A variety of joint space multidimensional scaling (MDS) methods have been utilized for the spatial analysis of two- or three-way dominance data involving subjects' preferences, choices, considerations, intentions, etc. so as to provide a parsimonious spatial depiction of the underlying relevant dimensions, attributes, stimuli, and/or subjects'…

A cyclical conditional maximum likelihood estimation procedure is developed for the multidimensional unfolding of two- or three-way dominance data (e.g., preference, choice, consideration) measured on ordered successive category rating scales. The technical description of the proposed model and estimation procedure are discussed, as well as the…

A multidimensional scaling procedure is introduced that attempts to derive a spatial representation of stimuli unconfounded by the effect of subjects' degrees of familiarity with these stimuli. A Monte Carlo study investigating the extent to which the procedure recovers known parameters shows that the procedure succeeds in adjusting for…

Presents an approach to multidimensional unfolding that reduces the occurrence of degenerate solutions and conducts a Monte Carlo study to demonstrate the superiority of the new method to the ALSCAL and KYST nonmetric procedures for student preference data. (SLD)

A stochastic multidimensional scaling procedure is presented for analysis of three-mode, three-way pick any/"J" data. The procedure fits both vector and ideal-point models and characterizes the effect of situations by a set of dimension weights. An application in the area of consumer psychology is discussed. (SLD)

TSCALE, a multidimensional scaling procedure based on the contrast model of A. Tversky for asymmetric three-way, two-mode proximity data, is presented. TSCALE conceptualizes a latent dimensional structure to describe the judgmental stimuli. A Monte Carlo analysis and two consumer psychology applications illustrate the procedure. (SLD)

This paper presents a new stochastic multidimensional scaling vector threshold model designed to analyze "pick any/n" choice data. A maximum likelihood procedure is formulated to estimate a joint space of both individuals and stimuli. The non-linear probit type model is described, and a Monte Carlo analysis is performed. (TJH)

A stochastic multidimensional unfolding (MDU) procedure is presented to represent individual differences in phased or sequential decision processes spatially. A Monte Carlo analysis demonstrates estimation proficiency and the appropriateness of the proposed model selection heuristic, and an application to capture awareness, consideration, and…