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Multivariate analysis of categorical data: theory

Author: Van de Geer, John P. Series: Advanced quantitative techniques in the social sciences ; 2 Publisher: Sage, 1993.Language: EnglishDescription: 99 p. ; 24 cm.ISBN: 0803945655Type of document: BookBibliography/Index: Includes bibliographical references and index
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Item type Current location Collection Call number Status Date due Barcode Item holds
Book Europe Campus
Main Collection
Print H61.25 .A38 G44 1993
(Browse shelf)
001177223
Available 001177223
Total holds: 0

Includes bibliographical references and index

Digitized

Multivariate Analysis of Categorical Data: Theory Contents Series Editor's Introduction Preface 1. General Concepts 1.1 Variables and Objects 1.2 Types of Variables 1.3 Missing Data 1.4 Active Versus Passive Data 1.5 Many Options 1.6 Transformation and Optimal Quantification 1.7 Classical Versus Nonlinear Multivariate Analysis 2. Classical Methods of Multivariate Analysis 2.1 Introduction 2.2 Principal Components Analysis 2.3 Canonical Analysis 2.4 Generalized Canonical Analysis 2.5 Relations Among PCA, CA, and GCA 3. Principal Components Analysis 3.1 Introduction ix xiii 1 1 2 3 5 6 6 7 9 9 9 12 14 15 17 17 3.2 Example With Two Categorical Variables 3.3 Graph 3.4 Regression Lines 3.5 Leading Coordinate Points 3.6 PCA solution 3.6.1 Introduction 3.6.2 PCA Solution in the Example 3.6.3 A Conclusion From the Example 3.6.4* Calculation of PCA for Two Variables 3.7 PCA Solution for Three Variables With Linear Dependence 3.7.1 Introduction 3.7.2 PCA Solution 3.7.3 PCA Results for Objects 3.8 PCA Example With Three Linearly Independent Variables 3.8.1 Introduction 3.8.2 PCA Solution 3.8.3 PCA Object Scores 3.8.4 PCA Solution for LC Points 3.9* Calculation of the PCA Solution 4. Optimal Quantification 4.1 Introduction 4.2 Single Quantification: Two Variables 4.2.1 Classical PCA 4.2.2 Solution With Single Optimal Quantification 4.2.3 Optimal Quantification Without a Priori Quantification 4.2.4 Second PCA Solution for the Example 4.3 Single Quantification With Three Variables 4.3.1 Single Optimal Quantification With Three Nominal Variables 4.3.2 Optimal Single Quantification 4.3.3 Some Additional Comments 4.4 Other Options for Optimal Quantification 4.5 Multiple Quantification With Two Variables 4.6 Multiple Quantification With Three Variables 4.6.1 Introduction 4.6.2 Multiple Solution 4.6.3 Correlations and Variances 18 19 20 20 23 23 23 28 29 30 30 31 33 35 35 37 39 43 44 46 46 47 47 47 50 51 51 51 52 56 56 57 61 61 62 66 4.7* Calculation of Optimal Quantification 4.7.1 * General Principle 4.7.2 * Example 4.7.3* Multiple Quantification 5. Indicator Matrices 5.1 Complete Indicator Matrix 5.2 Incomplete Indicator Matrix: Missing Data Passive 5.3 Indicator Matrix With Missing Data Active 5.4 Burt Table and Indicator Matrix 5.5* Some Algebraic Derivations 5.5.1 * Introduction 5.5.2 * Complete Indicator Matrix 5.5.3 * Incomplete Indicator Matrix 6. Properties and Risks of Optimal Quantification 6.1 Introduction 6.2 Marginal Frequencies 6.3 Number of Categories of a Variable 6.4 Effect of Special Category Combinations 6.5 Linear Regression and Reciprocal Averaging 6.6 Missing Data Treated as Passive 6.7 How Many Dimensions for Optimal Quantification? 6.7.1 Complete Indicator Matrix 6.7.2 Incomplete Indicator Matrix 6.7.3 Missing Data Active 7. Conclusions 7.1 Recapitulation 7.2 Preview References and Recommended Reading Name Index Subject Index About the Author 69 69 69 69 71 71 73 73 75 76 76 76 78 79 79 79 80 81 83 84 87 87 90 92 93 93 94 95 96 97 99

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