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

When it comes to designing and testing in the world of information architecture, it’s hard to beat card sorting. As a usability testing method, card sorting is easy to set up, simple to recruit for and can supply you with a range of useful insights. But there’s a long-standing debate in the world of card sorting, and that’s whether it’s better to run card sorts in person (moderated) or remotely over the internet (unmoderated).

‍

This article should give you some insight into the world of online card sorting. We've included an analysis of the benefits (and the downsides) as well as why people use this approach. Let's take a look!

‍

How an online card sort works

‍

Running a card sort remotely has quickly become a popular option just because of how time-intensive in-person card sorting is. Instead of needing to bring your participants in for dedicated card sorting sessions, you can simply set up your card sort using an online tool (like our very own OptimalSort) and then wait for the results to roll in.

‍

So what’s involved in a typical online card sort? At a very high level, here’s what’s required. We’re going to assume you’re already set up with an online card sorting tool at this point.

‍

1. Define the cards: Depending on what you’re testing, add the items (cards) to your study. If you were testing the navigation menu of a hotel website, your cards might be things like “Home”, “Book a room”, “Our facilities” and “Contact us”.
2. Work out whether to run a closed or open sort: Determine whether you’ll set the groups for participants to sort cards into (closed) or leave it up to them (open). You may also opt for a mix, where you create some categories but leave the option open for participants to create their own.
3. Recruit your participants: Whether using a participant recruitment service or by recruiting through your own channels, send out invites to your online card sort.
4. Wait for the data: Once you’ve sent out your invites, all that’s left to do is wait for the data to come in and then analyze the results.

‍

That’s online card sorting in a nutshell – not entirely different from running a card sort in person. If you’re interested in learning about how to interpret your card sorting results, we’ve put together this article on open and hybrid card sorts and this one on closed card sorts.

‍

Why is online card sorting so popular?

‍

Online card sorting has a few distinct advantages over in-person card sorting that help to make it a popular option among information architects and user researchers. There are downsides too (as there are with any remote usability testing option), but we’ll get to those in a moment.

‍

Where remote (unmoderated) card sorting excels:

‍

• Time savings: Online card sorting is essentially ‘set and forget’, meaning you can set up the study, send out invites to your participants and then sit back and wait for the results to come in. In-person card sorting requires you to moderate each session and collate the data at the end.
• Easier for participants: It’s not often that researchers are on the other side of the table, but it’s important to consider the participant’s viewpoint. It’s much easier for someone to spend 15 minutes completing your online card sort in their own time instead of trekking across town to your office for an exercise that could take well over an hour.
• Cheaper: In a similar vein, online card sorting is much cheaper than in-person testing. While it’s true that you may still need to recruit participants, you won’t need to reimburse people for travel expenses.
• Analytics: Last but certainly not least, online card sorting tools (like OptimalSort) can take much of the analytical burden off you by transforming your data into actionable insights. Other tools will differ, but OptimalSort can generate a similarity matrix, dendrograms and a participant-centric analysis using your study data.

‍

Where in-person (moderated) card sorting excels:

‍

• Qualitative insights: For all intents and purposes, online card sorting is the most effective way to run a card sort. It’s cheaper, faster and easier for you. But, there’s one area where in-person card sorting excels, and that’s qualitative feedback. When you’re sitting directly across the table from your participant you’re far more likely to learn about the why as well as the what. You can ask participants directly why they grouped certain cards together.

‍

Online card sorting: Participant numbers

‍

So that’s online card sorting in a nutshell, as well as some of the reasons why you should actually use this method. But what about participant numbers? Well, there’s no one right answer, but the general rule is that you need more people than you’d typically bring in for a usability test.

‍

This all comes down to the fact that card sorting is what’s known as a generative method, whereas usability testing is an evaluation method. Here’s a little breakdown of what we mean by these terms:

‍

Generative method: There’s no design, and you need to get a sense of how people think about the problem you’re trying to solve. For example, how people would arrange the items that need to go into your website’s navigation. As Nielsen Norman Group explains: “There is great variability in different people's mental models and in the vocabulary they use to describe the same concepts. We must collect data from a fair number of users before we can achieve a stable picture of the users' preferred structure and determine how to accommodate differences among users”.

‍

Evaluation method: There’s already a design, and you basically need to work out whether it’s a good fit for your users. Any major problems are likely to crop up even after testing 5 or so users. For example, you have a wireframe of your website and need to identify any major usability issues.

‍

Basically, because you’ll typically be using card sorting to generate a new design or structure from nothing, you need to sample a larger number of people. If you were testing an existing website structure, you could get by with a smaller group.

‍

Where to from here?

‍

Following on from our discussion of generative versus evaluation methods, you’ve really got a choice of 2 paths from here if you’re in the midst of a project. For those developing new structures, the best course of action is likely to be a card sort. However, if you’ve got an existing structure that you need to test in order to usability problems and possible areas of improvement, you’re likely best to run a tree test. We’ve got some useful information on getting started with a tree test right here on the blog.

In 2009, Bob Bailey and Cari Wolfson published published findings that changed how we approach first click testing and usability testing. They analyzed 12 scenario-based user tests and found that if someone gets their first click right, they're about twice as likely to complete their task successfully. This finding was so compelling that we built First Click Testing (formerly Chalkmark) specifically to help teams test this.  But we'd never actually validated their research using our own data, until now.

‍

Turns out, we're sitting on one of the world's largest databases of tree testing results. So we analyzed millions of task responses to see if the "first click predicts success" hypothesis holds up.

‍

It does. Convincingly.

‍

Users who get their first click correct are nearly three times more likely to complete their task successfully (70% vs 24% success rate).

‍

Here's how we validated the original study, what our data shows, and why first clicks matter more than you might think.

‍

‍

‍

Original first click testing study: 87% task success rate

‍

‍

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:

‍

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%.

‍

‍

‍

Our Tree Testing data: First clicks predict 70% task success rate

‍

‍

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:

‍

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%

‍

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

‍

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%

‍

‍

‍

How Tree Testing measures first click success and task completion

‍

‍

Bob and Cari proved the usefulness of the methodology by linking two key metrics in scenario-based usability studies: first clicks and task success. First Click Testing 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. Tree Testing (formerly Treejack) does measure task success — and first clicks.

‍

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.

‍

Thus, every single time a person completes an individual tree testing 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.

‍

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):

‍

‍

‍

‍

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

‍

‍

‍

‍

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

‍

‍

‍

‍

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):

‍

‍

‍

‍

‍

How to run first click tests: Best practices for usability testing

‍

‍

First click analysis is one of the most predictive metrics in usability testing. Whether you're testing wireframes, landing pages, or information architecture, measuring first click success gives you early insight into whether your design will work.

‍

This analysis reinforces something we already knew: first clicks 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 quantitative first click data, Optimal has you covered. First Click Testing works for wireframes and landing pages, while Tree Testing validates your information architecture.

‍

To finish, here are a few invaluable insights from other researchers on getting the most from first click testing:

‍

• 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.

‍

‍

About this study

‍

This analysis was conducted in 2015 using millions of task responses from Optimal’s First Click and Tree Testing tools. While the dataset predates recent UI trends, the underlying behavioral principle, that a correct first click strongly predicts task success, remains consistent with modern usability research.

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!