Standard MaxDiff tells you that one feature matters more than another. It never tells you whether either one actually matters at all. Anchored MaxDiff Scaling closes that gap by adding a single extra question that turns a relative ranking into an absolute one.<\/p>\n\n\n\n
In this article, we\u2019ll explore what Anchored MaxDiff is, how the two anchoring models work, and how to set it up in a MaxDiff survey.<\/p>\n\n\n\n\n\n
Anchored MaxDiff is a technique that upgrades a standard MaxDiff exercise from a relative model into an absolute one. A relative model only tells you how one option compares to another; an absolute model tells you whether an option matters at all.<\/p>\n\n\n\n
Traditional MaxDiff identifies which attributes matter more or less relative to each other. It never establishes whether any of them clear a real bar of importance:<\/p>\n\n\n\n
Anchored MaxDiff adds one extra question, the “anchor,” that asks respondents to draw a line between attributes that genuinely matter and those that don’t.<\/p>\n\n\n\n
Anchored MaxDiff works in almost any research scenario involving multiple competing options:<\/p>\n\n\n\n
Here’s a concrete example. A researcher studying credit card preferences shows respondents 6 card brands using a standard MaxDiff question<\/a>. Most respondents will only ever select 2 of the 6 as their most and least preferred, which leaves the remaining 4 unexplained.<\/p>\n\n\n\n Adding an anchor question fixes that gap. If respondents mark Visa, Mastercard, and American Express as “must-haves,” the researcher learns something important. Even though Visa ranked highest, Mastercard and Amex still matter to respondents rather than sitting in an ambiguous middle.<\/p>\n\n\n\n The same logic applies well beyond product features. In concept testing<\/a>, respondents can rank which design concepts they like most and least. An anchor question then identifies every concept they’d genuinely be happy with.<\/p>\n\n\n\n Anchored MaxDiff supports two models for adding that extra question. Both insert as a simple multiple-choice step into the existing MaxDiff exercise:<\/p>\n\n\n\n Each model is covered in more detail below.<\/p>\n\n\n\n The Binary Direct model is the simpler and more common of the two. Respondents see every available option at once and mark which ones are “must-haves.” A few things make this model easy to work with:<\/p>\n\n\n\n That single action draws a clean line between what genuinely matters to a respondent and what falls into “nice to have” territory.<\/p>\n\n\n\n The Dual Response model asks respondents to identify some, all, or none of the options using straightforward multiple-choice questions<\/a>. It gives researchers slightly more flexibility in how the anchor question is framed, at the cost of a bit more complexity for respondents.<\/p>\n\n\n\n Anchoring is available as a setting on the MaxDiff question type, but it’s off by default. Enabling it adds the anchor question automatically to the standard MaxDiff flow. A few things are worth knowing before you turn it on:<\/p>\n\n\n\n A single MaxDiff score means little without a reference point. Saying one feature scored a 10 and another a 5 only tells you the first is twice as preferred. It doesn’t tell you whether either one is worth building. Anchoring gives that missing context. The technique is commonly used to answer questions like the following.<\/p>\n\n\n\n A standard MaxDiff survey works well when you just need a high-level read on preferences. Anchored MaxDiff is the better choice when you need to draw a real line between what matters and what doesn’t. Ranking everything relative to everything else isn’t always enough.<\/p>\n\n\n\n <\/p>\n\n\n\n\n\tHow does anchoring work?<\/strong><\/h2>\n\n\n\n
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Binary Direct model<\/h3>\n\n\n\n
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Dual Response model<\/h3>\n\n\n\n
How do I set up Anchored MaxDiff in QuestionPro?<\/strong><\/h2>\n\n\n\n
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What questions can Anchored MaxDiff answer?<\/strong><\/h2>\n\n\n\n
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