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

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Résumé :
An accessible, practical guide to using PLS-SEM for social sciences research. With applications using SmartPLS - the primary software used in partial least squares structural equation modeling (PLS-SEM) - this practical guide provides concise instructions on how to use this evolving statistical technique to conduct research and obtain solutions. Featuring the latest research, new examples, and expanded discussions throughout, the Second Edition is designed to be easily understood by those with limited statistical and mathematical training who want to pursue research opportunities in new ways. New and Key Features. A new overview of the latest research on composite-based modeling (e.g., distinction between composite and causal indicators) enhances reader understanding of the conceptual foundation for PLS-SEM. A new in-depth discussion explores the various types of mediation, alternative mediation analysis procedures, mediated moderation, and moderated mediation. Coverage of several methods for constructing bootstrap confidence intervals includes percentile, studentized, bias-corrected, and accelerated, as well as two double bootstrap methods. Simple instructions give readers the "how-tos" of using SmartPLS software 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.

Biographie:
<p style=LINE-HEIGHT: normal>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.</p>

Sommaire:
Chapter 1: An Introduction to Structural Equation Modeling What Is Structural Equation Modeling? Considerations in Using Structural Equation Modeling Structural Equation Modeling With Partial Least Squares Path Modeling PLS-SEM, CB-SEM, and Regressions Based on Sum Scores Organization of Remaining Chapters Chapter 2: Specifying the Path Model and Examining Data 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 Path Model Creation Using the SmartPLS Software Chapter 3: Path Model Estimation Stage 4: Model Estimation and the PLS-SEM Algorithm Case Study Illustration: PLS Path Model Estimation (Stage 4) Chapter 4: Assessing PLS-SEM Results Part I: Evaluation of Reflective Measurement Models Overview of Stage 5: Evaluation of Measurement Models Stage 5a: Assessing Results of Reflective Measurement Models Case Study Illustration-Reflective Measurement Models Running the PLS-SEM Algorithm Reflective Measurement Model Evaluation Chapter 5: Assessing PLS-SEM Results Part II: Evaluation of the Formative Measurement Models Stage 5b: Assessing Results of Formative Measurement Models Bootstrapping Procedure Bootstrap Confidence Intervals Case Study Illustration-Evaluation of Formative Measurement Models Chapter 6: Assessing PLS-SEM Results Part III: Evaluation of the Structural Model Stage 6: Assessing PLS-SEM Structural Model Results Case Study Illustration-How Are PLS-SEM Structural Model Results Reported? Chapter 7: Mediator and Moderator Analysis Mediation Moderation Chapter 8: Outlook on Advanced Methods Importance-Performance Map Analysis Hierarchical Component Models Confirmatory Tetrad Analysis Dealing With Observed and Unobserved Heterogeneity Consistent Partial Least Squares