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Multi-Dimensional Scaling: An Introduction
Date
2012-12-07 (Creation date: 2012-12-07)
Main contributor
William G. Jacoby
Summary
Would you like to draw pictures of your data in ways that reveal structures not obvious from inspection of the data values alone? Multidimensional scaling (MDS) can accomplish that objective. MDS produces a “map” of stimuli based on information about the “proximities” among them. The stimuli are any objects of interest to the researcher (e.g., presidential candidates for a political scientist, consumer products for a market researcher, occupations for a sociologist), and many types of information can be interpreted as proximities (e.g., correlations, similarity judgments, profile dissimilarities, etc.). MDS methods have many potential applications in empirical research. They can be used to simplify the contents of large complex datasets, model similarities among sets of objects, estimate the cognitive structures underlying survey responses, and optimize the measurement characteristics of qualitative observations. MDS can be generalized to show individual differences across distinct data sources (e.g., subsets of survey respondents or data collected at different time points), and can be adapted to represent respondent preferences among a set of stimuli (i.e., “ideal points” models). This workshop provides an introduction to MDS. It is intended for a general audience and does not assume prior experience with MDS or familiarity with advanced statistical methods beyond basic regression analysis. Specific topics to be covered include: The basic idea of MDS; the general estimation procedure; interpretation of results; different varieties of MDS; and software options for performing MDS analyses.
Collection
Workshop in Methods
Unit
Social Science Research Commons
Notes

Performers

Dr. Jacoby is Professor of Political Science at Michigan State University. He is also a Research Scientist at the University of Michigan and the Director of ICPSR’s Summer Program in Quantitative Methods of Social Research.