until a final ranking is done among the remaining 2, 3, or 4 winning items. The sets can decrement in complexity, from 6 items at a time, to 5 items, etc. Until an overall winning item is identified. For each respondent, items that are selected "worst" are dropped from that same respondent's later MaxDiff sets. Tournament MaxDiff: Originally called "Adaptive MaxDiff" by the author (Orme) in 2006, but to avoid confusion it's probably easier to think of it as Tournament MaxDiff, because it proceeds similar to a round-robin tournament in sports competitions. Typical analysis is HB-MNL aggregate logit or latent class MNL may also be used. Other augmentations are possible and have been proposed, including augments that focus on obtaining more precision for the worst few items.
Augmented approaches can lead to even more accurate measurement of the top few items for each respondent, assuming the augment focuses on obtaining more information about best items. The rank-order judgments may be exploded into paired comparisons (or other related coding approaches) for those 6 items and added to the choice data set.
For example, respondents might be asked to rank-order the 6 items chosen "best" across the previous 6 MaxDiff sets. At Knowledge Excel, we have the experience to work on various types of Max Diffs like:Īugmented MaxDiff: Additional ranking, rating, or sorting tasks are completed outside the MaxDiff exercise and then added (augmented) as new choice tasks to each respondent's MaxDiff data for utility estimation.