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Primer on Partial Least Squares Structural Equation Modeling 3rd edition by Joe Hair, Tomas Hult, Christian Ringle, Marko Sarstedt ISBN 1544396406 978-1544396408

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ISBN 10: 1544396406
ISBN 13: 978-1544396408
Author: Joe Hair, Tomas Hult, Christian Ringle, Marko Sarstedt 

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.

Primer on Partial Least Squares Structural Equation Modeling 3rd Table of contents:

Chapter 1 • An Introduction to Structural Equation Modeling

Chapter Preview

What Is Structural Equation Modeling?

Considerations in Using Structural Equation Modeling

Composite Variables

Measurement

Measurement Scales

Coding

Data Distributions

Principles of Structural Equation Modeling

Path Models With Latent Variables

Testing Theoretical Relationships

Measurement Theory

Structural Theory

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

Considerations When Applying Pls-Sem

Key Characteristics of the PLS-SEM Method

Data Characteristics

Minimum Sample Size Requirement

Missing Value Treatment

Nonnormal Data

Scales of Measurement

Secondary Data

Model Characteristics

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

Mediation

Moderation

Control Variables

Stage 2: Specifying the Measurement Models

Reflective and Formative Measurement Models

Single-Item Measures and Sum Scores

Higher-Order Constructs

Stage 3: Data Collection and Examination

Missing Data

Suspicious Response Patterns

Outliers

Data Distribution

Case Study Illustration—Specifying the PLS-SEM Model

Application of Stage 1: Structural Model Specification

Application of Stage 2: Measurement Model Specification

Application of Stage 3: Data Collection and Examination

Path Model Creation Using the SmartPLS Software

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

How the Algorithm Works

Statistical Properties

Algorithmic Options and Parameter Settings to Run the Algorithm

Results

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

Model Estimation

Estimation Results

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

Step 1: Indicator Reliability

Step 2: Internal Consistency Reliability

Step 3: Convergent Validity

Step 4: Discriminant Validity

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

Running the PLS-SEM Algorithm

Reflective Measurement Model Evaluation

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

Step 1: Assess Convergent Validity

Step 2: Assess Formative Measurement Models for Collinearity Issues

Step 3: Assess the Significance and Relevance of the Formative Indicators

Bootstrapping Procedure

Concept

Bootstrap Confidence Intervals

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

Extending the Simple Path Model

Reflective Measurement Model Evaluation (Recap)

Formative Measurement Model Evaluation

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

Step 1: Assess the Structural Model for Collinearity

Step 2: Assess the Significance and Relevance of the Structural Model Relationships

Step 3: Assess the Model’s Explanatory Power

Step 4: Assess the Model’s Predictive Power

Number of Folds

Number of Repetitions

Prediction Statistic

Results Interpretation

Treating Predictive Power Issues

Step 5: Model Comparisons

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

Introduction

Measurement and Structural Model Evaluation in Mediation Analysis

Types of Mediating Effects

Testing Mediating Effects

Multiple Mediation

Case Study Illustration—Mediation

Moderation

Introduction

Types of Moderator Variables

Modeling Moderating Effects

Creating the Interaction Term

Product Indicator Approach

Orthogonalizing Approach

Two-Stage Approach

Guidelines for Creating the Interaction Term

Model Evaluation

Results Interpretation

Moderated Mediation and Mediated 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

Multigroup Analysis

Uncovering Unobserved Heterogeneity

Measurement Model Invariance

Consistent PLS-SEM

Summary

Review Questions

Critical Thinking Questions

Key Terms

Suggested Readings

Glossary

References

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