Abstract #301673

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JSM 2003 Abstract #301673
Activity Number: 208
Type: Contributed
Date/Time: Tuesday, August 5, 2003 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract - #301673
Title: Likelihood Approach to the Estimation of Random Effects Multidimensional Unfolding
Author(s): Shiang-Tung Jung*+ and Richard D. Gonzalez
Companies: University of Michigan and University of Michigan
Address: 2167 Stone Rd., Ann Arbor, MI, 48105-2534,
Keywords: multidimensional scaling ; random effects ; multidimensional unfolding ; maximum likelihood
Abstract:

Multidimensional unfolding is a scaling technique for rating data. In multidimensional unfolding, each subject rates a set of stimuli. The analysis provides a configuration that represents the stimuli and also represents each subject as an "ideal point"; in the same configuration. We propose a maximum likelihood approach that allows random effects parameters in the multidimensional unfolding model, i.e., the subject ideal points are treated as random effects. Two benefits of this model are the ability to estimate random effects within subjects and the ability to include linear predictors, or covariates, of the ideal points. We also explore the computation algorithm to estimate the unfolding model. The new algorithm is then compared to existing algorithms based on alternative least-squares. An illustrative data set in the domain of marketing will be presented.

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