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Modern multidimensional scaling theory and applications

Provides a comprehensive treatment of multidimensional scaling (MDS), a family of statistical techniques for analyzing the structure of (dis)similarity data. Containing chapters on asymmetric models and on unfolding, this book also includes many exercises. It is useful for students in psychology, sociology, and marketing.

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  • "Provides a comprehensive treatment of multidimensional scaling (MDS), a family of statistical techniques for analyzing the structure of (dis)similarity data. Containing chapters on asymmetric models and on unfolding, this book also includes many exercises. It is useful for students in psychology, sociology, and marketing."@en
  • "Provides a comprehensive treatment of multidimensional scaling (MDS), a family of statistical techniques for analyzing the structure of (dis)similarity data. Containing chapters on asymmetric models and on unfolding, this book also includes many exercises. It is useful for students in psychology, sociology, and marketing."
  • "The book provides a comprehensive treatment of multidimensional scaling (MDS), a statistical technique used to analyze the structure of similarity or dissimilarity data in multidimensional space. Such data are widespread, for example, intercorrelations of attitude items, direct ratings of similarity on choice objects, or trade indices for a set of countries. MDS models such data as distances among points in a geometric space of low dimensionality. This makes complex data sets accessible to visual exploration and thus aids in seeing structure not obvious from the numbers. Other uses of MDS interpret the geometry and, in particular, the distance function as a psychological composition rule. The book may be used as an introduction to MDS for students in many areas including statistics, psychology, sociology, political sciences, and marketing. The prerequisite is a two-semester course in statistics for the social or managerial sciences. The book is also suited for several varieties of advanced courses on MDS, either with an emphasis on data analysis or with a focus on the psychology of similarity. All the mathematics required for more advanced topics is developed systematically."
  • "(Publisher-supplied data) The book provides a comprehensive treatment of multidimensional scaling (MDS), a family of statistical techniques for analyzing the structure of (dis)similarity data. Such data are widespread, including, for example, intercorrelations of survey items, direct ratings on the similarity on choice objects, or trade indices for a set of countries. MDS represents the data as distances among points in a geometric space of low dimensionality. This map can help to see patterns in the data that are not obvious from the data matrices. MDS is also used as a psychological model for judgments of similarity and preference. This book may be used as an introduction to MDS for students in psychology, sociology, and marketing. The prerequisite is an elementary background in statistics. The book is also well suited for a variety of advanced courses on MDS topics. All the mathematics required for more advanced topics is developed systematically. This second edition is not only a complete overhaul of its predecessor, but also adds some 140 pages of new material. Many chapters are revised or have sections reflecting new insights and developments in MDS. There are two new chapters, one on asymmetric models and the other on unfolding. There are also numerous exercises that help the reader to practice what he or she has learned, and to delve deeper into the models and its intricacies. These exercises make it easier to use this edition in a course. All data sets used in the book can be downloaded from the web. The appendix on computer programs has also been updated and enlarged to reflect the state of the art."
  • "This book provides a comprehensive treatment of multidimensional scaling (MDS), a statistical technique used to analyze the structure of similarity or dissimilarity data in multidimensional space. There are many examples of such data, including intercorrelations of attitude items, direct ratings of similarity on choice objects, and trade indices for a set of countries. MDS models such data as distances between points in a geometric space of low dimensionality. This makes complex data sets accessible to visual exploration and thus makes it easier to see structure not obvious from the numbers. Other uses of MDS interpret the geometry and, in particular, the distance function as a psychological composition rule. The book may be used as an introduction to MDS for students in many areas, including statistics, psychology, sociology, political science, and marketing. The prerequisite is a two-semester course in statistics for the social or managerial sciences. The volume is also suited for various advanced courses on MDS, either with an emphasis on data analysis or a focus on the psychology of similarity. All the mathematics required for more advanced topics is developed systematically."
  • ""Este libro ofrece un tratamiento integral de escalamiento multidimensional (MDS), una familia de técnicas estadísticas para el análisis de la estructura de la (des) similitud de datos. Estos datos están muy extendidas, que incluyen, por ejemplo, intercorrelaciones de elementos de la encuesta, las calificaciones directas sobre la similitud en los objetos de elección, o los índices de comercio para un conjunto de países. MDS representa los datos como distancias entre puntos en un espacio geométrico de baja dimensionalidad. Este mapa puede ayudar a ver patrones en los datos que no son evidentes a partir de las matrices de datos. MDS también se utiliza como un modelo psicológico para los juicios de similitud y preferencia.""
  • ""This book provides a comprehensive treatment of multidimensional scaling (MDS), a family of statistical techniques for analyzing the structure of (dis)similarity data. Such data are widespread, including, for example, intercorrelations of survey items, direct ratings on the similarity on choice objects, or trade indices for a set of countries. MDS represents the data as distances among points in a geometric space of low dimensionality. This map can help to see patterns in the data that are not obvious from the data matrices. MDS is also used as a psychological model for judgments of similarity and preference."--Back cover."@en

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  • "Lehrbuch"
  • "Electronic books"@en
  • "Electronic books"
  • "Llibres electrònics"

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  • "Modern multidimensional scaling theory and applications"@en
  • "Modern multidimensional scaling theory and applications"
  • "Modern Multidimensional Scaling : Theory and Applications"
  • "Modern Multidimensional Scaling"
  • "Modern multidimensional scaling : theory and applications"@en
  • "Modern multidimensional scaling : theory and applications"
  • "Modern Multidimensional Scaling Theory and Applications"@en
  • "Modern Multidimensional Scaling Theory and Applications"

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