Card Sorting

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What is Card Sorting?

Card sorting is a user research method that helps designers understand how users categorize and organize information. Participants categorize content on index cards, physically or digitally. Designers use card sorting in the information architecture (IA) phase to build structures that align with users’ mental models.

In this video, Design Consultant and author Donna Spencer states, “The most important thing about this [card sorting] is that you learn about how people think about the ideas under question. You understand what things people think go together. You learn a lot about what people call those things…”

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If we were working with music content and we said, 'Here's a bunch of songs; arrange them,' we might see some people arrange them by band, we might see some people arrange them by genre, we might see some people say, 'Don't like any of those – toss them out.' So, we can learn about the *ways* that content can be organized, as well as the things that go together within those ways.

Table of contents

What is Card Sorting?
Why Is Card Sorting Important in Design?
Types of Card Sorting
How to Run a Card Sort
Card Sorting: Benefits and Drawbacks
1. Benefits
2. Drawbacks
Learn More about Card Sorting
Questions related to Card Sorting

Why Is Card Sorting Important in Design?

Card sorting is important because it helps designers understand user mental models, improves IA, and is a key part of a user-centered design approach. It contributes to the overall success of a design because it integrates the final product with user expectations and behaviors.

Card sorting impacts design in the following ways:

User-Centered Design: Card sorting allows designers to understand users’ unique perspectives and helps to design interfaces that align with users' mental models.
Informs Information Architecture: It helps designers organize information and content in the most intuitive way. When designers analyze how users categorize content, they can create IAs that represent user expectations which, in turn, improves navigation and usability.
Content Relevance: Card sorting helps identify and prioritize what content or features users consider most important so they are easily accessible.
Usability Improvement: When designers understand how users expect information to be organized and presented, they can make informed decisions to improve the overall usability of a design. Usability enhances the user experience and reduces cognitive load.
Validation of Design Decisions: Card sorting can validate existing IAs or test proposed structures. This ensures that design decisions are grounded in user preferences and allows for more effective designs.
Iterative Design Process: Design is an iterative process, and card sorting fits into this since it provides feedback that can be used to refine designs. Continuous testing and adjustment based on user input lead to designs that continually improve over time.
Collaborative Decision-Making: Card sorting can involve stakeholders, including clients and team members, in the decision-making process. It facilitates collaboration and considers diverse perspectives.
Optimizing Navigation: Designers can use the insights from a card sort workshop to optimize navigation paths—crucial for websites or applications with a large amount of content.
Enhancing Findability: Card sorting helps designers with discoverability. Users can find relevant information more easily when the design aligns with their mental models.
Adapting to User Diversity: Different user groups have different ways to cluster and label information. Card sorting allows designers to accommodate the diverse needs and preferences of various user segments.

A pair of hands arranging a set of different colored post it notes, in the manner of a card sort — Use plain index cards or post-it notes for an in-person card sort. Card sorting helps identify and group patterns naturally and can inform a website or app’s IA and overall design. Put the process to use from the design research phase and beyond.
© UX Indonesia, Unsplash License

Types of Card Sorting

There are several types of card sorting:

© Interaction Design Foundation, CC BY-SA 4.0

Open Card Sort: Participants organize website content into groups based on their understanding and name each group to reflect its content accurately. Designers opt for an open card sort to see how users naturally label and group content without predefined categories. This method is beneficial in the early stages of a project.
Closed Card Sort: Participants categorize website content into predetermined groups. This method is suitable when working with a set of pre-established categories and aims to understand how users sort content items into each.
Mixed Card Sort: Designers can combine open and closed card sorting when they have some established categories but want to explore new ones. This hybrid approach lets participants use provided categories and create new ones. It's useful to expand or restructure a partially established IA.

Designers should choose different types of card sorting depending on the project’s specific requirements.

How to Run a Card Sort

Here are some guidelines to successfully run a card sort.

Set Objectives

For any research methodology, it's crucial to clearly define the objectives and determine whether card sorting is the most effective approach to gain the intended insights.

Example:

Consider a card sorting session which focuses on improving a community-based platform that connects volunteers with local service opportunities.

The organization wants to revamp its website. The current platform is complex, and users find it challenging to discover relevant volunteer opportunities.

In this case, an objective could be “Enhance the website's menu and navigation structure to make it more user-friendly for volunteers seeking community service opportunities.”

Select Tools

Designers can use plain index cards or sticky notes for in-person card sorting, with a range of a few to about 100 cards or more, depending on the project. For a virtual workshop, however, limit the number of cards to around 50.

Various online card sorting tools exist, and they offer a key advantage—they’re readily available and easily facilitate analysis without the need to transfer results from cards to a spreadsheet.

Prepare Instructions

Meticulous preparation of instructions is essential. The instructions must be clear to ensure participants comprehend the task at hand. This becomes particularly crucial in an online card sort.

Designers should anticipate potential questions participants might pose. If the card sort is online, incorporate answers within the instructions. For an in-person card sort, prepare responses in advance.

Potential participant questions may include:

Can a card be placed in more than one group?
Can I exclude a card if I don't understand it?
Am I allowed to add a card if I sense content is missing?

Example:

For the volunteer platform, participants organize the cards into groups based on how they would expect to find volunteer opportunities on a community service platform.

The designer should encourage participants to vocalize their thought process and share any specific preferences.

They should also answer any questions the potential users might have.

Select Participants

It’s important to select the right people for the card sort. Determine the ideal number of participants—this is typically more straightforward for in-person sessions due to space and time constraints; 3 to 5 participants is ideal.

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The people who you invite to be involved are also super important – not quite as important as the content, but still very important. Small groups of people working in-person around a table is literally the best thing because you can listen to the people and the conversation that they have and that conversation is so much more useful than the actual results that you get. We still could do this remotely, especially if people can get together and you can moderate via video.
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Or... I haven't ever done this, but we could kind of do it with a Zoom call and like a MURAL board and get people to poke things around, but it's still – any time that you have a chance to do it face-to-face, do that. And one of the things that I like to do if I need to do remote card sorts is just at least a couple face-to-face because then I can really hear that discussion and understand what's going on with just some of the group and then I can look at the results from the others.
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We want to make sure that *the people are also of the same level*. And this goes for any kind of group research or group activities. If you have a boss and somebody much more junior, the junior people will always just defer to the boss. If you have technical people and non-technical people, the non-technical people might just go, 'I don't know!' and follow the technical people. So, you want to make sure that people are at the same kind of levels, and, if you can, the same kinds of work or life situation, or the same kinds of users
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because then you can listen to them discuss their experiences and they'll do that better if they're the same types. We do that for most kind of focus groups and things. If you are doing it in-person, just don't try to fill up a room with lots of people. Do it with one group or have one facilitator per group because, as I said, the conversation is the super-useful part of this and it's really worth listening.
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What I used to do when I did these in-person is I would kind of just sit up at the end of the table, kind of be typing in results from a previous sort; so, I don't look like I'm hovering over them. I look like I'm busy kind of just listening a bit. But I'm listening to every single word they're saying! So, again, if we're working remotely and we're doing a remote card sort, you still want to have a good representative set of people and see if you can figure out a way of getting
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people to work together remotely. Otherwise, they'll just do it by themselves one at a time, and you'll still get good results, but you just won't hear them discussing their reasoning.

In online card sorting, it's recommended to involve only a select few individuals initially. Handling an extensive volume of responses can be overwhelming during the analysis phase. Begin by distributing the card sorting exercise to a small group, providing space for refinement and adjustment. If necessary, consider expanding the participant pool to a larger audience in subsequent rounds or phases.

Example:

For the volunteer card sort, the designer or researcher running the session should recruit participants with varying levels of experience in volunteering, including newcomers and frequent volunteers. They should limit the number of participants to 3-5 per card sort.

Incentive

As with most research activities, incentivize the participants to ensure they are motivated to participate. This is especially true for online card sorts.

Example:

The volunteer platform could offer a year of free access to the platform or $10 gift cards to a charitable organization, etc.

Content Preparation

It’s essential to evaluate the content thoroughly before the card sort. The content participants receive will determine the quality of the results.

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So, what we want is content that is all at the *same level*. So, what I mean by that is we need it to be the same kind of granularity, the same kind of part of a hierarchy. An example that I saw a very long time ago that's a good example for this is I saw somebody running a card sort about tourism content. And in the tourism content, they had things like a hotel name, a zoo to visit,
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a winery to visit, they had some stuff about the geography, and then they had a card called 'accommodation'. So, you can think like if you've got a hotel – actually, I think they had a card of 'things to do' as well. So, you have like a 'hotel' card and an 'accommodation' card. They're different levels of hierarchy. A hotel is more detailed than the broad concept of accommodation. So, we don't want that 'accommodation' card in the mix because people will put those together
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– you cannot stop them from putting a hotel and accommodation together. And then, you haven't really learned about the content and the sort. You've just learned the thing that was going to happen. So, you want to make sure that they're – and the level is a hard concept, but you can see it in your content; like, you want either quite detailed things or fairly broad things, but they need to be the same. You want the cards to be a good selection that represents the content you'll be working with in the end.
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If you were card sorting for a supermarket, a grocery store, but you didn't include anything cold, so no frozen foods, no refrigerated foods, and then you ran the card sort, you're going to learn about some things but you're not going to learn about everything. And then you won't have information to cover the things that you haven't covered in the sort. So, you want to have really good coverage across everything that you're arranging.
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And this can be hard because if you're working on a site that's large, you need to be able to get good coverage. You also need people to be able to actually group the content. So, if you get a good broad selection that covers your content but then people look at it and they go, 'Well, I can see some chocolate and some apples and some cheese and some tinned foods and things, but I can't make a *group* out of those,'
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then you haven't learned anything from the activity. So, this is a juggle – you need to get a breadth that covers your content but still people can make groups out of.

For a larger content set, divide the process into multiple card sorts for the best results. Avoid using jargon, as unclear content can negatively impact effective card grouping. When individuals can't understand the information, they tend to force connections that aren't genuine. To achieve the best outcome, ensure the content is clear and direct.

Example:

In the volunteer website example, the lead designer creates physical or digital cards for categories like "Education," "Environmental Projects," "Health and Wellness," etc. They leave a few cards blank with markers available in case participants have an idea they’d like to add to the sort.

The designer prepares a set of sample volunteer opportunities within each category and identifies key tasks users often perform, such as finding tutoring opportunities or environmental cleanup projects.

Workshop

Prepare the note cards in advance and spread them out on a table before the participants arrive. Explain the instructions clearly and answer any questions that arise, then let the sorting begin! Don’t forget to take detailed notes throughout the process.
© Interaction Design Foundation, CC BY-SA 4.0

It’s important to provide participants with context and explain the instructions. In physical card sorts, communicate verbally; for virtual sessions, written instructions are essential.

Avoid excessive explanations or over-assistance. Participants may seek guidance, but refrain from delving into extensive detail. Encourage them to proceed independently for optimal results.

Refrain from telling participants that they’ll label the groups they create during the workshop. While impractical for online card sorts, this ensures the users focus on categories rather than being influenced by predefined labels, preserving the organic card sorting process.

Throughout the card sort, make note of participant comments. Maintain distance to facilitate natural participant conversation without disruption.

Example:

In the case of the volunteer site, the designer should explain the purpose of the session and emphasize that there are no right or wrong answers.

They can provide a brief overview of the current platform and its challenges in connecting volunteers with opportunities.

The lead designer presents specific tasks to participants, such as finding a tutoring opportunity or discovering environmental cleanup projects, and asks them to locate the relevant category in their card groups.

The designer observes and takes notes on how participants group the cards, the labels they assign to each group, and any specific comments they make.

After the sorting, the designer should ask participants about why they made certain choices, what terms they find most intuitive, and if they encountered any difficulties.

Analysis

“The first thing you want to do is just poke around… What did people do? What’s kind of interesting? What ways did they do things?”
—Donna Spencer

In the video, Donna Spencer speaks about how to analyze the data of a card sorting workshop and pinpoint what’s most interesting or relevant to reveal the final results.

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The first thing you want to do is just *poke around*. If you've got it in a big spreadsheet or something, you might print it out on the wall, you sort of look at: What *happened* here? What did people *do*? What's kind of *interesting*? What *ways* did they do things? So, just an initial – 'What is this?' You might look through and say, 'Okay, Participant 1 – how did they do that? Oh, look – they sorted the groceries into the way it's sorted in a physical store.'
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Or, 'They sorted the groceries according to taste – interesting. It's just interesting.' Then you might go through and go, 'What is interesting about those groupings? Ah, wow – okay, this is different to that. I didn't expect to see people put those things together.' And then look at like different participants and say, 'What did people do that is *different*? Did everybody do exactly the same thing? Did they do all different things?
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Did they do some similar things and some differents? What is interesting about that?' – What kind of variations did you notice? And then, also, *what words* did people put on the cards? What language and labeling came out of this activity is super interesting. So... you're kind of just burying yourself in data initially; you're not trying to get to a result really quickly.

First, get an initial overview. It’s more convenient to analyze an online card sorting session since results are directly available on your computer. However, for in-person card sorts it’s best to streamline the process with numbers and a spreadsheet. Assign a number to each card and enter it into the spreadsheet along with the card's content to simplify data input.

Then delve deeper into the analysis—scrutinize the participants' results for detailed comparisons and identify those similarities and differences. Examine variations to extract valuable insights. Note instances where similar groupings contain different cards. Additionally, evaluate specific cards and their placement by each participant.

This is a qualitative research method—focus on the discovery of interesting aspects within the results that align with the card sort’s objectives.

Finally, integrate your findings into the project, preferably along with other research methods.

Example:

Following the volunteer organization, the designer analyzes the collected data to identify common patterns, groupings, and potential improvements for the website's menu and navigation.

They present the results to the design team and relevant stakeholders.

From there, the designer can recommend changes for the menu structure based on user insights to improve the volunteer experience.

If the design team has completed all of the above, they should be be able to establish the following outcomes after their analysis:

Insights into how volunteers naturally categorize and expect to find community service opportunities.
Identification of intuitive groupings and potential improvements for the website's menu structure.
Greater understanding of user preferences in the platform’s navigation which adds to a more effective and user-friendly community service hub.

Card Sorting: Benefits and Drawbacks

Card sorting provides valuable insights into information organization and user preferences. Designers everywhere praise the practice because it improves usability, coordinates designs with how users think, and makes it easier to prioritize content. However, like any methodology, card sorting is not without its limitations.

Benefits

Card sorting offers several benefits, including but not limited to:

Information Organization: Card sorting helps organize and structure information and enhances accessibility and user understanding.
User-Centered Design: Facilitates a user-centric approach by involving participants in the categorization process; it helps to align the design with user mental models.
Content Prioritization: Identifies which content is most relevant or important to users during design and development.
Navigation Design: Card sorting informs the design of intuitive navigation systems that are aligned with how users naturally group and categorize information.
Reduced Cognitive Load: It minimizes cognitive load through logical content groupings and intuitive navigation.

Drawbacks

While card sorting is a valuable strategy, it does have some drawbacks:

Limited Quantitative Data: Card sorting provides qualitative insights but lacks quantitative data, which makes the prioritization of results objectively a challenge.
Time-Consuming: Sessions can be time-intensive, especially when they involve a lot of content to process or a large number of participants.
Difficulty with Divergent Thinking: Some participants may struggle with open-ended categorization tasks. This limits the effectiveness of capturing diverse perspectives.
Not Suitable for All Content: Card sorting may not be the best fit for highly complex or technical content; other methods like tree testing or mind mapping may be more appropriate.
Dependency on Participants: Participants’ knowledge, backgrounds, or cognitive biases can influence results and potentially lead to skewed outcomes. Too many participants also presents a problem, as does too few.

Learn More about Card Sorting

Read more about card sorting in Chapter 22 of IxDF’s The Encyclopedia of Human-Computer Interaction, 2nd Ed. by William Hudson.

If you want to learn more about how to run a card sorting session, read IxDF’s Card Sorting: The Ultimate Guide (in 2024).

Watch Donna Spencer’s Master Class, How To Use Card Sorting For Better Information Architecture.

Interested in learning more about card sorting and UX Design? Enroll in Interaction Design for Usability with user-centered design expert, William Hudson, today!

Read about card sorting on Usability.gov’s website.

Questions related to Card Sorting

How do I choose between open, closed, and hybrid card sorting?

Depending on your project goals, development stage, and the information you want, you can choose between open, closed, or hybrid card sorting.

Early in the project, choose open card sorting. Participants organize topics into categories without predefined labels. It helps understand users' mental models, especially when creating a new system.

For established categories, use closed card sorting. This method clarifies how users sort items into predefined groups, useful to validate or redefine existing structures like website navigation.

Explore a hybrid approach by starting with an open card sort to identify content categories, then transition to a closed card sort to evaluate the effectiveness of those category labels.

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Today we're going to talk all about card sorting. Learn about the ways that content can be organized, as well as the things that go together.

You can learn more about open and closed card sorting in Donna Spencer’s Master Class, “How To Use Card Sorting For Better Information Architecture.”

What tools do you need to conduct a card sorting session?

To conduct a card sorting session effectively, you need a combination of physical or digital tools. Here’s a breakdown:

Index cards or post-it notes are tools of the UX trade. It’s always beneficial to have them on hand.

© Interaction Design Foundation, CC BY-SA 4.0

Cards: In a physical session, use index cards or sticky notes. Each card should represent a concept, function, or item relevant to your information architecture (IA). For a digital session, use online card sorting tools that allow virtual card manipulation.
Labels: Clearly label each card. In digital tools, you can type the labels; in physical sessions, write legibly on the cards.
Recording Tools: For in-person sessions, use a camera or smartphone to record the final sorted groups for analysis. Digital tools automatically record this data.
Space: For a physical session, a large table or wall space is necessary to spread out and organize cards. Digital sessions require a reliable internet connection and a platform for participants to access the sorting tool.
Participants: Recruit a diverse group of participants who represent your target user base. Make sure they understand the scope and purpose of the session.
Instructions: Prepare clear instructions explaining the task to ensure participants understand their objective.
Note-taking Materials: For in-person sessions, have pens and paper for participants to jot down their thoughts. Digital sessions might allow for notes within the sorting tool.
Consent Forms: If recording the session, prepare consent forms in compliance with privacy and data protection laws.
Analysis Tools: Post-session, use tools like spreadsheets or specialized software to analyze the data collected.

Remember, the choice of tools should align with the goals of your card sorting session and the comfort level of your participants whether digital or in-person.

What are some recommended books that cover card sorting?

Several books offer comprehensive insights into card sorting and its application in user experience (UX) design.

Read Donna Spencer’s, Card Sorting: Designing Usable Categories, from Rosenfeld Media in 2009.

This book is a definitive guide on card sorting. It covers everything from planning and conducting sessions to analyzing results. It's especially useful for those new to card sorting, providing step-by-step guidance and practical advice.

The User Experience Team of One: A Research and Design Survival Guide by Leah Buley, Rosenfeld Media, 2013.

Buley’s book is apt for solo UX practitioners or small teams. It gives practical tips on various UX methods, like card sorting, and provides real-world advice for effective UX research and design.

Learn more from The MIT Press’, Sorting Things Out: Classification and Its Consequences, by Geoffrey C. Bowker and Susan Leigh Star (2000).

It takes a broader view, examining the role of categorization in society and technology. Bowker and Star provide a deeper understanding of how classification impacts web design and user experience.

What are some highly cited research on card sorting?

Some highly cited and influential research papers on card sorting, information architecture (IA), and beyond include:

Tullis, Tom, et al. "An Empirical Comparison of Lab and Remote User Testing of Web Interfaces." Human Interface Design Dept; Fidelity Investments, (2002).

Roth, Robert E., et al. "Card Sorting for Cartographic Research and Practice." Journal of Maps 13, no. 2 (2017): 705-715.

Finnie-Ansley, James, Paul Denny, and Andrew Luxton-Reilly. 2022. "Play Your Cards Right: Using Quantitative Card-Sort Data to Examine Students' Pattern-Like Concepts." In Proceedings of the 53rd ACM Technical Symposium on Computer Science Education - Volume 1 (SIGCSE 2022), Vol. 1, 857–863. New York, NY, USA: Association for Computing Machinery.

What is the best way to recruit participants for a card sorting session?

Recruiting participants for a card sort is crucial to obtain valuable insights. The best approach involves several key steps:

Define Your Target Audience: Identify the typical users of your product or service. Your participants should represent this group as closely as possible to ensure the results are relevant.
Determine the Number of Participants: A helpful guideline is to include 3-5 participants for every card sorting session. You can run multiple sessions with different participants. This range provides sufficient data for analysis without becoming too complicated.
Use Multiple Recruitment Methods: Use various channels like social media, email lists, forums related to your product, UX research tools, and professional networks. If you have an existing customer base, consider reaching out to them, too.
Offer Incentives: Incentives such as gift cards, discounts, or access to premium features can increase participation rates. Ensure the incentives are appealing to your target audience.
Screen Participants: Use screening questions to ensure participants fit your target user profile. This step is crucial for the validity of your results.
Schedule Sessions Flexibly: In in-person sessions, offer multiple time slots. For online sessions, provide clear instructions on how and when to participate.
Ensure Ethical Practices: Obtain informed consent, explain the purpose of the study and how you will use the data, and ensure privacy.
Prepare for No-Shows: Always recruit more participants than needed to account for no-shows or dropouts.

In the video below, HCI and UX expert, William Hudson, describes the best methods to use to recruit participants for UX research.

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I wanted to say a bit more about this important issue of recruiting participants. The quality of the results hinges entirely on the quality of the participants. If you're asking participants to do things and they're not paying attention or they're simply skipping through as quickly as they can – which does happen – then you're going to be very disappointed with the results
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and possibly simply have to write off the whole thing as an expensive waste of time. So, recruiting participants is a very important topic, but it's surprisingly difficult. Or, certainly, it can be. You have the idea that these people might want to help you improve your interactive solution – whatever it is; a website, an app, what have you – and lots of people *are* very motivated to do that. And you simply pay them a simple reward and everyone goes away quite happy.
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But it's certainly true with *online research* that there are people who would simply take part in order to get the reward and do very little for it. And it comes as quite a shock, I'm afraid, if you're a trusting person, that this kind of thing happens. I was involved in a fairly good-sized study in the U.S. – a university, who I won't name – and we had as participants in a series of studies students, their parents and the staff of the university.
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And, believe it or not, the students were the best behaved of the lot in terms of actually being conscientious in answering the questions or performing the tasks as required or as requested. Staff were possibly even the worst. And I think their attitude was "Well, you're already paying me, so why won't you just give me this extra money without me having to do much for it?" I really don't understand the background to that particular issue.
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And the parents, I'm afraid, were not a great deal better. So, we had to throw away a fair amount of data. Now, when I say "a fair amount", throwing away 10% of your data is probably pretty extreme. Certainly, 5% you might want to plan for. But the kinds of things that these participants get up to – particularly if you're talking about online panels, and you'll often come across panels if you go to the tool provider, if you're using, say for example, a card-sorting tool
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or a first-click test tool and they offer you respondents for a price each, then be aware that those respondents have signed up for this purpose, for the purpose of doing studies and getting some kind of reward. And some of them are a little bit what you might call on the cynical side. They do as little as possible. We've even on card sort studies had people log in, do nothing for half an hour and then log out and claim that they had done the study.
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So, it can be as vexing as that, I'm afraid. So, the kinds of things that people get up to: They do the minimum necessary; that was the scenario I was just describing. They can answer questions in a survery without reading them. So, they would do what's called *straightlining*. Straightlining is where they are effectively just answering every question the same in a straight line down the page or down the screen. And they also could attempt to perform tasks without understanding them.
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So, if you're doing a first-click test and you ask them, "Go and find this particular piece of apparel, where would you click first?", they'd just click. They're not reading it; they didn't really read the question. They're not looking at the design mockup being offered; they're just clicking, so as to get credit for doing this. Like I say, I don't want to paint all respondents with this rather black brush, but it's *some* people do this. And we just have to work out how to keep those people from polluting our results. So, the reward is sometimes the issue, that if you are too generous in the reward
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that you're offering, you will attract the wrong kind of participant. Certainly I've seen that happen within organizations doing studies on intranets, where somebody decided to give away a rather expensive piece of equipment at the time: a DVD reader, which was – when this happened – quite a valuable thing to have. And the quality of the results plummetted. Happily, it was something where we could actually look at the quality of the results and
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simply filter out those people who really hadn't been paying much attention to what they were supposed to be doing. So, like I say, you can expect for online studies to discard been 5 and 10% of your participants' results. You also – if you're doing face-to-face research – and you're trying to do quantitative sorts of numbers, say, you'd be having 20 or 30 participants, you probably won't have a figure quite as bad as that, but I still have seen, even in face-to-face card sorts, for example,
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people literally didn't *understand* what they were supposed to be doing, or didn't get what they were supposed to be doing, and consequently their results were not terribly useful. So, you're not going to get away with 100% valuable participation, I'm afraid. And so, I'm going to call these people who aren't doing it, and some of them are not doing it because they don't understand, but the vast majority are not doing it because they don't want to spend the time or the effort; I'm going to call them *failing participants*. And the thing is, we actually need to be able to *find* them in the data and take them out.
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You have to be careful how you select participants, how you filter them and how you actually measure the quality of their output, as it were. And one of the big sources of useful information are the actual tools that you are using. In an online survey, you can see how long people have spent, you can see how many questions they have answered. And, similarly, with first-click testing, you can see how many of the tasks they completed; you can see how long they spent doing it.
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And with some of these, we actually can also see how successful they were. In both of the early-design testing methods – card sorting and first-click testing – we are allowed to nominate "correct" answers – which is, I keep using the term in double-quotes here because there are no actually correct answers in surveys, for example; so, I'm using "correct" in a particular way: "Correct" is what we think they should be doing when they're doing a card sort, *approximately*, or, in particular, when they're doing a *first-click test*,
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that we think they ought to be clicking around about here. Surveys as a group are a completely different kettle of fish, as it were. There are really no correct answers when you start. You've got your list of research questions – things that you want to *know* – but what you need to do is to incorporate questions and answers in such a way that you can check that people are indeed *paying attention* and *answering consistently*. So, you might for example change the wording of a question and reintroduce it later on
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to see if you get the same answer. The idea is to be able to get a score for each participant. And the score is your own score, about basically how much you trust them or maybe the *inverse* of how much you trust them. So, as the score goes up, your trust goes down. So, if these people keep doing inconsistent or confusing things, like replying to questions with answers that aren't actually real answers – you've made them up – or not answering two questions which are effectively the same the same way, etc.,
00:07:33 --> 00:08:02

then you would get to a point where you'd say, "Well, I just don't trust this participant," and you would yank their data from your results. Happily, most of these tools do make it easy for you to yank individual results. So, we have to design the studies to *find* these failing participants. And, as I say, for some these tools – online tools we'll be using – that is relatively straightforward, but tedious. But with surveys, in particular, you are going to have to put quite a bit of effort into that kind of research.
00:08:02 --> 00:08:32

Steps we can take in particular: Provide consistency checks between tasks or questions. Ensure that "straightlined" results – where people are always answering in the same place on each and every question down the page – ask the same question again in slightly different wording or with the answers in a different order. Now, I wouldn't go around changing the order of answers on a regular basis. You might have one part of the questionnaire where "good" is on the right and "bad" is on the left;
00:08:32 --> 00:09:00

and you might decide to change it in a completely different part of the questionnaire and make it really obvious that you've changed it to those who are paying attention. But whatever it is that you do, what you're *trying* to do is to find people who really aren't paying much attention to the directions on the survey or whatever the research tool is, and catch them out and pull them out of your results. And of the issues you should be aware of if you're paying for participants from something
00:09:00 --> 00:09:30

like your research tool *supplier* is that you can go back to them and say, "These people did not do a very good job of completing this survey, this study." And ask them to refund you for the cost of those. You tell them that you're having to pull their data out of your results. Also, it helps to tidy up their respondent pool. Perhaps it's not your particular concern, but if you do end up using them again, it would be nice to know that some of these people who are simply gaming the system have been removed from the respondent pool.
00:09:30 --> 00:09:45

So, reporting them – getting them removed from the pool – is a sensible thing to be doing. And, finally, devising a scoring system to check the consistency and also checking for fake responses and people who are just not basically doing the research as you need them to do it.

To learn more about recruitment methods and best practices, dive into IxDF’s Data-Driven Design: Quantitative Research for UX course.

How long should a card sorting session last?

It usually depends on how complex the content is and the method. However, as a general guideline, a session should last between 15 to 30 minutes.

In the video, UX Consultant, Donna Spencer, explains the type and level of content of a card sort.

Show Hide video transcript

00:00:00 --> 00:00:30

So, what we want is content that is all at the *same level*. So, what I mean by that is we need it to be the same kind of granularity, the same kind of part of a hierarchy. An example that I saw a very long time ago that's a good example for this is I saw somebody running a card sort about tourism content. And in the tourism content, they had things like a hotel name, a zoo to visit,
00:00:30 --> 00:01:00

a winery to visit, they had some stuff about the geography, and then they had a card called 'accommodation'. So, you can think like if you've got a hotel – actually, I think they had a card of 'things to do' as well. So, you have like a 'hotel' card and an 'accommodation' card. They're different levels of hierarchy. A hotel is more detailed than the broad concept of accommodation. So, we don't want that 'accommodation' card in the mix because people will put those together
00:01:00 --> 00:01:34

– you cannot stop them from putting a hotel and accommodation together. And then, you haven't really learned about the content and the sort. You've just learned the thing that was going to happen. So, you want to make sure that they're – and the level is a hard concept, but you can see it in your content; like, you want either quite detailed things or fairly broad things, but they need to be the same. You want the cards to be a good selection that represents the content you'll be working with in the end.
00:01:34 --> 00:02:04

If you were card sorting for a supermarket, a grocery store, but you didn't include anything cold, so no frozen foods, no refrigerated foods, and then you ran the card sort, you're going to learn about some things but you're not going to learn about everything. And then you won't have information to cover the things that you haven't covered in the sort. So, you want to have really good coverage across everything that you're arranging.
00:02:04 --> 00:02:35

And this can be hard because if you're working on a site that's large, you need to be able to get good coverage. You also need people to be able to actually group the content. So, if you get a good broad selection that covers your content but then people look at it and they go, 'Well, I can see some chocolate and some apples and some cheese and some tinned foods and things, but I can't make a *group* out of those,'
00:02:35 --> 00:02:49

then you haven't learned anything from the activity. So, this is a juggle – you need to get a breadth that covers your content but still people can make groups out of.

Learn more in the Master Class How To Use Card Sorting For Better Information Architecture.

How can card sorting results inform website or app structure?

Card sorting results inform the structure of websites or apps, as they reflect how users categorize and understand content. Here's how you can leverage the results:

The way participants group items in card sorting provides direct insight into how they logically categorize information. This creates a structure that aligns with users' mental models, making navigation more intuitive and user-centered.
Analyze common groupings and labels to design navigation menus and categories that feel natural to users. This includes naming menu items and grouping content under these categories.
Card sorting helps identify which content potential users consider most important or related. This informs the hierarchy on your website or app and ensures the most relevant information is easily accessible.
When you organize content in a way that reflects users' expectations, you increase the findability of information. Users are more likely to quickly find what they're looking for, improving the overall user experience.
The process may reveal content areas that users find confusing (gaps) or perceive as overlapping (redundancies). From there, you can refine and clarify content or merge/split categories as needed.
Understanding how users categorize information can help your team design more efficient user flows and journeys through the website or app.
Card sorting is often part of an iterative design process. You can use participants’ feedback to make adjustments and then test again to refine the structure further.
Since card sorting often involves diverse participants, results are inclusive which helps your website or app’s structure cater to a broader audience.
Finally, perhaps a non-obvious use of card sorting is to highlight the differences in the way domain experts and non-domain experts think about the same website or app structure. Card sorting expert, Donna Spencer, explains more in the video below.

Show Hide video transcript

00:00:00 --> 00:00:30

One of the not-obvious uses of card sorting is to show people who have a really good, deep technical understanding of a domain – to show them that people who don't know this yet think really, really different about it. And that can actually be one of the big findings – it's: You think like this because you *know* stuff. The people who are using it think like this because *they don't*.
00:00:30 --> 00:00:54

If we arrange it your way, they won't be able to comprehend it. Or even different teams in an organization – you think that way; they think that way. There isn't a right way to do this. So, it's not only about putting content together to make a hierarchy for an IA. There are deeper and more useful aspects for this process.

What are some advantages of digital card sorting over physical card sorting?

Digital card sorting offers several advantages over physical card sorting, such as:

Digital tools automatically collect and analyze data quickly and with the ability to generate statistical reports and visual representations of the data.
It easily handles more cards and participants than physical sorting, which is often limited by physical space and materials.
It eliminates the need for physical materials, rental space, and to transcribe results from physical cards. Also, it reduces time spent on set-up and managing the session.
Participants might feel more at ease in their own environment, leading to more genuine responses. They can also complete the task at their convenience.
Digital card sorting is more environmentally friendly as it reduces the need for paper-based materials.

How can you integrate card sorting findings into wireframes and prototypes?

Integrating card sorting results into wireframes and prototypes involves a few key steps.

First, develop the information architecture (IA) using the categories and groups identified in the card sort. This IA acts as a blueprint for content organization, aligning it with user expectations. Next, design the navigation menus—use labels participants preferred during the card sorting exercise to create an intuitive experience. Organize each page or section based on card sorting findings and emphasize the most crucial items for clear content hierarchy.

Wireframes for different screen sizes. Card sorting helps inform wireframes and prototypes whether on paper or digitally. Designers can use the feedback from the sessions to inspire their initial low-fidelity (lo-fi) designs.

© Interaction Design Foundation, CC BY-SA 4.0

As you create wireframes, guarantee they illustrate the basic layout and content allocation based on these insights. Then, map out the website or app user flow with logical and efficient paths. Incorporate the initial data into early wireframes and prototypes, then validate and refine design decisions through user testing.

Consider the contextual placement of elements, not just categorization, to guide their relationship on individual screens or pages. Be responsive to feedback; adapt your wireframes and prototypes as needed. Finally, integrate visual design elements that enhance the IA so the design is accessible and inclusive for diverse users.

You can learn more about sketching wireframes in the course Interaction Design for Usability with William Hudson.

How do you set realistic objectives for a card sorting study?

Setting realistic objectives for a card sorting study is crucial for its success and relevance. Consider the following points:

Understand the project goals.
Identify key content areas.
Determine the scope of the session.
Choose the right method.
Consider participants’ profiles.
Be specific and measurable when giving instructions.
Plan for actionable results.
Consider resource availability and remain flexible.
Review and adjust objectives - iterate!
Ensure objectives are user-centric.

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Literature on Card Sorting

Here's the entire UX literature on Card Sorting by the Interaction Design Foundation, collated in one place:

Card Sorting: The Ultimate Guide (in 2025)

Whenever you’re working in user experience (UX) design, you’ve got to understand user expectations if you’re going to be able to group information effectively, and card sorting is a handy—and relatively simple—way to engage participants so they categorize topics in a way they find logical. This approach is an effective one to help you uncover user preferences for content arrangement to help create a coherent and user-friendly structure for websites or apps.

You’ve likely come across the concept of card sorting—whether or not you’re in UX—and researchers use the popular, low-tech research technique to organize data sets. What it’s especially useful for are information architecture, menu structures, workflows, and website navigation. You may find it easy to run a card sort, but—be that as it may—there’s an art to it and things that you’ll need to get right; let’s get into the do’s and don’ts of it now.

What Is Card Sorting in UX Research?

When you do a card sort, you get users involved in organizing information and they’ll sort labeled cards into groups from what they understand about the relevant things. The purpose behind the process is it shows how users expect to find information on a website, and the insights you get from card sorting are—potentially—very powerful tools you can use to create intuitive and user-friendly website structures as a designer, structures that run in line with the users' mental models for seamless and efficient navigation.

Here’s a little example of how a card sorting session might go. Picture a group of user participants and you’re the researcher for a grocery store brand that’s creating a new website. You want to see how customers are going to approach the store—namely, what they’d put various items under in terms of categories. So, you deal out cards to the participants, cards with things like “Dairy,” “Bread,” “Fruits,” “Toothpaste,” “Deodorant,” “Eggs,” “Insect Repellent” and “Garbage Bags.” Now, the users put these into categories, like “Dairy” and “Bread” under “Daily Essentials,” “Toothpaste” and “Deodorant” under “Toiletries,” and “Garbage Bags” and “Insect Repellent” under “Sundries.” But some participants might consider, say, toothpaste a daily essential, for example. Even though they wouldn’t need to buy a whole tube of it more than once every two weeks, they might still term it a daily essential.

Welcome to the “magic” of card sorting, where logic can be subjective! In the above example, you’d be able to harvest—no pun intended—these insights to organize the website's menu and categories in the way that works best, and make it easy for shoppers to find what they need.

Watch as Design Consultant Donna Spencer provides an excellent example of a card sort in her IxDF Master Class, “How To Use Card Sorting For Better Information Architecture.”

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00:00:00 --> 00:00:34

The first thing you want to do is just *poke around*. If you've got it in a big spreadsheet or something, you might print it out on the wall, you sort of look at: What *happened* here? What did people *do*? What's kind of *interesting*? What *ways* did they do things? So, just an initial – 'What is this?' You might look through and say, 'Okay, Participant 1 – how did they do that? Oh, look – they sorted the groceries into the way it's sorted in a physical store.'
00:00:34 --> 00:01:03

Or, 'They sorted the groceries according to taste – interesting. It's just interesting.' Then you might go through and go, 'What is interesting about those groupings? Ah, wow – okay, this is different to that. I didn't expect to see people put those things together.' And then look at like different participants and say, 'What did people do that is *different*? Did everybody do exactly the same thing? Did they do all different things?
00:01:03 --> 00:01:26

Did they do some similar things and some differents? What is interesting about that?' – What kind of variations did you notice? And then, also, *what words* did people put on the cards? What language and labeling came out of this activity is super interesting. So... you're kind of just burying yourself in data initially; you're not trying to get to a result really quickly.

Why Do We Use Card Sorting?

Card sorting helps you understand how people think—and that’s not just about items but about ideas and concepts, too—and when you’ve got a good idea of how users think about encountering a digital design, for instance, you’ll be able to arrange content in a way that makes sense to them when you are organizing a website's structure or navigation. A card sort can show how someone’s background and personal experience affect their thoughts and the way they envision things, and give you a handy vantage point to accommodate how they see their world and fine-tune your interface design to their way of seeing and grouping things.

When you’ve done a successful card sort, you’ll learn the following, too:

What goes together and why
Different ideas and methods of organizing content
If people think similarly or differently about something or things

While people use card sorting more often to create IAs, website navigation, and menu structures, the strength of card sorting is how it translates across the board, and you can also use it for internal communication and politics.

In this clip, Donna Spencer explains one of the less obvious uses of card sorting and what it can tell us.

Show Hide video transcript

00:00:00 --> 00:00:30

One of the not-obvious uses of card sorting is to show people who have a really good, deep technical understanding of a domain – to show them that people who don't know this yet think really, really different about it. And that can actually be one of the big findings – it's: You think like this because you *know* stuff. The people who are using it think like this because *they don't*.
00:00:30 --> 00:00:54

If we arrange it your way, they won't be able to comprehend it. Or even different teams in an organization – you think that way; they think that way. There isn't a right way to do this. So, it's not only about putting content together to make a hierarchy for an IA. There are deeper and more useful aspects for this process.

You’ll find card sorting effective for enhancing your IA, sure, but don’t expect it to actually create the IA for you—and that’s because card sorting is qualitative and exploratory, and therefore often yields results that are inconsistent and renders it unreliable to create an information architecture from scratch. On a similar note, it can’t tell you whether your IA works, and even though the card sort will inform your IA, you won’t know if it’s any good. Donna Spencer suggests you use a tree test instead.

What’s more, card sorting isn’t suitable when you’re after the following:

Definitive, black and white answers
Quantitative, statistically valid data

Advantages and Disadvantages of Card Sorting

Advantages of Card Sorting

Simplicity: You’ll find that very few techniques are as easy as just handing someone a deck of cards and then asking them to sort them out—it’s so direct and basic.
Cost-effectiveness: You’re talking about some plain card and either printer ink or pen ink. You may also use some sticky notes or some Sellotape.
Speed: You can run this exercise quickly—and as many times as you have to until you get the data you need out of it.
User-centric: Card sorting focuses on user input—and that’s crucial for product success—and since it values user perspectives over assumptions, it can save many a design headache later.
Familiarity: It’s a technique that’s been around for hundreds of years and people—including your users—can “get” it easily and not need much explaining on how to do it.
Insightful: It isn’t the most in-depth approach, to be sure; still, card sorting does manage to provide valuable insights as regards how users categorize information—and they’re insights you can leverage well.

Disadvantages of Card Sorting

Task ignorance: Card sorting doesn't always consider exactly what you’re going to do with the insights that come from it—the practical application of data, in other words—so it pays to be cautious about how you’re going to translate those results into your product.

Inconsistency: Card sorting results can vary thanks to all those little individual differences in perception and categorization; you know, the little quirks and idiosyncrasies people have about how they see things in their world—or, maybe more apt here, their worlds, plural.

Time-consuming analysis: It’s quick to perform a card sort, sure, but to analyze card sorting results—that’s another matter, and it can translate to long treatment times, especially when you’ve got complex data sets at hand.

Surface-level insights: Some fundamental issues or context may be beyond the technique’s scope, since it can often only go so deep, and the top-level view a card sort might give you can have deeper, more intricate matters under it—matters which you might well need to look more closely into.

Types of Card Sorting

Card sorting is a versatile UX research technique and there are different types of it that can suit various needs, with each one offering unique insights—so, without further ado, let's explore these card sorting methods in detail, plus some examples.

1. Open Card Sorting

In this one, participants sort cards into categories which they make themselves, and that’s great if you want to know how users naturally group information and without preconceived categories of yours (or someone else’s) that may guide them away from how they’d see things otherwise.

Say you get a stack of cards and you sort cards into groups and label these groups; that’s an open card sort.

2. Closed Card Sorting

Closed card sorting finds participants sorting cards into predefined categories, and it’s a method you’ll find useful whenever you want to test specific groupings or if you’ve got a basic structure.

You get a stack of cards, and you’ve got to sort them based on groups the researchers have created—that’s a closed card sort.

3. Hybrid Card Sorting

Hybrid card sorting is a blend of open and closed card sorting where participants sort cards into provided categories but they can create new ones of their own, too, which means a good deal of flexibility for them and deeper insights into user preferences for you.

Example: Picture a website you might be building for a travel operator or brand that handles tourist resorts or the like. You’re doing a card sort where users can sort destinations and activities into different categories (e.g., Beaches, Mountains, and Cities). The beauty of this card sort, though, is that it’s a hybrid one and users aren’t restricted to the categories you’ve given them—in fact, they can make up totally new ones like “Adventure Travel,” “Historic Trails,” “Family-friendly” (the list can go on) and, from this, you’ll be able to get more nuanced insights.

4. Reverse Card Sorting

Reverse card sorting—or, tree testing—is a card sort that (you guessed it) works in reverse and users have got to work backwards. So, instead of actually sorting cards, they deduce where a card should go inside of an existing structure, and that’s a great way to test how intuitive a site’s navigation is. Then, at the end of a session, you can evaluate the results—compare how many users placed the item in the “right” category.

Example: Let’s imagine in a website that’s all about cookery (recipes and maybe some products and services, too) that you hand out a specific recipe card to users (say, “Vegetarian Lasagna”) and they’ve got to find where it fits inside of a pre-made set of categories. That way, you can test if the organization of the site really does make sense to each user.

5. Modified-Delphi Card Sorting

Modified-Delphi card sorting is a method that evolves with each participant, and it begins with the first participant’s conducting a complete card sort where they organize and arrange the items in a way that makes sense to them. That forms the baseline model, and then subsequent participants refine this model—each one builds on the previous person's work, and so the process continues.

Example: We’ve got a team working on designing a health app, and they categorize features like “Exercise Tracking,” “Diet Plans,” and “Mental Wellness.” Each one of the participants first sorts these features on their own; then, when they’re done, they discuss their choices collectively and that way can reach a consensus.

6. Remote Card Sorting

Remote card sorting—just as it sounds—lets participants sort cards through online tools, and it’s a handy and convenient way to reach a broader and more diverse audience.

Example: An e-commerce brand’s site might use an online tool to have users sort product categories like Electronics, Home Goods, and Fashion, and participants do the task on their computers instead of having to attend a formal session.

Tree Testing vs Card Sorting: Which One Should You Prefer?

First off, here’s a quick overview of both techniques:

In tree testing, users navigate through a simplified text version of the site's structure to find items—it’s something that reveals how easily they can locate information.
Card sorting helps you understand how users categorize information, and, there, participants organize topics or items into groups that make sense to them.

William Hudson, UX Designer and Author, discusses tree testing in more detail here, so watch this video to understand more about getting started with tree testing.

Show Hide video transcript

00:00:00 --> 00:00:33

We're going to be talking about getting started with tree testing. Tree testing *simulates* a hierarchical navigation structure. So, those are just the layers of menu that you expect your users to wade through in a typical application or website or mobile app, for example. And it's purely text-based; so, this is simply how Optimal Workshop, and their solution is called "Treejacks".
00:00:33 --> 00:01:00

Here, for example, it's Home phone, My Account, Help and Support, Internet and so on. And this is how – you can change the colors. I believe you might even be able to change the fonts, but that's all you can do. There is nothing else that you can do in terms of the visual presentation. And, again, that's one of the strengths – that you're not distracting people with unnecessarily complex visual designs – you're looking purely at the navigation.
00:01:00 --> 00:01:31

So, starting from the home page, each level of navigation is shown to participants in turn. And so, that's the top level. So, being given a task, I now have to click on one of those *six* items. Most tree testing services ask you to upload a spreadsheet with your hierarchy. And I'll give you a full example of a project where we've done exactly that, and you can see the format; it's very straightforward. It's just done hierarchically in a spreadsheet. Separately, you provide a list of tasks. And, for a lot of these testing services,
00:01:31 --> 00:02:02

you provide a correct response or a set of correct responses. I did work on one site where we ended up giving people all kinds of correct responses because they could get to the products that were being sold a number of different ways and they were all valid; but, of course, that ended up contributing to users' confusion, as well, unfortunately. But, for basic tree testing, no visual design is needed. So, we just upload the spreadsheet of the menu hierarchy and off we go.
00:02:02 --> 00:02:33

As the researcher, you can specify how many of the tasks each participant must do and whether tasks can be skipped. And if you're really keen-eyed, you might see in the top right-hand corner of this screenshot on the right that it says "Skip this task" – which is user-definable, researcher-definable. You can decide whether your participants are able to skip tasks as they're going along. Optimal Workshop has some recommendations for their Treejack tool. They suggest a maximum of 10 tasks, and of course that can vary.
00:02:33 --> 00:03:02

If they're really complicated tasks, you might want to reduce that number. If they're really simple tasks and you've got a really short tree navigation hierarchy, then you might increase that number. Do write tasks that test the part of the website you want to improve. So, if you're working in a particular area, then focus on that – obviously. Write tasks as hypothetical scenarios based on your typical visitors. So, you want to make this sound like something realistic from their perspective.
00:03:02 --> 00:03:33

And don't mimic the language that's used in your navigation. Use different language than the labels on your tree. Otherwise, it just becomes a word-matching exercise and you will have achieved nothing; you will end up going to all this effort and expense and you will get results that are of no value to you because all people did was to look at what you wrote and match it to the text on the screen. So, don't do that, as you must use common words that are *natural* for your tasks without mirroring the navigation terms.
00:03:33 --> 00:04:00

This is a simple summary result for the tree testing. This one again is from Optimal Workshop, but most of the tools have a very similar kind of summary. In this case, we've got a pie chart and the successes are shown in green. And there is the idea of a direct success and an indirect success. The direct success is when people have gone straight to the correct answer without any variation.
00:04:00 --> 00:04:32

Indirect is where they've gone down one path in your navigation hierarchy and had to backtrack. So, if they still end up in the right place, that's still referred to as a success – but it's an *indirect* success. And you can analyze these figures separately if you wish. If you're getting a lot of indirect success, it means people are getting there in the end but they're confused. And, of course, in *real life*, if you're talking about an actual website or mobile app, you may see a lot of people simply giving up rather than persevering with the study, which is what they're doing when you're doing the research.
00:04:32 --> 00:05:03

The red is fail, and that too is direct and indirect. And in both, in the same situation, the same concepts apply that if they've wandered around a bit to fail, it's indirect, but if they've actually gone straight to an incorrect solution without backtracking, then it's called a direct failure. And then, finally, at the bottom of this chart, you have the skips, which hopefully will be zero, and certainly will be zero if you said that users are not allowed to skip tasks.

If you’re going to pick between these methods, it’ll depend on your project's stage and goals for which one’s best. If your current IA seems to be causing problems and you need to assess how effective it is, then do start with tree testing, as it’ll show you where users struggle to find information; what’s more, it’ll give you a clear picture of the areas that need improvement.

After you’ve grasped what’s wrong with your current IA through tree testing, then it’s time to go ahead with card sorting so you can get a helping hand at understanding how users think you should organize your content. An open card sort is what lets you capture your users' natural categorizations and terminologies—that’s a crucial ingredient for making an intuitive and user-friendly IA.

Last—but not least—do another tree test after you’ve reworked your IA based on card sorting insights. It’ll help you compare the new IA against the old one using similar tasks, and it’s a comparison that will highlight just how effective your changes actually are—plus, it’ll help you get your updated IA more in line with users’ expectations and behaviors.

What Tools Do You Need to Conduct a Card Sort?

Card sorting doesn’t call for any fancy tools, and you can just use basic office supplies to sort with, say, index cards and markers. With that said, though, there are several digital tools you’ll find that can enhance the research process, and below we show six online tools that can help you streamline your card-sorting process.

Considerations for Choosing a Card Sorting Tool

You’ve got to consider several factors while you’re picking the right card-sorting tool for your project, so let’s see four important ones:

Study size: Tools vary in their capacity to handle different study sizes, so, for larger studies, go for tools that have got robust data-handling capabilities.

Study format: Decide if your study is to be a remote or an in-person one, and that’s because tools for remote sorting have got features like online collaboration, while in-person tools might focus more on physical interaction.

Analysis needs: Some tools come equipped with advanced analytics, which are ideal for detailed data interpretation—so, work out how deeply you’ll need the analysis to go.

Budget constraints: Be sure to balance the need for features with what you’re prepared to pay for.

How to Run a Card Sort?

1. Set Clear Objectives

Just like any other research methodology, you’ve got to work out exactly what it is that you want to learn and then—once you’ve done that—consider whether card sorting is actually the best way to learn it.

2. Select the Right Tools

You’ll need some cards, and plain index cards or Post-it notes work well for an in-person card sort. You can use anywhere from a handful to about 100—sometimes more, but it’ll depend on the project—but do try to limit the number of cards to 50 if you’re going to do a virtual card sort.

Index cards or Post-it notes work well for card sorting.

You can find several card sorting tools online—such as OptimalSort, but applications like Trello, Miro, or GitHub can work well, too. If you use a card sorting tool, it’ll offer you a great benefit, and that’s immediate availability for analysis on the computer and—what’s more and a happy little extra—you’ll be spared the tedious task of transferring results from physical cards to a spreadsheet.

3. Write Clear Instructions for Effective Participation

You’ve got to prepare your instructions before your card sort begins—and that’s no matter if it’s to be done physically or digitally—and they’ve got to be clear so your participants know what to do.

Think about the questions people may ask, and, if you’re conducting your card sort online, be sure to include the answers to those questions in your instructions so you’ll pre-empt them. If you’re going to be doing an in-person card sort, prepare your answers in advance.

Participants may ask questions like:

“This card fits with more than one group; can I put it in multiple places?”
“I don’t understand this card; can I exclude it?”
“There seems to be some content missing; can I add a card?”

Another important factor for you to consider before you start is your analysis, and it’s here where you’ve got to think about the optimal results you want; as in, you may wish to have only five people in your sample or as many as 25. If you do an in-person card sort, then it’s important that you figure out how to get your data from the physical world into the digital.

4. Organize the Content

Your content is one of the most critical parts of card sorting—if not the most—and your results will come from the content you give your participants; and that’s why you’ve got to evaluate them well.

Donna Spencer describes some of the elements and their significance of your card sorting content:

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So, what we want is content that is all at the *same level*. So, what I mean by that is we need it to be the same kind of granularity, the same kind of part of a hierarchy. An example that I saw a very long time ago that's a good example for this is I saw somebody running a card sort about tourism content. And in the tourism content, they had things like a hotel name, a zoo to visit,
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a winery to visit, they had some stuff about the geography, and then they had a card called 'accommodation'. So, you can think like if you've got a hotel – actually, I think they had a card of 'things to do' as well. So, you have like a 'hotel' card and an 'accommodation' card. They're different levels of hierarchy. A hotel is more detailed than the broad concept of accommodation. So, we don't want that 'accommodation' card in the mix because people will put those together
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– you cannot stop them from putting a hotel and accommodation together. And then, you haven't really learned about the content and the sort. You've just learned the thing that was going to happen. So, you want to make sure that they're – and the level is a hard concept, but you can see it in your content; like, you want either quite detailed things or fairly broad things, but they need to be the same. You want the cards to be a good selection that represents the content you'll be working with in the end.
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If you were card sorting for a supermarket, a grocery store, but you didn't include anything cold, so no frozen foods, no refrigerated foods, and then you ran the card sort, you're going to learn about some things but you're not going to learn about everything. And then you won't have information to cover the things that you haven't covered in the sort. So, you want to have really good coverage across everything that you're arranging.
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And this can be hard because if you're working on a site that's large, you need to be able to get good coverage. You also need people to be able to actually group the content. So, if you get a good broad selection that covers your content but then people look at it and they go, 'Well, I can see some chocolate and some apples and some cheese and some tinned foods and things, but I can't make a *group* out of those,'
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then you haven't learned anything from the activity. So, this is a juggle – you need to get a breadth that covers your content but still people can make groups out of.

If you’ve got an extensive content set, it may be wise to split them into several card sorts so you get the desired results.

Steer clear of jargon. Avoiding technical terms like that is a vital thing, and that’s because if people can’t understand your content, they won’t be able to group the cards that well and it may affect your results and distort things. People tend to force associations and make connections that aren’t there when they don’t fully understand the information, and that’s why it’s so important to be sure you use clear and direct content; it’s one of the factors that your getting the best possible outcome depends on.

5. Choose the Participants

The people you select for your card sort are really important; Donna Spencer explains what you need to consider.

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The people who you invite to be involved are also super important – not quite as important as the content, but still very important. Small groups of people working in-person around a table is literally the best thing because you can listen to the people and the conversation that they have and that conversation is so much more useful than the actual results that you get. We still could do this remotely, especially if people can get together and you can moderate via video.
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Or... I haven't ever done this, but we could kind of do it with a Zoom call and like a MURAL board and get people to poke things around, but it's still – any time that you have a chance to do it face-to-face, do that. And one of the things that I like to do if I need to do remote card sorts is just at least a couple face-to-face because then I can really hear that discussion and understand what's going on with just some of the group and then I can look at the results from the others.
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We want to make sure that *the people are also of the same level*. And this goes for any kind of group research or group activities. If you have a boss and somebody much more junior, the junior people will always just defer to the boss. If you have technical people and non-technical people, the non-technical people might just go, 'I don't know!' and follow the technical people. So, you want to make sure that people are at the same kind of levels, and, if you can, the same kinds of work or life situation, or the same kinds of users
00:01:37 --> 00:02:06

because then you can listen to them discuss their experiences and they'll do that better if they're the same types. We do that for most kind of focus groups and things. If you are doing it in-person, just don't try to fill up a room with lots of people. Do it with one group or have one facilitator per group because, as I said, the conversation is the super-useful part of this and it's really worth listening.
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What I used to do when I did these in-person is I would kind of just sit up at the end of the table, kind of be typing in results from a previous sort; so, I don't look like I'm hovering over them. I look like I'm busy kind of just listening a bit. But I'm listening to every single word they're saying! So, again, if we're working remotely and we're doing a remote card sort, you still want to have a good representative set of people and see if you can figure out a way of getting
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people to work together remotely. Otherwise, they'll just do it by themselves one at a time, and you'll still get good results, but you just won't hear them discussing their reasoning.

You might wonder about what the ideal number of participants is for your card sort. Sure, due to space and time constraints, you’ll find in-person card sorts easier to manage. For an online card sort, send the exercise to a small group first, as it’ll let you revise and adapt your card sort and, if you have to, you can always reach out to more people later.

6. Facilitate the Process

It’s best to start out with an introduction, give your participants a bit of context, and then go through the instructions. In a physical card sort, you can speak to your participants, but, if you do it remotely via Zoom or something similar, then you’ll have to write them for most virtual card sorts.

Don’t keep explaining or overhelp. One or some of your participants may likely look to you for guidance in the session; you can repeat the instructions, sure, but don’t go into more detail. You’ll get better results if they carry on without further assistance from you.

It’s better not to tell the people in your card sort that they’ll label the groups they create. Unfortunately, that’s not something you can do in a virtual card sort with an online tool. Language and labels will influence the categories—your participants are going to think more about the categories than the content that goes into them, and you’ll lose the fluidity of the card-sorting process.

Take notes during the card sorting process, and be sure to listen to what your participants say. Try not to hover, though, as this can get in the way of the organic conversation between the participants.

Offer incentives—they’re remarkably effective—and you’ll find this especially valuable in virtual settings where people commit less. Think about what might motivate your participants—such as a small gift, a discount voucher, or even a chance to win something bigger.

7. Analyze the Results

You'll find analysis tends to be easier with an online card sort, and that’s because it directly inputs results into your computer. For an in-person card sort, though, you can use numbers and a spreadsheet to simplify things, and number each card and put it into your spreadsheet, along with the card’s content; then, all you’ve got to do is input the numbers.

First, you want to get an overview. Donna Spencer shares how you do this:

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The first thing you want to do is just *poke around*. If you've got it in a big spreadsheet or something, you might print it out on the wall, you sort of look at: What *happened* here? What did people *do*? What's kind of *interesting*? What *ways* did they do things? So, just an initial – 'What is this?' You might look through and say, 'Okay, Participant 1 – how did they do that? Oh, look – they sorted the groceries into the way it's sorted in a physical store.'
00:00:34 --> 00:01:03

Or, 'They sorted the groceries according to taste – interesting. It's just interesting.' Then you might go through and go, 'What is interesting about those groupings? Ah, wow – okay, this is different to that. I didn't expect to see people put those things together.' And then look at like different participants and say, 'What did people do that is *different*? Did everybody do exactly the same thing? Did they do all different things?
00:01:03 --> 00:01:26

Did they do some similar things and some differents? What is interesting about that?' – What kind of variations did you notice? And then, also, *what words* did people put on the cards? What language and labeling came out of this activity is super interesting. So... you're kind of just burying yourself in data initially; you're not trying to get to a result really quickly.

Then, go into more depth, compare participants in detail, and see what the similarities and differences in their results are. And then look at what insights you can pull from the variations you find. For an IA, did they all make an “About Us” section? If they did, then what cards did they put into that group? Sometimes, people will create the same groupings but put different cards into the groupings. You can look at specific cards, too, and see where each person puts them.

Remember, it’s a qualitative research exercise, so you’re trying to get insights—and you want to discover what’s interesting or what stands out in your results. Review your user research objectives and what you wanted to learn from the card sort.

Learn more about quantitative and qualitative user research in this video.

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Ah, well – it's a lovely day here in Tiree. I'm looking out the window again. But how do we know it's a lovely day? Well, I could – I won't turn the camera around to show you, because I'll probably never get it pointing back again. But I can tell you the Sun's shining. It's a blue sky. I could go and measure the temperature. It's probably not that warm, because it's not early in the year. But there's a number of metrics or measures I could use. Or perhaps I should go out and talk to people and see if there's people sitting out and saying how lovely it is
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or if they're all huddled inside. Now, for me, this sunny day seems like a good day. But last week, it was the Tiree Wave Classic. And there were people windsurfing. The best day for them was not a sunny day. It was actually quite a dull day, quite a cold day. But it was the day with the best wind. They didn't care about the Sun; they cared about the wind. So, if I'd asked them, I might have gotten a very different answer than if I'd asked a different visitor to the island
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or if you'd asked me about it. And it can be almost a conflict between people within HCI. It's between those who are more *quantitative*. So, when I was talking about the sunny day, I could go and measure the temperature. I could measure the wind speed if I was a surfer – a whole lot of *numbers* about it – as opposed to those who want to take a more *qualitative* approach. So, instead of measuring the temperature, those are the people who'd want to talk to people to find out more about what *it means* to be a good day.
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And we could do the same for an interface. I can look at a phone and say, "Okay, how long did it take me to make a phone call?" Or I could ask somebody whether they're happy with it: What does the phone make them feel about? – different kinds of questions to ask. Also, you might ask those questions – and you can ask this in both a qualitative and quantitative way – in a sealed setting. You might take somebody into a room, give them perhaps a new interface to play with. You might – so, take the computer, give them a set of tasks to do and see how long they take to do it. Or what you might do is go out and watch
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people in their real lives using some piece of – it might be existing software; it might be new software, or just actually observing how they do things. There's a bit of overlap here – I should have mentioned at the beginning – between *evaluation techniques* and *empirical studies*. And you might do empirical studies very, very early on. And they share a lot of features with evaluation. They're much more likely to be wild studies. And there are advantages to each. In a laboratory situation, when you've brought people in,
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you can control what they're doing, you can guide them in particular ways. However, that tends to make it both more – shall we say – *robust* that you know what's going on but less about the real situation. In the real world, it's what people often call "ecologically valid" – it's about what they *really* are up to. But it is much less controlled, harder to measure – all sorts of things. Very often – I mean, it's rare or it's rarer to find more quantitative in-the-wild studies, but you can find both.
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You can both go out and perhaps do a measure of people outside. You might – you know – well, go out on a sunny day and see how many people are smiling. Count the number of smiling people each day and use that as your measure – a very quantitative measure that's in the wild. More often, you might in the wild just go and ask people. It's a more qualitative thing. Similarly, in the lab, you might do a quantitative thing – some sort of measurement – or you might ask something more qualitative – more open-ended. Particularly quantitative and qualitative methods,
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which are often seen as very, very different, and people will tend to focus on one *or* the other. *Personally*, I find that they fit together. *Quantitative* methods tend to tell me whether something happens and how common it is to happen, whether it's something I actually expect to see in practice commonly. *Qualitative* methods – the ones which are more about asking people open-ended questions – either to both tell me *new* things that I didn't think about before,
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but also give me the *why* answers if I'm trying to understand *why* it is I'm seeing a phenomenon. So, the quantitative things – the measurements – say, "Yeah, there's something happening. People are finding this feature difficult." The qualitative thing helps me understand what it is about it that's difficult and helps me to solve it. So, I find they give you *complementary things* – they work together. The other thing you have to think about when choosing methods is about *what's appropriate for the particular situation*. And these things don't always work.
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Sometimes, you can't do an in-the-wild experiment. If it's about, for instance, systems for people in outer space, you're going to have to do it in a laboratory. You're not going to go up there and experiment while people are flying around the planet. So, sometimes you can't do one thing or the other. It doesn't make sense. Similarly, with users – if you're designing something for chief executives of Fortune 100 companies, you're not going to get 20 of them in a room and do a user study with them.
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That's not practical. So, you have to understand what's practical, what's reasonable and choose your methods accordingly.

Last—but not least—apply your findings to your project, and it’s best to combine them with other research methods.

Card Sorting Tips and Best Practices

You can leverage card sorting as a powerful tool in user experience research, but its success really does hinge on how you conduct it. You can consider this method a vital step in designing intuitive interfaces.

With that said, though, you’ve got to stick to certain tips and best practices if you’re to fully capitalize on its benefits. Follow these guidelines (which are numbered to match the diagram above) as they’ll help you conduct productive card-sorting sessions that yield actionable results.

Use relevant and straightforward cards. Avoid jargon and complex terms—remember, the clarity of your cards can impact the quality of your results.
Choose a diverse group of participants who represent your target audience; diversity is a powerful engine that brings varied perspectives.
Whether it’s online or in-person, make sure you provide a user-friendly sorting environment for participants. For online sorts, pick a tool that's easy to navigate. For in-person ones, make sure there’s a comfortable and distraction-free space.
Give your participants concise, understandable instructions. Be sure to clarify any ambiguities and be ready to answer questions, remembering that good instructions lead to more accurate sorting.
Too many cards can swamp and overwhelm participants, so it’s best to aim for a balanced number that's comprehensive yet manageable, typically around 40-50 cards.
Encourage participants to think freely in open sorts, as that openness is what can lead to innovative categorizations and unexpected insights.
Observe things quietly during the sort, and make notes about how participants interact with the cards and listen to their comments. The user observations you make can end up being as valuable as the sort itself.
“Post-sort” (i.e. after the session), spend time analyzing the results. Look for patterns, outliers, and surprising groupings, and this detailed analysis is where the real value of card sorting lies.
Don't hesitate to run multiple card sorting sessions, and that’s because iterations can refine your understanding and lead to more robust conclusions.
Use card sorting with other research methods like surveys or user interviews—it’s wise as this multi-method approach will give you a more comprehensive view.
Test your method before using card sorting, and it’ll ensure that it’s not just effective but means it’s nice and tailorable for meaningful insights, too.

The Take Away

When you want to know how your users see and think about concepts and items you need to design for, then card sorting is a handy way to reveal patterns so you can organize websites, apps, and the like in a way that matches their mental models best. Key factors like approach (i.e., in-person or remote), objectives, tools, content, participants (including the number of them who are participating in the card sort) are things to bear in mind, although you’ll likely find the process straightforward—but you’ll need to consider each element with care.

To be sure, card sorting is one technique that can offer invaluable insights, but it’ll still call for you to be mindful that the results you get from it may not always be clear. That said, it’s still a handy go-to thanks to its low cost, simplicity, and low investment of effort—so try it, use it carefully, and you may come away with some of the most helpful insights you can hope for to guide how your website or app ends up looking and functioning successfully.

References and Where to Learn More

Dive deeper into card sorting with Donna Spencer’s Master Class, How To Use Card Sorting For Better Information Architecture

Learn how to run card sorts and what tools to use:

William Hudson explains How to Screen Research Participants

Learn more about Card Sorting

Take a deep dive into Card Sorting with our course Interaction Design for Usability .

This course will teach you fundamental usability concepts and methods and will tie them together with interaction and visual design. By completing the course, you will become equipped with the tools required to create products with outstanding user experience and usability. Your newly acquired knowledge will also enable you to reduce the costs, risk, and time required to design and implement such products.

You’ll learn to adopt a user-centered approach to UX design and usability so you can create user-friendly products that people love to use—for example, by allowing for user errors and providing timely feedback messages. More importantly, it is crucial that your entire team—developers, project managers, and product owners alike—adopt this holistic, user-centered mindset. This course therefore aims to provide any team member with just that: it will not only equip you with fundamental usability and design concepts, but also introduce you to lean and agile processes that will allow your whole team to become design-centric.

You should take this course if you belong to a team whose goal is to create a great product—whatever role you play in that. The fact of the matter is that usability experts and UX designers are just the tip of the iceberg when it comes to the people who influence the design of a product. A sound understanding of user-centered design processes is thus greatly beneficial whether you’re a UX designer, developer, or a newcomer to design who wants to be part of a product team one day.

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