Table of Contents 1
Cover
5
Preface
6
1 Introduction
1.1 Data Visualisation
1.2 Correspondence Analysis in a “Nutshell”
1.3 Data Sets
1.4 Symmetrical Versus Asymmetrical Association
1.5 Notation
1.6 Formal Test of Symmetrical Association
1.7 Formal Test of Asymmetrical Association
1.8 Correspondence Analysis and R
1.9 Overview of the Book
7
Part I: Classical Analysis of Two Categorical Variables
2 Simple Correspondence Analysis
2.1 Introduction
2.2 Reducing Multi-dimensional Space
2.3 Measuring Symmetric Association
2.4 Decomposing the Pearson Residual for Nominal Variables
2.5 Constructing a Low-Dimensional Display
2.6 Practicalities of the Low-Dimensional Plot
2.7 The Biplot Display
2.8 The Case for No Visual Display
2.9 Detecting Statistically Significant Points
2.10 Approximate p-values
2.11 Final Comments
3 Non-Symmetrical Correspondence Analysis
3.1 Introduction
3.2 Quantifying Asymmetric Association
3.3 Decomposing 8
Part II: Ordinal Analysis of Two Categorical Variables
4 Simple Ordinal Correspondence Analysis
4.1 Introduction
4.2 A Simple Correspondence Analysis of the Temperature Data
4.3 On the Mean and Variation of Profiles with Ordered Categories
4.4 Decomposing the Pearson Residual for Ordinal Variables
4.5 Constructing a Low-Dimensional Display
4.6 The Biplot Display
4.7 Final Comments
5 Ordered Non-symmetrical Correspondence Analysis
5.1 Introduction
5.2 The Goodman–Kruskal tau Index Revisited
5.3 Decomposing 9
Part III: Analysis of Multiple Categorical Variables
6 Multiple Correspondence Analysis
6.1 Introduction
6.2 Crisp Coding and the Indicator Matrix
6.3 The Burt Matrix
6.4 Stacking
6.5 Final Comments
7 Multi-way Correspondence Analysis
7.1 An Introduction
7.2 Pearson’s Residual 10
