An Introduction to Correspondence Analysis. Eric J. Beh

      1  Cover

      2  Title Page

      3  Copyright

      4  Dedication

      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

and the Partition of

7.5 The Marcotorchino Residual

and the Partition of

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