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Please use this identifier to cite or link to this item: http://hdl.handle.net/11299/104074

Title: A stochastic three-way unfolding model for asymmetric binary data
Authors: DeSarbo, Wayne S.
Lehmann, Donald R.
Holbrook, Morris B.
Havlena, William J.
Gupta, Sunil
Issue Date: 1987
Citation: DeSarbo, Wayne S, Lehmann, Donald R, Holbrook, Morris B, Havlena, William J & et al. (1987). A stochastic three-way unfolding model for asymmetric binary data. Applied Psychological Measurement, 11, 397-418. doi:10.1177/014662168701100406
Abstract: 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.
URI: http://purl.umn.edu/104074
Appears in Collections:Volume 11, 1987

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