1. Multivariate Data Analysis Chapter 1 - Introduction

2. Chapter 1 What is Multivariate Analysis? Impact of the Computer Revolution Multivariate Analysis Defined

3. Some Basic Concepts of Multivariate Analysis The Variate (a linear combination of variables with weights) Measurement Scale  Nonmetric Measurement Scales Nominal and ordinal scales  Metric Measurement Scales Interval and ration scales Measurement Error and Multivariate Measurement  Validity and reliability Statistical Significance Versus Statistical Power  Type I error (alpha)  Type II error (beta)  Power: Effect size, Alpha, Sample size

4. Chapter 1 Types of Multivariate Techniques Principal Components and Common Factor Analysis Multiple Regression Multiple Discriminant Analysis Multivariate Analysis of Variance Conjoint Analysis Canonical Correlation

5. Chapter 1 Types of Multivariate Techniques Cluster Analysis Multidimensional Scaling Correspondence Analysis Linear Probability Models Structural Equation Modeling Other Emerging Multivariate Techniques

6. Guidelines for Multivariate Analysis and Interpretation Establish Practical Significance as well as Statistical Significance Sample Size Affects All Results Know Your Data  Influences of outliners  Missing values  Violations of assumptions

7. Guidelines for Multivariate Analysis and Interpretation (Cont.) Strive for Model Parsimony  Multicollinearity Look at Your Errors Validate Your Results  Splitting the sample  Employing a bootstraping technique  Gathering a separate sample

8. A structured Approach to Multivariate Model Building Stage 1: Define the Research Problem, Objectives, and Multivariate Techniques to Be Used Stage 2: Develop the Analysis Plan Stage 3: Evaluate the Assumptions Underlying the Multivariate Technique Stage 4: Estimate the Multivariate Model and Assess Overall Model Fit Stage 5: Interpret the Variate(s) Stage 6: Validate the Multivariate Model

9. Databases Primary Database Perceptions of HATCO Purchase Outcomes Purchaser Characteristics Other Databases