Detecting dominancy and accounting for scale differences when using stated choice data to estimate logit models

Abstract

Stated choice surveys have been used for several decades to estimate preferences of agents using choice models, and are widely applied in the transportation domain. Typically orthogonal or efficient experimental designs underlie such surveys. These experimental designs may suffer from choice tasks containing a dominant alternative, which we show is problematic because it affects scale and therefore may bias parameter estimates. We propose a new measure based on minimum regret to calculate dominancy and automatically detect such choice tasks in an experimental design. This measure is then used to define a new experimental design type that ensures tradeoffs within the design. Finally, we propose a new regret-scaled multinomial logit model that takes the level of dominancy within a choice task into account. Results using simulated and empirical data show that the presence of dominant alternatives can bias model estimates, but that making scale a function of a smooth approximation of normalised minimum regret can properly account for scale differences without the need to remove choice tasks with dominant alternatives from the dataset.