August 15, 2022
2 min

Card Sorting vs Tree Testing: what's the best?

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.

What is card sorting and tree testing? 🧐

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.

What are the differences? 🂱 👉🌴

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.

What method should you use? 🤷

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.

Ok, I'm using both, but which comes first? 🐓🥚

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.

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Ready for take-off: Best practices for creating and launching remote user research studies

"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!

Creating remote research studies

Use screener questions and post-study questions wisely

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.

Do a practice run

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!

Sending out remote research studies

Manage expectations about how long the study will be open for

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.

Provide contact details and be open to questions

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.

Don’t forget to include the link!

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:

  • Have I checked my spelling and grammar?
  • Have I replaced all the template placeholder content with the correct information?
  • Have I mentioned when the study will close?
  • Have I included contact details?
  • Have I launched my study and received confirmation that it is live?
  • Have I included the link to the study in my communications to participants?
  • Does the link work? (yep, I’ve broken it before)

General tips for both creating and sending out remote research studies

Know your audience

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.

Keep it brief

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.

Set clear instructions

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!

Create a template

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!

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Our latest feature session replay has landed 🥳

What is session replay?

Session replay allows you to record participants completing a card sort without the need for plug-ins or integrations. This great new feature captures the participant's interactions and creates a recording for each participant completing the card sort that you can view in your own time. It’s a great way to identify where users may have struggled to categorize information to correlate with the insights you find in your data.  

Watch the video 📹 👀

How does session replay work?

  • Session replay interacts with a study and nothing else. It does not include audio or face recording in the first release, but we’re working on it for the future.
  • There is no set-up or plug-in required; you control the use of screen replay in the card sort settings.  
  • For enterprise customers, the account admin will be required to turn this feature on for teams to access.
  • Session replay is currently only available on card sort, but it’s coming soon to other study types.

Help article 🩼


Guide to using session replay

How do you activate session replay?

To activate session replay, create a card sort or open an existing card sort that has not yet been launched. Click on ‘set up,’ then ‘settings’; here, you will see the option to turn on session replay for your card sort. This feature will be off by default, and you must turn it on for each card study.

How do I view a session replay?

To view a session replay of a card sort, go to Results > Participants > Select a participant > Session replay. 

I can't see session replay in the card sort settings 👀

If this is the case, you will need to reach out to your organization's account admin to ask for this to be activated at an organizational level. It’s really easy for session replay to be enabled or disabled by the organization admin just by navigating to Settings > Features > Session Replay, where it can be toggled on/off. 

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The powerful analysis features in our card sorting tool

You’ve just finished running your card sort. The study has closed and the data is waiting to be analyzed. It’s time to take a look at the analysis side of card sorting, specifically in our tool OptimalSort. Let’s get started.

A note on analysis 📌

When it comes to analysis, there are essentially two types. There’s exploratory analysis (when you look through data to get impressions, pull out useful ideas and be creative) and statistical analysis (which really just comes down to the numbers). These two types of analysis also go by qualitative and quantitative, respectively.

You’re able to get fantastic insights from both forms.

“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, Maadmob.

Getting started with analysis 🏁

Whenever you wrap up a study using our card sorting tool, you’ll want to kick off your analysis by heading to the Results Overview section. It’s here that you’ll be able to see how many people actually took part in the study, the average time taken and general statistics about the study itself.

This is useful data to include in presentations to interested stakeholders, just to give them a more holistic view of your research.

Digging into your participant data ⛏

With the Results Overview section out of the way, you can make your way over to the Participants Table. This is where you can find information about the individual people who took part in your card sort. You can also start to filter your data here.

Here are just a few of the different actions that you can take:

  • Review your participants, and include or exclude certain individuals based on their card sorts. This is a useful tool if you want to use your data in different ways.
  • Segment and reload your results. This function can allow you to view data from individuals or groups of your choosing.
  • Add additional card sorts. If you also decided to run manual (in-person) card sorts using printed cards, you can add this data here.

Analysing open and hybrid card sort data 🕵️♂

The Categories tab is the best place to go for open and hybrid card sort results. Take some time to scan the categories people came up with and you’ll be able to quickly build up a good understanding of their ‘mental models’, or how they perceived the theme of your cards.

Consider how different the categories might look for cards containing food items, for example. Some participants might create categories reflecting supermarket aisles, while others might create categories reflecting food groups.

A good place to get started here is by refining your data. Standardize any categories that have similar labels (whether that’s wording, spelling or capitalizations etc). Hybrid card sorts have some set categories, and these will already be standardized.

Note: Before you start throwing categories with similar labels together, take a closer look to see if people had the same conceptual approach. Here’s an example from our card sorting 101 guide:

Of the 15 groups with the word ‘Animal’ in the label, 13 had a similar set of cards, but two participants had labeled their categories slightly differently (Animals and Environment’ and ‘Animals and Nature’) and had thus included extra cards the others didn’t have (‘Glaciers melting faster than previously thought’, for example).

Reviewing the Similarity Matrix 🤔

One really useful tool for understanding how your participants think is the Similarity Matrix. This view shows you the percentage of people who grouped 2 cards together.

The most closely related pairings are clustered along the right edge. Higher agreement between participants on which cards go together equates to darker and larger clusters.

There are a few different ways to use the insights from the Similarity Matrix:

  • Put together a draft website structure based on the clusters you see on the right.
  • Identify which card pairings are most common (and as a result should probably go together on your website).
  • Identify which card pairings are least common so you don’t need to waste time considering how they might work on your website.

Spotting popular card groupings 🔍

Dendrograms are a tool to enable you to spot popular groups of cards, as well to get a general feel of how similar or different your participants’ card sorts were to each other.

There are two dendrograms to explore:

  • More than 30 card sort participants: The Actual Agreement Method (AAM) dendrogram gives you the data straight: “X% of participants agree with this exact grouping”.
  • Fewer than 30 card sort participants: The Best Merge Method (BMM) tells you “X% of participants agree with parts of this grouping”, and so enables you to extract as much as you can from the data.

Looking for alternative approaches 👀

The Participant-Centric Analysis (PCA) view can be useful when you have a lot of results. It’s quite simple. Basically, it aims to find the most popular grouping strategy, and then find two more popular alternatives among participants who agreed with the first strategy.

This approach is called Participant-Centric Analysis because every response (from every participant) is treated as a potential solution, and then ranked for similarity with other responses. What this is telling you is that if you see a card sort with a 11/43 agreement score, this means 10 other participants sorted their cards into groups similar to these ones. 

Taking the next step: Run a card sort and try analysis for yourself 🃏

Now that we’ve taken a bit of a deep dive into the analysis side of card sorting in OptimalSort, it’s time to take the tool for a spin and start generating your own data.

Getting started is easy. If you haven’t already, simply sign up for a free account (you don’t need a credit card) and start a card sort. You can also practice by creating a card sort and sending it out to your coworkers, friends or family. Once you start to see results trickling in, you can start to make sense of the data.

For more information, check out the card sorting 101 guide that we’ve put together, or our introduction to card sorting on the Optimal Workshop Blog.

Happy testing! 

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