Augmenting discrete-choice data to identify common preference scales for inter-subject analyses

Discrete-choice experiments are commonly used to measure subjects’ preference structures and are often preferred to other measurement methods because they better align with actual choice behavior and avoid some of the well-documented biases inherent in alternative elicitation methods. A limitation of discrete-choice methods is the loss of inter-subject comparability because preference estimates are invariant to linear transformations necessitating indentifying constraints that remove a common, between-subjects utility scale. This constraint limits the application of discrete-choice results to situations where within-subject comparisons are meaningful. They enable one to sort options for each subject but not to sort subjects according to the relative intensity of their preferences. This paper uses auxiliary data to recover a common preference scale for between-subject comparisons. The model combines discrete-choice data with ratings data while adjusting for response biases due to method effects. The joint model moves the identification constraints from the sub-model for the discrete-choice data to the sub-model for the ratings data. The proposed methodology is complementary to willingness-to-pay computations when studies lack price or its economic foundation is untenable.