This paper brings together several research streams and concepts that have been evolving in random utility choice theory: first, it reviews the literature on stated preference (SP) elicitation methods and introduces the concept of testing data generation process invariance across SP and revealed preference (RP) choice data sources; second, it proposes a general data generation process an useful framework for viewing this data combination process; third, it describes the evolution of discrete choice models within the random utility family, where progressively more behavioural realism is being achieved by relaxing strong assumptions on the role of the variance structure (specifically heteroscedasticity) of the unobserved effects. This latter topic is central to the issue of combining multiple data sources. Particular choice model formulations incorporating heteroscedastic effects are presented, discussed and applied to data. The rich insights possible from modeling heteroscedasticity in choice processes is illustrated in each of the empirical applications, which examine its relevance to issues of data combination and taste heterogeneity
