DESCRIPTION:
For graduate and upper-level undergraduate marketing research courses. 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.
CONTENTS:
1. Introduction: Models and Model Building.
Section I Understanding and Preparing for Multivariate Analysis.
2. Cleaning and Transforming Data.
3. Factor Analysis.
Section II Analysis Using Dependece 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.
