# Card Sorting

> A research method where participants group your content into categories, revealing the mental model they already hold.

- Category: Process & Methods
- Canonical: https://www.themasterly.com/glossary/card-sorting

Card sorting asks participants to group your content into categories that make sense to them. It reveals the model people already hold, and a product organised around that model needs no explanation, while one organised around any other model needs a help article.

It is the generative half of a pair. Card sorting produces a structure; [tree testing](https://www.themasterly.com/glossary/tree-testing) checks whether the structure you built from it actually works.

## Open, closed and hybrid

| | Participants | Reveals | Use when |
|---|---|---|---|
| **Open** | Create and name their own groups | Grouping and vocabulary | The structure is undecided |
| **Closed** | File into your categories | Whether your categories are understood | You have a structure to validate |
| **Hybrid** | Use yours, may add their own | Both, and what is missing | You have a draft and suspect gaps |

Open sorting is where the surprises live, and the category names participants invent are frequently more useful than the grouping. When six people independently create a group called "money" containing your billing, plan, seats and usage items, you have learned both the structure and the label.

## Running one that produces a decision

**Write cards as content, not as navigation.** "Change how many seats we pay for" is a thing somebody wants. "Seat management" is your existing menu label, and putting it on a card tests whether people can regroup your current structure rather than what they actually think.

**Use the words users use.** Where you are unsure, that uncertainty is itself the finding, and an open sort will settle it.

**Thirty to sixty cards.** Enough to show structure, few enough that nobody gets tired and starts filing at random.

**Fifteen to twenty participants per audience**, more if you plan to cluster statistically.

**Split B2B audiences.** Admins and daily users hold different models. Merging them produces an average structure that fits neither, which is the most common way a card sort produces a worse result than doing nothing.

**Ask them to think aloud** where the format allows. Why something went where it did is often more useful than where it went.

## Reading the output

**Agreement matrices** show how often any two cards were grouped together. High-agreement clusters are the parts of the structure you can be confident about.

**Disagreement is information, not noise.** A card that lands in four different groups usually means one of three things: the label is ambiguous, the item genuinely belongs in two places, or you have two audiences with different models. All three are worth knowing, and averaging them away loses the finding.

**The names people invent** are the vocabulary your labels should use.

**Orphans** — cards nobody knew what to do with — usually name something whose purpose is unclear rather than something misfiled.

Resist the temptation to let software cluster the results and take the dendrogram as the answer. The statistics show agreement; the decision about what to do with a genuinely two-homed item is still a design judgement.

## What it cannot tell you

Card sorting reveals how people categorise content when they are looking at all of it at once, laid out on a table. That is not the situation anybody is in when using your product, where they arrive with one goal, see part of the structure, and are in a hurry.

So a card sort tells you which groupings are plausible and which labels are understood. It does not tell you whether somebody can find something under time pressure, which is what tree testing measures, and it says nothing about whether the thing is any good once found.

Treating a card sort as the final answer produces a structure that is defensible and untested. It is the first of two steps.

## Moderated, unmoderated, and which to run

Both formats work and they return different things.

**Unmoderated** is cheap, scales to the numbers the analysis wants, and gives you the grouping without the reasoning. It suits a first pass and any study where you intend to cluster statistically.

**Moderated** costs an hour per participant and returns the why. Somebody hesitating over a card, saying "this could go in two places, depending on whether I'm setting it up or checking it", has explained a structural problem that no matrix will show.

The pragmatic combination is a small moderated round first, to hear the reasoning and catch labels that turn out to be ambiguous, then an unmoderated round at volume once the cards are right. Running the large study first usually means discovering the ambiguity in the data, where it looks like disagreement rather than like a badly worded card.

## In practice

A settings area has grown to forty options and support keeps hearing that people cannot find things they have used before.

An open card sort with eighteen participants returns a consistent grouping that differs from the product's in one specific way. Everything involving money goes together: invoices, plan, seat count, usage limits. Fourteen of eighteen participants make that group, and eleven of them call it some version of "billing".

The product splits those items across three sections, because three teams built them at different times. Nobody had designed that split; it accumulated.

The regrouping and three renamed labels follow from the sort. A tree test afterwards confirms the money tasks now pass, and catches one section that got worse in the process.

## Where teams get it wrong

- **Cards written as menu labels.** Testing whether people can reproduce your structure.
- **Merging distinct audiences.** An average model that fits nobody.
- **Averaging away disagreement.** The split was the finding.
- **Too many cards.** Tired participants filing carelessly, producing confident-looking data.
- **Stopping at the sort.** Generative without evaluative is half the method.

## Related terms

- [Information Architecture](https://www.themasterly.com/glossary/information-architecture)
- [Tree Testing](https://www.themasterly.com/glossary/tree-testing)
- [User Research](https://www.themasterly.com/glossary/user-research)
- [Sitemap](https://www.themasterly.com/glossary/sitemap)

## FAQ

**What is card sorting?**

A method where participants group your labels into categories that make sense to them, and often name those categories. It reveals the model people already hold, which is the structure your product should match if you want it to need no explanation.

**What is the difference between open and closed card sorting?**

In an open sort participants create and name the groups themselves, which surfaces their vocabulary as well as their grouping. In a closed sort you supply the categories and they file items into them, which tests whether categories you have already chosen are understood. Open first when the structure is undecided; closed to validate one.

**How many participants do you need for a card sort?**

Fifteen to twenty per audience for a qualitative read, and thirty or more if you intend to cluster the results statistically. Unlike usability testing, more participants genuinely help here, because the output is agreement across people rather than problems found.

**What is the difference between card sorting and tree testing?**

Card sorting is generative: it asks people to build a structure. Tree testing is evaluative: it asks whether the structure you built works. Sort to learn the model, design against it, then tree test to check that what you designed matches.

**How many cards should a card sort have?**

Thirty to sixty. Below thirty there is not enough to reveal a structure; past about eighty participants get tired and start grouping carelessly, which produces data that looks like a finding. If you have more content than that, sort a representative sample.

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