Maximum Difference Scaling

Have you ever been asked to measure respondents' preferences for things such as brands, product features, job-related benefits, or product packaging? Have you ever been asked to prioritize a list of performance attributes or gauge the potential impact of different advertising claims? If so, you may wish to consider the class of trade-off related techniques available within MaxDiff/Web, a component within the SSI Web system.

MaxDiff is an approach for obtaining preference/importance scores for multiple items (brand preferences, brand images, product features, advertising claims, etc.) using marketing or social survey research. Although MaxDiff has much in common with conjoint analysis, it is easier to use (for the researcher, respondent, and end client) and is applicable to a wider variety of research situations. (It is not a substitute for conjoint analysis, however, as conjoint offers unique benefits for studying products or services made up of complex features added together.)

With MaxDiff, respondents are shown a set (subset) of the possible items in the exercise, and are asked to indicate (among this subset) the best and worst items (or most and least important, etc.):



Respondents typically complete a dozen or more such sets where each set contains a different subset of items. The combinations of items are designed very carefully with the goal that each item is shown an equal number of times and pairs of items are shown an equal number of times. Each respondent typically sees each item two or more times across the MaxDiff sets. MaxDiff exercises focus on estimating preference or importance scores for typically about 15 to 40 items--though hundreds of items could be accommodated in advanced applications.

See a MaxDiff/Web Example Survey

Why use MaxDiff instead of standard rating scales? Research has shown that MaxDiff scores demonstrate greater discrimination between items and between respondents on the items. The MaxDiff question is simple to understand, so respondents from children to adults with a variety of educational and cultural backgrounds can provide reliable data. Since respondents make choices rather than expressing strength of preference using some numeric scale, there is no opportunity for scale use bias. This is an extremely valuable property for cross-cultural research studies.

MaxDiff/Web makes it easy for researchers with only minimal exposure to statistics to conduct sophisticated research for the scaling of multiple items. The trade-off techniques used in MaxDiff/Web are robust and easy to apply. The resulting item scores are also easy to interpret, as they are placed on a 0 to 100 point common scale and sum to 100.

Projects may be conducted over the Internet, using computers not connected to the internet (CAPI interviewing), or via paper-and-pencil questionnaires (using the included MaxDiff paper-and-pencil utility and the separately purchased CBC/HB system). MaxDiff/Web may be used designing, fielding, and analyzing:

MaxDiff (best-worst scaling) experiments
Choices from subsets of three items, four items, etc. (no "worst" choice)
Method of Paired Comparisons (MPC) experiments (choices from pairs)

Item scores are estimated for each individual using a hierarchical Bayes (HB) methodology. The HB tool is built right into the interface, and with a few clicks the estimation begins. The default settings are quite robust, so users with very little background in statistics can obtain good results. HB is a powerful approach for stabilizing scores for each individual from sparse choice data. However, it is a computationally-intensive program that takes between 15 minutes to an hour for a typical MaxDiff dataset.
Advanced users may export their MaxDiff data to a *.CHO file for analysis using the standard CBC/HB system or using our Latent Class v2.5 software.

MaxDiff products:
- MaxDiff/Web 30
- MaxDiff/Web 500
- MaxDiff Designer

The MaxDiff/Web System is sold in two sizes, the 30 item version and a 500 item version. For more details, refer to the "MaxDiff/Web Technical Paper."