An Introduction to Max Diff Analysis

For Max Diff (maximum difference scaling), respondents are shown a set of the possible attributes and are asked to indicate the best and worst attribute (or most and least important). The respondent can only make ONE selection per column.

Vital for Research: This question goes beyond a standard rating. It forces respondents to pick the most and least important attribute, giving you actionable data.

When choosing a resort to stay at, what is the least and most important factor in your decision? 

Set 1 of 3

Least Important Most Important
Least Important Most Important
Least Important Most Important

When to Use It

Max Diff is similar to a rating scale, which asks a respondent to rate how important an attribute is. The above example could be asked as a matrix or as five separate rating questions; but this is not efficient.

The Max Diff question combines all the options of multiple matrix / rating questions into one. By forcing respondents to make choices between options Max Diff delivers results that show the relative importance of the items being rated.

Looking at the example results below; it seems that price is the most important and a hotel gym is of least importance to the sample audience. This conclusion would likely not have been drawn by other traditional question types. For example, if asked as multiple standalone questions, many people would likely rate a gym as being pretty important, but when compared to other items it becomes less important overall.

Options and Settings

For this question you can choose the following:

  • Require an answer (can not proceed until answered)
  • You can choose to show up to 10 sets of attributes - If you wish to show only one set with all attributes, simple leave the "attributes" and "sets" input boxes blank.


For each attribute, you'll see the percentage and count of the times it was ranked as most appealing, least appealing, or not chosen.

Individual results (displayed as a spreadsheet) will display the line item as a column header and the respondent's value below that. The attributes will be ranked based on the score which is computed using the below formula:

# times item was selected as best - # times attribute was selected as worst

# times the item appeared
  • The higher the score, the more popular the feature is to respondents
  • A positive score means that the attribute was selected as MOST appealing more often than LEAST appealing
  • A negative score means that the attribute was selected as LEAST appealing more often than MOST appealing
  • A score of zero means that the attribute was chosen as MOST and LEAST appealing an equal number of times OR it has never been chosen
  • If a score of an item is two times larger than that of another item, it can be interpreted that it is twice as most or least appealing

Example Data

Rank Attribute Score Times Marked Most Important Times Marked Least Important Times Shown
2Room cleanliness0.2215
3All inclusive pacakge0224
4Customer service-0.14017
5Hotel gym -0.5134