Abstract
AbstractThe interpretation of multidimensional data in consumer research has traditionally required property fitting to determine the structure underlying the data. As an alternative, Cognitive Differentiation Analysis (CDA) regresses a matrix of pairwise product judgments on vectors of product‐attribute ratings. The estimated multiple regression equation indicates the degree of differentiation in the pairwise data that is accounted for by each attribute. In this article CDA is applied to the analysis of simulated multidimensional data and to actual data from a benefit segmentation study. The implications of improved interpretability of multidimensional data for understanding the competitive environment are discussed. © 1995 John Wiley & Sons, Inc.
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