Card sorting is a technique used to understand how users mentally organize information by having them physically sort topics into categories. It involves converting content into cards that participants group, which provides insights into their mental models. Analysis of the card sorts reveals how users naturally cluster topics, which can then be used to improve an organization's structure, labeling, and findability.

1. Information Architecture Technique
Card Sorting

2. Why use Card Sorting
Method for Addressing Problems of Find-ability

3. Product Team asks,
‘Where does this piece of content or functionality belong?’
UX Team asks,
‘Where would the user START this task?
User asks,
‘Where do I find what I’m looking for?’
Problems of Find-ability

4. What is Information Architecture?

5. 5 Planes of User Experience
Skeleton
Scope
Strategy
Surface
1
2 3
Structure

6. Skeleton
Scope
Strategy
Surface
1
2 3
Structure
5 Planes of User Experience
Information
Architecture

7. Card Sorting Overview

8. • Each piece of content or functionality is converted to a ‘Card’
• Cards are organized into groups by participants
• Participant’s responses are analyzed to better understand the user’s
mental model
• Analysis provides recommendations for improved organization, labeling,
and find-ability
Card Sorting Overview

9. Each ‘Card’ consists of:
• Name
• Description
• Fit Confidence
Perfect, Good, Fair
• Importance (optional)
Very Important - Not Important
Card Sorting Overview

10. Two Primary Methods
Open Card Sort vs Closed Card Sort

11. Closed Card Sort
• Used when you know the group
names.
• Quantitative method used for
validation of group names and the
placement of items within those
groups.
Two Primary Methods
Open Card Sort
• Used when you do not know the
group names.
• Qualitative method used for
generating insights into group names
and user mental models.

12. Open Card Sort
Method

13. Phase 1
• Mix the cards up and hand them to a participant.
• Ask them to group each card with other cards they feel it fits with.
• Ask them to use the think out loud method and to mark any annotations on the cards.
Pay special attention to the mental model the participant uses to group items, and the
alternate names and descriptions they write.
Is it by frequency, relatedness, something else?
Open Card Sort - Method

14. Phase 2
• For each group, have them go through each card and select the fit confidence. “How well
do you think X fits in this group?”
Have them also select the “Importance” of each card (optional).
• Then, ask each user to come up with a name for each group. The group name also helps
drive insights.
Open Card Sort - Method

15. Open Card Sort
Analysis

16. Shows how often an item was
grouped with another item,
factoring quality of fit.
Analysis - Similarity Matrix / Contour Map

17. How to construct using Excel:
• Each sheet is a new participant’s matrix
with a total sheet which sums the cell
across participants.
• For each cell, if the two items were in the
same group, average their fit confidence.
e.g. If Item A was a Perfect Fit and Item B
was a Good fit, mark a 2.5 in that cell.
• Note: A Perfect has a value of 3,
good has a 2, and fair has a 1.
Analysis - Similarity Matrix / Contour Map

18. How to construct using Excel:
• Use Conditional formatting with a Graded
Color Scale
• Sort columns to try and group darker
colors together into ‘clusters’
• Note: The Row and Column are
transposed, i.e. if you move a column, you
must move the row as well.
Analysis - Similarity Matrix / Contour Map

19. Shows the relationship between
features.
Larger the height, less connected the item.
e.g. 11 and 12 are tightly connected.
15 is loosely connected to16 and 17.
Analysis - Dendrogram based on Cluster Analysis

20. How to Use:
• Use this as a starting point to find logical
groupings, based on height.
• Consider the number of groupings
participants created.
• Remember, this is the result of qualitative
data, not quantitatively significant data.
Analysis - Dendrogram based on Cluster Analysis

21. Example of 5 Groups,
Based on height.
Analysis - Dendrogram based on Cluster Analysis

22. Example of 3 Groups,
Based on height.
Analysis - Dendrogram based on Cluster Analysis

23. Example of 10 Groups,
Based on height.
Analysis - Dendrogram based on Cluster Analysis

24. How to construct using Past
(or other statistics software):
• Use the data from the Similarity Matrix
• Multivariate -> Clustering -> Classical
Analysis - Dendrogram based on Cluster Analysis
Hammer, Ø., Harper, D.A.T., Ryan, P.D. 2001. PAST: Paleontological statistics software
package for education and data analysis. Palaeontologia Electronica 4(1): 9pp.
https://folk.uio.no/ohammer/past/

25. Closed Card Sort
Methods

26. Method 1
• Similar to the Open Card Sort Method with one exception.
• Create Pre-defined Header Groups which the participants place cards into.
Closed Card Sort - Methods

27. Method 2 - Survey
Create a survey where each item has a
single selection of each group name.
For each item, capture attributes such
as importance or confidence
(optional).
Closed Card Sort - Methods

28. Closed Card Sort
Method 2 - Analysis

29. Shows the percentage of
participants that grouped an item
into a particular category.
Analysis - Correlation Table

30. How to construct using Excel:
• For each cell, calculate the percentage of
participants that selected an item for
each group compared to other groups.
• Color code based on the percentages.
• Green highlight indicates the highest percentage
each item received.
• Red highlight indicates percentages over the
margins of error (e.g. 10%) that were not the highest
percentage for that feature. This indicates lower
agreement.
• For each group, sort based on the items
that contained the highest percentage
Analysis - Similarity Matrix / Contour Map

31. Shows the average importance participants assigned to each item.
The higher the value, the more important it is to the participants.
Analysis - Item Importance Chart (optional)

32. Results

33. Show how items were placed
between the initial proposal and
the result of the Close Card Sort.
Results

34. Summary

35. • If problems of find-ability exist, Card Sorting can solve these pain points
• To gain insights into those pain points, use an Open Card Sort method.
To validate pain points or solutions, use a Closed Card Sort method.
• Analysis of a Card Sort will help craft better item names or groupings.
• Better naming and grouping will increase the use of features or content.
• An increase in the use of features or content provides business value to
the organization, which can be validated through analytics.
Summary