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Applied Multidimensional Scaling and Unfolding
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Table of Contents

1 First steps.- 2 The purpose of MDS and Unfolding.- 3 The fit of MDS and Unfolding solutions.- 4 Proximities.- 5 Variants of MDS models.- 6 Confirmatory MDS.- 7 Typical mistakes in MDS.- 8 Unfolding.- 9 MDS algorithms.- 10 MDS Software.- Subject Index.

About the Author

Ingwer Borg is visiting professor of psychology at WWU Munster (Germany). He was scientific director at GESIS (Mannheim, Germany), psychology professor at JLU (Giessen, Germany), and research director at HRC (Munich, Germany). He has authored or edited 20 books and numerous articles on data analysis, survey research, theory construction, and various substantive fields of psychology, from psychophysics to job satisfaction.Patrick J.F. Groenen is professor of statistics at the Econometric Institute, Erasmus University Rotterdam, the Netherlands. His main research interests are in data science visualization techniques, such as multidimensional scaling, unfolding, and nonlinear multivariate analysis techniques. He has coauthored both technical and more applied papers in a variety of international journals. Patrick Mair received his PhD in statistics from the University of Vienna in 2005. Since 2013 he has worked as senior lecturer in statistics at the Department of Psychology, Harvard University. His research focuses on computational and applied statistics with special emphasis on psychometric methods, such as latent variable models and multivariate exploratory techniques.

Reviews

"'This book introduces the multidimensional scaling (MDS) as a psychological model and as a data analysis technique for the applied researcher. ... The book is unique in its orientation on the applied researcher, whose primary interest is in using MDS as a tool to build substantive theories. ... The primary audience of this book are psychologists, social scientists, and market researchers. No particular background knowledge is required, beyond a basic knowledge of statistics.'" (Ludwig Paditz, zbMATH 1409.62006, 2019)

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