A Bayesian multidimensional scaling procedure for the spatial analysis of revealed choice data

We present a new Bayesian formulation of a vector multidimensional scaling procedure for the spatial analysis of binary choice data. The Gibbs sampler is gainfully employed to estimate the posterior distribution of the specified scalar products, bilinear model parameters. The computational procedure allows for the explicit estimation of a covariance matrix which can accommodate violations of IIA due to context effects. In addition, posterior standard errors can be estimated which reflect differential degrees of consumer choice uncertainty and/or brand position instability. A marketing application concerning the analysis of consumersʹ consideration sets for luxury automobiles is provided to illustrate the use of the proposed methodology.