A model for preferential and triadic choice is derived in terms of weighted sums of central F distribution functions. It is a probabilistic generalization of Coombs' (1964) unfolding model from which special cases can be derived easily. This model for binary choice can be easily related to preference ratio judgments. (SLD)

A model is proposed in which different sets of linear constraints are imposed on different dimensions in component analysis and classical multidimensional scaling frameworks. An algorithm is presented for fitting the model to the data by least squares. Examples demonstrate the method. (SLD)

Cognitive Differentiation Analysis (CDA) represents a method to measure the correspondence of an individual vector or a composite vector of descriptor ratings to a matrix of pair-wise dissimilarity judgments where both sets of judgments are assumed to be ordinal. The zero intercept regression extension of CDA is described. (TJH)