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.
- 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;The Psychometric Society, vol. 45(1), pages 3-24, March.
- 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;The Psychometric Society, vol. 58(1), pages 7-35, March.
- Ulf Böckenholt & Ingo Böckenholt, 1991. "Constrained latent class analysis: Simultaneous classification and scaling of discrete choice data," Psychometrika, Springer;The Psychometric Society, vol. 56(4), pages 699-716, December.
- Joseph Bennett & William Hays, 1960. "Multidimensional unfolding: Determining the dimensionality of ranked preference data," Psychometrika, Springer;The Psychometric Society, vol. 25(1), pages 27-43, March.
- 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.
- Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
- Amos Tversky & Itamar Simonson, 1993. "Context-Dependent Preferences," Management Science, INFORMS, vol. 39(10), pages 1179-1189, October.
- 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;The Classification Society, vol. 1(1), pages 147-186, December.
- Frank Busing & Patrick Groenen & Willem Heiser, 2005. "Avoiding degeneracy in multidimensional unfolding by penalizing on the coefficient of variation," Psychometrika, Springer;The Psychometric Society, vol. 70(1), pages 71-98, March.
- Stef Buuren & Willem Heiser, 1989. "Clusteringn objects intok groups under optimal scaling of variables," Psychometrika, Springer;The Psychometric Society, vol. 54(4), pages 699-706, 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;The Psychometric Society, vol. 70(1), pages 45-69, March.
- Puto, Christopher P, 1987. " The Framing of Buying Decisions," Journal of Consumer Research, Oxford University Press, vol. 14(3), pages 301-15, December.
- Wayne DeSarbo & Duncan Fong & John Liechty & Jennifer Coupland, 2005. "Evolutionary preference/utility functions: A dynamic perspective," Psychometrika, Springer;The Psychometric Society, vol. 70(1), pages 179-202, March.
- Geert Soete & Suzanne Winsberg, 1993. "A latent class vector model for preference ratings," Journal of Classification, Springer;The Classification Society, vol. 10(2), pages 195-218, December.
- Belk, Russell W, 1975. " Situational Variables and Consumer Behavior," Journal of Consumer Research, Oxford University Press, vol. 2(3), pages 157-64, December.
- Geert Soete & Willem Heiser, 1993. "A latent class unfolding model for analyzing single stimulus preference ratings," Psychometrika, Springer;The Psychometric Society, vol. 58(4), pages 545-565, December.
- Henk Kiers & Donatella Vicari & Maurizio Vichi, 2005. "Simultaneous classification and multidimensional scaling with external information," Psychometrika, Springer;The Psychometric Society, vol. 70(3), pages 433-460, September.
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 you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.