Maximum Difference Scaling is used when we need to rank a large number of items (attributes of a product, attributes of advertising messages etc.) reliably and with sufficient discrimination and confidence. While respondents may be asked to rate individual items on a typical MR scale, scale responses over a large number of items usually show the problem of low discrimination. For example, it is difficult to confidently rank two choice attributes that score averages of 7.44 and 7.52 on a 10-point scale. The other approach can be to ask respondents to directly assign ranks to the items. Research has shown that ranking more than seven items though direct assignment of ranks is a cognitively demanding task for most individuals. MaxDiff provides us with a reliable method for ranking large number of items while avoiding both of the problems above.
In a MaxDiff survey, each respondent sees sets of different items from which they are asked to chose their maximum and minimum on the construct of focus (say value, importance, preference etc.). Each respondent sees multiple such tasks while different sets of respondents may see different sets of tasks. The design of the experiment tries to minimize bias through considerations like a) one way frequency balance – showing each item an equal number of times b) two-way freq. balance – each task occurring with each other task an equal number of times etc. Attempt is made to minimize variations from such balance.
The data is collated and analyzed using Logit analysis (a form of regression analysis where the dependent and independent variables are all categorical in nature). The final results are a scaled ranking of the items for the aggregate of respondents. Analysis may also be performed separately for different demographic and other segments.