Abstract
Similarity data in management research are typically collected in order to understand the underlying dimensions determining perceptions of stimuli such as brands or companies. One advantage of such data is that it is cognitively easier for respondents to provide subjective assessments of the similarity between objects than to rate these objects on a number of attributes that they may not even be aware of. Furthermore, when asking respondents to rate objects on attributes, the selection of the attributes proposed may influence the results while, in fact, it is not clear that these attributes are the relevant ones. In multidimensional scaling, the methodology allows you to infer the structure of perceptions. In particular, the researcher is able to make inferences regarding the number of dimensions that are necessary to fit the similarity data. In this chapter, we first describe the type of data collected to perform multidimensional scaling and we then present metric and nonmetric methods of multidimensional scaling. Multidimensional scaling explains the similarity of objects such as brands. We then turn to the analysis of preference data, where the objective is to model and explain preferences for objects. These explanations are based on the underlying dimensions of preferences that are discovered through the methodology.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Bibliography
Basic Technical Readings
Carroll, J. D., & Arabie, P. (1980). Multidimensional scaling. Annual Review of Psychology, 31, 607–49.
Kruskal, J. B., & Wish, M. (1978). Multidimensional scaling. Beverly Hills, CA: Sage Publications.
Shepard, R. N. (1980). Multidimensional scaling, tree-fitting, and clustering. Science, 210(24), 390–398.
Application Readings
Bijmolt, T. H. A., & Wedel, M. (1999). A comparison of multidimensional scaling methods for perceptual mapping. Journal of Marketing Research, 36, 277–285.
Cooper, L. G. (1983). A review of multidimensional scaling in marketing research. Applied Psychological Measurement, 7(4), 427–450.
DeSarbo, W. S., Young, M. R., & Rangaswamy, A. (1997). A parametric multidimensional unfolding procedure for incomplete nonmetric preference/choice set data in marketing research. Journal of Marketing Research, 34(4), 499–516.
DeSarbo, W. S., & De Soete, G. (1984). On the use of hierarchical clustering for the analysis of nonsymmetric proximities. Journal of Consumer Research, 11, 601–610.
Green, P. E. (1975). Marketing applications of MDS: Assessment and outlook. Journal of Marketing, 39, 24–31.
Green, P. E., & Carmone, F. J. (1989). Multidimensional scaling: An introduction and comparison of nonmetric unfolding techniques. Journal of Marketing Research, 6, 330–341.
Helsen, K., & Green, P. E. (1991). A computational study of replicated clustering with an application to market segmentation. Decision Sciences, 22, 1124–1141.
Johnson, R. M. (1971). Market segmentation: A strategic management tool. Journal of Marketing Research, 8, 13–18.
Malhotra, N. K., Jain, A. K., Patil, A., Pinson, C., & Lan, W. (2010). Consumer cognitive complexity and the dimensionality of multidimensional scaling configurations. Review of Marketing Research, 7, 199–253.
Neidell, L. A. (1969). The use of nonmetric multidimensional scaling in marketing analysis. Journal of Marketing, 33, 37–43.
Sexton, D. E., Jr. (1974). A cluster analytic approach to market response functions. Journal of Marketing Research, 11, 109–114.
Srivatsava, R. K., Leone, R. P., & Shocker, A. D. (1981). Market structure analysis: Hierarchical clustering of products based on substitution in use. Journal of Marketing, 45(Summer), 38–48.
Stuart, T. E., & Podolny, J. M. (1996). Local search and the evolution of technological capabilities. Strategic Management Journal, 17, 21–38.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer Science+Business Media New York
About this chapter
Cite this chapter
Gatignon, H. (2014). Analysis of Similarity and Preference Data. In: Statistical Analysis of Management Data. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-8594-0_13
Download citation
DOI: https://doi.org/10.1007/978-1-4614-8594-0_13
Published:
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4614-8593-3
Online ISBN: 978-1-4614-8594-0
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)