis

 
Examining Endogeneity

 
Treating Observed and Unobserved Heterogeneity

 
Measurement Model Invariance

 
Consistent PLS-SEM

 
Summary

 
Review Questions

 
Critical Thinking Questions

 
Key Terms

 
Suggested Readings

 
 
Glossary
 
References
 
Index
Key features

NEW TO THIS EDITION:

  • All screenshots and instructions have been updated for the latest edition of Smart PLS software, SmartPLS 3
  • A thoroughly revised and updated Chapter 7 on Mediator and Moderator Analysis covers more types of mediation, and provides updated explanations and guidelines on moderated mediation.
  • The latest research on the nature of composite-based modeling provides a more thorough conceptual foundation for PLS-SEM
  • Applications of PLS-SEM with secondary or archival data shows readers even more applications for this approach
  • Coverage of model fit, analysis of predictive power, and metrics of model comparison have all been expanded for a richer discussion of PLS-SEM
  • Improved guidelines for generating and validating single-item measures for redundancy analyses and determining minimum sample sizes help clarify these steps
  • More on the distinction between PLS-SEM and CB-SEM helps readers choose the right technique for their project
  • Extended coverage of more advanced concepts helps readers go further with PLS-SEM
  • Summaries are now structured by learning outcome for improved organization

 KEY FEATURES:

  • Simple instructions give readers the “how-tos” of using the SmartPLS software (www.smartpls.com) to obtain solutions, and prepare manuscripts using PLS-SEM for academic journal submissions.
  • Rules of Thumb in every chapter provide guidelines on best practices in the application and interpretation of PLS-SEM.
  • A focus on accessibility is reflected through limited equations, formulas, and Greek symbols to facilitate clear understanding.
  • Chapters organized around learning outcomes simplify the process of learning the concepts and statistical terms.
  • Concepts consistently defined in plain language further facilitate comprehension of methods.

For instructors

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