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

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.

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About this guide

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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.

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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.

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Understanding the two types of analysis: exploratory and statistical

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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.

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• 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.

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• 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.

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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.

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Start with the big picture

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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.

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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).

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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).

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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.

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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!

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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’.

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Drill down and cross-reference

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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.

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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.

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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.

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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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Further reading

• Card Sorting 101 – Learn about the differences between open, closed and hybrid card sorts, and how to run your own using OptimalSort.
• Which comes first: card sorting or tree testing? – Tree testing and card sorting can give you insight into the way your users interact with your site, but which comes first?
• How to pick cards for card sorting – What should you put on your cards to get the best results from a card sort? Here are some guidelines.

In 2009, Bob Bailey and Cari Wolfson published apaper entitled “FirstClick Usability Testing: A new methodology for predicting users’ success on tasks”. They’d analyzed 12 scenario-based user tests and concluded that the first click people make is a strong leading indicator of their ultimate success on a given task. Their results were so compelling that we got all excited and created Chalkmark, a tool especially for first click usability testing. It occurred to me recently that we’ve never revisited the original premise for ourselves in any meaningful way.

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And then one day I realized that, as if by magic, we’re sitting on quite possibly the world’s biggest database of tree test results. I wondered: can we use these results to back up Bob and Cari’s findings (and thus the relevanceof Chalkmark)?Hell yes we can.So we’ve analyzed tree testing data from millions of responses in Treejack, and we're thrilled (relieved) that it confirmed the findings from the 2009 paper — convincingly.

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What the original study found

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Bob and Cari analyzed data from twelve usability studies on websites and products ‘with varying amounts and types of content, a range of subject matter complexity, and distinct user interfaces’. They found that people were about twice as likely to complete a task successfully if they got their first click right, than if they got it wrong:

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If the first click was correct, the chances of getting the entire scenario correct was 87%If the first click was incorrect, the chances of eventually getting the scenario correct was only 46%

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What our analysis of tree testing data has found

We analyzed millions of tree testing responses in our database. We've found that people who get the first click correct are almost three times as likely to complete a task successfully:

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If the first click was correct, the chances of getting the entire scenario correct was 70%If the first click was incorrect, the chances of eventually getting the scenario correct was 24%

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To give you another perspective on the same data, here's the inverse:

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If the first click was correct, the chances of getting the entire scenario incorrect was 30%If the first click was incorrect, the chances of getting the whole scenario incorrect was 76%

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How Treejack measures first clicks and task success

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Bob and Cari proved the usefulness of the methodology by linking two key metrics in scenario-based usability studies: first clicks and task success. Chalkmark doesn't measure task success — it's up to the researcher to determine as they're setting up the study what constitutes 'success', and then to interpret the results accordingly. Treejack does measure task success — and first clicks.

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In a tree test, participants are asked to complete a task by clicking though a text-only version of a website hierarchy, and then clicking 'I'd find it here' when they've chosen an answer. Each task in a tree test has a pre-determined correct answer — as was the case in Bob and Cari's usability studies — and every click is recorded, so we can see participant paths in detail.

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Thus, every single time a person completes an individual Treejack task, we record both their first click and whether they are successful or not. When we came to test the 'correct first click leads to task success' hypothesis, we could therefore mine data from millions of task.

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To illustrate this, have a look at the results for one task.The overall Task result, you see a score for success and directness, and a breakdown of whether each Success, Fail, or Skip was direct (they went straight to an answer), or indirect (they went back up the tree before they selected an answer):

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In the pietree for the same task, you can look in more detail at how many people went the wrong way froma label (each label representing one page of your website):

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In the First Click tab, you get a percentage breakdown of which label people clicked first to complete the task:

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And in the Paths tab, you can view individual participant paths in detail (including first clicks), and can filter the table by direct and indirect success, fails, and skips (this table is only displaying direct success and direct fail paths):

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How to get busy with first click testing

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This analysis reinforces something we already knew that firstclicks matter. It is worth your time to get that first impression right.You have plenty of options for measuring the link between first clicks and task success in your scenario-based usability tests. From simply noting where your participants go during observations, to gathering quantitative first click data via online tools, you'll win either way. And if you want to add the latter to your research, Chalkmark can give you first click data on wireframes and landing pages,and Treejack on your information architecture.

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To finish, here's a few invaluable insights from other researchers ongetting the most from first click testing:

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• Jeff Sauro details a useful approach to running a first click test, and shares the findings from a test he ran on 13 people.
• An article on Neoinsight describes three common usability problems that first click testing can solve.
• Gianna LaPin describes a first click test she ran on Netflix, VUDU, and Hulu Plus.

"Dear Optimal Worshop

I have two huge content structures I would like to A/B test. Do you think Treejack would be appropriate?"

— Mike

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Hi Mike (and excellent question)!

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Firstly, yes, Treejack is great for testing more than one content structure. It’s easy to run two separate Treejack studies — even more than two. It’ll help you decide which structure you and your team should run with, and it won’t take you long to set them up.

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When you’re creating the two tree tests with your two different content structures, include the same tasks in both tests. Using the same tasks will give an accurate measure of which structure performs best. I’ve done it before and I found that the visual presentation of the results — especially the detailed path analysis pietrees — made it really easy to compare Test A with Test B.

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Plus (and this is a big plus), if you need to convince stakeholders or teammates of which structure is the most effective, you can’t go past quantitative data, especially when its presented clearly — it’s hard to argue with hard evidence!

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Here’s two example of the kinds of results visualizations you could compare in your A/B test: the pietree, which shows correct and incorrect paths, and where people ended up:

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And the overall Task result, which breaks down success and directness scores, and has plenty of information worth comparing between two tests:

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Keep in mind that running an A/B tree test will affect how you recruit participants — it may not be the best idea to have the same participants complete both tests in one go. But it’s an easy fix — you could either recruit two different groups from the same demographic, or test one group and have a gap (of at least a day) between the two tests.

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I’ve one more quick question: why are your two content structures ‘huge’?

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I understand that sometimes these things are unavoidable — you potentially work for a government organization, or a university, and you have to include all of the things. But if not, and if you haven’t already, you could run an open card sort to come up with another structure to test (think of it as an A/B/C test!), and to confirm that the categories you’re proposing work for people.

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You could even run a closed card sort to establish which content is more important to people than others (your categories could go from ‘Very important’ to ‘Unimportant’, or ‘Use everyday’ to ‘Never use’, for example). You might be able to make your content structure a bit smaller, and still keep its usefulness. Just a thought... and of course, you could try to get this information from your analytics (if available) but just be cautious of this because of course analytics can only tell you what people did and not what they wanted to do.

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All the best Mike!