I’ve got the perfect technical paper for you to check out to answer this question. It’s called “MaxDiff Analysis: Simple Counting, Individual-Level Logit, and HB (2009)”. I am personally a fan of using Hierarchical Bayes (HB) to get the scores. You can access the paper here:
http://www.sawtoothsoftware.com/education/techpap.shtml.

Here’s a summary:

“This paper compares different methods of obtaining individual-level scores for MaxDiff surveys at the individual level: Simple counting, individual-level logit, and HB. Key to the success for all these methods was having enough information available for each respondent to estimate stable scores.

The author (Orme) finds that counting analysis provides reasonable population estimates of scores, but that the individual-level scores can lack precision. Precision is better under the logit model estimation methods: either individual-level logit or HB, which “borrows” information across the sample to improve the individual-level logit scores for individuals.

Despite the simplicity of the counting approach and its weaknesses, it tends to do quite well in predicting responses to holdout choices. But, across-respondent variance (heterogeneity) tends to be weaker than the other methods studied.”

Good luck!

For an online group brainstorming tool we’re developing, we’re currently looking at pairwise comparison as a method for ranking a (potentially long) list of items.

With MassDiff, is there a standard system for “counting” the respondents’ answers? How do the answers get translated into the MaxDiff score?

Thanks!