Statistical Analysis Of Management Data

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Overview

This book covers multivariate statistical analyses that are important for researchers in all fields of management whether finance, production, accounting, marketing, strategy, technology or human resources management. Although multivariate statistical techniques such as those described in this book play key roles in fundamental disciplines of the social sciences (e.g., economics and econometrics or psychology and psychometrics), the methodologies particularly relevant and typically used in management research are the center of focus of this study. Statistical Analysis of Management Data is especially designed to provide doctoral students with a theoretical knowledge of the basic concepts underlying the most important multivariate techniques and with an overview of actual applications in various fields. The content herein addresses both the underlying mathematics and problems of application. As such, a reasonable level of competence in both statistics and mathematics is needed. This book is not intended as a first introduction to statistics and statistical analysis. Instead it assumes that the student is familiar with basic statistical techniques. The techniques are presented in a fundamental way but in a format accessible to students in a doctoral program, to practicing academicians, and to data analysts.

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Product Details

  • ISBN-13: 9781402073151
  • Publisher: Springer-Verlag New York, LLC
  • Publication date: 4/3/2008
  • Pages: 352
  • Product dimensions: 0.81 (w) x 6.14 (h) x 9.21 (d)

Meet 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 ofInternational 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).

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Table of Contents

Preface
1 Introduction 1
2 Multivariate Normal Distribution 9
3 Measurement Theory: Reliability and Factor Analysis 29
4 Multiple Regression with a Single Dependent Variable 55
5 System of Equations 79
6 Categorical Dependent Variables 105
7 Rank Ordered Data 137
8 Error in Variables - Analysis of Covariance Structure 159
9 Analysis of Similarity and Preference Data 253
App. A Rules in Matrix Algebra 309
App. B Statistical Tables 310
App. C Description of Data Sets 314
Index 329
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