Multivariate data analysis
- 8th
- New Delhi Cengage Learning India Pvt. Ltd. 2019
- xvii, 813 p.
Table of Content
Chapter 1 Overview of Multivariate Methods
Section 1: Preparing for Multivariate Analysis
Chapter 2: Examining Your Data
Section 2: Interdependence Techniques
Chapter 3: Exploratory Factor Analysis
Chapter 4: Cluster Analysis
Section 3: Dependence Techniques
Chapter 5: Multiple Regression
Chapter 6: MANOVA: Extending ANOVA
Chapter 7: Discriminant Analysis
Chapter 8: Logistic Regression: Regression with a Binary Dependent Variable
Section 4: Moving Beyond the Basic Techniques
Chapter 9: Structural Equation Modeling: An Introduction
Chapter 10: Confirmatory Factor Analysis
Chapter 11: Testing Structural Equation Models
Chapter 12: Advanced Topics in SEM
Chapter 13: Partial Least Squares Modeling (PLS-SEM)
In addition to the chapters in the print book, e-copies of all other chapters in the previous editions are available to download on the companion website, including canonical correlation, conjoint analysis, multidimensional scaling, and correspondence analysis.
For over 30 years, this text has provided students with the information they need to understand and apply multivariate data analysis. The eighth edition of Multivariate Data Analysis provides an updated perspective on the analysis of all types of data as well as introducing some new perspectives and techniques that are foundational in today’s world of analytics. Multivariate Data Analysis serves as the perfect companion for graduate and postgraduate students undertaking statistical analysis for business degrees, providing an application-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.