“Dear Optimal, I want to test the structure of a university website (well certain sections anyway). My gut instinct is that it's pretty 'broken'. Lots of sections feel like they're in the wrong place. I want to test my hypotheses before proposing a new structure. I'm definitely going to do some card sorting, and was planning a mixture of online and offline. My question is about when to bring in tree testing. Should I do this first to test the existing IA? Or is card sorting sufficient? I do intend to tree test my new proposed IA in order to validate it, but is it worth doing it upfront too?" — Matt
Dear Matt,
Ah, the classic chicken or the egg scenario: Which should come first, tree testing or card sorting?
It’s a question that many researchers often ask themselves, but I’m here to help clear the air! You should always use both methods when changing up your information architecture (IA) in order to capture the most information.
Tree testing and card sorting, when used together, can give you fantastic insight into the way your users interact with your site. First of all, I’ll run through some of the benefits of each testing method.
Card sorting is a great method to gauge the way in which your users organize the content on your site. It helps you figure out which things go together and which things don’t. There are two main types of card sorting: open and closed.
Closed card sorting involves providing participants with pre-defined categories into which they sort their cards. For example, you might be reorganizing the categories for your online clothing store for women. Your cards would have all the names of your products (e.g., “socks”, “skirts” and “singlets”) and you also provide the categories (e.g.,“outerwear”, “tops” and “bottoms”).
Open card sorting involves providing participants with cards and leaving them to organize the content in a way that makes sense to them. It’s the opposite to closed card sorting, in that participants dictate the categories themselves and also label them. This means you’d provide them with the cards only, and no categories.
Card sorting, whether open or closed, is very user focused. It involves a lot of thought, input, and evaluation from each participant, helping you to form the structure of your new IA.
Tree testing is a fantastic way to determine how your users are navigating your site and how they’re finding information. Your site is organized into a tree structure, sorted into topics and subtopics, and participants are provided with some tasks that they need to perform. The results will show you how your participants performed those tasks, if they were successful or unsuccessful, and which route they took to complete the tasks. This data is extremely useful for creating a new and improved IA.
Tree testing is an activity that requires participants to seek information, which is quite the contrast to card sorting. Card sorting is an activity that requires participants to sort and organize information. Each activity requires users to behave in different ways, so each method will give its own valuable results.
Tree testing and card sorting are complementary methods within your UX toolkit, each unlocking unique insights about how users interact with your site structure. The difference is all about direction.
Card sorting is generative. It helps you understand how users naturally group and label your content; revealing mental models, surfacing intuitive categories, and informing your site’s information architecture (IA) from the ground up. Whether using open or closed methods, card sorting gives users the power to organize content in ways that make sense to them.
Tree testing is evaluative. Once you’ve designed or restructured your IA, tree testing puts it to the test. Participants are asked to complete find-it tasks using only your site structure – no visuals, no design – just your content hierarchy. This highlights whether users can successfully locate information and how efficiently they navigate your content tree.
In short:
Using both methods together gives you clarity and confidence. One builds the structure. The other proves it works.
The right method depends on where you are in your IA journey. If you're beginning from scratch or rethinking your structure, starting with card sorting is ideal. It will give you deep insight into how users group and label content.
If you already have an existing IA and want to validate its effectiveness, tree testing is typically the better fit. Tree testing shows you where users get lost and what’s working well. Think of card sorting as how users think your site should work, and tree testing as how they experience it in action.
In this scenario, I’d recommend running a tree test first in order to find out how your existing IA currently performs. You said your gut instinct is telling you that your existing IA is pretty “broken”, but it’s good to have the data that proves this and shows you where your users get lost.
An initial tree test will give you a benchmark to work with – after all, how will you know your shiny, new IA is performing better if you don’t have any stats to compare it with? Your results from your first tree test will also show you which parts of your current IA are the biggest pain points and from there you can work on fixing them. Make sure you keep these tasks on hand – you’ll need them later!
Once your initial tree test is done, you can start your card sort, based on the results from your tree test. Here, I recommend conducting an open card sort so you can understand how your users organize the content in a way that makes sense to them. This will also show you the language your participants use to name categories, which will help you when you’re creating your new IA.
Finally, once your card sort is done you can conduct another tree test on your new, proposed IA. By using the same (or very similar) tasks from your initial tree test, you will be able to see that any changes in the results can be directly attributed to your new and improved IA.
Once your test has concluded, you can use this data to compare the performance from the tree test for your original information architecture.
Card sorting and tree testing aren’t rivals, view them as allies. Used together, they give you end-to-end clarity. Card sorting informs your IA design based on user mental models. Tree testing evaluates that structure, confirming whether users can find what they need. This combination creates a feedback loop that removes guesswork and builds confidence. You'll move from assumptions to validation, and from confusion to clarity – all backed by real user behavior.
If you missed our live training, don’t worry, we’ve got you covered! In this session, our product experts Katie and Aidan discuss why, how and when to benchmark an existing structure using Treejack.
They also talk through some benchmarking use cases, demo how to compare tasks between different studies, and which results are most helpful.
A great information architecture (IA) is essential for a great user experience (UX). And testing your website or app’s information architecture is necessary to get it right.
Card sorting and tree testing are the very best UX research methods for exactly this. But the big question is always: which one should you use, and when? Very possibly you need both. Let’s find out with this quick summary.
Card sorting is used to test the information architecture of a website or app. Participants group individual labels (cards) into different categories according to criteria that makes best sense to them. Each label represents an item that needs to be categorized. The results provide deep insights to guide decisions needed to create an intuitive navigation, comprehensive labeling and content that is organized in a user-friendly way.
Tree testing is also used to test the information architecture of a website or app. When using tree testing participants are presented with a site structure and a set of tasks they need to complete. The goal for participants is to find their way through the site and complete their task. The test shows whether the structure of your website corresponds to what users expect and how easily (or not) they can navigate and complete their tasks.
Card sorting is a UX research method which helps to gather insights about your content categorization. It focuses on creating an information architecture that responds intuitively to the users’ expectations. Things like which items go best together, the best options for labeling, what categories users expect to find on each menu.
Doing a simple card sort can give you all those pieces of information and so much more. You start understanding your user’s thoughts and expectations. Gathering enough insights and information to enable you to develop several information architecture options.
Tree testing is a UX research method that is almost a card sort in reverse. Tree testing is used to evaluate an information architecture structure and simply allows you to see what works and what doesn’t.
Using tree testing will provide insights around whether your information architecture is intuitive to navigate, the labels easy to follow and ultimately if your items are categorized in a place that makes sense. Conversely it will also show where your users get lost and how.
You’ve got this far and fine-tuning your information architecture should be a priority. An intuitive IA is an integral component of a user-friendly product. Creating a product that is usable and an experience users will come back for.
If you are still wondering which method you should use - tree testing or card sorting. The answer is pretty simple - use both.
Just like many great things, these methods work best together. They complement each other, allowing you to get much deeper insights and a rounded view of how your IA performs and where to make improvements than when used separately. We cover more reasons why card sorting loves tree testing in our article which dives deeper into why to use both.
Wanting full, rounded insights into your information architecture is great. And we know that tree testing and card sorting work well together. But is there an order you should do the testing in? It really depends on the particular context of your research - what you’re trying to achieve and your situation.
Tree testing is a great tool to use when you have a product that is already up and running. By running a tree test first you can quickly establish where there may be issues, or snags. Places where users get caught and need help. From there you can try and solve potential issues by moving on to a card sort.
Card sorting is a super useful method that can be instigated at any stage of the design process, from planning to development and beyond. As long as there is an IA structure that can be tested again. Testing against an already existing website navigation can be informative. Or testing a reorganization of items (new or existing) can ensure the organization can align with what users expect.
However, when you decide to implement both of the methods in your research, where possible, tree testing should come before card sorting. If you want a little more on the issue have a read of our article here.
Check out our OptimalSort and Treejack tools - we can help you with your research and the best way forward. Wherever you might be in the process.
In Part 1 of this series we looked at how to interpret results from open and hybrid card sorts and now in Part 2, we’re going to talk about closed card sorts. In closed card sorts, participants are asked to sort the cards into predetermined categories and are not allowed to create any of their own. You might use this approach when you are constrained by specific category names or as a quick checkup before launching a new or newly redesigned website.In Part 1, we also discussed the two different - but complementary - types of analysis that are generally used together for interpreting card sort results: exploratory and statistical. Exploratory analysis is intuitive and creative while statistical analysis is all about the numbers. Check out Part 1 for a refresher or learn more about exploratory and statistical analysis in Donna Spencer’s book.
Closed card sort analysis is generally much quicker and easier than open and hybrid card sorts because there are no participant created category names to analyze - it’s really just about where the cards were placed. There are some similarities about how you might start to approach your analysis process but overall there’s a lot less information to take in and there isn’t much in the way of drilling down into the details like we did in Part 1.Just like with an open card sort, kick off your analysis process by taking an overall look at the results as a whole. Quickly cast your eye over each individual card sort and just take it all in. Look for common patterns in how the cards have been sorted. Does anything jump out as surprising? Are there similarities or differences between participant sorts?
If you’re redesigning an existing information architecture (IA), how do your results compare to the current state? If this is a final check up before launching a live website, how do these results compare to what you learned during your previous research studies?If you ran your card sort using information architecture tool OptimalSort, head straight to the Overview and Participants Table presented in the results section of the tool. If you ran a moderated card sort using OptimalSort’s printed cards, you’ve probably been scanning them in after each completed session, but now is a good time to double check you got them all. And if you didn’t know about this handy feature of OptimalSort, it’s something to keep in mind for next time!
The Participants Table shows a breakdown of your card sorting data by individual participant. Start by reviewing each individual card sort one by one by clicking on the arrow in the far left column next to the Participants numbers. From here you can easily flick back and forth between participants without needing to close that modal window. Don’t spend too much time on this — you’re just trying to get a general impression of how the cards were sorted into your predetermined categories. Keep an eye out for any card sorts that you might like to exclude from the results. For example participants who have lumped everything into one group and haven’t actually sorted the cards.
Don’t worry- excluding or including participants isn’t permanent and can be toggled on or off at anytime.Once you’re happy with the individual card sorts that will and won’t be included in your results visualizations, it’s time to take a look at the Results Matrix in OptimalSort. The Results Matrix shows the number of times each card was sorted into each of your predetermined categories- the higher the number, the darker the shade of blue (see below).
This table enables you to quickly and easily get across how the cards were sorted and gauge the highest and lowest levels of agreement among your participants. This will tell you if you’re on the right track or highlight opportunities for further refinement of your categories.If we take a closer look (see below) we can see that in this example closed card sort conducted on the Dewey Decimal Classification system commonly used in libraries, The Interpretation of Dreams by Sigmund Freud was sorted into ‘Philosophy and psychology’ 38 times in study a completed by 51 participants.
In the real world, that is exactly where that content lives and this is useful to know because it shows that the current state is supporting user expectations around findability reasonably well. Note: this particular example study used image based cards instead of word label based cards so the description that appears in both the grey box and down the left hand side of the matrix is for reference purposes only and was hidden from the participants.Sometimes you may come across cards that are popular in multiple categories. In our example study, How to win friends and influence people by Dale Carnegie, is popular in two categories: ‘Philosophy & psychology’ and ‘Social sciences’ with 22 and 21 placements respectively. The remaining card placements are scattered across a further 5 categories although in much smaller numbers.
When this happens, it’s up to you to determine what your number thresholds are. If it’s a tie or really close like it is in this case, you might review the results against any previous research studies to see if anything has changed or if this is something that comes up often. It might be a new category that you’ve just introduced, it might be an issue that hasn’t been resolved yet or it might just be limited to this one study. If you’re really not sure, it’s a good idea to run some in-person card sorts as well so you can ask questions and gain clarification around why your participants felt a card belonged in a particular category. If you’ve already done that great! Time to review those notes and recordings!You may also find yourself in a situation where no category is any more popular than the others for a particular card. This means there’s not much agreement among your participants about where that card actually belongs. In our example closed card sort study, the World Book Encyclopedia was placed into 9 of 10 categories. While it was placed in ‘History & geography’ 18 times, that’s still only 35% of the total placements for that card- it’s hardly conclusive.
Sometimes this happens when the card label or image is quite general and could logically belong in many of the categories. In this case, an encyclopedia could easily fit into any of those categories and I suspect this happened because people may not be aware that encyclopedias make up a very large part of the category on the far left of the above matrix: ‘Computer science, information & general works’. You may also see this happening when a card is ambiguous and people have to guess where it might belong. Again - if you haven’t already - if in doubt, run some in-person card sorts so you can ask questions and get to the bottom of it!After reviewing the Results Matrix in OptimalSort, visit the Popular Placements Matrix to see which cards were most popular for each of your categories based on how your participants sorted them (see below 2 images).
The diagram shades the most popular placements for each category in blue making it very easy to spot what belongs where in the eyes of your participants. It’s useful for quickly identifying clusters and also highlights the categories that didn’t get a lot of card sorting love. In our example study (2 images above) we can see that ‘Technology’ wasn’t a popular card category choice potentially indicating ambiguity around that particular category name. As someone familiar with the Dewey Decimal Classification system I know that ‘Technology’ is a bit of a tricky one because it contains a wide variety of content that includes topics on medicine and food science - sometimes it will appear as ‘Technology & applied sciences’. These results appear to support the case for exploring that alternative further!
Now that we’ve looked at how to interpret your open, hybrid and closed card sorts, here are some next steps to help you turn those insights into action!Once you’ve analyzed your card sort results, it’s time to feed those insights into your design process and create your taxonomy which goes hand in hand with your information architecture. You can build your taxonomy out in Post-it notes before popping it into a spreadsheet for review. This is also a great time to identify any alternate labelling and placement options that came out of your card sorting process for further testing.From here, you might move into tree testing your new IA or you might run another card sort focussing on a specific area of your website. You can learn more about card sorting in general via our 101 guide.
When interpreting card sort results, don’t forget to have fun! It’s easy to get overwhelmed and bogged down in the results but don’t lose sight of the magic that is uncovering user insights.I’m going to leave you with this quote from Donna Spencer that summarizes the essence of card sort analysis quite nicely:Remember that you are the one who is doing the thinking, not the technique... you are the one who puts it all together into a great solution. Follow your instincts, take some risks, and try new approaches. - Donna Spencer