Multidimensional scaling has been applied to a wide range of marketing prob
lems, in particular to perceptual mapping based on dissimilarity judgments.
The introduction of methods based on the maximum likelihood principle is o
ne of the most important developments. In this article, the authors compare
the three available Maximum Likelihood Multidimensional Scaling (MLMDS) me
thods, namely, MULTISCALE, MAXSCAL, and PROSCAL, and the traditional multid
imensional scaling (MDS) method KYST in a Monte Carlo study with 243 synthe
tic data sets. The MLMDS methods outperform KYST with respect to recovering
the perceptual maps. MAXSCAL recovers the true distances between brands so
mewhat better than MULTISCALE, which is somewhat better than PROSCAL. With
regard to distance recovery, the MLMDS methods are quite robust to violatio
ns of distributional assumptions. The decision criteria for selecting the n
umber of dimensions are less robust to distributional violations. The resul
ts support the use of Consistent Akaike Information Criterion for the selec
tion of the number of dimensions. The authors recommend that dissimilarity
judgments be collected on interval scales or on ordinal scales with a subst
antial number of scale values. The authors discuss implications of the resu
lts for the design and analysis of perceptual mapping studies.