Applied Multidimensional Scaling and Unfolding

1. Front Matter

   Pages i-ix

   PDF

2. First Steps

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

   Pages 1-10

3. The Purpose of MDS and Unfolding

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

   Pages 11-27

4. The Fit of MDS and Unfolding Solutions

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

   Pages 29-41

5. Proximities

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

   Pages 43-51

6. Variants of MDS Models

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

   Pages 53-66

7. Confirmatory MDS

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

   Pages 67-76

8. Typical Mistakes in MDS

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

   Pages 77-93

9. Unfolding

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

   Pages 95-104

10. MDS Algorithms

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

    Pages 105-110

11. MDS Software

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

    Pages 111-120

12. Back Matter

    Pages 121-122

    PDF

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).

 

 

• MSC 91C15
• Multidimensional scaling
• Unfolding
• Multivariate data analysis
• Proximity data
• Preference data
• Psychometrics
• Visualizing proximity data
• R package smacof
• Proxscal
• computational social sciences

“‘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)

• Westfälische Wilhelms-Universität, Münster, Germany

  Ingwer Borg

• Econometric Institute, Erasmus University Rotterdam, Rotterdam, The Netherlands

  Patrick J.F. Groenen

• Department of Psychology, Harvard University, Cambridge, USA

  Patrick Mair

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.

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