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Multivariate Data Analysis,
8th Edition

Joseph F Hair, Barry J. Babin, Rolph E. Anderson, William C. Black

ISBN-13: 9781473756540 | ISBN-10: 1473756545

Copyright 2018

| Published 2018

| 832 pages

List Price USD $82.00

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Meet the Authors

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Joseph F. Hair, Jr. is Cleverdon Chair of Business at University of South Alabama, USA. He previously held the Copeland Endowed Chair of Entrepreneurship and was Director of the Entrepreneurship Institute at the E.J. Ourso College of Business, Louisiana State University. He was a United States Steel Foundation Fellow at the University of Florida, Gainesville, where he earned his Ph.D. in Marketing. He has authored over 40 books and has published numerous articles in professional journals. He is a Distinguished Fellow of the Academy of Marketing Sciences, the Society for Marketing Advances, and the Southwestern Marketing Association.

Barry J. Babin has authored more than 100 research publications in some of today's most prestigious peer-reviewed research periodicals, such as the Journal of Marketing, Journal of Consumer Research, Journal of Business Research, Journal of Retailing, Psychology and Marketing, Journal of the Academy of Marketing Science and the International Journal of Wine Business Research. Dr. Babin is currently Morris Lewis Professor of Marketing and chair of marketing in the Ole Miss Business School. He has won numerous honors for his research, including the Louis K. Brandt Faculty Research Award from the University of Southern Mississippi, Outstanding Researcher in the COB at Louisiana Tech University, the 1996 Society for Marketing Advances (SMA) Steven J. Shaw Award, the 1997 Omerre Deserres Award for Outstanding Contributions to Retail and Service Environment Research and the Academy of Marketing Science’s prestigious Harold W. Berkman Distinguished Service Award. Dr. Babin is a past president of the Academy of Marketing Science and the Society for Marketing Advances and has been recognized as a distinguished fellow of both organizations. His research focuses on the effect of the service environment in creating value for employees and customers and on the impact of big data on consumer liberty. He offers expertise in building and understanding value that leads to long-lasting, mutually beneficial relationships with employees and customers. His teaching specialties involve advanced methods of marketing and consumer research and creative problem solving. A frequent international lecturer, he has presented in Australia, South Korea, France, Germany, Mexico, New Zealand, South Africa, Canada, Sweden, Norway, Peru, Israel and the United Kingdom.

Dr. Anderson is the Royal H. Gibson Sr. Professor of Business Administration and former Head of the Department of Marketing at Drexel University. He earned his Ph.D. from the University of Florida, and his MBA. and BA degrees from Michigan State University. He is author or co-author of 18 textbooks. His research has been widely published in the major professional journals in his field. Dr. Anderson has been selected twice by Drexel’s LeBow College of Business students to receive the Faculty Appreciation Award, and he isa distinguished fellow in the Center for Teaching Excellence. He serves on the editorial boards of five academic journals and on the Faculty Advisory Board of the Fisher Institute for Professional Selling.

Dr. Black is a Professor Emeritus in the Department of Marketing, at the E. J. Ourso College of Business, Louisiana State University (LSU). He received his MBA in 1976 and Ph.D. in 1980, both from the University of Texas at Austin. He held positions at the University of Arizona from 1980 to 1985, and has been at LSU since 1985. He has published numerous articles in professional journals, along with a number of chapters in scholarly books. He is a member of the Editorial Review Board for the Journal of Business Research.

  • The chapters on SEM have been updated to include: greater emphasis on psychometrics and scale development, discussions on the use of reflective versus formative scaling, an alternative approach for handing interactions (orthogonal moderators), higher order models, multi-group analyses, Bayesian SEM, and revised information on software availability (e.g. Lavaan and SmartPLS). The multi-group discussion also includes an alternative to partial metric invariance when cross-group variance problems are small.
  • Online resources for researchers including continued coverage from past editions of all of the analyses from the latest versions of both SAS and SPSS (commands and outputs).
  • New chapter on partial least squares structural equation modeling (PLS-SEM), an emerging technique which can be applied by researchers in both the academic and business domains.
  • Each chapter has been updated to reflect technical improvements, (for example adding material on multiple imputation for missing data treatments, and the merging of basic principles from the fields of data mining and its applications).
  • Extended discussions of emerging topics, including causal treatments/inference (i.e. causal analysis of non-experimental data as well as propensity score models) along with multi-level and panel data models (extending regression into new research areas and providing a framework for cross-sectional/time-series analysis).
  • Each chapter highlights the implications of Big Data, underlining the role of multivariate data analysis in this new era of analytics.
  • Provides an application-oriented introduction to multivariate analysis for the non-statistician.
  • Unique “Rule of Thumb” feature helps students learn how to best use different techniques.
  • Aimed at students taking postgraduate and high-level graduate degrees across all the business areas.
  • Assumes that students will come from a business, rather than mathematics, background. The authors use non-complex language to make complex techniques accessible.

Table of Contents

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Chapter 1 Overview of Multivariate Methods
Section 1: Preparing for Multivariate Analysis
Chapter 2: Examining Your Data
Section 2: Interdependence Techniques
Chapter 3: Exploratory Factor Analysis
Chapter 4: Cluster Analysis
Section 3: Dependence Techniques
Chapter 5: Multiple Regression
Chapter 6: MANOVA: Extending ANOVA
Chapter 7: Discriminant Analysis
Chapter 8: Logistic Regression: Regression with a Binary Dependent Variable
Section 4: Moving Beyond the Basic Techniques
Chapter 9: Structural Equation Modeling: An Introduction
Chapter 10: Confirmatory Factor Analysis
Chapter 11: Testing Structural Equation Models
Chapter 12: Advanced Topics in SEM
Chapter 13: Partial Least Squares Modeling (PLS-SEM)

In addition to the chapters in the print book, e-copies of all other chapters in the previous editions are available to download on the companion website, including canonical correlation, conjoint analysis, multidimensional scaling, and correspondence analysis.

Cengage provides a range of supplements that are updated in coordination with the main title selection. For more information about these supplements, contact your Learning Consultant.

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Multivariate Data Analysis

  • ISBN-10: 1473756545
  • ISBN-13: 9781473756540

Price USD$ 82.00

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