Would you like to draw pictures of your data, in ways that reveal structures that are not obvious from inspection of the data values, alone? Multidimensional scaling (MDS) tries to accomplish exactly that objective. To be more precise, MDS produces a map of stimuli, based upon information about the proximities among those stimuli.

Multidimensional scaling 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). It also can be adapted to represent respondent preferences among a set of stimuli (so-called ideal points models).

Specific topics to be covered in the short course include: The basic idea of MDS; the general estimation procedure; interpretation of results; different varieties of MDS; and software options for performing MDS analyses. These lectures are intended for a general audience. They do not assume any prior experience with MDS or familiarity with advanced statistical methods (i.e., beyond basic regression analysis).

Location:	106 Farrall Agricultural Engineering Hall [map]
Price:	Free to Graduate Students, Faculty and Research Staff
Sponsor:	public
Contact:	Dr. Brian Silver bsilver@msu.edu 517-353-9288
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