Simple and Weighted Unfolding Threshold Models for the Spatial Representation of Binary Choice Data

Abstract

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.