A Monte Carlo Evaluation of Maximum Likelihood Multidimensional Scaling Methods
49 Pages Posted: 10 Dec 1996
Date Written: March 1996
We compare three alternative Maximum Likelihood Multidimensional Scaling methods for pairwise dissimilarity ratings, namely MULTISCALE, MAXSCAL, and gurations very well. The recovery of the true dimensionality depends on the test criterion (likelihood ratio test, AIC, or CAIC), as well as on the MLMDS method. The three MLMDS methods test the dissimilarity data equally well. The methods are relatively robust against violations of their distributional assumptions. MULTISCALE outperforms separate Monte Carlo study, it is shown that the MLMDS methods frequently converge to local optima, especially if a random start is used. Rational starts, however, turn out to provide a satisfactory solution for the local optima problem. Implications for researchers intending to apply MLMDS are provided.
JEL Classification: C15, C61, C63
Suggested Citation: Suggested Citation