A contingent grouping approach for stated preferences
This paper introduces the theoretical and statistical foundations of contingent grouping, a variant of attribute-based stated preference methods, and checks for its implementation difficulty. Respondents are asked to simply group a number of alternatives as better or worse than a baseline or the status quo situation. A Monte Carlo approach shows that it performs statistically better than contingent choice according to the MSE criterion, and worse than contingent ranking, while a case study suggests that contingent grouping is less difficult to answer than contingent ranking. Furthermore, contingent choice shows a greater consistency in preferences with contingent grouping than with contingent ranking.
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