From the course: UX Foundations: Information Architecture

Understanding that card sorting isn't a precise technique

From the course: UX Foundations: Information Architecture

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Understanding that card sorting isn't a precise technique

As you've probably realized, card sorting is not a precise technique. Different participants will sort cards in different ways, and so the cluster analysis and dendrogram won't magically produce the ideal information architecture for you. However, once you start diving into your own data, you'll quickly start seeing the patterns and the strength of the relationships. You'll also be able to cross reference that, with what you heard individual participants say as they performed the sort. Working from the Cluster analysis, the Dendrogram, the raw sort results and the notes you took of participants' comments, you can pull together a suitable grouping of items. Of course, there might also be business rules or real world contraints that mean certain items have to go in certain places. Sometimes, unfortunately, politics gets in the way of a good information architecture. Luckily, you can also use the data from your sessions to help convince management that it's time for a change. I like to print all the information off, arrange it on my desk, and just absorb it for awhile. Then, I try creating groups that seem to best match the majority view based on the sort results. I'll check the groups I create against the individual raw data, in case there was some people who sorted an entirely different way. But my aim is to make a hierarchy that will be acceptable to everyone who participates in the sort. In the end, you need to apply a combination of knowledge from the cluster analysis, dendrogram, and what participants said during the sort, in order to create a good first pass at an information architecture. You should also use other data you have, such as usability studies, customer support data, search logs, and web logs, to inform your analysis. Don't blindly follow the statistical output. Think about what participants said, and about real world implications. Back up your decisions with this data. If you can't find data to back up your decision, it indicates you might not have it right. The next step, after we have our abstract hierarchy, is to refine it by testing it with a reverse sort. That's the next topic.

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