The Advanced MaxDiff test is a great way to compare many alternatives without overwhelming respondents by asking them to read and consider all items at once. It takes a list of your items to be compared, and shows them in a balanced order to each respondent several items at a time.

The method is  focused on collecting general aggregate information, without the intention to obtain individual-level estimates. In typical settings respondents would see 3-5 screens.

In deciding which items to show on the next screen of the MaxDiff, the system focuses on ensuring equal coverage of pairs of items among all respondents. In other words, items are chosen randomly, with bias toward pairs of items that were seen less frequently overall across respondents.

The core analysis of respondents' preferences is performed by Maximum Likelihood Multinomial Logit model. 

The statistics page has three display modes: 

  • Preference Likelihood (X/screen) represents the likelihood that an item would be preferred over (X-1) other randomly selected items in the set. This score is appropriate when the MaxDiff exercise shows X items per exposure. 
  • Utility Scores are the raw regression coefficients estimated at the aggregate level. They are zero-centered so that 0 represents the average performance. The more positive an item's utility, the more it is preferred by respondents and the more negative an item's utility, the less it is preferred.
  • Average-based PL (50% baseline) - represents the Preference likelihood [PL] that an item would be preferred over one other randomly selected item in the set. A score above 50% indicates that an item is a better-than-average performer.

Export includes:

  • Raw data Export: data on what each respondent saw, and what decision was made on each task
Did this answer your question?