Abstract
We formulate multiple correspondence analysis (MCA) as a nonlinear multivariate analysis method that integrates ideas from multidimensional scaling. MCA is introduced as a graphical technique that minimizes distances between connecting points in a graph plot. We use this geometrical approach to show how questions posed of categorical marketing research data may be answered with MCA in terms of closeness. We introduce two new displays, the star plot and line plot, which help illustrate the primary geometric features of MCA and enhance interpretation. Our approach, which extends Gifi (1981, 1990), emphasizes easy-to-interpret and managerially relevant MCA maps.
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The authors thank J. Douglas Carroll, Don Lehmann, Donald Morrison, and two anonymous reviewers for their helpful comments on a previous version of this manuscript.
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Hoffman, D.L., De Leeuw, J. Interpreting multiple correspondence analysis as a multidimensional scaling method. Marketing Letters 3, 259–272 (1992). https://doi.org/10.1007/BF00994134
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DOI: https://doi.org/10.1007/BF00994134