A three-way clusterwise multidimensional unfolding procedure for the spatial representation of context dependent preferences
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.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Ben-Akiva, Moshe & Morikawa, Takayuki & Shiroishi, Fumiaki, 1992. "Analysis of the reliability of preference ranking data," Journal of Business Research, Elsevier, vol. 24(2), pages 149-164, March.
- Frank Busing & Patrick Groenen & Willem Heiser, 2005. "Avoiding degeneracy in multidimensional unfolding by penalizing on the coefficient of variation," Psychometrika, Springer, vol. 70(1), pages 71-98, March.
- J. Douglas Carroll & Sandra Pruzansky & Joseph Kruskal, 1980. "Candelinc: A general approach to multidimensional analysis of many-way arrays with linear constraints on parameters," Psychometrika, Springer, vol. 45(1), pages 3-24, March.
- Joseph Bennett & William Hays, 1960. "Multidimensional unfolding: Determining the dimensionality of ranked preference data," Psychometrika, Springer, vol. 25(1), pages 27-43, March.
- Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer, vol. 2(1), pages 193-218, December.
- Puto, Christopher P, 1987. " The Framing of Buying Decisions," Journal of Consumer Research, University of Chicago Press, vol. 14(3), pages 301-15, December.
- Geert Soete & Willem Heiser, 1993. "A latent class unfolding model for analyzing single stimulus preference ratings," Psychometrika, Springer, vol. 58(4), pages 545-565, December.
- Wayne DeSarbo & Duncan Fong & John Liechty & Jennifer Coupland, 2005. "Evolutionary preference/utility functions: A dynamic perspective," Psychometrika, Springer, vol. 70(1), pages 179-202, March.
- Geert Soete & Suzanne Winsberg, 1993. "A latent class vector model for preference ratings," Journal of Classification, Springer, vol. 10(2), pages 195-218, December.
- Stef Buuren & Willem Heiser, 1989. "Clusteringn objects intok groups under optimal scaling of variables," Psychometrika, Springer, vol. 54(4), pages 699-706, September.
- Wayne DeSarbo & Vithala Rao, 1984. "GENFOLD2: A set of models and algorithms for the general UnFOLDing analysis of preference/dominance data," Journal of Classification, Springer, vol. 1(1), pages 147-186, December.
- Belk, Russell W, 1975. " Situational Variables and Consumer Behavior," Journal of Consumer Research, University of Chicago Press, vol. 2(3), pages 157-64, December.
- Jacqueline Meulman & Peter Verboon, 1993. "Points of view analysis revisited: Fitting multidimensional structures to optimal distance components with cluster restrictions on the variables," Psychometrika, Springer, vol. 58(1), pages 7-35, March.
- Henk Kiers & Donatella Vicari & Maurizio Vichi, 2005. "Simultaneous classification and multidimensional scaling with external information," Psychometrika, Springer, vol. 70(3), pages 433-460, September.
- K. Deun & P. Groenen & W. Heiser & F. Busing & L. Delbeke, 2005. "Interpreting degenerate solutions in unfolding by use of the vector model and the compensatory distance model," Psychometrika, Springer, vol. 70(1), pages 45-69, March.
- Amos Tversky & Itamar Simonson, 1993. "Context-Dependent Preferences," Management Science, INFORMS, vol. 39(10), pages 1179-1189, October.
- Ulf Böckenholt & Ingo Böckenholt, 1991. "Constrained latent class analysis: Simultaneous classification and scaling of discrete choice data," Psychometrika, Springer, vol. 56(4), pages 699-716, December.
- Vichi, Maurizio & Kiers, Henk A. L., 2001. "Factorial k-means analysis for two-way data," Computational Statistics & Data Analysis, Elsevier, vol. 37(1), pages 49-64, July.
When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:53:y:2009:i:8:p:3217-3230. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei)
If references are entirely missing, you can add them using this form.