A comparison of multidimensional scaling methods for perceptual mapping

Multidimensional scaling has been applied to a wide range of marketing problems, in particular to perceptual mapping based on dissimilarity judgments. The introduction of methods based on the maximum likelihood principle is one of the most important developments. In this article, the authors compare the three available Maximum Likelihood Multidimensional Scaling (MLMDS) methods, namely, MULTISCALE, MAXSCAL, and PROSCAL, and the traditional multidimensional scaling (MDS) method KYST in a Monte Carlo study with 243 synthetic data sets. The MLMDS methods outperform KYST with respect to recovering the perceptual maps. MAXSCAL recovers the true distances between brands somewhat better than MULTISCALE, which is somewhat better than PROSCAL. With regard to distance recovery, the MLMDS methods are quite robust to violations of distributional assumptions. The decision criteria for selecting the number of dimensions are less robust to distributional violations. The results support the use of Consistent Akaike Information Criterion for the selection of the number of dimensions. The authors recommend that dissimilarity judgments be collected on interval scales or on ordinal scales with a substantial number of scale values. The authors discuss implications of the results for the design and analysis of perceptual mapping studies.