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Card sorting

"small-scale affinity"

Card sorting :: 1 of 2

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What is it?

Card sorting is a technique for exploring how ordinary people group items, so that you can develop structures that maximize the probability of users being able to find items.

Card sorting can be thought of as a particular form of (related but simplified) affinity diagramming exercise. But whereas affinity diagramming is used primarily for finding patterns in apparently unstructured data, card sorting is used primarily for discovering what kinds of patterns ordinary people find most salient and therefore meaningful.


Card sorting is very common technique. It differs from affinity diagramming primarily in that it's undertaken by one or more concurrent users (between 5 and 10 is best), with these separate "sorts" being summed together in a subsequent synthesis exercise to comprise the final categorisation.

  • Easy and cheap to conduct
  • Enables you to understand how "real people" are likely to group items
  • Identifies items that are likely to be difficult to categorize and find
  • Identifies terminology that is likely to be misunderstood

What's it for?

Card sorting is appropriate when you have identified (via a prior affinity diagramming exercise, for example) a set of items that you need to categorize. It is particularly useful for informing web taxonomies, e.g. sitemaps or other kinds of site structures).

Methods and use overview
The following is the basic process for a card sorting exercise.

  • Names of items to be categorized are printed on individual cards.
    Cards should be large enough to accommodate the names in a font that participants can read easily when spread out on a desk or table-at least 14 point
  • Participants are asked to group items in a way that makes sense to them.
    They may also be asked to name the resulting groups.
  • Once all participants have completed the exercise, enter the data in a spreadsheet, and examine the groupings.
    There will be general agreement about many items, and these groupings will be fairly apparent.
  • You can use cluster analysis to get a pictorial representation of the resultant groupings.
  • Pay special attention to items about which a consensus does not exist. Would re-naming the item improve the situation, or does it need to be included in more than one category?

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