Anchored Max Diff

MaxDiff Scaling has become a widely used market research technique that provides a hierarchy of the relative importance or appeal for a list of statements. max diff analysis Scaling utilizes tradeoff technique in which respondents are forced to choose between items.
MaxDiff is used to capture relative preferences for multiple items. Respondents choose the ‘best’ and ‘worst’ of the options shown to them. They do not have the option of saying they prefer ‘none’ of the options or ‘all’ of the options.
Anchored MaxDiff is a simple yet effective model to enhance a max-diff exercise from a relative model (where one option’s utility is comparable to the others) – to an absolute model. Anchored MaxDiff experiments supplement standard MaxDiff questions with additional questions designed to determine the attributes’ absolute importance.
An ‘anchored max diff’ works the same as the traditional max diff above, but at the end of the exercise each respondent is presented with all of the items in turn and asked to indicate which ones “would definitely motivate them to take action”.
This one additional question frames the exercise by establishing what will and won’t work for the business
Because we have asked the question to identify those items that are truly motivating, we can differentiate in our hierarchy between those messages that will engage customers and those that won’t (in practical terms it means the analysis draws a line in our results, and anything above the line will result in action from our target market and anything below the line won’t).
Jordan Louviere, the inventor of max diff analysis, proposed a dual-response, indirect method for scaling the items relative to a threshold anchor of importance or desirability.

This additional question appears as:

Considering only the items above….

o None of these are important to me
o Some of these are important to me
o All of these are important to me

•If the respondent clicks "None of these are important to me" then we inform utility estimation that all four items shown in this MaxDiff set should have lower utility than the anchor threshold. The answer is coded in the data file as "3".

•If the respondent clicks "Some of these are important to me" then we know that the "best" item selected within the MaxDiff set should have higher utility than the anchor and the "worst" item selected should have lower utility than the anchor. The answer is coded in the data file as "2".

•If the respondent clicks "All of these are important to me" then we inform utility estimation that all four items should have higher utility than the anchor threshold. the answer is coded in the data file as "1".

When "Some of these are important to me" is selected, we know that the best item is better than the anchor and the worst item is worse than the anchor, but we do not have any information about how the two non-selected items (from this set of four) relate to the anchor. Thus, the dual-response indirect method provides incomplete information about how each of the items shown in a MaxDiff set relates to the anchor.

In the output it given an anchor point. The anchor is the ‘threshold’ or ‘null’ point where respondents give zero importance to a feature. It is the minimum point at which a feature reaches the standard set. Just like any other feature, the null point also gets a utility at the end of the exercise. To determine if a feature is important, its utility should be more than the utility of the null vector.
The anchor point is the threshold level, which can be set as the benchmark. Anything above it is deemed important, while anything below is less important. It is always easier to explain the relationships between features when numbers are comparable to a benchmark.

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