For the ALSCAL multidimensional scaling computer program, it is reported that (1) a new coordinate estimation routine is superior to the original; (2) an oversight in the interval measurement level case has been found and corrected; and (3) a new initial configuration routine is also superior to the original. (Author/JKS)

The issue of whether one should use a single or several random initial configurations in multidimensional scaling is disucssed in this brief note. It is a response to the preceding article (TM 503 491), which commented on another article by Arabie (TM 503 490). (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)

This paper presents a simple random procedure for selecting subsets of stimulus pairs for presentation to subjects. The resulting set of ratings from the group of subjects allows the construction of a group space through the use of an existing computer program. (Author/JKS)

Multidimensional scaling has recently been enhanced so that data defined at only the nominal level of measurement can be analyzed. The efficacy of ALSCAL, an individual differences multidimensional scaling program which can analyze nominal, ordinal, interval and ratio data is presented. (Author/JKS)

A computational method for weighted euclidean distance scaling (a method of multidimensional scaling) which combines aspects of an "analytic" solution with an approach using loss functions is presented. (Author/JKS)

A Monte Carlo study was carried out to investigate the ability of the ALSCAL multidimensional scaling program to recover true structure inherent in simulated proximity data. The results under varying conditions were mixed. Practical implications and suggestions for further research are discussed. (Author/JKS)

A family of models for the representation and assessment of individual differences for multivariate data called PINDIS (Procrustean Individual Differences Scaling) is presented. PINDIS sheds new light on the interpretability and applicability of a variety of multidimensional scaling models. (Author/JKS)

A single-step maximum likelihood estimation procedure is developed for multidimensional scaling of dissimilarity data measured on rating scales. The procedure can fit the euclidian distance model to the data under various assumptions about category widths and under two distributional assumptions. Practical uses of the method are demonstrated.…

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)

Two revisions of computer-interactive multidimensional scaling data selection procedures are presented. One revision--based on randomly ordering the list of stimuli--improves the estimates of the multidimensional scaling parameters and the other permits more efficient data designs. (Author/JKS)

A maximum likelihood estimation procedure was developed to fit weighted and unweighted additive models of conjoint data obtained by categorical rating, paired comparisons or directional ranking methods. Practical uses of the procedure are presented to demonstrate various advantages of the procedure as a statistical method. (Author/JKS)

Some aspects of the small sample behavior of maximum likelihood estimates in multidimensional scaling are investigated with Monte Carlo techniques. In particular, the chi square test for dimensionality is examined and a correction for bias is proposed and evaluated. (Author/JKS)

A class of closely related hierarchical grouping methods are discussed and a procedure which implements them in an integrated fashion is presented. These methods avoid some theoretical anomalies inherent in clustering and provide a framework for viewing partitioning and nonpartitioning grouping. Significant relationships between these methods and…

Many data analysis problems in psychology may be posed conveniently in terms which place the parameters to be estimated on one side of an equation and an expression in these parameters on the other side. A rule for improving the rate of convergence of the iterative solution of such equations is developed and applied to four problems. (Author/RC)