August 15, 2022
1 min read

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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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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Card Sorting outside UX: How I use online card sorting for in-person sociological research

Hello, my name is Rick and I’m a sociologist. All together, “Hi, Rick!” Now that we’ve got that out of the way, let me tell you about how I use card sorting in my research. I'll soon be running a series of in-person, moderated card sorting sessions. This article covers why card sorting is an integral part of my research, and how I've designed the study toanswer specific questions about two distinct parts of society.

Card sorting to establish how different people comprehend their worlds

Card sorting,or pile sorting as it’s sometimes called, has a long history in anthropology, psychology and sociology. Anthropologists, in particular, have used it to study how different cultures think about various categories. Researchers in the 1970s conducted card sorts to understand how different cultures categorize things like plants and animals. Sociologists of that era also used card sorts to examine how people think about different professions and careers. And since then, scholars have continued to use card sorts to learn about similar categorization questions.

In my own research, I study how different groups of people in the United States imagine the category of 'religion'. Asthose crazy 1970s anthropologists showed, card sorting is a great way to understand how people cognitively understand particular social categories. So, in particular,I’m using card sorting in my research to better understand how groups of people with dramatically different views understand 'religion' — namely, evangelical Christians and self-identified atheists. Thinkof it like this. Some people say that religion is the bedrock of American society.

Others say that too much religion in public life is exactly what’s wrong with this country. What's not often considered is these two groups oftenunderstand the concept of 'religion' in very different ways. It’s like the group of blind men and the elephant: one touches the trunk, one touches the ears, and one touches the tail. All three come away with very different ideas of what an elephant is. So you could say that I study how different people experience the 'elephant' of religion in their daily lives. I’m doing so using primarily in-person moderated sorts on an iPad, which I’ll describe below.

How I generated the words on the cards

The first step in the process was to generate lists of relevant terms for my subjects to sort. Unlike in UX testing, where cards for sorting might come from an existing website, in my world these concepts first have to be mined from the group of people being studied. So the first thing I did was have members of both atheist and evangelical groups complete a free listing task. In a free listing task, participants simply list as many words as they can that meet the criteria given. Sets of both atheist and evangelical respondents were given the instructions: "What words best describe 'religion?' Please list as many as you can.” They were then also asked to list words that describe 'atheism', 'spirituality', and 'Christianity'.

I took the lists generated and standardizedthem by combining synonyms. For example, some of my atheists used words like 'ancient', 'antiquated', and 'archaic' to describe religion. SoI combined all of these words into the one that was mentioned most: 'antiquated'. By doing this, I created a list of the most common words each group used to describe each category. Doing this also gave my research another useful dimension, ideal for exploring alongside my card sorting results. Free lists can beanalyzed themselves using statistical techniques likemulti-dimensional scaling, so I used this technique for apreliminary analysis of the words evangelicals used to describe 'atheism':

Optimalsort and sociological research

Now that I’m armed with these lists of words that atheist and evangelicals used to describe religion, atheism etc., I’m about to embark on phase two of the project: the card sort.

Why using card sorting software is a no-brainer for my research

I’ll be conducting my card sorts in person, for various reasons. I have relatively easy access to the specific population that I’m interested in, and for the kind of academic research I’m conducting, in-person activities are preferred. In theory, I could just print the words on some index cards and conduct a manual card sort, but I quickly realized that a software solution would be far preferable, for a bunch of reasons.

First of all, it's important for me to conductinterviews in coffee shops and restaurants, and an iPad on the table is, to put it mildly, more practical than a table covered in cards — no space for the teapot after all.

Second, usingsoftwareeliminates the need for manual data entry on my part. Not only is manual data entry a time consuming process, but it also introduces the possibly of data entry errors which may compromise my research results.

Third, while the bulk of the card sorts are going to be done in person, having an online version will enable meto scale the project up after the initial in-person sorts are complete. The atheist community, in particular, has a significant online presence, making a web solution ideal for additional data collection.

Fourth, OptimalSort gives the option to re-direct respondents after they complete a sort to any webpage, which allows multiple card sorts to be daisy-chained together. It also enables card sorts to be easily combined with complex survey instruments from other providers (e.g. Qualtrics or Survey Monkey), so card sorting data can be gathered in conjunction with other methodologies.

Finally, and just as important, doing card sorts on a tablet is more fun for participants. After all, who doesn’t like to play with an iPad? If respondents enjoy the unique process of the experiment, this is likely to actually improve the quality of the data, andrespondents are more likely to reflect positively on the experience, making recruitment easier. And a fun experience also makes it more likely that respondents will complete the exercise.

What my in-person, on-tablet card sorting research will look like

Respondents will be handed an iPad Air with 4G data capability. While the venues where the card sorts will take place usually have public Wi-Fi networks available, these networks are not always reliable, so the cellular data capabilities are needed as a back-up (and my pre-testing has shown that OptimalSort works on cellular networks too).

The iPad’s screen orientation will be locked to landscape and multi-touch functions will be disabled to prevent respondents from accidentally leaving the testing environment. In addition, respondents will have the option of using a rubber tipped stylus for ease of sorting the cards. While I personally prefer to use a microfiber tipped stylus in other applications, pre-testing revealed that an old fashioned rubber tipped stylus was easier for sorting activities.

using a tablet to conduct a card sort

When the respondent receives the iPad, the card sort first page with general instructions will already be open on the tablet in the third party browser Perfect Web. A third party browser is necessary because it is best to run OptimalSort locked in a full screen mode, both for aesthetic reasons and to keep the screen simple and uncluttered for respondents. Perfect Web is currently the best choice in the ever shifting app landscape.

participants see the cards like this

I'll give respondents their instructions and then go to another table to give them privacy (because who wants the creepy feeling of some guy hanging over you as you do stuff?). Altogether, respondents will complete two open card sorts and a fewsurvey-style questions, all chained together by redirect URLs. First, they'll sort 30 cards into groups based on how they perceive 'religion', and name the categories they create. Then, they'll complete a similar card sort, this time based on how they perceive 'atheism'.

Both atheist and evangelicals will receive a mixture of some of the top words that both groups generated in the earlier free listing tasks. To finish, they'll answer a few questions that will provide further data on how they think about 'religion'. After I’ve conducted these card sorts with both of my target populations, I’ll analyze the resulting data on its own and also in conjunction with qualitative data I’ve already collected via ethnographic research and in-depth interviews. I can't wait, actually. In a few months I’ll report back and let you know what I’ve found.

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How to interpret your card sort results Part 1: open and hybrid card sorts

Cards have been created, sorted and sorted again. The participants are all finished and you’re left with a big pile of awesome data that will help you improve the user experience of your information architecture. Now what?Whether you’ve run an open, hybrid or closed card sort online using an information architecture tool or you’ve run an in person (moderated) card sort, it can be a bit daunting trying to figure out where to start the card sort analysis process.

About this guide

This two-part guide will help you on your way! For Part 1, we’re going to look at how to interpret and analyze the results from open and hybrid card sorts.

  • In open card sorts, participants sort cards into categories that make sense to them and they give each category a name of their own making.
  • In hybrid card sorts, some of the categories have already been defined for participants to sort the cards into but they also have the ability to create their own.

Open and hybrid card sorts are great for generating ideas for category names and labels and understanding not only how your users expect your content to be grouped but also what they expect those groups to be called.In both parts of this series, I’m going to be talking a lot about interpreting your results using Optimal Workshop’s online card sorting tool, OptimalSort, but most of what I’m going to share is also applicable if you’re analyzing your data using a spreadsheet or using another tool.

Understanding the two types of analysis: exploratory and statistical

Similar to qualitative and quantitative methods, exploratory and statistical analysis in card sorting are two complementary approaches that work together to provide a detailed picture of your results.

  • Exploratory analysis is intuitive and creative. It’s all about going through the data and shaking it to see what ideas, patterns and insights fall out. This approach works best when you don’t have the numbers (smaller sample sizes) and when you need to dig into the details and understand the ‘why’ behind the statistics.

  • Statistical analysis is all about the numbers. Hard data that tells you exactly how many people expected X to be grouped with Y and more and is very useful when you’re dealing with large sample sizes and when identifying similarities and differences across different groups of people.

Depending on your objectives - whether you are starting from scratch or redesigning an existing IA - you’ll generally need to use some combination of both of these approaches when analyzing card sort results. Learn more about exploratory and statistical analysis in Donna Spencer’s book.

Start with the big picture

When analyzing card sort results, start 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 and the category names given by participants. Does anything jump out as surprising? Are there similarities or differences between participant sorts? If you’re redesigning an existing IA, how do your results compare to the current state?If you ran your card sort using OptimalSort, your first port of call will be 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, 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.

A screenshot of the individual participant card sort results pop-up in OptimalSort.
Viewing individual participant card sorts in detail.

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 what happened.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.If you have a good number of responses, then the Participant Centric Analysis (PCA) tab (below) can be a good place to head next. It’s great for doing a quick comparison of the different high-level approaches participants took when grouping the cards.The PCA tab provides the most insight when you have lots of results data (30+ completed card sorts) and at least one of the suggested IAs has a high level of agreement among your participants (50% or more agree with at least one IA).

A screenshot of the Participant Centric Analysis (PCA) tab in OptimalSort, showing an example study.
Participant Centric Analysis (PCA) tab for an open or hybrid card sort in OptimalSort.

The PCA tab compares data from individual participants and surfaces the top three ways the cards were sorted. It also gives you some suggestions based on participant responses around what these categories could be called but try not to get too bogged down in those - you’re still just trying to gain an overall feel for the results at this stage.Now is also a good time to take a super quick peek at the Categories tab as it will also help you spot patterns and identify data that you’d like to dive deeper into a bit later on!Another really useful visualization tool offered by OptimalSort that will help you build that early, high-level picture of your results is the Similarity Matrix. This diagram helps you spot data clusters, or groups of cards that have been more frequently paired together by your participants, by surfacing them along the edge and shading them in dark blue. It also shows the proportion of times specific card pairings occurred during your study and displays the exact number on hover (below).

A screenshot of the Similarity Matrix tab in OptimalSort, with the results from an example study displaying.
OptimalSort’s Similarity Matrix showing that ‘Flat sandals’ and ‘Court shoes’ were paired by 91% of participants (31 times) in this example study.

In the above screenshot example we can see three very clear clusters along the edge: ‘Ankle Boots’ to ‘Slippers’ is one cluster, ‘Socks’ to ‘Stockings & Hold Ups’ is the next and then we have ‘Scarves’ to ‘Sunglasses’. These clusters make it easy to spot the that cards that participants felt belonged together and also provides hard data around how many times that happened.Next up are the dendrograms. Dendrograms are also great for gaining an overall sense of how similar (or different) your participants’ card sorts were to each other. Found under the Dendrogram tab in the results section of the tool, the two dendrograms are generated by different algorithms and which one you use depends largely on how many participants you have.

If your study resulted in 30 or more completed card sorts, use the Actual Agreement Method (AAM) dendrogram and if your study had fewer than 30 completed card sorts, use the Best Merge Method (BMM) dendrogram.The AAM dendrogram (see below) shows only factual relationships between the cards and displays scores that precisely tell you that ‘X% of participants in this study agree with this exact grouping’.In the below example, the study shown had 34 completed card sorts and the AAM dendrogram shows that 77% of participants agreed that the cards highlighted in green belong together and a suggested name for that group is ‘Bling’. The tooltip surfaces one of the possible category names for this group and as demonstrated here it isn’t always the best or ‘recommended’ one. Take it with a grain of salt and be sure to thoroughly check the rest of your results before committing!

A screenshot of the Actual Agreement Method (AAM) dendrogram in OptimalSort.
AAM Dendrogram in OptimalSort.

The BMM dendrogram (see below) is different to the AAM because it shows the percentage of participants that agree with parts of the grouping - it squeezes the data from smaller sample sizes and makes assumptions about larger clusters based on patterns in relationships between individual pairs.The AAM works best with larger sample sizes because it has more data to work with and doesn’t make assumptions while the BMM is more forgiving and seeks to fill in the gaps.The below screenshot was taken from an example study that had 7 completed card sorts and its BMM dendrogram shows that 50% of participants agreed that the cards highlighted in green down the left hand side belong to ‘Accessories, Bottoms, Tops’.

A screenshot of the Best Merge Method (BMM) dendrogram in OptimalSort.
BMM Dendrogram in OptimalSort.

Drill down and cross-reference

Once you’ve gained a high level impression of the results, it’s time to dig deeper and unearth some solid insights that you can share with your stakeholders and back up your design decisions.Explore your open and hybrid card sort data in more detail by taking a closer look at the Categories tab. Open up each category and cross-reference to see if people were thinking along the same lines.Multiple participants may have created the same category label, but what lies beneath could be a very different story. It’s important to be thorough here because the next step is to start standardizing or chunking individual participant categories together to help you make sense of your results.In open and hybrid sorts, participants will be able to label their categories themselves. This means that you may identify a few categories with very similar labels or perhaps spelling errors or different formats. You can standardize your categories by merging similar categories together to turn them into one.OptimalSort makes this really easy to do - you pretty much just tick the boxes alongside each category name and then hit the ‘Standardize’ button up the top (see below). Don’t worry if you make a mistake or want to include or exclude groupings; you can unstandardize any of your categories anytime.

A screenshot of the categories tab in OptimalSort, showing how categorization works.
Standardizing categories in OptimalSort.

Once you’ve standardized a few categories, you’ll notice that the Agreement number may change. It tells you how many participants agreed with that grouping. An agreement number of 1.0 is equal to 100% meaning everyone agrees with everything in your newly standardized category while 0.6 means that 60% of your participants agree.Another number to watch for here is the number of participants who sorted a particular card into a category which will appear in the frequency column in dark blue in the right-hand column of the middle section of the below image.

A screenshot of the categories tab after the creation of two groupings.
Categories table after groupings called ‘Accessories’ and ‘Bags’ have been standardized.

A screenshot of the Categories tab showing some of the groupings under 'Accessories'.
A closer look at the standardized category for ‘Accessories’.

From the above screenshot we can see that in this study, 18 of the 26 participant categories selected agree that ‘Cat Eye Sunglasses’ belongs under ‘Accessories’.Once you’ve standardized a few more categories you can head over to the Standardization Grid tab to review your data in more detail. In the below image we can see that 18 participants in this study felt that ‘Backpacks’ belong in a category named ‘Bags’ while 5 grouped them under ‘Accessories’. Probably safe to say the backpacks should join the other bags in this case.

A screenshot of the Standardization grid tab in OptimalSort.
Standardization Grid in OptimalSort.

So that’s a quick overview of how to interpret the results from your open or hybrid card sorts.Here's a link to Part 2 of this series where we talk about interpreting results from closed card sorts as well as next steps for applying these juicy insights to your IA design process.

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