Statistical Analysis of Management Data / Edition 2 available in Hardcover
- Pub. Date:
- Springer New York
Statistical Analysis of Management Data provides a comprehensive approach to multivariate statistical analyses that are important for researchers in all fields of management, including finance, production, accounting, marketing, strategy, technology, and human resources. This book is especially designed to provide doctoral students with a theoretical knowledge of the concepts underlying the most important multivariate techniques and an overview of actual applications. It offers a clear, succinct exposition of each technique with emphasis on when each technique is appropriate and how to use it. This second edition, fully revised, updated, and expanded, reflects the most current evolution in the methods for data analysis in management and the social sciences. In particular, it places a greater emphasis on measurement models, and includes new chapters and sections on:
- confirmatory factor analysis
- canonical correlation analysis
- cluster analysis
- analysis of covariance structure
- multi-group confirmatory factor analysis and analysis of covariance structures.
Featuring numerous examples, the book may serve as an advanced text or as a resource for applied researchers in industry who want to understand the foundations of the methods and to learn how they can be applied using widely available statistical software.
|Publisher:||Springer New York|
|Edition description:||2nd ed. 2010|
|Product dimensions:||6.40(w) x 9.40(h) x 1.20(d)|
About the Author
Hubert Gatignon is the Claude Janssen Chaired Professor of Business Administration at INSEAD. He joined INSEAD in 1994 from the Wharton School of the University of Pennsylvania where he was Professor of Marketing. He holds a Ph.D. in Marketing from the University of California, Los Angeles.
His research interests involve (1) modeling the factors influencing the adoption and diffusion of innovations and (2) explaining and econometrically measuring how the effects of marketing mix variables change over conditions and over time. His most recent research concerns strategies for entering a market and for defending a brand's position, as well as international marketing strategy.
Dr. Gatignon's publications have appeared in Communications Research, International Journal of Research in Marketing, Journal of Business Research, Journal of Consumer Research, Journal of International Business Studies, Journal of Law, Economics and Organization, Journal of Marketing, Journal of Marketing Research, Management Science, Marketing Letters, Marketing Science, Planning Review, and in Strategic Management Journal. He is the author of Statistical Analysis of Management Data and he is also a co-author of MARKSTRAT3: The Strategic Marketing Simulation, ADSTRAT: An Advertising Decision Support System and COMPTRACK: A Competitive Tracking Software. He co-edited The INSEAD-Wharton Alliance on Globalizing: Strategies for Building Successful Global Businesses.
Dr. Gatignon is an Associate Editor of the Journal of Marketing Research and he serves on the editorial boards of International Journal of Research in Marketing (he was the Editor-in-Chief from 2000 until 2006), Journal of Business-to-Business Marketing, Journal of Marketing, Journal of the Academy of Marketing Science, Marketing Letters, Marketing Science and Recherche et Applications en Marketing (He was the Editor-in-Chief from 1998 to 2000). He has also served on the editorial board of Journal of International Business Studies and Journal of International Marketing. Dr. Gatignon is on the advisory board of The Quantitative Marketing Network of the Social Sciences Research Network. He has been an Academic Trustee of the Marketing Science Institute from 1998 to 2004 and is now an Academic Trustee at AiMark (Center for Advanced International Marketing Knowledge).
Table of ContentsMultivariate Normal Distribution.- Reliability Alpha, Principle Component Analysis, and Exploratory Factor Analysis.- Confirmatory Factor Analysis.- Multiple Regression with a Single Dependent Variable.- System of Equations.- Canonical Correlation Analysis.- Categorical Dependent Variables.- Rank-Ordered Data.- Error in Variables – Analysis of Covariance Structure.- Cluster Analysis.- Analysis of Similarity and Preference Data.- Appendices.