A Three-Way Clusterwise Multidimensional Unfolding Procedure for the Spatial Representation of Context Dependent Preferences

Computational Statistics & Data Analysis, Volume 53, Issue 8, Pages 3217–3230

Georgetown McDonough School of Business Research Paper No. 2796213

Posted: 17 Jun 2016

See all articles by Wayne S. DeSarbo

Wayne S. DeSarbo

Pennsylvania State University

Selin Atalay

Frankfurt School of Finance and Management

Simon J. Blanchard

Georgetown University - Robert Emmett McDonough School of Business

Date Written: June 1, 2009

Abstract

Various deterministic and latent structure approaches for combining forms of multidimensional scaling and cluster analysis have been previously discussed. A new clusterwise three-way unfolding methodology for the analysis of two-way or three-way metric dominance/preference data is proposed. The purpose of this proposed methodology is to simultaneously estimate a joint space of stimuli and cluster ideal point representations, as well as the clusters themselves, such that the geometry underlying the clusterwise model renders some indication of the underlying structure in the data. In the three-way case, it is shown how multiple ideal points can represent preference change over contexts or situations. Partitions, overlapping clusters, stationary and context dependent preference representations are allowed. After a literature review of related methodological research, the technical details of the proposed three-way clusterwise spatial unfolding model are presented in terms of modeling context/situational dependent preferences (i.e., preferences for various stimuli collected over the same set of subjects over time, situation, etc.). The psychological basis for the models is provided in terms of the extensive behavioral decision theory and consumer psychology literature on contextual preferences and situational effects. An application to a data set exploring preferences for breakfast/snack food data over a number of different usage situations is then presented, followed by a discussion on future potential research directions.

Suggested Citation

DeSarbo, Wayne S. and Atalay, A. Selin and Blanchard, Simon J., A Three-Way Clusterwise Multidimensional Unfolding Procedure for the Spatial Representation of Context Dependent Preferences (June 1, 2009). Computational Statistics & Data Analysis, Volume 53, Issue 8, Pages 3217–3230, Georgetown McDonough School of Business Research Paper No. 2796213, Available at SSRN: https://ssrn.com/abstract=2796213

Wayne S. DeSarbo (Contact Author)

Pennsylvania State University ( email )

University Park
State College, PA 16802
United States

A. Selin Atalay

Frankfurt School of Finance and Management ( email )

Adickesallee 32-34
Frankfurt am Main, 60322
Germany
60322 (Fax)

Simon J. Blanchard

Georgetown University - Robert Emmett McDonough School of Business ( email )

3700 O Street, NW
Washington, DC 20057
United States

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