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 third 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, this edition includes:
· A new chapter on the analysis of mediation and moderation effects
· Examples using STATA for most of the statistical methods
· Example of XLSTAT applications
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 particularly relevant and typically used in management research, and to learn how they can be applied using widely available statistical software.
Reviews / Votes
From the book reviews:
"The book covers the main statistical techniques that can be used in analyzing management data. Due to its complexity, the book is dedicated to doctoral students, having a good background both in mathematics and in statistics. The book is full of illustrative examples, helping the readers to better understand the theoretical notions and the statistical techniques used throughout it. . the book is also intended for applied researchers in industry, or other practitioners in the management field." (Marina Gorunescu, zbMATH, Vol. 1298, 2014)
"This edition deals with statistical topics useful to researchers and graduate students in many fields of management . . updated and expanded according to current needs and demands of the field. . I really enjoyed reading this book. . In summary, it is a nicely packaged book for the intended audience and I have no hesitation to recommend it as a textbook for a graduate course in management sciences and related areas. . used as an excellent reference book for researchers in these areas." (Technometrics, Vol. 52 (3), August, 2010)
Edition
Softcover reprint of the original 3rd ed. 2014
Language
Place of publication
Target group
Professional and scholarly
Edition type
Illustrations
229 s/w Abbildungen, 34 farbige Abbildungen
XV, 563 p. 263 illus., 34 illus. in color.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 32 mm
Weight
ISBN-13
978-1-4899-7725-0 (9781489977250)
DOI
10.1007/978-1-4614-8594-0
Schweitzer Classification
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, and the Habilitation à Diriger des Recherches from Université Paul Cézanne-Aix Marseille.His research interests involve (1) the marketing of innovations (e.g., modeling the factors influencing the adoption and diffusion of innovations), (2) marketing strategy (e.g., explaining and econometrically measuring how the effects of marketing mix variables change over conditions and over time), and (3) statistical analysis of management data.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, Journal of Retailing, Management Science, Marketing Letters, Marketing Science, Planning Review, and Strategic Management Journal. He has recently edited New Products and Services Development (4 volumes), and he is 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 through 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 theEditor-in-Chief from 1998 through 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 was an Academic Trustee of the Marketing Science Institute from 1998 through 2004 and is now an Academic Trustee at AiMark (Center for Advanced International Marketing Knowledge).ISI Web of Knowledge identifies Hubert Gatignon as a Highly Cited Researcher in the Economics and Business category. He has been named a Fellow of the European Marketing Academy and is included in the 25th Anniversary Edition of Who's Who in the World and in the Who's Who in France.
1: Introduction.- 2: Multivariate Normal Distribution.- 3: Reliability Alpha, Principles Component Analysis and Exploratory Factor Analysis.- 4: Confirmatory Factor Analysis.- 5: Multiple Regression with a Single Dependent Variable.- 6: System of Equations.- 7: Canonical Correlation Analysis.- 8: Categorical Dependent Variables.- 9: Rank-Ordered Data.- 10: Error in Variables - Analysis of Covariance Structure - Structural Equation Models.- 11: Testing Mediation and Moderation Effects.- 12: Cluster Analysis.- 13: Analysis of Similarity and Preference Data.- Appendices: A: Rules in Matrix Algebra.- B: Statistical Tables.- C: Description of Data Sets.
