Multidimensional Scaling (or MDS) is a method of separating univariate data based upon variance. Conceptually, MDS takes the dissimilarities, or distances, between items described in the data and generates a map between the items. The number of dimensions in this map are often provided prior to generation by the analyst. Usually, the highest variance dimension corresponds to the largest distances being described in the data. The map solution relies on univariate data, so the rotation and orientation of the map dimensions is not significant. MDS uses dimensional analysis similar to Principle Components.
A classic example of MDS (which is the basis of the accompanying Multidimensional Scaling Sample) is taking the straight-line distance between cities across the US to produce a map of the US. A common business application of MDS in a marketing research context is to obtain the number and nature of the perceptual dimensions used by customers to judge the similarity between different items.
