MaxDiff Scaling

MaxDiff is a methodology developed by Jordan Louviere et al., that results in interval scale measurements that are based on comparative judgments that can be easily performed, even when the number of attributes is not small. The task presents respondents with a set of items, usually 3 to 6, and simply asks them to select the most preferred and the least preferred in the set (or the most important, least important; best item, worst item, etc. In fact the method is also referred to as Best/Worst scaling). Respondents can typically handle a number of these evaluations, and may be asked to respond to 15, 20, or more sets.

Maximum Difference Scaling is a very effective method of establishing the relative priority attached by an audience to a large set of items (up to 30). These items might be:

  • Features or benefits of a service
  • Areas for potential investment of resources
  • Interests and activities
  • Potential marketing messages for a new product
  • Products or Services used

Traditionally, for a large number of items (more than 10) this might have been addressed via a rating scale. For example, we might ask on a numeric scale of 1..10 where 1 is not important at all and 10 is Extremely important, how important is each item? Each item's average score can then be presented for any subgroup.

Survey Analytics Max Diff Analysis


MaxDiff analysis can be used in new product features research or or even market segmentation research to get accurate orderings of the most important product features, Survey Analytics help's you discriminate among feature strengths better than derived importance methodologies. Like other trade-off analyses, the analysis derives utilities for each of the most important product features which can be used to derive optimal products, using market segmentation to put respondents into groups with similar preference structures, or to prioritize strategic product goals.

You can have your respondents perform Forced-choice nature of the tasks, where in Survey Analytics MaxDiff can disentangle the relative feature importance in cases where average Likert-style ratings might all have very similar ratings.
  • Unlimited Attributes
  • Random Sub-Selection and Rotation
  • Utility / Share of Preference Calculation

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Max Diff Analysis - In Practice


Related Links and Resources

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