This dissertation consists of three essays on forms of preference heterogeneity in discrete choice models.

The first essay uses a model of heterogeneity conditional on observed individual-specific characteristics to tailor financial incentives to enhance weight loss program participation among target demographics. Financial incentives in weight loss programs have received attention mostly with respect to effectiveness rather than participation and representativeness. This essay examines the impact of financial incentives on participation with respect to populations vulnerable to obesity and understudied in the weight loss literature. We found significant heterogeneity across target sub-populations and suggest a strategy of offering multiple incentive designs to counter the dispersive effects of preference heterogeneity.

The second essay investigates the ability of a novel elicitation format to reveal decision strategy heterogeneity. Attribute non-attendance, the behaviour of ignoring some attributes when performing a choice task, violates fundamental assumptions of the random utility model. However, self-reported attendance behaviour on dichotomous attendance scales has been shown to be unreliable. In this essay, we assess the ability of a polytomous attendance scale to ameliorate self-report unreliability. We find that the lowest point on the attendance scale corresponds best to non-attendance, attendance scales need be no longer than two or three points, and that the polytomous attendance scale had limited success in producing theoretically consistent results.

The third essay explores available approaches to model different features of unobserved heterogeneity. Unobserved heterogeneity is popularly modelled using the mixed logit model, so called because it is a mixture of standard conditional logit models. Although the mixed logit model can, in theory, approximate any random utility model with an appropriate mixing distribution, there is little guidance on how to select such a distribution. This essay contributes to suggestions on distribution selection by describing the heterogeneity features which can be captured by established parametric mixing distributions and more recently introduced nonparametric mixing distributions, both of a discrete and continuous nature. We provide empirical illustrations of each feature in turn using simple mixing distributions which focus on the feature at hand.