Two-Dimensional Max-Diff (2DMD)
Published in Quirk's, October 2018
Prioritization and Max-Diff
A common problem in research is prioritization of a list of items (features, messages, etc.). With only a handful of items a ranking exercise works nicely, though it doesn’t really provide information on the spacing between the ranks. A constant sum slcale is better, though it cannot be used when there are more than a handful of items. The research industry has gravitated toward widespread use of maximum difference scaling (Max-Diff) as a standard way of handling this task. The Max-Diff approach can easily handle many items, and provides both rank ordering and spacing between the ranks (in the form of individual-level attractiveness scores).
In the standard Max-Diff approach, respondents are presented sets of (generally) 3-5 items and asked to choose the most and least important items in each set. This information is used to estimate the prefer ence scores at the individual respondent level using Hierarchical Bayesian (HB) analysis. With this information we can array the items on a chart from most to least important. While this is now an industry standard approach, it does have a significant drawback: the items are arrayed on only one dimension (usually, importance). But sometimes in practice, it is important to sort the items on a second dimension (say, uniqueness or innovativeness). In such cases the practice is to conduct another Max-Diff exercise with that framing or to ask rating scaled questions to get at that information. This can be a tedious process for the respondents, especially when more than a handful of items are involved. The information from the two exercises are plotted together in a quadrant chart to provide insights.
A More Efficient Approach
The question then is whether there is a more efficient way to get at the answer – one that does not require the respondent to go through the exercise twice. Well, since a standard Max-Diff task does ask respondents to provide two evaluations per question set (most and least important), why not ask them to evaluate each set of items for the two dimensions instead? Let’s say we are interested in the importance and innovativeness of the items. Then, let each respondent evaluate each set of items and pick two – the most important and the most innovative items in that set (See Figure-1). Other than that, the data collection process can proceed just as it would in a regular Max-Diff study.