We introduce a new statistical procedure for the identification of unobserved categories that vary between individuals and in which objects may span multiple categories. This procedure can be used to analyze data from a proposed sorting task in which individuals may simultaneously assign objects to multiple piles. The results of a synthetic…

The p-median offers an alternative to centroid-based clustering algorithms for identifying unobserved categories. However, existing p-median formulations typically require data aggregation into a single proximity matrix, resulting in masked respondent heterogeneity. A proposed three-way formulation of the p-median problem explicitly considers…

Identifying price sensitive consumers is an important problem in marketing. We develop a Bayesian multi-level factor analytic model of the covariation among household-level price sensitivities across product categories that are substitutes. Based on a multivariate probit model of category incidence, this framework also allows the researcher to…

This paper presents a new procedure called TREEFAM for estimating ultrametric tree structures from proximity data confounded by differential stimulus familiarity. The objective is to quantitatively filter out effects of stimulus unfamiliarity. Superiority of TREEFAM over conventional methods is illustrated through a Monte Carlo study and an…

This manuscript introduces a new Bayesian finite mixture methodology for the joint clustering of row and column stimuli/objects associated with two-mode asymmetric proximity, dominance, or profile data. That is, common clusters are derived which partition both the row and column stimuli/objects simultaneously into the same derived set of clusters.…

A new parametric maximum likelihood procedure is proposed for estimating ultrametric trees for the analysis of conditional rank order proximity data. Technical aspects of the model and the estimation algorithm are discussed, and Monte Carlo results illustrate its application. A consumer psychology application is also examined. (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)

A method is proposed to simultaneously estimate regression functions and subject membership in "k" latent classes or groups given a censored dependent variable for a cross-section of subjects. Maximum likelihood estimates are obtained using an EM algorithm. The method is illustrated through a consumer psychology application. (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)

An extension is described to a product-testing model to account for misinformation among subjects that would lead them to perform incorrectly on "pick one of two" tests. The model is applied to a data set of 367 subjects picking 1 of 2 colas. Misinformation does exist. (SLD)

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…