MaxDiff vs rating scale is one of the first questions researchers run into when they need to score how important several features or benefits are to their audience. A rating scale asks people to score each item on its own. MaxDiff asks them to pick a best and a worst from a small group instead.
The two question types can look similar on a survey page, but the data behind them is not the same. A rating scale tends to flatten preferences toward the middle. MaxDiff forces a real trade-off, which is why so many research teams have switched over.
In this guide, we’ll break down how each method works, when MaxDiff earns its place over a standard rating scale, and how to set up and interpret your first MaxDiff study.
What is MaxDiff scaling?
MaxDiff, short for Maximum Difference Scaling, is a survey method that measures the relative importance of a set of items by repeatedly asking respondents to pick the best and worst option from a small group. It is also called best-worst scaling, a term first developed through the academic work of Jordan Louviere in the 1990s.
MaxDiff is not the same as conjoint analysis, which tests full combinations of attributes and price together. MaxDiff instead ranks a flat list of items against each other, which makes it a better fit when your list does not break down into clean attribute levels.
Researchers commonly use MaxDiff to score items that people struggle to rank on their own, including:
- Brand or product features
- Marketing messages and advertising claims
- Employee benefits or perks
- Purchase drivers and buying criteria
What is a rating scale, and what’s wrong with it?
A rating scale asks respondents to score each item by itself, usually on a 5-point or 7-point range such as a Likert scale, a labeled scale that measures agreement or satisfaction in fixed steps. It is quick to build and familiar to almost every respondent.
The problem shows up in the results. Three biases repeat across nearly every rating scale study:
- Central tendency bias: the tendency to avoid the highest and lowest points and cluster answers around the middle.
- Straight-lining: giving the same or similar score to every item on the list, whether or not it deserves it.
- Scale-use bias: using the scale differently by culture or personality, which distorts comparisons between groups or countries.
This bias shows up constantly in real research. One respondent gives everything a five out of five, since nothing feels worth marking down. Another treats a four as generous and rarely goes higher. Neither pattern tells you which item that person actually values most, and averaging the two only hides the problem further.
MaxDiff vs rating scale: Key differences
The table below lines up both methods across the factors that matter most when you are choosing a question type for your next study.
| Factor | Rating scale | MaxDiff |
|---|---|---|
| Task for respondent | Scores each item alone | Picks a best and worst from a small set |
| Common bias | Central tendency, straight-lining | Very low, choices force real trade-offs |
| Best for | Quick satisfaction or agreement checks | Ranking a long list of features or claims |
| Output | Raw average scores per item | Preference scores that sum to 100 |
| Cross-cultural reliability | Lower, scale use varies by culture | Higher, choices are less culture-dependent |
| Setup effort | Minimal | Needs more planning and a larger sample |
When to use MaxDiff vs when a rating scale is enough
Neither method wins every study. The right choice depends on what you need to learn and how many items you are comparing.
Use MaxDiff when:
- You have 10 or more items to compare
- Past studies show respondents rating everything the same
- You need a ranked list, not just individual scores
- The study spans multiple countries, languages, or cultures
A rating scale still works well when:
- You only have two or three items to compare
- You need an absolute score, not just a rank
- You are tracking the same metric over time, such as satisfaction
- The survey needs to stay very short
Real-world example: Prioritizing product features with MaxDiff
Picture a software company deciding which five features to build into its next release. A rating scale would likely show every feature scoring between 7 and 9 out of 10, since respondents rarely call a feature they were specifically asked about unimportant.
A MaxDiff study avoids that trap. Respondents see small groups of features, four at a time, and pick the one they want most and the one they care about least. After a dozen rounds, the responses produce a clear ranking, often expressed as scores that sum to 100 across every feature tested.
That structure matters for the decision that follows. A feature scoring 20 is genuinely twice as valuable to customers as one scoring 10, a distinction a rating scale cannot support. Teams running this kind of study alongside broader market segmentation work can see which features matter most to each customer group, not just the audience as a whole.
How to run a MaxDiff study step by step
Setting up a MaxDiff study follows a similar process across most survey platforms.
- List your items.
Gather the full set of features, claims, or attributes you want to test, ideally between 10 and 30.
- Set the group size.
Decide how many items respondents see per screen, typically four or five.
- Build the question.
Add a MaxDiff question type to your survey and load in your item list.
- Preview and test.
Run the survey yourself first to confirm the number of sets feels reasonable to complete.
- Launch and collect responses.
Aim for at least 150 to 300 completed responses, with 100 to 150 per segment if you plan to compare subgroups.
- Analyze the output.
Review the preference scores, or run a deeper model if you need results broken out by respondent.
How to interpret MaxDiff scores
MaxDiff scores are reported on a 0 to 100 scale that sums to 100 across every item tested, which makes results easy to compare at a glance.
Most platforms calculate these scores using logit regression, a statistical model that estimates the odds one item is chosen over the others, or Hierarchical Bayes, a more advanced model that estimates preferences for each individual respondent rather than only the group average. Either method produces the same type of output: a ranked, ratio-scaled result where the size of the gap between two scores has real meaning.
That last part is what sets MaxDiff apart from a rating scale average. A score of 20 means an item is twice as preferred as one scoring 10. A rating scale average of 4.0 versus 2.0 on a 5-point scale does not carry the same guarantee, since respondents rarely spread their answers across the full range.
Common mistakes to avoid with MaxDiff
MaxDiff produces strong data, but only when the study is set up correctly.
- Testing too few items, which wastes the method’s main strength
- Repeating the same item pairings across every set, which weakens the comparisons
- Skipping a pilot test, which can hide a confusing or overlapping item list
- Ignoring segment-level analysis, which hides real differences between customer groups
- Confusing MaxDiff with MaxDiff vs conjoint style attribute testing, when a flat item list is what the study actually needs
- Skipping the anchoring step when the study needs absolute, not just relative, importance, a gap covered in anchored MaxDiff scaling
Using MaxDiff in QuestionPro surveys
QuestionPro includes a built-in MaxDiff question type inside its survey builder, so researchers can set item lists, group sizes, and scoring without exporting data to a separate statistics package. Teams running broader studies inside QuestionPro’s Market Research Software can add a MaxDiff question alongside other question types in the same project, then compare the results directly against any rating scale data already in the study.
Better data starts with the right question type
The method you choose shapes the story your data can tell. A rating scale is fast and familiar, but it flattens differences that actually matter to a product or marketing decision. MaxDiff takes more planning to set up, yet it rewards that effort with a ranked, bias-resistant view of what your audience truly prefers.
Try replacing one rating scale question in your next study with a MaxDiff question, and compare the clarity of the results for yourself.
Frequently Asked Questions (FAQs)
Yes, the two terms describe the same method in most industry use. Academically, best-worst scaling is the broader theory, and MaxDiff is one specific way of applying it, though researchers and vendors use the two terms interchangeably outside academic papers.
Most MaxDiff studies work best with 10 to 30 items. Below that range, a rating scale or a simple ranking question is usually faster to run and just as reliable, so MaxDiff’s added setup time may not pay off.
No. MaxDiff measures relative importance between items, not overall satisfaction or loyalty. Run it alongside tracking metrics like NPS or CSAT rather than instead of them, since those metrics answer a different question about how customers feel overall.
Most researchers aim for at least 150 to 300 completed responses, though the right number depends on how many items and versions the study includes. Smaller samples can still work for internal, directional research rather than public reporting.
Yes, and it often performs better than rating scales in this setting. Because MaxDiff relies on choices rather than numeric scores, it avoids the scale-use differences that make cross-cultural rating comparisons unreliable.
