MaxDiff is an approach for obtaining preference/importance scores for multiple items. This presentation will help understanding why max diff is required, what are the types of max diff and outputs of max diff. We have also given our demo links in the end.

1. Measure what attributes are most important when making a
purchase decision & by how much

2. WHAT’S WRONG WITH ASKING PEOPLE TO RATE OR RANK ITEMS?
Problems with RATING:
• Results may not be discriminating (may
rate everything as important)!
• The scale is arbitrary, does not tell the
strength of importance
• Cannot handle a long list
Problems with RANKING:
• People are good at picking the extremes
but their preferences for anything in
between might be inaccurate
• Only tells you the order of importance,
not the strength of importance
• Cannot handle a long list

3. HOW DOES MAX-DIFF SOLVE THESE PROBLEMS?
Best-Worst Scaling…
• Always generates discriminating results as respondents are asked to choose the BEST
and WORST option which simulates real-world behavior – people make choices and
trade-offs
• The results will tell you the order and strength of importance of all items
• There is no scale-bias
• Can handle a long list of items as people are given a few items in each task to evaluate
• Can get accurate preferences of all items

4. TYPES OF MAX-DIFF
Standard Max Diff
Anchored Max Diff
Adaptive Max Diff
Max Diff Scores on Fly
In Standard Max Diff, respondents are shown a set (subset) of the possible items in the study, and
are asked to indicate (among this subset with 4-5 items) the most and least important attribute.
In this method, an indirect scaling question is added below Max diff question as:
On selection of above options, we inform utility estimation that items shown in this MaxDiff set
should have lower/higher utility than the anchor threshold.
This is designed for larger number of attributes. If we have large set of attributes, we would need
more number of sets for good design. In adaptive, we can pick selected attributes to go in Max
Diff. This selection can be random or based on response of some question before Max diff.
In this method, we return max diff scores as soon as exercise is completed. The labels and utility
values can be used in further questions.

5. OUTPUTS OF MAX-DIFF
Attribute Importance
If a score of an item is two times bigger than another item, it can be interpreted that it is
twice as appealing

6. OUTPUTS OF MAX-DIFF
Best-Worst scores on an the aggregate level
0.151
0.141
0.109
0.087
0.086
0.085
0.029
0.009
-0.019
-0.023
-0.066
-0.074
-0.091
-0.137
-0.282
-0.35 -0.3 -0.25 -0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2
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Best-Worst scores =
Times(BEST) – Times(WORST)
No. of times an item appears
The higher the score, the more the feature is appealing to respondents
– A positive score: it is chosen as MOST appealing more often than least appealing
– A negative score: it is chosen as LEAST appealing more often than most appealing
– A zero score: it is chosen as MOST and LEAST appealing an equal number of times OR it
has never been chosen as most and least appealing

7. BEYOND MAX-DIFF
Total Unduplicated Reach and Frequency (TURF)
It is an optimization approach for finding a subset of items that "reach" the maximum number of
respondents possible.
This method assesses the quality of each subset of items by setting its "reach" equal to the sum of
the probabilities across the items in the subset, where the probability for each item is the likelihood
that the item would be selected from a set including all items, according to the Logit rule. Intuitively,
we are finding sets of items that maximize the likelihood that respondents will like one or more
items within the set.

8. MAX-DIFF DEMO LINKS
Standard Max Diff
Standard Max Diff with Images
Anchored Max Diff
Adaptive Max Diff
Max Diff with Scores on Fly

9. For any additional questions, write us at contactus@knowledgexcel.com
Visit us at http://knowledgexcel.com