A new procedure for nonmetric multidimensional scaling is proposed and evaluated in this extensive article. The procedure generalizes to a wide variety of situations and types of data and is robust with respect to measurement error. The statistical development of the procedure and examples of its use are presented. (JKS)

Tree representations of similarity data are investigated. Hierarchical clustering is critically examined, and a more general procedure, called the additive tree, is presented. The additive tree representation is then compared to multidimensional scaling. (Author/JKS)

Two-dimensional euclidean planes and additive trees are two of the most common representations of proximity data for multidimensional scaling. Guidelines for comparing these representations and discovering properties that could help identify which representation is more appropriate for a given data set are presented. (Author/JKS)

A Monte Carlo study investigated the ability of the ALSCAL multidimensional scaling program to recover true structure inherent in simulated proximity measures when data were missing. The program worked well with up to 60 percent missing data as long as sample size was large and random error was low. (Author/JKS)