The book "Modern Multidimensional Scaling: Theory and Applications" by Ingwer Borg and Patrick J.F. Groenen is a comprehensive guide to the theory and applications of multidimensional scaling (MDS). MDS is a technique used to analyze similarity or dissimilarity data by modeling it as distances in a geometric space, providing a visual representation that is easier to understand than raw data. The book covers various aspects of MDS, including its basic concepts, technical aspects, unfolding, geometric interpretations, and related methods. The first six chapters introduce the fundamental ideas of MDS, explaining its four main purposes: data exploration, testing structural hypotheses, discovering psychological dimensions, and modeling mental arithmetic. Subsequent chapters delve into technical details such as matrix algebra, solving MDS problems, and handling degeneracies and local minima. The book also explores unfolding, a special case of MDS where preference data from different individuals are represented by distances between points. Part IV focuses on the geometric interpretation of MDS, discussing how MDS can be seen as a psychological model and how to interpret distances and scalar products. Part V covers techniques and models closely associated with MDS, including Procrustean methods, three-way models, and methods related to principal component analysis and correspondence analysis. The book includes 24 chapters divided into five parts, with 176 illustrations. It provides a detailed appendix on major MDS computer programs and a summary of notation used throughout the book. The authors recommend that beginners start with the first six chapters, while more advanced readers can explore specific topics of interest. The book is designed to be accessible to those with only elementary knowledge of descriptive statistics and includes exercises to help readers apply MDS to empirical data sets.The book "Modern Multidimensional Scaling: Theory and Applications" by Ingwer Borg and Patrick J.F. Groenen is a comprehensive guide to the theory and applications of multidimensional scaling (MDS). MDS is a technique used to analyze similarity or dissimilarity data by modeling it as distances in a geometric space, providing a visual representation that is easier to understand than raw data. The book covers various aspects of MDS, including its basic concepts, technical aspects, unfolding, geometric interpretations, and related methods. The first six chapters introduce the fundamental ideas of MDS, explaining its four main purposes: data exploration, testing structural hypotheses, discovering psychological dimensions, and modeling mental arithmetic. Subsequent chapters delve into technical details such as matrix algebra, solving MDS problems, and handling degeneracies and local minima. The book also explores unfolding, a special case of MDS where preference data from different individuals are represented by distances between points. Part IV focuses on the geometric interpretation of MDS, discussing how MDS can be seen as a psychological model and how to interpret distances and scalar products. Part V covers techniques and models closely associated with MDS, including Procrustean methods, three-way models, and methods related to principal component analysis and correspondence analysis. The book includes 24 chapters divided into five parts, with 176 illustrations. It provides a detailed appendix on major MDS computer programs and a summary of notation used throughout the book. The authors recommend that beginners start with the first six chapters, while more advanced readers can explore specific topics of interest. The book is designed to be accessible to those with only elementary knowledge of descriptive statistics and includes exercises to help readers apply MDS to empirical data sets.