Applied Multidimensional Scaling and Unfolding

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

Part of the SpringerBriefs in Statistics book series (BRIEFSSTATIST)

Table of contents

  1. Front Matter
    Pages i-ix
  2. Ingwer Borg, Patrick J. F. Groenen, Patrick Mair
    Pages 1-10
  3. Ingwer Borg, Patrick J. F. Groenen, Patrick Mair
    Pages 11-27
  4. Ingwer Borg, Patrick J. F. Groenen, Patrick Mair
    Pages 29-41
  5. Ingwer Borg, Patrick J. F. Groenen, Patrick Mair
    Pages 43-51
  6. Ingwer Borg, Patrick J. F. Groenen, Patrick Mair
    Pages 53-66
  7. Ingwer Borg, Patrick J. F. Groenen, Patrick Mair
    Pages 67-76
  8. Ingwer Borg, Patrick J. F. Groenen, Patrick Mair
    Pages 77-93
  9. Ingwer Borg, Patrick J. F. Groenen, Patrick Mair
    Pages 95-104
  10. Ingwer Borg, Patrick J. F. Groenen, Patrick Mair
    Pages 105-110
  11. Ingwer Borg, Patrick J. F. Groenen, Patrick Mair
    Pages 111-120
  12. Back Matter
    Pages 121-122

About this book

Introduction

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).

Keywords

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

Authors and affiliations

  • Ingwer Borg
    • 1
  • Patrick J.F. Groenen
    • 2
  • Patrick Mair
    • 3
  1. 1.Westfälische Wilhelms-UniversitätMünsterGermany
  2. 2.Econometric InstituteErasmus University RotterdamRotterdamThe Netherlands
  3. 3.Department of PsychologyHarvard UniversityCambridgeUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-73471-2
  • Copyright Information The Author(s) 2018
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-319-73470-5
  • Online ISBN 978-3-319-73471-2
  • Series Print ISSN 2191-544X
  • Series Online ISSN 2191-5458
  • About this book