We propose and describe a comprehensive theoretical framework that integrates choice models and structural equation models. Referred to as "structural choice modelling" the framework easily combines data from separate but related choice experiments. We describe the mathematical properties of the...

We show how to combine statistically efficient ways to design discrete choice experiments based on random utility theory with new ways of collecting additional information that can be used to expand the amount of available choice information for modeling the choices of individual decision...

We briefly review and discuss traditional conjoint analysis (CA) and discrete choice experiments (DCEs), widely used stated preference elicitation methods in several disciplines. We pay particular attention to the origins and basis of CA, and show that it is generally inconsistent with economic...

Disagreement among researchers regarding types of optimal choice experiments is often best seen as resulting from differences in the set of assumptions researchers are willing to make about the underlying data generating process. Much of the current debate may have confused, rather than...

The concept of a consideration set has become a central concept in the study of consumer behavior. This paper shows that the common practice of estimating models using only the set of alternatives deemed to be in the set considered by a consumer will usually result in estimated parameters that...

This paper describes and applies a general approach for incorporating factors with structural equations into models for discrete choice. The approach gives form to the covariance matrix in random coefficient models. The factors act directly on the random coefficients as unobserved attributes....