PART 1 Overview 1 Introduction 1.1 The Nature of Multivariate Data 1.2 Overview of Multivariate Methods 1.3 Format of Succeeding Chapters 2 Vectors and Matrices 2.1 Introduction 2.2 Definitions 2.3 Geometric Interpretation of Operations 2.4 Matrix Properties 2.5 Learning Summary Exercises 3 Regression Analysis 3.1 Introduction 3.2 Regression Analysis: How It Works 3.3 Sample Problem: Leslie Salt Property 3.4 Questions Regarding the Application of Regression Analysis 3.5 Learning Summary PART II Analysis of Interdependence 4 Principal Components Analysis 4.1 Introduction 4.2 Principal Components: How It Works 4.3 Sample Problem: Gross State Product 4.4 Questions Regarding the Application of Principal Components 4.5 Learning Summary 5 Exploratory Factor Analysis 5.1 Introduction 5.2 Exploratory Factor Analysis: How It Works 5.3 Sample Problem: Perceptions of Ready-to-Eat Cereals 5.4 Questions Regarding the Application of Factor Analysis 5.5 Learning Summary 6 Confirmatory Factor Analysis 6.1 Introduction 6.2 Confirmatory Factor Analysis: How It Works 6.3 Sample Problems 6.4 Questions Regarding the Application of Confirmatory Factor Analysis 6.5 Learning Summary 7 Multidimensional Scaling 7.1 Introduction 7.2 Classical Metric MDS: How It Works 7.3 Nonmetric MDS: How It Works 7.4 The INDSCAL Model and Method for Individual Differences Scaling: How It Works 7.5 Multidimensional Analysis of Preference: How It Works 7.6 Learning Summary 7.7 Selected Readings 8 Cluster Analysis 8.1 Introduction 8.2 Objectives of Cluster Analysis 8.3 Measures of Distance, Dissimilarity, and Density 8.4 Agglomerative Clustering: How It Works 8.5 Partitioning: How It Works 8.6 Sample Problem: Preference Segmentation 8.7 Questions Regarding the Application of Cluster Analysis 8.8 Learning Summary PART III Analysis of Dependence 9 Canonical Correlation 9.1 Introduction 9.2 Canonical Correlation: How It Works 9.3 Sample Problem 9.4 Questions Regarding the Application of Canonical Correlation 9.5 Learning Summary 10 Structural Equation Models with Latent Variables 10.1 Introduction 10.2 Structural Equation Models with Latent Variables: How It Works 10.3 Sample Problem: Modeling the Adoption of Innovation 10.4 Questions Regarding the Application of Structural Equations with Latent Variables 10.5 Learning Summary 11 Analysis of Variance 11.1 Introduction 11.2 ANOVA/ANCOVA: How It Works 11.3 Sample Problem: Test Marketing a New Product 11.4 Multiple Analysis of Variance (MANOVA): How It Works 11.5 Sample Problem: Testing Advertising Message Strategy 11.6 Questions Regarding the Application of ANOVA 11.7 Learning Summary 12 Discriminant Analysis 12.1 Introduction 12.2 Two-Group Discriminant Analysis: How It Works 12.3 Sample Problem: Books by Mail 12.4 Questions Regarding the Application of Two-Group Discriminant Analysis 12.5 Multiple Discriminant Analysis: How It Works 12.6 Sample Problem: Real Estate 12.7 Questions Regarding the Application of Multiple Discriminant Analysis 12.8 Learning Summary 13 Logit Choice Models 13.1 Introduction 13.2 Binary Logit Model: How It Works 13.3 Sample Problem: Books by Mail 13.4 Multinomial Logit Model: How It Works 13.5 Sample Problem: Brand Choice 13.6 Questions Regarding the Application of Logit Choice Models 13.7 Learning Summary Statistical Tables Bibliography Index particular statistical packages (e.g., SAS and SPSS). These workbooks explain how the concepts in the text are linked to the application software and show the student how to perform the analyses presented in each chapter. The program templates provided in the workbooks enable students to run their own analyses of the more than 100 data sets (most taken from real applications in the pub- lished literature) contained the CD-ROM that accompanies the text. Be able to interpret the results of the analysis. In each chapter, we raise the im- portant issues and problems that tend to come up with the application of each method. We place special emphasis on assessing the generalizability of the re- sults of an analysis, and suggest ways in which students can test the validity of their findings.
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