Analyzing Multivariate Data / With Cd - 03 edition

Lattin, Jim : Stanford University

Carroll, Doug : Rutgers University

Green, Paul : University of Pennsylvania

1. INTRODUCTION

The Nature of Multivariate Data
Overview of Multivariate Methods
Format of Succeeding Chapters

2. VECTORS AND MATRIXES

Introduction
Definitions
Geometric Interpretation of Operations
Matrix Properties
Learning Summary
Exercises

3. REGRESSION ANALYSIS

Introduction
Regression Analysis: How it Works
Sample Problem: Leslie Salt Property
Learning Summary
Exercises

4. PRINCIPAL COMPONENTS ANALYSIS

Introduction
Principal Components: How it Works
Sample Problem: Gross State Production
Questions Regarding the Application of Principal Components
Learning Summary
Exercises

5. EXPLORATORY FACTOR ANALYSIS

Introduction
Exploratory Factor Analysis: How it Works
Sample Problem: Perceptions of Ready-to-Eat Cereals
Questions Regarding the Application of Factor Analysis
Learning Summary
Exercises

6. CONFIRMATORY FACTOR ANALYSIS

Introduction
Confirmatory Factor Analysis: How Does it Work? Sample Problems
Questions Regarding the Application of Confirmatory Factor Analysis
Learning Summary
Exercises

7. MULTIDIMENSIONAL SCALING

Introduction
Metric MDS: How Does it Work? Non-Metric MDS: How Does it Work? Individual Differences Scaling: How Does It Work? Centroid Scaling: How Does it Work? A Note on Model Validation
Learning Summary
Exercises

8. CLUSTERING

Introduction
Objectives of Cluster Analysis
Measures of Distance, Dissimilarity, and Density
Agglomerative Clustering: How IT Works
Partitioning: How it Works
Sample Problem: Preference Segmentation
Questions Regarding the Application of Cluster Analysis
Learning Summary
Exercises

9. CANONICAL CORRELATION

Introduction
Canonical Correlation: How Does it Work? Sample Problem
Questions Regarding the Application of Canonical Correlation
Learning Summary
Exercises

10. STRUCTURAL EQUATION MODELS WITH LATENT VARIABLES

Introduction
Structural Equations with Latent Variables: How Does it Work? Sample Problem: Modeling the Adoption of Innovation
Questions Regarding the Application of Structural Equations with Latent Variables
Learning Summary
Exercises

11. ANALYSIS OF VARIANCE

Introduction
ANOLVA and ANCOVA: How Does it Work? Sample Problem: Test Marketing a New Product
Multiple Analysis of Variance (MANOVA): How Does it Work
Sample Problem: Testing Advertising Message Strategy
Questions Regarding the Application of MANOVA and MANCOVA
Learning Summary
Exercises

12. DISCRIMINANT ANALYSIS

Introduction
Two-Group Discriminant Analysis: How Does it Work? Sample Problem: Book Club Data
Questions Regarding the Application of Two-Group Discriminant Analysis
Multiple Discriminant Analysis: How Does it Work? Sample Problem: Real Estate
Questions Regarding the Application of Multiple Discriminant Analysis
Learning Summary
Exercises

13. LOGIT CHOICE MODELS

Introduction
Binary Logit Model: How Does it Work? Sample Problem: Books Direct
Multinomial Logit Model: How Does it Work? Sample Problem: Brand Choice
Questions Regarding the Application of Logit Choice Models
Learning Summary
Exercises