Bootstrap Confidence Regions for Multidimensional Scaling Solutions

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 t...

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Bibliographic Details
Published in:American journal of political science 2014-01, Vol.58 (1), p.264-278
Main Authors: Jacoby, William G., Armstrong II, David A.
Format: Article
Language:English
Subjects:
AJPS WORKSHOP
Bootstrap mechanism
Bootstrap method
Bootstrap resampling
bootstrapping
Coordinate systems
Data analysis
Elections
Ellipses
Estimation
Geometry
ideology in public opinion
Line-of-sight (LOS) dissimilarities
Methodology
Methodology (Data Analysis)
Multidimensional scales
Multidimensional scaling
Political candidates
Political parties
Political partisanship
Political science
Political Science Research
Sampling
Sampling errors
Scales
Stability
Studies
Trust
Voting
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Summary: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.
ISSN:0092-5853
1540-5907
DOI:10.1111/ajps.12056