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

ISBN-10: 1452217440
ISBN-13: 9781452217444
Edition: 2014
List price: $43.00
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Description: A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), by Hair, Hult, Ringle, and Sarstedt, provides a concise yet very practical guide to understanding and using PLS structural equation modeling (PLS-SEM). PLS-SEM is evolving as  More...

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Book details

List price: $43.00
Copyright year: 2014
Publisher: SAGE Publications, Incorporated
Publication date: 4/3/2013
Binding: Paperback
Pages: 328
Size: 6.00" wide x 9.00" long x 0.75" tall
Weight: 1.166

A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), by Hair, Hult, Ringle, and Sarstedt, provides a concise yet very practical guide to understanding and using PLS structural equation modeling (PLS-SEM). PLS-SEM is evolving as a statistical modeling technique and its use has increased exponentially in recent years within a variety of disciplines, due to the recognition that PLS-SEM’s distinctive methodological features make it a viable alternative to the more popular covariance-based SEM approach. This text—the only comprehensive book available to explain the fundamental aspects of the method—includes extensive examples on SmartPLS software, and is accompanied by multiple data sets that are available for download from the accompanying website (www.pls-sem.com).

Dr. Joe Hair is Founder and Senior Scholar of the Doctoral Degree in Business Administration, Coles College, Kennesaw State University, USA. He previously held the Copeland Endowed Chair of Entrepreneurship and was Director, Entrepreneurship Institute, Ourso College of Business Administration, Louisiana State University. He has authored over 40 books, including Multivariate Data Analysis, Prentice-Hall, 7 th edition, 2010 (cited 22,000+ times); Marketing, South-Western Publishing Company, 12 th edition 2012; Essentials of Business Research Methods, M.E. Sharpe, 2011; Research Methods for Business, Wiley, 2007; and Essentials of Marketing Research, McGraw-Hill/Irwin, 3 rd edition 2013. He also has published numerous articles in scholarly journals such as the Journal of Marketing Research, Journal of Academy of Marketing Science, Journal of Business/Chicago, Journal of Advertising Research, Journal of Business Research, Journal of Long Range Planning, Journal of Marketing Theory and Practice, International Marketing Review, Journal of Experimental Education, Business Horizons, Journal of Retailing, Multivariate Behavioral Research, and others. He was recognized as the 2011 Academy of Marketing Science Marketing Educator of the year. He often presents seminars on research techniques, multivariate data analysis and marketing issues for organizations in Europe, Australia and other locations outside the U.S.

Dr. G. Tomas M. Hult is the Eli Broad Professor of Marketing and International Business and Director of the International Business Center in the Eli Broad College of Business at Michigan State University. He has been Executive Director of the Academy of International Business and President of the AIB Foundation since 2004; Editor-in-Chief of the Journal of the Academy of Marketing Science since 2009; and been on the U.S. Department of Commerce's District Export Council since 2012. Professor Hult is one of some 80 elected Fellows of the Academy of Internat ional Business. He is one of the world's leading authorities in global strategy, with a particular focus on topics dealing with the intersection of global marketing and supply chain management. Hult was ranked the 75th "most-cited scientist in economics and business" in the world by Thomson Reuters in their Essential Science Indicators covering a period from 1997 to 2007. In a 2012 study in the Academy of Management Perspectives, Hult was ranked 6th among business scholars who received their degrees since 1991. His research has been cited more than 10,000 times per Google Scholar. Hult has been Deputy Editor and Department Editor of the Journal of International Business Studies; Associate Editor of Decision Sciences and Journal of Operations Management; and currently serves as Associate Editor of the Journal of Supply Chain Management as well as on the review boards of the Journal of Marketing, Academy of Management Journal, Strategic Management Journal, and Journal of Retailing. He regularly teaches doctoral seminars on multivariate statistics, structural equation modeling, and hierarchical linear modeling worldwide. Dr. Hult is a dual citizen of Sweden and the USA. More information about Tomas Hult can be found at tomashult.com.

Preface
About the Authors
An Introduction to Structural Equation Modeling
Learning Outcomes
Chapter Preview
What Is Structural Equation Modeling?
Considerations in Using Structural Equation Modeling
The Variate
Measurement
Measurement Scales
Coding
Data Distributions
Structural Equation Modeling With Partial Least Squares Path Modeling
Path Models With Latent Variables
Measurement Theory
Structural Theory
PLS-SEM and CB-SEM
Data Characteristics
Model Characteristics
Organization of Remaining Chapters
Summary
Review Questions
Critical Thinking Questions
Key Terms
Suggested Readings
Specifying the Path Model and Collecting Data
Learning Outcomes
Chapter Preview
Specifying the Structural Model
Mediation
Moderation
Higher-Order and Hierarchical Component Models
Specifying the Measurement Models
Reflective and Formative Measurement Models
Single-Item Measures
Data Collection and Examination
Missing Data
Suspicious Response Patterns
Outliers
Data Distribution
Case Study Illustration: Specifying the PLS-SEM Model
Structural Model Specification
Measurement Model Specification
Data Collection and Examination
Path Model Creation Using the SmartPLS Software
Summary
Review Questions
Critical Thinking Questions
Key Terms
Suggested Readings
Path Model Estimation
Learning Outcomes
Chapter Preview
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
Assessing PLS-SEM Results Part I: Evaluation of Reflective Measurement Models
Learning Outcomes
Chapter Preview
Evaluation of Measurement Models
Assessing Results of Reflective Measurement Models
Internal Consistency Reliability
Convergent Validity
Discriminant Validity
Case Study Illustration-Reflective Measurement Models
Running the PLS-SEM Algorithm
Reflective Measurement Model Evaluation
Summary
Review Questions
Critical Thinking Questions
Key Terms
Suggested Readings
Assessing PLS-SEM Results Part II: Evaluation of the Formative Measurement Models
Learning Outcomes
Chapter Preview
Assessing Results of Formative Measurement Models
Assess Convergent Validity
Assess Formative Measurement Models for Collinearity Issues
Assess the Significance and Relevance of the Formative Indicators
Bootstrapping Procedure
Concept and Justification
Bootstrap Confidence Intervals
Case Study Illustration-Evaluation of Formative Measurement Models
Extending the Simple Path Model
Reflective Measurement Model Evaluation
Formative Measurement Model Evaluation
Summary
Review Questions
Critical Thinking Questions
Key Terms
Suggested Readings
Assessing PLS-SEM Results Part III: Evaluation of the Structural Model
Learning Outcomes
Chapter Preview
Assessing PLS-SEM Structural Model Results
Collincarity Assessment
Structural Model Path Coefficients
Coefficient of Determination (R<sup>2</sup> Value)
Effect Size f<sup>2</sup>
Blindfolding and Predictive Relevance Q<sup>2</sup>
Heterogeneity
Goodness-of-Fit Index
Case Study Illustration: How Are PLS-SEM Structural Model Results Reported?
Summary
Review Questions
Critical Thinking Questions
Key Terms
Suggested Readings
Advanced Topics in PLS-SEM
Learning Outcomes
Chapter Preview
Importance-Performance Matrix Analysis
Method
Case Study Illustration
Mediator Analysis
Method
Case Study Illustration
Higher-Order Models/Hierarchical Component Models
Method
Case Study Illustration
Summary
Review Questions
Critical Thinking Questions
Key Terms
Suggested Readings
Modeling Heterogeneous Data
Learning Outcomes
Chapter Preview
Modeling Categorical Moderator Effects
Introduction
The Parametric Approach to PLS-MGA
Measurement Invariance
Case Study Illustration
Modeling Unobserved Heterogeneity
Continuous Moderator Effects
Method
Modeling Continuous Moderating Effects
Three-Way Interactions
Creating the Interaction Term
Case Study Illustration
Summary
Review Questions
Critical Thinking Questions
Key Terms
Suggested Readings
References
Author Index
Subject Index

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