In order to determine the effectiveness of multidimensional scaling (MDS) in recovering the dimensionality of a set of dichotomously-scored items, data were simulated in one, two, and three dimensions for a variety of correlations with the underlying latent trait. Similarity matrices were constructed from these data using three margin-sensitive and three margin-free coefficients and used as input to MDS. Stress (S1), S1 by dimension plots, and plots of the scaled items were examined to determine the effect of varying the magnitude and pattern of correlations. The results suggested that items with similar patterns of correlations tend to cluster together, that distance from the center of a cluster is a function of the amount of random error in the item, and that as the number of latent traits underlying the data increases, the dimensionality of the representational space increases. Cluster analysis using MDS coordinates is suggested to isolate homogeneous sets of items, whereas consideration of the S1 coefficient is recommended to determine the number of latent traits in the data. (Author)