Extending discrete choice models to incorporate attitudinal and other latent variables

One of the nagging issues in using discrete choice models is how softer attributes, such as attitudes and perceptions, that are not explicitly manipulated within the context of the choice experiment can be accommodated. In many cases, it is reasonable to expect that the choice of a particular alternative may be influenced by non-product-related attributes. For example, latent attitudes and perceptions may play as much of a role in shaping choice as the attributes that have been manipulated and used to define the alternative offerings. In this article, the authors present several full information models that can accommodate latent variables such as attitudes and satisfaction within the context of binary and multinomial choice models. The models proposed are particularly useful when the focus is on understanding how softer attributes can influence choice decisions. The authors accomplish this by integrating structural equation models within the basic framework of binary and multinomial choice models. Two empirical applications are provided. In addition to illustrating the proposed models, these applications provide insights into the circumstances under which the simultaneous factor-choice modeling approach makes a difference.