Multivariate Data Analysis
For graduate (Master & PhD students) and upper-level undergraduate marketing research courses.
By: Joseph Hair, William Black, Barry Babin, Rolph Anderson
Mar 2009, Paperback, 816 pages
For over 30 years, this text has provided students with the information they need to understand and apply multivariate data analysis.
Hair et. al provides an applications-oriented introduction to multivariate analysis for the non-statistician. By reducing heavy statistical research into fundamental concepts, the text explains to students how to understand and make use of the results of specific statistical techniques.
In this seventh revision, the organization of the chapters has been greatly simplified. New chapters have been added on structural equations modeling, and all sections have been updated to reflect advances in technology, capability, and mathematical techniques.
1 Introduction: Models and
Section I Understanding and Preparing for Multivariate Analysis
2 Cleaning and Transforming Data
3 Factor Analysis
Section II Analysis Using Dependence Techniques
4 Simple and Multiple Regression Analysis
5 Canonical correlation
6 Conjoint analysis
7 Multiple Discriminant Analysis and Logistic Regression
8 ANOVA and MANOVA
Section III Analysis using Interdependence Techniques
9 Group data and Cluster Analysis
10 MDS and Correspondence Analysis
Structural Equation Modeling
11 SEM: An Introduction
12 Application of SEM