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.…

The distance approach to nonlinear multivariate analysis proposed by J. J. Meulman (1986) is reviewed. Several generalizations are discussed by combining features from the conventional multivariate analysis approach, which seeks weighted sums of variables, with the alternative approach, which seeks to fit distances. (SLD)

An algorithm based on the majorization theory of J. de Leeuw and W. J. Heiser is presented for fitting the slide-vector model. It views the model as a constrained version of the unfolding model. A three-way variant is proposed, and two examples from market structure analysis are presented. (SLD)

This paper proposes a generalization of Thurstonian probabilistic choice models for analyzing both multiple preference responses and their relationships. The approach is illustrated by modeling data from two multivariate preference experiments. Preliminary data analyses show that the extension can yield an adequate representation of multivariate…

An alternative formulation for Guttman scaling is presented. The new formulation is described, and advantages over Guttman's formulation are detailed. The method is assumption-free and capable of multidimensional analysis. (Author/JKS)

Paper shows that an obvious generalization of the subjective metrics model by Bloxom, Horan, Carroll and Chang has a very simple algebraic solution which was previously considered by Meredith in a different context. (Author)

Two models for psychological distances, the hierarchical clustering model and the multidimensional Euclidean model, are compared to see how they relate to each other. (Author/RK)

Paper develops a model which expresses pair comparisons as a function of (a) affective values which form a perfect simplex, (b) systematic (constant over replications) deviations from the simplex-structured affective values, and (c) errors of measurement for the pair comparisons. (Author)

Structural equation modeling is applied in conjunction with constrained monotone distance analysis. These alternative methods are used in an evaluation of a social-psychological model derived from Likert's theory of organizational behavior. (Author/JKS)

Restricted multidimensional scaling models, allowing constraints on parameters, are extended to the case of asymmetric data. Examples of several models are provided, using journal citation data. Possible extensions of the models are considered. (Author/JKS)

A review of existing techniques for the analysis of three-way data revealed that none were appropriate to the wide variety of data usually encountered in psychological research, and few were capable of both isolating common information and systematically describing individual differences. Such a model is developed and evaluated. (Author/JKS)

In studies involving judgments of similarity or dissimilarity, a variety of other variables may also be measured. In such cases, there are important advantages to joint analyses of the dissimilarity and collateral variables. A variety of models are described for relating these and algorithms are described for fitting these to data. (Author/JKS)

A method for multidimensional scaling that is highly resistant to the effects of outliers is described. Some Monte Carlo simulations illustrate the efficacy of the procedure, which performs well with or without outliers. (SLD)

Ideal point discriminant analysis (IPDA) is proposed for the analysis of contingency tables of cross-classified data. Several data sets illustrate IPDA, which combines log-linear and dual scaling models to provide a spatial representation of row and column categories and allow statistical evaluation of various structural hypotheses about…

The current state of multidimensional scaling using the city-block metric is reviewed, with attention to (1) substantive and theoretical issues; (2) recent algorithmic developments and their implications for analysis; (3) isometries with other metrics; (4) links to graph-theoretic models; and (5) prospects for future development. (SLD)