Switch preference analysis by the drift vector method

Artur Zaborski

doi:10.18778/0208-6018.314.04

Authors

Artur Zaborski Wroclaw University of Economics Chair of Econometrics and Computer Science

DOI:

https://doi.org/10.18778/0208-6018.314.04

Keywords:

preference analysis, multidimensional scaling, asymmetric data, drift vectors

Abstract

The matrix of switch preference data (e.g. one’s preference for brand j, given that one has already defined his/her first choice for brand i) is not symmetric. The averaging of appropriate off-diagonal proximity entries for such data leads to the loss of information, hence the necessity to use appropriate methods for asymmetric data. Among the chosen methods of asymmetric multidimensional scaling, particular attention was paid to the drift vectors method. This method enables to present simultaneously on the perceptual map both the configuration of points representing the analyzed objects and the vectors indicating the direction and the strength of changes in the respondents preferences.

Downloads

Download data is not yet available.

References

Borg, I., Groenen, P. (2005), Modern multidimensional scaling. Theory and applications. Second Edition, Springer-Verlag, New York.
Google Scholar

Chino N. (1978), A graphical technique for representing the asymmetric relationship between N objects, Behaviometrika, no 5, 23-40.
Google Scholar

DeSarbo W. S., Johnson M.D., Manrai A.K., Manrai L.A., Edward E.A. (1992), TSCALE: A New Multidimensional Scaling Procedure Based on Tversky’s Contrast Model, Psycho-metrika, 57, 43-69.
Google Scholar

Harshman R.A., Green P.E., Wind Y., Lundy M.E. (1982), A model for the analysis of asymmetric data in marketing research, Marketing Science, vol. I, no 2, 205-242.
Google Scholar

Holyoak K.J., Gordon P.C. (1983), Social reference points, Journal of Personality and Social Psychology, no 44, 881-887.
Google Scholar

Okada A., Imaizumi T. (1987), Nonmetric multidimensional scaling of asymmetric prox-imities, Behaviometrika, no 21, 81-96.
Google Scholar

Okada A., Imaizumi T. (2007), Multidimensional scaling of asymmetric proximities with a dominance point, Advances in Data Analysis Studies in Classification, Data Analysis, and Knowledge Organization, (red.) R. Decker, H.J. Lenz, Springer-Verlag, Berlin, Heidel-berg, 307-318.
Google Scholar

Tversky A., Gati I. (1982), Features of similarity, Psychological Review, no 89, 123-154.
Google Scholar

Zielman B., Heiser W.J. (1996), Analysis of Asymmetry by a Slide-Vector, Psychometrika, 58, 101-114.
Google Scholar