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Informationen zum Autor Michael Greenacre, Jorg Blasius Klappentext Using a practical approach, Multiple Correspondence Analysis and Related Methods brings together the theory and applications of multiple correspondence analysis into a single volume. Because multiple correspondence analysis methods are capable of handling high-dimensional categorical data, they are applicable to survey research efforts in areas such as the social sciences, marketing, health economics, and biomedicine. This book contains a comprehensive overview of these applications. Written by leading experts, the text also features software notes for each chapter, an appendix on available software, R programs for applying the various methods, and numerous worked examples and case studies. Zusammenfassung As multiple correspondence analysis methods are capable of handling high-dimensional categorical data, they are applicable to survey research efforts in areas like the social sciences, marketing, health economics, and biomedicine. Using a practical approach, this book brings together the theory and applications of multiple correspondence analysis. Inhaltsverzeichnis Correspondence Analysis and Related Methods in Practice. From Simple to Multiple Correspondence Analysis. Divided by a Common Language: Analyzing and Visualizing Two-Way Arrays. Nonlinear Principal Component Analysis and Related Techniques. The Geometric Analysis of Structured Individuals × Variables Tables. Correlational Structure of Multi-Choice Data as Viewed from Dual Scaling. Validation Techniques in Multiple Correspondence Analysis. Multiple Correspondence Analysis of Subsets of Response Categories. Scaling Unidimensional Models with Multiple Correspondence Analysis. The Unfolding Fallacy Unveiled: A Comparison of Multiple Correspondence Analysis and Non-Metric IRT Models. Regularized Multiple Correspondence Analysis. The Evaluation of "Don't Know" Responses by Generalized Canonical Analysis. Multiple Factor Analysis for Contingency Tables. Simultaneous Analysis. Multiple Factor Analysis of Mixed Tables. Correspondence Analysis and Classification. Multiblock Canonical Correlation Analysis for Categorical Variables: Application to Epidemiological Data. Projection Pursuit Approach for Categorical Data. Correspondence Analysis and Categorical Conjoint Measurement. A Three-Step Approach to Assessing the Behavior of Survey Items in Cross-National Research using Biplots. Additive and Multiplicative Models for Three-Way Contingency Tables: Darroch (1974) Revisited. A New Model for Visualizing Interactions in Analysis of Variance. Logistic Biplots. Appendix: Computational of Multiple Correspondence Analysis, with Code in R....
Table des matières
Correspondence Analysis and Related Methods in Practice. From Simple to Multiple Correspondence Analysis. Divided by a Common Language: Analyzing and Visualizing Two-Way Arrays. Nonlinear Principal Component Analysis and Related Techniques. The Geometric Analysis of Structured Individuals × Variables Tables. Correlational Structure of Multi-Choice Data as Viewed from Dual Scaling. Validation Techniques in Multiple Correspondence Analysis. Multiple Correspondence Analysis of Subsets of Response Categories. Scaling Unidimensional Models with Multiple Correspondence Analysis. The Unfolding Fallacy Unveiled: A Comparison of Multiple Correspondence Analysis and Non-Metric IRT Models. Regularized Multiple Correspondence Analysis. The Evaluation of "Don't Know" Responses by Generalized Canonical Analysis. Multiple Factor Analysis for Contingency Tables. Simultaneous Analysis. Multiple Factor Analysis of Mixed Tables. Correspondence Analysis and Classification. Multiblock Canonical Correlation Analysis for Categorical Variables: Application to Epidemiological Data. Projection Pursuit Approach for Categorical Data. Correspondence Analysis and Categorical Conjoint Measurement. A Three-Step Approach to Assessing the Behavior of Survey Items in Cross-National Research using Biplots. Additive and Multiplicative Models for Three-Way Contingency Tables: Darroch (1974) Revisited. A New Model for Visualizing Interactions in Analysis of Variance. Logistic Biplots. Appendix: Computational of Multiple Correspondence Analysis, with Code in R.
