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Each participant is likely to have sorted the cards into slightly different groups, and quote those groups slightly different things. Still, hopefully just from watching the card sort sessions you'll already have noticed some general agreement between participants. Or the emotions of maybe two separate ways of looking to a site's contents or tasks. Now, we want to get a bit more rigorous with our analysis. We already talked about capturing the raw data in an Excel file. And turning into a grid of participant card names for each task.
We could probably have recorded our sort data directly into this grid format after each card sort session. But it's really useful to have both views, with the data sorted by group name and also sorted by task name. Sorting by group name lets you quickly tell how many groups each participant created, and how large each group was. Sorting by task name let's you know how many groups or group names participants placed each task into. Now we have the data in a more compact format, it's time to rationalize those group names. It's likely that several participants used similar names for groups, like maybe about us, or company information, or even just the company name. If those groupings tend to contain similar cards, it's fair to give them all the same name.
This isn't necessarily the final name we'll give to this category. But it's a good way of reducing the range of different group names to a common set. Make a copy of your original data in a new sheet to the spreadsheet. Then replace all the original category names the participants used. With the smallest set of standardized names. Whenever you can, you want to draw out the underlined contents of the category and the name you give it. Look for synonyms like check out at the card, or basket, or common nouns and verbs that participants used.
In our data, participants 6, 7, and 8 have given the first task group labels of general store information, store FAQs, and information about the store. Those 3 can proabbly be combined into one standard label. Choose the most frequently used label, or the one that best fits in a family of labels with other that people have chosen. For instance if participants tended to give action-based labels choose one that is verb-based. If instead participants tended to be descriptive choose a label that is noun-based.
Most of the time you can use search and replace to find all instances of a word and replace it with your standardized category name, but before you do that, just make sure that all of your participants use the term that you're replacing to mean the same thing. For instance, it's possible that two people used the term support, but one applied it just to questions about shipping, whereas the other applied it to questions about products. In that instance, we would want to create two standardized labels that actually differentiate those groups. Like shipping support and frequently asked questions.
Here you can see the results of my standardized groupings. Once you have your reduced set of catgory names it's time to work out how many particpants used that category in their groupings. You can do this in a standardization grid. On another new Excel work sheet type each of your standardized category names across the top, and then list all your tasks down the side. For each participant check off the standardized catogory name they placed each task into. You'll end up with a tally of matches for each cell.
Now if you apply a conditional formatting rule to show more color with more matches, you can easily see which categories are chosen most frequently for each task. Here, for instance, you can see that tasks 12 and 13 get a corsage for a prom and get Valentine's day roses, were both very frequently grouped into the special events category. The process of working with your data and creating the standardized group names from participant's names will make you very familiar with the types of groupings that participants chose. There really is no short cut, no special algorithm to apply, in order to get this familiarity.
Eyeballing the data this way is the easiest but least precise technique for creating a information architecture. This will give you the general idea about the groupings that participants used and the type of contents they expect in each group. Sometimes this is sufficient as the basis for creating your information architecture. But most likely, you'll want a more robust understanding of which items were consistently placed together by different groups of participants and the hierarchical structure of those groups. Luckily, there's software that helps us do deep analysis of the clusters of results between participants the two techniques, eye balling the data and using cluster analysis, compliment each other.
And the next section will describe the visual methods you can use. We'll discuss the software-based cluster analysis tools when we talk about computer based sorts in the next chapter.
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