Applied Multidimensional Scaling and Unfolding (SpringerBriefs in Statistics) - Softcover

Borg, Ingwer; Groenen, Patrick J.F.; Mair, Patrick

Softcover

ISBN 10: 3319734709 ISBN 13: 9783319734705

Publisher: Springer, 2018

Synopsis

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.).

This second edition has been completely revised to reflect new developments and the coverage of unfolding has also been substantially expanded. Intended for applied researchers whose main interests are in using these methods as tools for building substantive theories, it discusses numerous applications (classical and recent), highlights practical issues (such as evaluating model fit), presents ways to enforce theoretical expectations for the scaling solutions, and addresses the typical mistakes that MDS/unfolding users tend to make. Further, it shows how MDS and unfolding can be used in practical research work, primarily by using the smacof package in the R environment but also Proxscal in SPSS. It is a valuable resource for psychologists, social scientists, and market researchers, with a basic understanding of multivariate statistics (such as multiple regression and factor analysis).

 

 

About this title

Review:

“‘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)

About the Author:

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 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.

About this edition

• Publisher: Springer
• Publication date: 2018
• ISBN 10: 3319734709
• ISBN 13: 9783319734705
• Binding: Paperback
• Edition number: 2
• Number of pages: 131