{"product_id":"correspondence-analysis-isbn-9781119953241","title":"Correspondence Analysis","description":"\u003cb\u003eA comprehensive overview of the internationalisation of correspondence analysis\u003c\/b\u003e \u003cp\u003e\u003ci\u003eCorrespondence Analysis: Theory, Practice and New Strategies\u003c\/i\u003e examines the key issues of correspondence analysis, and discusses the new advances that have been made over the last 20 years.\u003c\/p\u003e \u003cp\u003eThe main focus of this book is to provide a comprehensive discussion of some of the key technical and practical aspects of correspondence analysis, and to demonstrate how they may be put to use.  Particular attention is given to the history and mathematical links of the developments made. These links include not just those major contributions made by researchers in Europe (which is where much of the attention surrounding correspondence analysis has focused) but also the important contributions made by researchers in other parts of the world.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eKey features include\u003c\/i\u003e:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eA comprehensive international perspective on the key developments of correspondence analysis.\u003c\/li\u003e \u003cli\u003eDiscussion of correspondence analysis for nominal and ordinal categorical data.\u003c\/li\u003e \u003cli\u003eDiscussion of correspondence analysis of contingency tables with varying association structures (symmetric and non-symmetric relationship between two or more categorical variables).\u003c\/li\u003e \u003cli\u003eExtensive treatment of many of the members of the correspondence analysis family for two-way, three-way and multiple contingency tables.\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eCorrespondence Analysis\u003c\/i\u003e offers a comprehensive and detailed overview of this topic which will be of value to academics, postgraduate students and researchers wanting a better understanding of correspondence analysis. Readers interested in the historical development, internationalisation and diverse applicability of correspondence analysis will also find much to enjoy in this book.\u003c\/p\u003e  \u003cp\u003eForeword xv\u003c\/p\u003e \u003cp\u003ePreface xvii\u003c\/p\u003e \u003cp\u003ePart One Introduction 1\u003c\/p\u003e \u003cp\u003e1 Data Visualisation 3\u003c\/p\u003e \u003cp\u003e1.1 A Very Brief Introduction to Data Visualisation 3\u003c\/p\u003e \u003cp\u003e1.1.1 A Very Brief History 3\u003c\/p\u003e \u003cp\u003e1.1.2 Introduction to Visualisation Tools for Numerical Data 4\u003c\/p\u003e \u003cp\u003e1.1.3 Introduction to Visualisation Tools for Univariate Categorical Data 6\u003c\/p\u003e \u003cp\u003e1.2 Data Visualisation for Contingency Tables 10\u003c\/p\u003e \u003cp\u003e1.2.1 Fourfold Displays 11\u003c\/p\u003e \u003cp\u003e1.3 Other Plots 12\u003c\/p\u003e \u003cp\u003e1.4 Studying Exposure to Asbestos 13\u003c\/p\u003e \u003cp\u003e1.4.1 Asbestos and Irving J. Selikoff 13\u003c\/p\u003e \u003cp\u003e1.4.2 Selikoff’s Data 17\u003c\/p\u003e \u003cp\u003e1.4.3 Numerical Analysis of Selikoff’s Data 17\u003c\/p\u003e \u003cp\u003e1.4.4 A Graphical Analysis of Selikoff’s Data 18\u003c\/p\u003e \u003cp\u003e1.4.5 Classical Correspondence Analysis of Selikoff’s Data 20\u003c\/p\u003e \u003cp\u003e1.4.6 Other Methods of Graphical Analysis 22\u003c\/p\u003e \u003cp\u003e1.5 Happiness Data 25\u003c\/p\u003e \u003cp\u003e1.6 Correspondence Analysis Now 29\u003c\/p\u003e \u003cp\u003e1.6.1 A Bibliographic Taste 29\u003c\/p\u003e \u003cp\u003e1.6.2 The Increasing Popularity of Correspondence Analysis 29\u003c\/p\u003e \u003cp\u003e1.6.3 The Growth of the Correspondence Analysis Family Tree 32\u003c\/p\u003e \u003cp\u003e1.7 Overview of the Book 34\u003c\/p\u003e \u003cp\u003e1.8 R Code 35\u003c\/p\u003e \u003cp\u003eReferences 36\u003c\/p\u003e \u003cp\u003e2 Pearson’s Chi-Squared Statistic 44\u003c\/p\u003e \u003cp\u003e2.1 Introduction 44\u003c\/p\u003e \u003cp\u003e2.2 Pearson’s Chi-Squared Statistic 44\u003c\/p\u003e \u003cp\u003e2.2.1 Notation 44\u003c\/p\u003e \u003cp\u003e2.2.2 Measuring the Departure from Independence 45\u003c\/p\u003e \u003cp\u003e2.2.3 Pearson’s Chi-Squared Statistic 47\u003cbr\u003e 2.2.4 Other 2 Measures of Association 48\u003c\/p\u003e \u003cp\u003e2.2.5 The Power Divergence Statistic 49\u003c\/p\u003e \u003cp\u003e2.2.6 Dealing with the Sample Size 50\u003c\/p\u003e \u003cp\u003e2.3 The Goodman--Kruskal Tau Index 51\u003c\/p\u003e \u003cp\u003e2.3.1 Other Measures and Issues 52\u003cbr\u003e 2.4 The 2 × 2 Contingency Table 52\u003c\/p\u003e \u003cp\u003e2.4.1 Yates’ Continuity Correction 53\u003c\/p\u003e \u003cp\u003e2.5 Early Contingency Tables 54\u003c\/p\u003e \u003cp\u003e2.5.1 The Impact of Adolph Quetelet 55\u003c\/p\u003e \u003cp\u003e2.5.2 Gavarret’s (1840) Legitimate Children Data 58\u003c\/p\u003e \u003cp\u003e2.5.3 Finley’s (1884) Tornado Data 58\u003c\/p\u003e \u003cp\u003e2.5.4 Galton’s (1892) Fingerprint Data 59\u003c\/p\u003e \u003cp\u003e2.5.5 Final Comments 61\u003c\/p\u003e \u003cp\u003e2.6 R Code 61\u003c\/p\u003e \u003cp\u003e2.6.1 Expectation and Variance of the Pearson Chi-Squared Statistic 61\u003c\/p\u003e \u003cp\u003e2.6.2 Pearson’s Chi-Squared Test of Independence 62\u003c\/p\u003e \u003cp\u003e2.6.3 The Cressie--Read Statistic 64\u003c\/p\u003e \u003cp\u003eReferences 67\u003c\/p\u003e \u003cp\u003ePart Two Correspondence Analysis of Two-Way Contingency Tables 71\u003c\/p\u003e \u003cp\u003e3 Methods of Decomposition 73\u003c\/p\u003e \u003cp\u003e3.1 Introduction 73\u003c\/p\u003e \u003cp\u003e3.2 Reducing Multidimensional Space 73\u003c\/p\u003e \u003cp\u003e3.3 Profiles and Cloud of Points 74\u003c\/p\u003e \u003cp\u003e3.4 Property of Distributional Equivalence 79\u003c\/p\u003e \u003cp\u003e3.5 The Triplet and Classical Reciprocal Averaging 79\u003c\/p\u003e \u003cp\u003e3.5.1 One-Dimensional Reciprocal Averaging 80\u003c\/p\u003e \u003cp\u003e3.5.2 Matrix Form of One-Dimensional Reciprocal Averaging 81\u003c\/p\u003e \u003cp\u003e3.5.3 -Dimensional Reciprocal Averaging 83\u003c\/p\u003e \u003cp\u003e3.5.4 Some Historical Comments 83\u003c\/p\u003e \u003cp\u003e3.6 Solving the Triplet Using Eigen-Decomposition 84\u003c\/p\u003e \u003cp\u003e3.6.1 The Decomposition 84\u003c\/p\u003e \u003cp\u003e3.6.2 Example 85\u003c\/p\u003e \u003cp\u003e3.7 Solving the Triplet Using Singular Value Decomposition 86\u003c\/p\u003e \u003cp\u003e3.7.1 The Standard Decomposition 86\u003c\/p\u003e \u003cp\u003e3.7.2 The Generalised Decomposition 88\u003c\/p\u003e \u003cp\u003e3.8 The Generalised Triplet and Reciprocal Averaging 89\u003c\/p\u003e \u003cp\u003e3.9 Solving the Generalised Triplet Using Gram--Schmidt Process 91\u003c\/p\u003e \u003cp\u003e3.9.1 Ordered Categorical Variables and a priori Scores 91\u003c\/p\u003e \u003cp\u003e3.9.2 On Finding Orthogonalised Vectors 92\u003c\/p\u003e \u003cp\u003e3.9.3 A Recurrence Formulae Approach 94\u003c\/p\u003e \u003cp\u003e3.9.4 Changing the Basis Vector 96\u003c\/p\u003e \u003cp\u003e3.9.5 Generalised Correlations 97\u003c\/p\u003e \u003cp\u003e3.10 Bivariate Moment Decomposition 100\u003c\/p\u003e \u003cp\u003e3.11 Hybrid Decomposition 100\u003c\/p\u003e \u003cp\u003e3.11.1 An Alternative Singly Ordered Approach 102\u003c\/p\u003e \u003cp\u003e3.12 R Code 103\u003c\/p\u003e \u003cp\u003e3.12.1 Eigen-Decomposition in R 103\u003c\/p\u003e \u003cp\u003e3.12.2 Singular Value Decomposition in R 103\u003c\/p\u003e \u003cp\u003e3.12.3 Singular Value Decomposition for Matrix Approximation 104\u003c\/p\u003e \u003cp\u003e3.12.4 Generating Emerson’s Polynomials 106\u003c\/p\u003e \u003cp\u003e3.13 A Preliminary Graphical Summary 109\u003c\/p\u003e \u003cp\u003e3.14 Analysis of Analgesic Drugs 112\u003c\/p\u003e \u003cp\u003eReferences 115\u003c\/p\u003e \u003cp\u003e4 Simple Correspondence Analysis 120\u003c\/p\u003e \u003cp\u003e4.1 Introduction 120\u003c\/p\u003e \u003cp\u003e4.2 Notation 121\u003c\/p\u003e \u003cp\u003e4.3 Measuring Departures from Complete Independence 122\u003c\/p\u003e \u003cp\u003e4.3.1 The ‘Duplication Constant’ 123\u003c\/p\u003e \u003cp\u003e4.3.2 Pearson Ratios 123\u003c\/p\u003e \u003cp\u003e4.4 Decomposing the Pearson Ratio 124\u003c\/p\u003e \u003cp\u003e4.5 Coordinate Systems 126\u003c\/p\u003e \u003cp\u003e4.5.1 Standard Coordinates 126\u003c\/p\u003e \u003cp\u003e4.5.2 Principal Coordinates 127\u003c\/p\u003e \u003cp\u003e4.5.3 Biplot Coordinates 132\u003c\/p\u003e \u003cp\u003e4.6 Distances 136\u003c\/p\u003e \u003cp\u003e4.6.1 Distance from the Origin 136\u003c\/p\u003e \u003cp\u003e4.6.2 Intra-Variable Distances and the  Metric 137\u003c\/p\u003e \u003cp\u003e4.6.3 Inter-Variable Distances 138\u003c\/p\u003e \u003cp\u003e4.7 Transition Formulae 140\u003c\/p\u003e \u003cp\u003e4.8 Moments of the Principal Coordinates 141\u003c\/p\u003e \u003cp\u003e4.8.1 The Mean of  142\u003c\/p\u003e \u003cp\u003e4.8.2 The Variance of  142\u003c\/p\u003e \u003cp\u003e4.8.3 The Skewness of  143\u003c\/p\u003e \u003cp\u003e4.8.4 The Kurtosis of  143\u003c\/p\u003e \u003cp\u003e4.8.5 Moments of the Asbestos Data 144\u003c\/p\u003e \u003cp\u003e4.9 How Many Dimensions to Use? 145\u003c\/p\u003e \u003cp\u003e4.10 R Code 147\u003c\/p\u003e \u003cp\u003e4.11 Other Theoretical Issues 154\u003c\/p\u003e \u003cp\u003e4.12 Some Applications of Correspondence Analysis 156\u003c\/p\u003e \u003cp\u003e4.13 Analysis of a Mother’s Attachment to Her Child 158\u003c\/p\u003e \u003cp\u003eReferences 165\u003c\/p\u003e \u003cp\u003e5 Non-Symmetrical Correspondence Analysis 177\u003c\/p\u003e \u003cp\u003e5.1 Introduction 177\u003c\/p\u003e \u003cp\u003e5.2 The Goodman--Kruskal Tau Index 180\u003c\/p\u003e \u003cp\u003e5.2.1 The Tau Index as a Measure of the Increase in Predictability 180\u003c\/p\u003e \u003cp\u003e5.2.2 The Tau Index in the Context of ANOVA 182\u003c\/p\u003e \u003cp\u003e5.2.3 The Sensitivity of  182\u003c\/p\u003e \u003cp\u003e5.2.4 A Demonstration: Revisiting Selikoff’s Asbestos Data 185\u003c\/p\u003e \u003cp\u003e5.3 Non-Symmetrical Correspondence Analysis 186\u003c\/p\u003e \u003cp\u003e5.3.1 The Centred Column Profile Matrix 186\u003c\/p\u003e \u003cp\u003e5.3.2 Decomposition of  187\u003c\/p\u003e \u003cp\u003e5.4 The Coordinate Systems 188\u003c\/p\u003e \u003cp\u003e5.4.1 Standard Coordinates 188\u003c\/p\u003e \u003cp\u003e5.4.2 Principal Coordinates 189\u003c\/p\u003e \u003cp\u003e5.4.3 Biplot Coordinates 193\u003c\/p\u003e \u003cp\u003e5.5 Transition Formulae 197\u003c\/p\u003e \u003cp\u003e5.5.1 Supplementary Points 198\u003c\/p\u003e \u003cp\u003e5.5.2 Reconstruction Formulae 198\u003c\/p\u003e \u003cp\u003e5.6 Moments of the Principal Coordinates 199\u003c\/p\u003e \u003cp\u003e5.6.1 The Mean of  199\u003c\/p\u003e \u003cp\u003e5.6.2 The Variance of  200\u003c\/p\u003e \u003cp\u003e5.6.3 The Skewness of  201\u003c\/p\u003e \u003cp\u003e5.6.4 The Kurtosis of  201\u003c\/p\u003e \u003cp\u003e5.7 The Distances 201\u003c\/p\u003e \u003cp\u003e5.7.1 Column Distances 201\u003c\/p\u003e \u003cp\u003e5.7.2 Row Distances 203\u003c\/p\u003e \u003cp\u003e5.8 Comparison with Simple Correspondence Analysis 204\u003c\/p\u003e \u003cp\u003e5.9 R Code 204\u003c\/p\u003e \u003cp\u003e5.10 Analysis of a Mother’s Attachment to Her Child 209\u003c\/p\u003e \u003cp\u003eReferences 212\u003c\/p\u003e \u003cp\u003e6 Ordered Correspondence Analysis 216\u003c\/p\u003e \u003cp\u003e6.1 Introduction 216\u003c\/p\u003e \u003cp\u003e6.2 Pearson’s Ratio and Bivariate Moment Decomposition 221\u003c\/p\u003e \u003cp\u003e6.3 Coordinate Systems 222\u003c\/p\u003e \u003cp\u003e6.3.1 Standard Coordinates 222\u003c\/p\u003e \u003cp\u003e6.3.2 The Generalised Correlations 223\u003c\/p\u003e \u003cp\u003e6.3.3 Principal Coordinates 225\u003c\/p\u003e \u003cp\u003e6.3.4 Location, Dispersion and Higher Order Components 229\u003c\/p\u003e \u003cp\u003e6.3.5 The Correspondence Plot and Generalised Correlations 230\u003c\/p\u003e \u003cp\u003e6.3.6 Impact on the Choice of Scores 232\u003c\/p\u003e \u003cp\u003e6.4 Artificial Data Revisited 233\u003c\/p\u003e \u003cp\u003e6.4.1 On the Structure of the Association 233\u003c\/p\u003e \u003cp\u003e6.4.2 A Graphical Summary of the Association 233\u003c\/p\u003e \u003cp\u003e6.4.3 An Interpretation of the Axes and Components 234\u003c\/p\u003e \u003cp\u003e6.4.4 The Impact of the Choice of Scores 235\u003c\/p\u003e \u003cp\u003e6.5 Transition Formulae 236\u003c\/p\u003e \u003cp\u003e6.6 Distance Measures 238\u003c\/p\u003e \u003cp\u003e6.6.1 Distance from the Origin 238\u003c\/p\u003e \u003cp\u003e6.6.2 Intra-Variable Distances 239\u003c\/p\u003e \u003cp\u003e6.7 Singly Ordered Analysis 239\u003c\/p\u003e \u003cp\u003e6.8 R Code 241\u003c\/p\u003e \u003cp\u003e6.8.1 Generalised Correlations and Principal Inertias 241\u003c\/p\u003e \u003cp\u003e6.8.2 Doubly Ordered Correspondence Analysis 245\u003c\/p\u003e \u003cp\u003eReferences 248\u003c\/p\u003e \u003cp\u003e7 Ordered Non-Symmetrical Correspondence Analysis 251\u003c\/p\u003e \u003cp\u003e7.1 Introduction 251\u003c\/p\u003e \u003cp\u003e7.2 General Considerations 252\u003c\/p\u003e \u003cp\u003e7.2.1 Orthogonal Polynomials Instead of Singular Vectors 253\u003c\/p\u003e \u003cp\u003e7.3 Doubly Ordered Non-Symmetrical Correspondence Analysis 254\u003c\/p\u003e \u003cp\u003e7.3.1 Bivariate Moment Decomposition 254\u003c\/p\u003e \u003cp\u003e7.3.2 Generalised Correlations in Bivariate Moment Decomposition 255\u003c\/p\u003e \u003cp\u003e7.4 Singly Ordered Non-Symmetrical Correspondence Analysis 257\u003c\/p\u003e \u003cp\u003e7.4.1 Hybrid Decomposition for an Ordered Predictor Variable 257\u003c\/p\u003e \u003cp\u003e7.4.2 Hybrid Decomposition in the Case of Ordered Response Variables 258\u003c\/p\u003e \u003cp\u003e7.4.3 Generalised Correlations in Hybrid Decomposition 258\u003c\/p\u003e \u003cp\u003e7.5 Coordinate Systems for Ordered Non-Symmetrical Correspondence Analysis 259\u003c\/p\u003e \u003cp\u003e7.5.1 Polynomial Plots for Doubly Ordered Non-Symmetrical Correspondence Analysis 260\u003c\/p\u003e \u003cp\u003e7.5.2 Polynomial Biplot for Doubly Ordered Non-Symmetrical Correspondence Analysis 262\u003c\/p\u003e \u003cp\u003e7.5.3 Polynomial Plot for Singly Ordered Non-Symmetrical Correspondence Analysis with an Ordered Predictor Variable 262\u003c\/p\u003e \u003cp\u003e7.5.4 Polynomial Biplot for Singly Ordered Non-Symmetrical Correspondence Analysis with an Ordered Predictor Variable 263\u003c\/p\u003e \u003cp\u003e7.5.5 Polynomial Plot for Singly Ordered Non-Symmetrical Correspondence Analysis with an Ordered Response Variable 264\u003c\/p\u003e \u003cp\u003e7.5.6 Polynomial Biplot for Singly Ordered Non-Symmetrical Correspondence Analysis with an Ordered Response Variable 265\u003c\/p\u003e \u003cp\u003e7.6 Tests of Asymmetric Association 265\u003c\/p\u003e \u003cp\u003e7.7 Distances in Ordered Non-Symmetrical Correspondence Analysis 266\u003c\/p\u003e \u003cp\u003e7.7.1 Distances in Doubly Ordered Non-Symmetrical Correspondence Analysis 267\u003c\/p\u003e \u003cp\u003e7.7.2 Distances in Singly Ordered Non-Symmetrical Correspondence Analysis 269\u003c\/p\u003e \u003cp\u003e7.8 Doubly Ordered Non-Symmetrical Correspondence of Asbestos Data 269\u003c\/p\u003e \u003cp\u003e7.8.1 Trends 270\u003c\/p\u003e \u003cp\u003e7.9 Singly Ordered Non-Symmetrical Correspondence Analysis of Drug Data 277\u003c\/p\u003e \u003cp\u003e7.9.1 Predictability of Ordered Rows Given Columns 278\u003c\/p\u003e \u003cp\u003e7.10 R Code for Ordered Non-Symmetrical Correspondence Analysis 283\u003c\/p\u003e \u003cp\u003eReferences 300\u003c\/p\u003e \u003cp\u003e8 External Stability and Confidence Regions 302\u003c\/p\u003e \u003cp\u003e8.1 Introduction 302\u003c\/p\u003e \u003cp\u003e8.2 On the Statistical Significance of a Point 303\u003c\/p\u003e \u003cp\u003e8.3 Circular Confidence Regions for Classical Correspondence Analysis 304\u003c\/p\u003e \u003cp\u003e8.4 Elliptical Confidence Regions for Classical Correspondence Analysis 306\u003c\/p\u003e \u003cp\u003e8.4.1 The Information in the Optimal Correspondence Plot 306\u003c\/p\u003e \u003cp\u003e8.4.2 The Information in the First Two Dimensions 308\u003c\/p\u003e \u003cp\u003e8.4.3 Eccentricity of Elliptical Regions 309\u003c\/p\u003e \u003cp\u003e8.4.4 Comparison of Confidence Regions 309\u003c\/p\u003e \u003cp\u003e8.5 Confidence Regions for Non-Symmetrical Correspondence Analysis 311\u003c\/p\u003e \u003cp\u003e8.5.1 Circular Regions in Non-Symmetrical Correspondence Analysis 312\u003c\/p\u003e \u003cp\u003e8.5.2 Elliptical Regions in Non-Symmetrical Correspondence Analysis 312\u003c\/p\u003e \u003cp\u003e8.6 Approximate -values and Classical Correspondence Analysis 313\u003c\/p\u003e \u003cp\u003e8.6.1 Approximate -values Based on Confidence Circles 313\u003c\/p\u003e \u003cp\u003e8.6.2 Approximate -values Based on Confidence Ellipses 314\u003c\/p\u003e \u003cp\u003e8.7 Approximate -values and Non-Symmetrical Correspondence Analysis 315\u003c\/p\u003e \u003cp\u003e8.8 Bootstrap Elliptical Confidence Regions 315\u003c\/p\u003e \u003cp\u003e8.9 Ringrose’s Bootstrap Confidence Regions 316\u003c\/p\u003e \u003cp\u003e8.9.1 Confidence Ellipses and Covariance Matrix 317\u003c\/p\u003e \u003cp\u003e8.10 Confidence Regions and Selikoff’s Asbestos Data 318\u003c\/p\u003e \u003cp\u003e8.11 Confidence Regions and Mother--Child Attachment Data 322\u003c\/p\u003e \u003cp\u003e8.12 R Code 325\u003c\/p\u003e \u003cp\u003e8.12.1 Calculating the Path of a Confidence Ellipse 326\u003c\/p\u003e \u003cp\u003e8.12.2 Constructing Elliptical Regions in a Correspondence Plot 327\u003c\/p\u003e \u003cp\u003eReferences 335\u003c\/p\u003e \u003cp\u003e9 Variants of Correspondence Analysis 337\u003c\/p\u003e \u003cp\u003e9.1 Introduction 337\u003c\/p\u003e \u003cp\u003e9.2 Correspondence Analysis Using Adjusted Standardised Residuals 337\u003c\/p\u003e \u003cp\u003e9.3 Correspondence Analysis Using the Freeman--Tukey Statistic 340\u003c\/p\u003e \u003cp\u003e9.4 Correspondence Analysis of Ranked Data 342\u003c\/p\u003e \u003cp\u003e9.5 R Code 343\u003c\/p\u003e \u003cp\u003e9.5.1 Adjusted Standardised Residuals 343\u003c\/p\u003e \u003cp\u003e9.5.2 Freeman--Tukey Statistic 349\u003c\/p\u003e \u003cp\u003e9.6 The Correspondence Analysis Family 353\u003c\/p\u003e \u003cp\u003e9.6.1 Detrended Correspondence Analysis 353\u003c\/p\u003e \u003cp\u003e9.6.2 Canonical Correspondence Analysis 354\u003c\/p\u003e \u003cp\u003e9.6.3 Inverse Correspondence Analysis 355\u003c\/p\u003e \u003cp\u003e9.6.4 Ordered Correspondence Analysis 355\u003c\/p\u003e \u003cp\u003e9.6.5 Grade Correspondence Analysis 355\u003c\/p\u003e \u003cp\u003e9.6.6 Symbolic Correspondence Analysis 356\u003c\/p\u003e \u003cp\u003e9.6.7 Correspondence Analysis of Proximity Data 356\u003c\/p\u003e \u003cp\u003e9.6.8 Residual (Scaling) Correspondence Analysis 360\u003c\/p\u003e \u003cp\u003e9.6.9 Log-Ratio Correspondence Analysis 362\u003c\/p\u003e \u003cp\u003e9.6.10 Parametric Correspondence Analysis 364\u003c\/p\u003e \u003cp\u003e9.6.11 Subset Correspondence Analysis 364\u003c\/p\u003e \u003cp\u003e9.6.12 Foucart’s Correspondence Analysis 365\u003c\/p\u003e \u003cp\u003e9.7 Other Techniques 365\u003c\/p\u003e \u003cp\u003eReferences 366\u003c\/p\u003e \u003cp\u003ePart Three Correspondence Analysis of Multi-Way Contingency Tables 373\u003c\/p\u003e \u003cp\u003e10 Coding and Multiple Correspondence Analysis 375\u003c\/p\u003e \u003cp\u003e10.1 Introduction to Coding 375\u003c\/p\u003e \u003cp\u003e10.2 Coding Data 377\u003c\/p\u003e \u003cp\u003e10.2.1 B-Splines 377\u003c\/p\u003e \u003cp\u003e10.2.2 Crisp Coding 380\u003c\/p\u003e \u003cp\u003e10.2.3 Fuzzy Coding 382\u003c\/p\u003e \u003cp\u003e10.3 Coding Ordered Categorical Variables by Orthogonal Polynomials 382\u003c\/p\u003e \u003cp\u003e10.4 Burt Matrix 384\u003c\/p\u003e \u003cp\u003e10.5 An Introduction to Multiple Correspondence Analysis 386\u003c\/p\u003e \u003cp\u003e10.6 Multiple Correspondence Analysis 388\u003c\/p\u003e \u003cp\u003e10.6.1 Notation 388\u003c\/p\u003e \u003cp\u003e10.6.2 Decomposition Methods 389\u003c\/p\u003e \u003cp\u003e10.6.3 Coordinates, Transition Formulae and Adjusted Inertia 393\u003c\/p\u003e \u003cp\u003e10.7 Variants of Multiple Correspondence Analysis 395\u003c\/p\u003e \u003cp\u003e10.7.1 Joint Correspondence Analysis 396\u003c\/p\u003e \u003cp\u003e10.7.2 Stacking and Concatenation 397\u003c\/p\u003e \u003cp\u003e10.8 Ordered Multiple Correspondence Analysis 398\u003c\/p\u003e \u003cp\u003e10.8.1 Orthogonal Polynomials in Multiple Correspondence Analysis 398\u003c\/p\u003e \u003cp\u003e10.8.2 Hybrid Decomposition of Multiple Indicator Tables 399\u003c\/p\u003e \u003cp\u003e10.8.3 Two Ordered Variables and Their Contingency Table 400\u003c\/p\u003e \u003cp\u003e10.8.4 Test of Statistical Significance 401\u003c\/p\u003e \u003cp\u003e10.8.5 Properties of Ordered Multiple Correspondence Analysis 403\u003c\/p\u003e \u003cp\u003e10.8.6 Graphical Displays in Ordered Multiple Correspondence Analysis 404\u003c\/p\u003e \u003cp\u003e10.9 Applications 405\u003c\/p\u003e \u003cp\u003e10.9.1 Customer Satisfaction in Health Care Services 406\u003c\/p\u003e \u003cp\u003e10.9.2 Two Quality Aspects 411\u003c\/p\u003e \u003cp\u003e10.10 R Code 417\u003c\/p\u003e \u003cp\u003e10.10.1 B-Spline Function 417\u003c\/p\u003e \u003cp\u003e10.10.2 Crisp and Fuzzy Coding Using B-Splines in R 421\u003c\/p\u003e \u003cp\u003e10.10.3 Crisp Coding and the Burt Table by Indicator Functions in R 425\u003c\/p\u003e \u003cp\u003e10.10.4 Classical and Multiple Correspondence Analysis in R 428\u003c\/p\u003e \u003cp\u003eReferences 444\u003c\/p\u003e \u003cp\u003e11 Symmetrical and Non-Symmetrical Three-Way Correspondence Analysis 451\u003c\/p\u003e \u003cp\u003e11.1 Introduction 451\u003c\/p\u003e \u003cp\u003e11.2 Notation 453\u003c\/p\u003e \u003cp\u003e11.3 Symmetric and Asymmetric Association in Three-Way Contingency Tables 454\u003c\/p\u003e \u003cp\u003e11.4 Partitioning Three-Way Measures of Association 455\u003c\/p\u003e \u003cp\u003e11.4.1 Partitioning Pearson’s Three-Way Statistic 457\u003c\/p\u003e \u003cp\u003e11.4.2 Partitioning Marcotorchino’s and Gray--William’s Three-Way Indices 458\u003c\/p\u003e \u003cp\u003e11.4.3 Marcotorchino’s Index 460\u003c\/p\u003e \u003cp\u003e11.4.4 Partitioning the Three-Way Delta Index 461\u003c\/p\u003e \u003cp\u003e11.4.5 Three-Way Delta Index 463\u003c\/p\u003e \u003cp\u003e11.5 Formal Tests of Predictability 463\u003c\/p\u003e \u003cp\u003e11.5.1 Testing Pearson’s Statistic 464\u003c\/p\u003e \u003cp\u003e11.5.2 Testing the Marcotorchino’s Index 464\u003c\/p\u003e \u003cp\u003e11.5.3 Testing the Delta Index 465\u003c\/p\u003e \u003cp\u003e11.5.4 Discussion 465\u003c\/p\u003e \u003cp\u003e11.6 Tucker3 Decomposition for Three-Way Tables 466\u003c\/p\u003e \u003cp\u003e11.7 Correspondence Analysis of Three-Way Contingency Tables 467\u003c\/p\u003e \u003cp\u003e11.7.1 Symmetrically Associated Variables 467\u003c\/p\u003e \u003cp\u003e11.7.2 Asymmetrically Associated Variables 468\u003c\/p\u003e \u003cp\u003e11.7.3 Additional Property 469\u003c\/p\u003e \u003cp\u003e11.8 Modelling of Partial and Marginal Dependence 470\u003c\/p\u003e \u003cp\u003e11.9 Graphical Representation 471\u003c\/p\u003e \u003cp\u003e11.9.1 Interactive Plot 471\u003c\/p\u003e \u003cp\u003e11.9.2 Interactive Biplot 472\u003c\/p\u003e \u003cp\u003e11.9.3 Category Contribution 474\u003c\/p\u003e \u003cp\u003e11.10 On the Application of Partitions 474\u003c\/p\u003e \u003cp\u003e11.10.1 Olive Data: Partitioning the Asymmetric Association 474\u003c\/p\u003e \u003cp\u003e11.10.2 Job Satisfaction Data: Partitioning the Asymmetric Association 476\u003c\/p\u003e \u003cp\u003e11.11 On the Application of Three-Way Correspondence Analysis 477\u003c\/p\u003e \u003cp\u003e11.11.1 Job Satisfaction and Three-Way Symmetrical Correspondence Analysis 477\u003c\/p\u003e \u003cp\u003e11.11.2 Job Satisfaction and Three-Way Non-Symmetrical Correspondence Analysis 483\u003c\/p\u003e \u003cp\u003e11.12 R Code 490\u003c\/p\u003e \u003cp\u003eReferences 511\u003c\/p\u003e \u003cp\u003ePart Four The Computation of Correspondence Analysis 517\u003c\/p\u003e \u003cp\u003e12 Computing and Correspondence Analysis 519\u003c\/p\u003e \u003cp\u003e12.1 Introduction 519\u003c\/p\u003e \u003cp\u003e12.2 A Look Through Time 519\u003c\/p\u003e \u003cp\u003e12.2.1 Pre-1990 519\u003c\/p\u003e \u003cp\u003e12.2.2 From 1990 to 2000 520\u003c\/p\u003e \u003cp\u003e12.2.3 The Early 2000s 522\u003c\/p\u003e \u003cp\u003e12.3 The Impact of R 523\u003c\/p\u003e \u003cp\u003e12.3.1 Overview of Correspondence Analysis in R 523\u003c\/p\u003e \u003cp\u003e12.3.2 MASS 524\u003c\/p\u003e \u003cp\u003e12.3.3 Nenadi´c and Greenacre’s (2007) ca 525\u003c\/p\u003e \u003cp\u003e12.3.4 Murtagh (2005) 527\u003c\/p\u003e \u003cp\u003e12.3.5 ade4 530\u003c\/p\u003e \u003cp\u003e12.4 Some Stand-Alone Programs 533\u003c\/p\u003e \u003cp\u003e12.4.1 JMP 533\u003c\/p\u003e \u003cp\u003e12.4.2 SPSS 533\u003c\/p\u003e \u003cp\u003e12.4.3 PAST 534\u003c\/p\u003e \u003cp\u003e12.4.4 DtmVic5.6+ 535\u003c\/p\u003e \u003cp\u003eReferences 540\u003c\/p\u003e \u003cp\u003eIndex 545\u003c\/p\u003e \"the book is outstandingly comprehensive and informative, well written, and clear. If the book is adopted for courses in Statistics for not only students in applied fields, but also for students in Statistics, it will provide them with an excellent up-to-date knowledge of the entire spectrum of correspondence analysis. I would also like to recommend the book very strongly to most researchers including seasoned researchers in data analysis, for the book will undoubtedly fill in the gap of their knowledge about versatile correspondence analysis. I learned a lot, reading the book.\" (Psychometrika 2016) \u003cp\u003e\u003cb\u003eEric J. Beh\u003c\/b\u003e\u003cbr\u003eSchool of Mathematics \u0026amp; Physical Sciences, University of Newcastle, Australia\u003c\/p\u003e \u003cp\u003e\u003cb\u003eRosaria Lombardo\u003c\/b\u003e\u003cbr\u003eDepartment of Economics, Second University of Naples, Italy\u003c\/p\u003e  \u003cp\u003eA comprehensive overview of the internationalisation of correspondence analysis\u003c\/p\u003e \u003cp\u003e\u003ci\u003eCorrespondence Analysis: Theory, Practice and New Strategies\u003c\/i\u003e examines the key issues of correspondence analysis, and discusses the new advances that have been made over the last 20 years.\u003c\/p\u003e \u003cp\u003eThe main focus of this book is to provide a comprehensive discussion of some of the key technical and practical aspects of correspondence analysis, and to demonstrate how they may be put to use. Particular attention is given to the history and mathematical links of the developments made. These links include not just those major contributions made by researchers in Europe (which is where much of the attention surrounding correspondence analysis has focused) but also the important contributions made by researchers in other parts of the world.\u003c\/p\u003e \u003cp\u003eKey features include:\u003cbr\u003e \u003cbr\u003e • A comprehensive international perspective on the key developments of correspondence analysis.\u003cbr\u003e \u003cbr\u003e • Discussion of correspondence analysis for nominal and ordinal categorical data.\u003cbr\u003e \u003cbr\u003e • Discussion of correspondence analysis of contingency tables with varying association structures (symmetric and non-symmetric relationship between two or more categorical variables).\u003cbr\u003e \u003cbr\u003e • Extensive treatment of many of the members of the correspondence analysis family for two-way, three-way and multiple contingency tables.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eCorrespondence Analysis\u003c\/i\u003e offers a comprehensive and detailed overview of this topic which will be of value to academics, postgraduate students and researchers who want to have a better understanding of correspondence analysis. Readers interested in the historical development, internationalisation and diverse applicability of correspondence analysis will also find much to enjoy in this book.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988995064037,"sku":"NP9781119953241","price":118.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119953241.jpg?v=1761782357","url":"https:\/\/k12savings.com\/es\/products\/correspondence-analysis-isbn-9781119953241","provider":"K12savings","version":"1.0","type":"link"}