Analysis of Similarity and Preference Data
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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.
KeywordsPreference Data Nonmetric Methods Ideal Point Model Dissimilarity Data Stimulus Points
Basic Technical Readings
- Kruskal, J. B., & Wish, M. (1978). Multidimensional scaling. Beverly Hills, CA: Sage Publications.Google Scholar
- 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.Google Scholar