Synthesizing MaxDiffs

After the analysis determines the utility coefficients, outputs and deliverables can be prepared to showcase the findings of the study. The utilities are the building blocks of all of the summary metrics.

The core summary metrics that typically accompany MaxDiff analysis are detailed below:

• Preference Share: The preference share is the measurement of the probability that an item would be chosen over another if a respondent was asked to select the best from all options. It is a product of the utilities calculated using a Multinomial * Logistical Regression model, and is derived by exponentiating the item utility and dividing that by the sum of all of the exponentiated items’ utilities.
• Average Utility: The average utility score of each item across all respondents. These are ordinal in nature and will show the relative preference between items. The average utilities can give some directional understanding but should not be a standalone metric to summarize the MaxDiff analysis.
• Counts Analysis: The count analysis is a metric that simply tells us the percentage of time each item was selected most/least when it was shown.
• Mean100 Score: The mean100 score creates a relative scale to judge the attribute importance on. In the example below, you can see that Starbucks for example is 143% more likely to be preferred over the average brand in this case. Hershey is 66% less preferred than the average brand.

The typical output of a MaxDiff Synthesis is a table displaying the choice options (attributes) and the utilities (importance). This importance is typically displayed on a Mean100 scale to make it easier to interpret.

Next to a table, a graphic makes it much more visible and easy to digest. In our package g6tr.md we have a function to create an automated version of the plot, see example below;