Author argues that simplistic and/or heuristic approaches to the Tucker and Messick model (an individual differences model for multidimensional scaling, 1963) are often inadequate. (Author/CB)

A method for externally constraining certain distances in multidimensional scaling configurations is introduced and illustrated. The method is described in detail and several examples are presented. (Author/JKS)

Techniques are developed for constructing confidence regions for each of the points in a multidimensional scaling solution. Bayesian credibility regions are discussed, and a technique for displaying these regions is described. (Author/JKS)

Two popular models for performing multidimensional scaling, Tucker and Messick's points-of-view model, and Tucker's three mode model, are combined into a single analytic procedure, the 3M-POV model. The procedure is described and its strengths are discussed. Carroll and Chang's INDSCAL model is also mentioned. (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)

Detailed reanalyses of data reported in Studies in Educational Evaluation: Monograph No. 1 by Y. Kashti and Monograph No. 5 by U. Kattmann, 1979, were performed using an explicitly structurally oriented approach via target analysis (PINDIS). Results contradict those reached by Kashti and Kattmann. (RL)

A globally optimal solution is presented for a class of functions composed of a linear regression function and a penalty function for the sums of squared regression weights. A completing-the-squares approach is used, rather than calculus, because it yields global minimality easily in two of three cases examined. (SLD)

A flexible data analysis approach is proposed that combines the psychometric procedures seriation and multidimensional scaling. The method, which is particularly appropriate for analysis of proximities containing temporal information, is illustrated using a matrix of cocitations in publications by 18 presidents of the Psychometric Society.…

Using Goodman's (1975) notion of quasi-independence as a method of obtaining goodness of fit measures for non-scalable types in a scalogram analysis, archival data sets were examined using available Guttman scaling techniques, recent developments in latent structure analysis, and multidimensional scaling procedures. The Stouffer-Toby (1951) data…

Multidimensional scaling (MDS) a highly reliable measurement technique, often requires an overwhelming task of the subject in the data collection procedure. This investigation was designed to determine the loss of precision in solution associated with five degrees of systematic reduction in the data collection task. Data were simulated via Monte…

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)

An algorithm for assessing the correspondence of one or more attribute rating variables to a symmetric matrix of dissimilarities is presented. It is useful as an alternative to fitting property variables into a multidimensional scaling space. The relation between the matrix and the variables is determined by evaluating pairs of pairs relations.…

A model for the analysis of paired comparison data is presented which is metric, mathematically tractable, and has an exact algebraic solution. (Authors/MB)

The coincidence hypothesis predicts that dissimilarity between objects that differ on two separable dimensions is larger than predicted from their unidimensional differences on the basis of triangle inequality and segmental additivity. The coincidence hypothesis was supported in two-dimensional stimuli studies. (Author/CM)

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)