Card Sorting and Similarity Analysis

What Is Card Sorting?

Card sorting asks participants to sort physical or digital cards (each labelled with a piece of content, a function, or a concept) into groups that make sense to them. It reveals users’ natural mental models for organising information and directly informs Information Architecture.

Type	Procedure	Used for
Open card sort	Participants create and name their own groups. No predefined categories.	Discovering a new IA — you do not yet know how users categorise the content.
Closed card sort	Groups are predefined by the researcher; participants only assign cards to them.	Validating an existing IA — testing whether the proposed categories match user expectations.

Similarity Analysis

The quantitative output of a card-sorting study is a similarity matrix — for every pair of cards $(A, B)$, compute:

$\text{Similarity}(A,B) = \frac{\text{number of participants who placed } A \text{ and } B \text{ in the same group}}{\text{total number of participants}} \times 100\%$

Interpretation:

• High similarity ($\to 100\%$): most users consistently co-group those cards — they should be co-located in the final IA.
• Low similarity: users hold varied mental models — the cards may not belong together, or the concept is ambiguous. A single IA hierarchy will not satisfy all users.
• Mixed similarity across pairs: indicates genuine variation in mental models — further testing (or a larger sample) is needed before committing to a final IA.

Worked Example: 4 Cards, 3 Users (2019 Q5c Style)

Given the following groupings from three participants:

• User A: {1,4}, {2,4}, {1}
• User B: {1,2,3}, {1,2,4}, {3,4}
• User C: {1,3}, {4}, {3,4}

We count for each pair which users placed them in the same group at least once:

Pair	Co-grouped by	Similarity
(1,2)	B only (appears together in {1,2,3} and {1,2,4})	$1/3 \approx 33\%$
(1,3)	B ({1,2,3}), C ({1,3})	$2/3 \approx 67\%$
(1,4)	A ({1,4}), B ({1,2,4})	$2/3 \approx 67\%$
(2,3)	B only ({1,2,3})	$1/3 \approx 33\%$
(2,4)	A ({2,4}), B ({1,2,4})	$2/3 \approx 67\%$
(3,4)	B ({3,4}), C ({3,4})	$2/3 \approx 67\%$

Commentary: pairs (1,3), (1,4), (2,4), and (3,4) show moderate agreement (67%); pairs (1,2) and (2,3) show weaker agreement (33%). No pair reaches 100% consensus — genuine variation in mental models. A reasonable IA might treat {1,3,4} as a loosely overlapping cluster with card 2 more weakly attached (mainly via card 4).

Critical caveat: with only $n=3$ participants, this sample is far too small to commit to a final structure with confidence. The study should be repeated with more participants before the IA is finalised.

Exam Tips for Card Sorting Questions

1. Always show your working for every pair — even where the arithmetic is straightforward. Method marks are awarded per pair.
2. Use the formula explicitly — state it, then substitute the numbers.
3. End with an explicit IA recommendation — which cards should be co-located based on the similarity data?
4. Always comment on sample size — is $n$ large enough to draw firm conclusions? If not, state that explicitly.
5. Link back to IA principles — how does this similarity matrix inform the organisation system, labelling system, and navigation structure?

Card Sorting and the UCD Cycle

Card sorting fits into the UCD cycle at the mid-stage: after requirements and initial user research, before detailed prototyping. The similarity matrix from card sorting directly determines the IA tree, which then feeds into wireframing and prototyping.

Card sorting is often complemented by:

• Interviews to understand why users group items as they do.
• Tree testing (reverse card sort) to validate the proposed IA: users are given the IA tree and asked where they would find a specific item — testing navigation effectiveness, not grouping preference.

Expected Learning

• Distinguish open from closed card sorts and explain when each is appropriate.
• Apply the similarity formula to a given set of user groupings, computing the similarity for every card pair.
• Interpret a similarity matrix: identify strong clusters, weak associations, and areas of user disagreement.
• Recommend an IA structure based on similarity data, with explicit caveats about sample size.
• Explain how card sorting fits into the UCD cycle and what other methods complement it.

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Past Paper Questions

Card sorting and similarity analysis appears in 2019 Q5(c) (4 cards, 3 users — compute similarity for all pairs, comment on implications for IA). The similarity formula and the importance of showing working are explicit exam requirements.