Accounting for respondent's preference uncertainty in choice experiments

Preference uncertainty is an important aspect affecting respondents' choices and attribute valuation. However, elicitation of preference uncertainty and its modelling is strongly restricted within choice experiments. This paper applies modelling techniques to account for the preference uncertainty data to evaluate road traffic noise. The paper argues that modelling the preference uncertainty data to examine the error structure can shed significant light on the potential causes of preference uncertainty. The results also reveal that accounting for preference uncertainty data within modelling can have important implications for the valuation exercise. It is found that the nested logit model can examine significant correlation between similar preference certainty levels arising from choice-set characteristics while the error components logit model can be used to examine the effect of inherent respondent uncertainty and stochastic factors on preference uncertainty. The paper therefore recommends treating and accounting for preference uncertainty within choice experiments and thereby examine its impact on any subsequent valuations.