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.
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.
The key purpose of running a card sort is to learn something new about how people conceptualize and organize the information that’s found on your website. The insights you gain from running a card sort can then help you develop a site structure with content labels or headings that best represent the way your users think about this information. Card sorts are in essence a simple technique, however it’s the details of the sort that can determine the quality of your results.
In most cases, each item in a card sort has only a short label, but there are instances where you may wish to add additional context to the items in your sort. Currently, the cards tab in OptimalSort allows you to include a tooltip description, a link within the tooltip description or to format the card as an image (with or without a label).
We generally don’t recommend using tooltip descriptions and links, unless you have a specific reason to do so. It’s likely that they’ll provide your participants with more information than they would normally have when navigating your website, which may in turn influence your results by leading participants to a particular solution.
Legitimate reasons that you may want to use descriptions and links include situations where it’s not possible or practical to translate complex or technical labels (for example, medical, financial, legal or scientific terms) into plain language, or if you’re using a card sort to understand your participants’ preferences or priorities.
If you do decide to include descriptions in your sort, it’s important that you follow the same guidelines that you would otherwise follow for writing card labels. They should be easy for your participants to understand and you should avoid obvious patterns, for example repeating words and phrases, or including details that refer to the current structure of the website.
I was curious to find out how often people were including descriptions in their card sorts, so I asked our development team to look into this data. It turns out that around 15% of cards created in OptimalSort have at least some text entered in the description field. In order to dig into the data a bit further, both Ania and I reviewed a random sample of recent sorts and noted how descriptions were being used in each case.
We found that out of the descriptions that we reviewed, 40% (6% of the total cards) had text that should not have impacted the sort results. Most often, these cards simply had the card label repeated in the description (to be honest, we’re not entirely sure why so many descriptions are being used this way! But it’s now in our roadmap to stop this from happening — stay tuned!). Approximately 20% (3% of the total cards) used descriptions to add context without obviously leading participants, however another 40% of cards have descriptions that may well lead to biased results. On occasion, this included linking to the current content or using what we assumed to be the current top level heading within the description.
So, how much influence could potentially leading card descriptions have on the results of a card sort? I decided to put it to the test by running a series of card sorts to compare the effect of different descriptions. As I also wanted to test the effect of linking card descriptions to existing content, I had to base the sort on a live website. In addition, I wanted to make sure that the card labels and descriptions were easily comprehensible by a general audience, but not so familiar that participants were highly likely to sort the cards in a similar manner.
I selected the government immigration website New Zealand Now as my test case. This site, which provides information for prospective and new immigrants to New Zealand, fit the above criteria and was likely unfamiliar to potential participants.
When I reviewed the New Zealand Now site, I found that the top level navigation labels were clear and easy to understand for me personally. Of course, this is especially important when much of your target audience is likely to be non-native English speaking! On the whole, the second level headings were also well-labeled, which meant that they should translate to cards that participants were able to group relatively easily.
There were, however, a few headings such as “High quality” and “Life experiences”, both found under “Study in New Zealand”, which become less clear when removed from the context of their current location in the site structure. These headings would be particularly useful to include in the test sorts, as I predicted that participants would be more likely to rely on card descriptions in the cases where the card label was ambiguous.
I selected 30 headings to use as card labels from under the sections “Choose New Zealand”, “Move to New Zealand”, “Live in New Zealand”, “Work in New Zealand” and “Study in New Zealand” and tweaked the language slightly, so that the labels were more generic.
I then created four separate sorts in OptimalSort:Round 1: No description: Each card showed a heading only — this functioned as the control sort
Round 2: Site section in description: Each card showed a heading with the site section in the description
Round 3: Short description: Each card showed a heading with a short description — these were taken from the New Zealand Now topic landing pages
Round 4:Link in description: Each card showed a heading with a link to the current content page on the New Zealand Now website
For each sort, I recruited 30 participants. Each participant could only take part in one of the sorts.
An interesting initial finding was that when we queried the participants following the sort, only around 40% said they noticed the tooltip descriptions and even fewer participants stated that they had used them as an aid to help complete the sort.
Participant recognition of descriptions
Of course, what people say they do does not always reflect what they do in practice! To measure the effect that different descriptions had on the results of this sort, I compared how frequently cards were sorted with other cards from their respective site sections across the different rounds.Let’s take a look at the “Study in New Zealand” section that was mentioned above. Out of the five cards in this section,”Where & what to study”, “Everyday student life” and “After you graduate” were sorted pretty consistently, regardless of whether a description was provided or not. The following charts show the average frequency with which each card was sorted with other cards from this section. For example in the control round, “Where & what to study” was sorted with “After you graduate” 76% of the time and with “Everyday day student life” 70% of the time, but was sorted with “Life experiences” or “High quality” each only 10% of the time. This meant that the average sort frequency for this card was 42%.
Untitled chartCreate bar charts
On the other hand, the cards “High quality” and “Life experiences” were sorted much less frequently with other cards in this section, with the exception of the second sort, which included the site section in the description.These results suggest that including the existing site section in the card description did influence how participants sorted these cards — confirming our prediction! Interestingly, this round had the fewest number of participants who stated that they used the descriptions to help them complete the sort (only 10%, compared to 40% in round 3 and 20% in round 4).Also of note is that adding a link to the existing content did not seem to increase the likelihood that cards were sorted more frequently with other cards from the same section. Reasons for this could include that participants did not want to navigate to another website (due to time-consciousness in completing the task, or concern that they’d lose their place in the sort) or simply that it can be difficult to open a link from the tooltip pop-up.
This quick investigation into the impact of descriptions illustrates some of the intricacies around using additional context in your card sorts, and why this should always be done with careful consideration. It’s interesting that we correctly predicted some of these results, but that in this case, other uses of the description had little effect at all. And the results serve as a good reminder that participants can often be influenced by factors that they don’t even recognise themselves!If you do decide to use card descriptions in your cards sorts, here are some guidelines that we recommend you follow:
We’d love to hear your thoughts on how we tested the effects of card descriptions and the results that we got. Would you have done anything differently?Have you ever completed a card sort only to realize later that you’d inadvertently biased your results? Or have you used descriptions in your card sorts to meet a genuine need? Do you think there’s a case to make descriptions more obvious than just a tooltip, so that when they are used legitimately, most participants don’t miss this information?
Let us know by leaving a comment!
"Hi Optimal Work,I was wondering if there are some best practices you stick to when creating or sending out different UX research studies (i.e. Card sorts, Prototyye Test studies, etc)? Thank you! Mary"
Indeed I do! Over the years I’ve learned a lot about creating remote research studies and engaging participants. That experience has taught me a lot about what works, what doesn’t and what leaves me refreshing my results screen eagerly anticipating participant responses and getting absolute zip. Here are my top tips for remote research study creation and launch success!
Screener questions are really useful for eliminating participants who may not fit the criteria you’re looking for but you can’t exactly stop them from being less than truthful in their responses. Now, I’m not saying all participants lie on the screener so they can get to the activity (and potentially claim an incentive) but I am saying it’s something you can’t control. To help manage this, I like to use the post-study questions to provide additional context and structure to the research.
Depending on the study, I might ask questions to which the answers might confirm or exclude specific participants from a specific group. For example, if I’m doing research on people who live in a specific town or area, I’ll include a location based question after the study. Any participant who says they live somewhere else is getting excluded via that handy toggle option in the results section. Post-study questions are also great for capturing additional ideas and feedback after participants complete the activity as remote research limits your capacity to get those — you’re not there with them so you can’t just ask. Post-study questions can really help bridge this gap. Use no more than five post-study questions at a time and consider not making them compulsory.
No matter how careful I am, I always miss something! A typo, a card with a label in the wrong case, forgetting to update a new version of an information architecture after a change was made — stupid mistakes that we all make. By launching a practice version of your study and sharing it with your team or client, you can stop those errors dead in their tracks. It’s also a great way to get feedback from the team on your work before the real deal goes live. If you find an error, all you have to do is duplicate the study, fix the error and then launch. Just keep an eye on the naming conventions used for your studies to prevent the practice version and the final version from getting mixed up!
Something that has come back to bite me more than once is failing to clearly explain when the study will close. Understandably, participants can be left feeling pretty annoyed when they mentally commit to complete a study only to find it’s no longer available. There does come a point when you need to shut the study down to accurately report on quantitative data and you’re not going to be able to prevent every instance of this, but providing that information upfront will go a long way.
You may think you’re setting yourself up to be bombarded with emails, but I’ve found that isn’t necessarily the case. I’ve noticed I get around 1-3 participants contacting me per study. Sometimes they just want to tell me they completed it and potentially provide additional information and sometimes they have a question about the project itself. I’ve also found that sometimes they have something even more interesting to share such as the contact details of someone I may benefit from connecting with — or something else entirely! You never know what surprises they have up their sleeves and it’s important to be open to it. Providing an email address or social media contact details could open up a world of possibilities.
It might seem really obvious, but I can’t tell you how many emails I received (and have been guilty of sending out) that are missing the damn link to the study. It happens! You’re so focused on getting that delivery right and it becomes really easy to miss that final yet crucial piece of information.
To avoid this irritating mishap, I always complete a checklist before hitting send:
First and foremost, before you create or disseminate a remote research study, you need to understand who it’s going to and how they best receive this type of content. Posting it out when none of your followers are in your user group may not be the best approach. Do a quick brainstorm about the best way to reach them. For example if your users are internal staff, there might be an internal communications channel such as an all-staff newsletter, intranet or social media site that you can share the link and approach content to.
And by that I’m talking about both the engagement mechanism and the study itself. I learned this one the hard way. Time is everything and no matter your intentions, no one wants to spend more time than they have to. Even more so in situations where you’re unable to provide incentives (yep, I’ve been there). As a rule, I always stick to no more than 10 questions in a remote research study and for card sorts, I’ll never include more than 60 cards. Anything more than that will see a spike in abandonment rates and of course only serve to annoy and frustrate your participants. You need to ensure that you’re balancing your need to gain insights with their time constraints.
As for the accompanying approach content, short and snappy equals happy! In the case of an email, website, other social media post, newsletter, carrier pigeon etc, keep your approach spiel to no more than a paragraph. Use an audience appropriate tone and stick to the basics such as: a high level sentence on what you’re doing, roughly how long the study will take participants to complete, details of any incentives on offer and of course don’t forget to thank them.
The default instructions in Optimal Workshop’s suite of tools are really well designed and I’ve learned to borrow from them for my approach content when sending the link out. There’s no need for wheel reinvention and it usually just needs a slight tweak to suit the specific study. This also helps provide participants with a consistent experience and minimizes confusion allowing them to focus on sharing those valuable insights!
When you’re on to something that works — turn it into a template! Every time I create a study or send one out, I save it for future use. It still needs minor tweaks each time, but I use them to iterate my template.What are your top tips for creating and sending out remote user research studies? Comment below!