Browsing by Author "DeSarbo, Wayne S."

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    A probabilistic multidimensional scaling vector model
    (1986) DeSarbo, Wayne S.; Oliver, Richard L.; De Soete, Geert
    This article presents the development of a new stochastic multidimensional scaling (MDS) method, which operates on paired comparisons data and renders a spatial representation of subjects and stimuli. Subjects are represented as vectors and stimuli as points in a T-dimensional space, where the scalar products, or projections of the stimulus points onto the subject vectors, provide respective information as to the utility (or whatever latent construct is under investigation) of the stimuli to the subjects. The psychometric literature concerning related MDS methods that also operate on paired comparisons data is reviewed, and a technical description of the new method is provided. A small monte carlo analysis performed on synthetic data with the new method is also presented. To illustrate the versatility of the model, an application measuring consumer satisfaction and investigating the impact of hypothesized determinants, using one of the optional reparameterized models, is described. Future areas of further research are identified.
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    Simple and weighted unfolding threshold models for the spatial representation of binary choice data
    (1986) DeSarbo, Wayne S.; Hoffman, Donna L.
    This paper describes the development of an unfolding methodology designed to analyze "pick any" or "pick any/n" binary choice data (e.g., decisions to buy or not to buy various products). Maximum likelihood estimation procedures are used to obtain a joint space representation of both persons and objects. A review of the relevant literature concerning the spatial treatment of such binary choice data is presented. The nonlinear logistic model type is described, as well as the alternating maximum likelihood algorithm used to estimate the parameter values. The results of an application of the spatial choice model to a synthetic data set in a monte carlo analysis are presented. An application concerning consumer (intended) choices for nine competitive brands of sports cars is discussed. Future research may provide a means of generalizing the model to accommodate three-way choice data.
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    A stochastic three-way unfolding model for asymmetric binary data
    (1987) DeSarbo, Wayne S.; Lehmann, Donald R.; Holbrook, Morris B.; Havlena, William J.; Gupta, Sunil
    This paper presents a new stochastic three-way unfolding method designed to analyze asymmetric three-way, two-mode binary data. As in the metric three-way unfolding models presented by DeSarbo (1978) and by DeSarbo and Carroll (1980, 1981, 1985), this procedure estimates a joint space of row and column objects, as well as weights reflecting the third way of the array, such as individual differences. Unlike the traditional metric three-way unfolding model, this new methodology is based on stochastic assumptions using an underlying threshold model, generalizing the work of DeSarbo and Hoffman (1986) to three-way and asymmetric binary data. The literature concerning the spatial treatment of such binary data is reviewed. The nonlinear probit-like model is described, as well as the maximum likelihood algorithm used to estimate its parameter values. Results of a monte carlo study applying this new method to synthetic datasets are presented. The new method was also applied to real data from a study concerning word (emotion) associations in consumer behavior. Possibilities for future research and applications are discussed.