If you choose not to decide, you still have made a choice

When designing stated-choice experiments modellers may consider offering respondents an “indifference” alternative to avoid stochastic choices when utility differences between alternatives are perceived as too small. By doing this, the modeller avoids adding white noise to the data and may gain additional information. This paper proposes a framework to model discrete choices in the presence of indifference alternatives. The approach allows depicting the likelihood function, independent of the number of alternatives in the choice-set and in the subset of indifference alternatives, offering a new approach to existing methods that are only defined for binary choice situations. The method is tested with the help of simulated and real data observing that the proposed framework allows recovering the parameters used in the generation of the synthetic datasets without major difficulties in most cases. Alternative approaches, such as considering the indifference option as an opt-out alternative or ignoring the indifference choices are clearly outperformed by the proposed framework and appear not capable of recovering parameters in the simulated set.