In an open card sort block, testers group cards into categories they create and name. This more flexible approach works well in the early stages of a project, when you're looking to get a more accurate grasp of your users' mental models for grouping and labeling content. Learn more about creating open card sort tests here: Have testers categorize information using Card Sorting
As testers complete your live maze, you will start seeing insights on the Results dashboard.
This article shows you how to interact with your open card sort results. To learn more about card sort results, check out this article: Understanding your card sorting results
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When viewing your maze results, categories with the exact same name are auto-combined by default. For instance, if multiple people created a "Lunch" category, those results are all grouped together.
The dashboard shows you the average agreement for all cards in each of these aggregated categories.
If you expand the category, you'll see which cards have been sorted into that category, the single card agreement rate for each of those cards, and how many people sorted each card into that category (frequency).
This can be a powerful insight into how your testers think. In the example screenshot below, we see that half of the testers agree that "Buffalo Wings" falls under "Snacks".
We can also identify a few outliers: the cards "Chicken Salad", "Oven Roasted Vegetables", "Turkey Meatballs", and "Chicken Breasts with Cream Sauce" also appear grouped under this category, though only 1 tester made that choice.
These results can also be helpful to identify labeling patterns. In the example screenshot below, we see that users created both "Desserts" and "Sweets" to categorize the same cards. However, 58.33% of the testers (7 testers) opted for "Desserts". This may be an indicator that your users will prefer this label, though you might also want to ensure that the content is discoverable using both terms.
Manually merge categories
Created categories are only automatically combined if they have the exact same name. Typos or spelling/format variations in the category name will appear as separate categories (e.g. "Dessert" and "Desserts").
To clean up your data and analyze those categories together, click each checkbox next to the category names to reveal the Category grouping editor. If needed, change the name for the new grouped category, and review the new agreement rate. When you're ready, click Create category to save the new grouped category.
This grouping will also be reflected in the agreement matrix and similarity matrix.
To remove that manual grouping, click the More menu (•••) next to the category name and click Undo last grouping.
You can rename any of the categories by clicking directly on the category name and typing the new category name.
To revert the name change, click the More menu (•••) next to the category name and click Reset to original name.
Going through the results, you might see some categories which are not particularly relevant.
To exclude a category from the results, click the More menu (•••) next to the category name and click Hide category or Hide grouping. You can easily reverse this action later.
Hidden categories will also be hidden in the agreement matrix.
Category display options
Show hidden: Displays any hidden categories. If there are no hidden categories in your results, this toggle appears as disabled.
Show raw results: Displays the original results collected from the testers, without automatic grouping. For instance, if 3 testers have created a "Snacks" category, this category will appear 3 times, each displaying the cards for each tester.
More menu (•••):
- Undo all groupings: Removes all manually created category groups and displays the default categories again. Please note that this permanently deletes all manual category groups you've created.
The Your cards tab lists all cards used in the card sort block and their respective average agreement. It allows you to dig further into the trends you were able to identify in the Categories tab.
If you expand each card, you'll see the categories it has been sorted into, the agreement rate for each of those categories, and how many people sorted the card into that category.
This can help you make informed decisions. In the example screenshot below, we see that "Buffalo Wings" has, on the surface, a low agreement rate. However, half of the testers identified that card as "Snacks".
The agreement matrix groups cards under each category based on their highest agreement rates. It gives you a visual representation of how often cards appeared in each category. Learn more
The similarity matrix evaluates the relationship between pairs of cards. It gives you insight into which cards were most frequently grouped together. Learn more