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Bootstrap Confidence Regions for Multidimensional Scaling Solutions

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  • William G. Jacoby
  • David A. Armstrong

Abstract

Multidimensional scaling (or MDS) is a methodology for producing geometric models of proximities data. Multidimensional scaling has a long history in political science research. However, most applications of MDS are purely descriptive, with no attempt to assess stability or sampling variability in the scaling solution. In this article, we develop a bootstrap resampling strategy for constructing confidence regions in multidimensional scaling solutions. The methodology is illustrated by performing an inferential multidimensional scaling analysis on data from the 2004 American National Election Study (ANES). The bootstrap procedure is very simple, and it is adaptable to a wide variety of MDS models. Our approach enhances the utility of multidimensional scaling as a tool for testing substantive theories while still retaining the flexibility in assumptions, model details, and estimation procedures that make MDS so useful for exploring structure in data.

Suggested Citation

  • William G. Jacoby & David A. Armstrong, 2014. "Bootstrap Confidence Regions for Multidimensional Scaling Solutions," American Journal of Political Science, John Wiley & Sons, vol. 58(1), pages 264-278, January.
    • Handle: RePEc:wly:amposc:v:58:y:2014:i:1:p:264-278
    DOI: 10.1111/ajps.12056
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    Cited by:

    1. Malek Abduljaber & Ilker Kalin, 2019. "Globalization and the Transformation of Political Attitude Structures at the Party Level in the Arab World: Insights from the Cases of Egypt and Jordan," Societies, MDPI, vol. 9(1), pages 1-19, March.

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