Multidimensional Scaling I

Kwanghee Jung, Yoshio Takane

Research output: Chapter in Book/Report/Conference proceedingChapter

3 Scopus citations

Abstract

Multidimensional scaling (MDS) is a set of data-analytic tools for deriving a graphical representation of objects in a multidimensional space based on proximity relations among them. By the graphical representation, we gain intuitive understanding of the regularity governing the relationships among the objects. In this article, we introduce the basic concept and models of MDS along with its essential ingredients such as distance models, proximity data, fitting criteria, dimensionality selection. To illustrate the use of MDS, three useful MDS procedures (simple MDS, individual differences MDS, and unfolding analysis) are presented with empirical examples.

Original languageEnglish
Title of host publicationInternational Encyclopedia of the Social & Behavioral Sciences: Second Edition
PublisherElsevier Inc.
Pages34-39
Number of pages6
ISBN (Electronic)9780080970875
ISBN (Print)9780080970868
DOIs
StatePublished - Mar 26 2015

Keywords

  • Euclidean distance model
  • Graphical representation
  • Ideal points
  • Individual differences
  • Multidimensional scaling
  • Preference data
  • Proximity data
  • Unfolding analysis

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    Jung, K., & Takane, Y. (2015). Multidimensional Scaling I. In International Encyclopedia of the Social & Behavioral Sciences: Second Edition (pp. 34-39). Elsevier Inc.. https://doi.org/10.1016/B978-0-08-097086-8.42045-3