Applied Multidimensional Scali

This book introduces multidimensional scaling (MDS) and unfolding as data analysis techniques for applied researchers. MDS is used for the analysis of proximity data on a set of objects, representing the data as distances between points in a geometric space (usually of two dimensions). Unfolding is a related method that maps preference data (typically evaluative ratings of different persons on a set of objects) as distances between two sets of points (representing the persons and the objects, resp.).

Ingwer Borg is visiting professor of psychology at WWU Münster (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 ofPsychology, 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.