Drawing out users' mental categories
Rugg and McGeorge (1997) framed sorting as a way to elicit knowledge structures. In UX, users group cards naming content. Sherwin and Tankala (2024) distinguish open sorts, where users name groups, closed sorts into predefined categories and hybrids. Like the mental models measured by Roth et al. (2010), card sorting finds an information architecture matching user expectations rather than the org chart.
Running sessions and sample size
Spencer (2009) recommends roughly 30–60 cards and piloting to weed out ambiguous ones. A few participants suffice for qualitative insight, but 15 or more is commonly advised for quantitative patterns (Sherwin & Tankala, 2024). Recruit people representing real personas. Online tools allow mobile sessions, but cut card counts because small screens tire people quickly.
From analysis to menus
Tullis and Albert (2013) explain similarity matrices showing how often two cards were grouped together and dendrograms drawing them as a tree; Capra (2005) proposes factor analysis as an alternative. Results are a starting point: refine labels and schemes (Rosenfeld et al., 2015) and validate with tree testing. On mobile, more than four or five top-level groups will not fit bottom navigation.
FAQ
- Open or closed sort first?
- Use open sorts to find user language for new structures and closed sorts to validate existing ones.
- How does card sorting differ from tree testing?
- Card sorting builds a structure; tree testing checks people can find items in it.
Sources reviewed: 2026-10-27
References
- Spencer, D. (2009). Card sorting: Designing usable categories. Rosenfeld Media. A practical guide to planning, running and analysing card sorts.
- Rugg, G., & McGeorge, P. (1997). The sorting techniques: A tutorial paper on card sorts, picture sorts and item sorts. Expert Systems, 14(2), 80–93. Academic tutorial on sorting techniques.
- Capra, M. G. (2005). Factor analysis of card sort data: An alternative to hierarchical cluster analysis. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 49(5), 691–695. Proposes factor analysis as an alternative to cluster analysis.
- Sherwin, K., & Tankala, S. (2024, February 2). Card sorting: Uncover users' mental models for better information architecture. Nielsen Norman Group. Defines open and closed sorts and recommends participant numbers.
- Rosenfeld, L., Morville, P., & Arango, J. (2015). Information architecture: For the web and beyond (4th ed.). O'Reilly Media. Standard IA text on organisation and labelling systems.
- Roth, S. P., Schmutz, P., Pauwels, S. L., Bargas-Avila, J. A., & Opwis, K. (2010). Mental models for web objects: Where do users expect to find the most frequent objects in online shops, news portals, and company web pages? Interacting with Computers, 22(2), 140–152. Measures user mental models, the target of card sorting.
- Tullis, T., & Albert, B. (2013). Measuring the user experience (2nd ed.). Morgan Kaufmann. Explains similarity matrices and dendrograms for card sort data.