Which comes first: card sorting or tree testing?

Usability guru Jared Spool has written extensively about the 'scent of information'. This term describes how users are always 'on the hunt' through a site, click by click, to find the content they’re looking for. Tree testing helps you deliver a strong scent by improving organisation (how you group your headings and subheadings) and labelling (what you call each of them).

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Anyone who’s seen a spy film knows there are always false scents and red herrings to lead the hero astray. And anyone who’s run a few tree tests has probably seen the same thing — headings and labels that lure participants to the wrong answer. We call these 'evil attractors'.In Part 1 of this article, we’ll look at what evil attractors are, how to spot them at the answer end of your tree, and how to fix them. In Part 2, we’ll look at how to spot them in the higher levels of your tree.

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The false scent — what it looks like in practice

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One of my favourite examples of an evil attractor comes from a tree test we ran for consumer.org.nz, a New Zealand consumer-review website (similar to Consumer Reports in the USA). Their site listed a wide range of consumer products in a tree several levels deep, and they wanted to try out a few ideas to make things easier to find as the site grew bigger.We ran the tests and got some useful answers, but we also noticed there was one particular subheading (Home > Appliances > Personal) that got clicks from participants looking for very different things — mobile phones, vacuum cleaners, home-theatre systems, and so on:

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The website intended the Personal appliance category to be for products like electric shavers and curling irons. But apparently, Personal meant many things to our participants: they also went there for 'personal' items like mobile phones and cordless drills that actually lived somewhere else.This is the false scent — the heading that attracts clicks when it shouldn’t, leading participants astray. Hence this definition: an evil attractor is a heading that draws unwanted traffic across several unrelated tasks.

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Evil attractors lead your users astray

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Attracting clicks isn’t a bad thing in itself. After all, that’s what a good heading does — it attracts clicks for the content it contains (and discourages clicks for everything else). Evil attractors, on the other hand, attract clicks for things they shouldn’t. These attractors lure users down the wrong path, and when users find themselves in the wrong place they'll either back up and try elsewhere (if they’re patient) or give up (if they’re not). Because these attractor topics are magnets for the user’s attention, they make it less likely that your user will get to the place you intended. The other evil part of these attractors is the way they hide in the shadows. Most of the time, they don’t get the lion’s share of traffic for a given task. Instead, they’ll poach 5–10% of the responses, luring away a fraction of users who might otherwise have found the right answer.

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Find evil attractors easily in your data

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The easiest attractors to spot are those at the answer end of your tree (where participants ended up for each task). If we can look across tasks for similar wrong answers, then we can see which of these might be evil attractors.In your Treejack results, the Destinations tab lets you do just that. Here’s more of the consumer.org.nz example:

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Normally, when you look at this view, you’re looking down a column for big hits and misses for a specific task. To look for evil attractors, however, you’re looking for patterns across rows. In other words, you’re looking horizontally, not vertically. If we do that here, we immediately notice the row for Personal (highlighted yellow). See all those hits along the row? Those hits indicate an attractor — steady traffic across many tasks that seem to have little in common. But remember, traffic alone is not enough. We’re looking for unwanted traffic across unrelated tasks. Do we see that here? Well, it looks like the tasks (about cameras, drills, laptops, vacuums, and so on) are not that closely related. We wouldn’t expect users to go to the same topic for each of these. And the answer they chose, Personal, certainly doesn’t seem to be the destination we intended. While we could rationalise why they chose this answer, it is definitely unwanted from an IA perspective. So yes, in this case, we seem to have caught an evil attractor red-handed. Here’s a heading that’s getting steady traffic where it shouldn’t.

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Evil attractors are usually the result of ambiguity

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It’s usually quite simple to figure out why an item in your tree is an evil attractor. In almost all cases, it’s because the item is vague or ambiguous — a word or phrase that could mean different things to different people. Look at our example above. In the context of a consumer-review site, Personal is too general to be a good heading. It could mean products you wear, or carry, or use in the bathroom, or a number of things. So, when those participants come along clutching a task, and they see Personal, a few of them think 'That looks like it might be what I’m looking for', and they go that way.Individually, those choices may be defensible, but as an information architect, are you really going to group mobile phones with vacuum cleaners? The 'personal' link between them is tenuous at best.

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Destroy evil attractors by being specific

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Just as it’s easy to see why most attractors attract, it’s usually easy to fix them. Evil attractors trade in vagueness and ambiguity, so the obvious remedy is to make those headings more concrete and specific. In the consumer-site example, we looked at the actual content under the Personal heading. It turned out to be items like shavers, curling irons, and hair dryers. A quick discussion yielded Personal care as a promising replacement — one that should deter people looking for mobile phones and jewellery and the like.In the second round of tree testing, among the other changes we made to the tree, we replaced Personal with Personal Care. A few days later, the results confirmed our thinking. Our former evil attractor was no longer luring participants away from the correct answers:

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Testing once is good, testing twice is magic

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This brings up a final point about tree testing (and about any kind of user testing, really): you need to iterate your testing —  once is not enough.The first round of testing shows you where your tree is doing well (yay!) and where it needs more work so you can make some thoughtful revisions. Be careful though. Even if the problems you found seem to have obvious solutions, you still need to make sure your revisions actually work for users, and don’t cause further problems. The good news is, it’s dead easy to run a second test, because it’s just a small revision of the first. You already have the tasks and all the other bits worked out, so it’s just a matter of making a copy in Treejack, pasting in your revised tree, and hooking up the correct answers. In an hour or two, you’re ready to pilot it again (to err is human, remember) and send it off to a fresh batch of participants.

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Two possible outcomes await.

• Your fixes are spot-on, the participants find the correct answers more frequently and easily, and your overall score climbs. You could have skipped this second test, but confirming that your changes worked is both good practice and a good feeling. It’s also something concrete to show your boss.
• Some of your fixes didn’t work, or (given the tangled nature of IA work) they worked for the problems you saw in Round 1, but now they’ve caused more problems of their own. Bad news, for sure. But better that you uncover them now in the design phase (when it takes a few days to revise and re-test) instead of further down the track when the IA has been signed off and changes become painful.

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Stay tuned for more on evil attractors

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In Part 1, we’ve covered what evil attractors are and how to spot them at the answer end of your tree: that is, evil attractors that participants chose as their destination when performing tasks. Hopefully, a future version of Treejack will be able to highlight these attractors to make your analysis that much easier.

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In Part 2, we’ll look at how to spot evil attractors in the intermediate levels of your tree, where they lure participants into a section of the site that you didn’t intend. These are harder to spot, but we’ll see if we can ferret them out.Let us know if you've caught any evil attractors red-handed in your projects.

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

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Card sorting to establish how different people comprehend their worlds

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

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

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

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How I generated the words on the cards

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The key purpose of running a card sort is to learn something new about how people conceptualize and organize the information that’s found on your website. The insights you gain from running a card sort can then help you develop a site structure with content labels or headings that best represent the way your users think about this information. Card sorts are in essence a simple technique, however it’s the details of the sort that can determine the quality of your results.

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Adding context to cards in OptimalSort – descriptions, links and images

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In most cases, each item in a card sort has only a short label, but there are instances where you may wish to add additional context to the items in your sort. Currently, the cards tab in OptimalSort allows you to include a tooltip description, a link within the tooltip description or to format the card as an image (with or without a label).

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We generally don’t recommend using tooltip descriptions and links, unless you have a specific reason to do so. It’s likely that they’ll provide your participants with more information than they would normally have when navigating your website, which may in turn influence your results by leading participants to a particular solution.

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Legitimate reasons that you may want to use descriptions and links include situations where it’s not possible or practical to translate complex or technical labels (for example, medical, financial, legal or scientific terms) into plain language, or if you’re using a card sort to understand your participants’ preferences or priorities.

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If you do decide to include descriptions in your sort, it’s important that you follow the same guidelines that you would otherwise follow for writing card labels. They should be easy for your participants to understand and you should avoid obvious patterns, for example repeating words and phrases, or including details that refer to the current structure of the website.

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A quick survey of how card descriptions are used in OptimalSort

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I was curious to find out how often people were including descriptions in their card sorts, so I asked our development team to look into this data. It turns out that around 15% of cards created in OptimalSort have at least some text entered in the description field. In order to dig into the data a bit further, both Ania and I reviewed a random sample of recent sorts and noted how descriptions were being used in each case.

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We found that out of the descriptions that we reviewed, 40% (6% of the total cards) had text that should not have impacted the sort results. Most often, these cards simply had the card label repeated in the description (to be honest, we’re not entirely sure why so many descriptions are being used this way! But it’s now in our roadmap to stop this from happening — stay tuned!). Approximately 20% (3% of the total cards) used descriptions to add context without obviously leading participants, however another 40% of cards have descriptions that may well lead to biased results. On occasion, this included linking to the current content or using what we assumed to be the current top level heading within the description.

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Use of card descriptions

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‍Create pie charts

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Testing the effect of card descriptions on sort results

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So, how much influence could potentially leading card descriptions have on the results of a card sort? I decided to put it to the test by running a series of card sorts to compare the effect of different descriptions. As I also wanted to test the effect of linking card descriptions to existing content, I had to base the sort on a live website. In addition, I wanted to make sure that the card labels and descriptions were easily comprehensible by a general audience, but not so familiar that participants were highly likely to sort the cards in a similar manner.

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I selected the government immigration website New Zealand Now as my test case. This site, which provides information for prospective and new immigrants to New Zealand, fit the above criteria and was likely unfamiliar to potential participants.

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Navigating the New Zealand Now website

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When I reviewed the New Zealand Now site, I found that the top level navigation labels were clear and easy to understand for me personally. Of course, this is especially important when much of your target audience is likely to be non-native English speaking! On the whole, the second level headings were also well-labeled, which meant that they should translate to cards that participants were able to group relatively easily.

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There were, however, a few headings such as “High quality” and “Life experiences”, both found under “Study in New Zealand”, which become less clear when removed from the context of their current location in the site structure. These headings would be particularly useful to include in the test sorts, as I predicted that participants would be more likely to rely on card descriptions in the cases where the card label was ambiguous.

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I selected 30 headings to use as card labels from under the sections “Choose New Zealand”, “Move to New Zealand”, “Live in New Zealand”, “Work in New Zealand” and “Study in New Zealand” and tweaked the language slightly, so that the labels were more generic.

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I then created four separate sorts in OptimalSort:Round 1: No description: Each card showed a heading only — this functioned as the control sort

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Round 2: Site section in description: Each card showed a heading with the site section in the description

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Round 3: Short description: Each card showed a heading with a short description — these were taken from the New Zealand Now topic landing pages

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Round 4:Link in description: Each card showed a heading with a link to the current content page on the New Zealand Now website

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For each sort, I recruited 30 participants. Each participant could only take part in one of the sorts.

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What the results showed

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An interesting initial finding was that when we queried the participants following the sort, only around 40% said they noticed the tooltip descriptions and even fewer participants stated that they had used them as an aid to help complete the sort.

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Participant recognition of descriptions

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‍Create bar charts

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Of course, what people say they do does not always reflect what they do in practice! To measure the effect that different descriptions had on the results of this sort, I compared how frequently cards were sorted with other cards from their respective site sections across the different rounds.Let’s take a look at the “Study in New Zealand” section that was mentioned above. Out of the five cards in this section,”Where & what to study”, “Everyday student life” and “After you graduate” were sorted pretty consistently, regardless of whether a description was provided or not. The following charts show the average frequency with which each card was sorted with other cards from this section. For example in the control round, “Where & what to study” was sorted with “After you graduate” 76% of the time and with “Everyday day student life” 70% of the time, but was sorted with “Life experiences” or “High quality” each only 10% of the time. This meant that the average sort frequency for this card was 42%.

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Untitled chartCreate bar charts

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On the other hand, the cards “High quality” and “Life experiences” were sorted much less frequently with other cards in this section, with the exception of the second sort, which included the site section in the description.These results suggest that including the existing site section in the card description did influence how participants sorted these cards — confirming our prediction! Interestingly, this round had the fewest number of participants who stated that they used the descriptions to help them complete the sort (only 10%, compared to 40% in round 3 and 20% in round 4).Also of note is that adding a link to the existing content did not seem to increase the likelihood that cards were sorted more frequently with other cards from the same section. Reasons for this could include that participants did not want to navigate to another website (due to time-consciousness in completing the task, or concern that they’d lose their place in the sort) or simply that it can be difficult to open a link from the tooltip pop-up.

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What we can take away from these results

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This quick investigation into the impact of descriptions illustrates some of the intricacies around using additional context in your card sorts, and why this should always be done with careful consideration. It’s interesting that we correctly predicted some of these results, but that in this case, other uses of the description had little effect at all. And the results serve as a good reminder that participants can often be influenced by factors that they don’t even recognise themselves!If you do decide to use card descriptions in your cards sorts, here are some guidelines that we recommend you follow:

• Avoid repeating words and phrases, participants may sort cards by pattern-matching rather than based on the actual content
• Avoid alluding to a predetermined structure, such as including references to the current site structure
• If it’s important that participants use the descriptions to complete the sort, you should mention this in your task instructions. It may also be worth asking them a post-survey question to validate if they used them or not

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We’d love to hear your thoughts on how we tested the effects of card descriptions and the results that we got. Would you have done anything differently?Have you ever completed a card sort only to realize later that you’d inadvertently biased your results? Or have you used descriptions in your card sorts to meet a genuine need? Do you think there’s a case to make descriptions more obvious than just a tooltip, so that when they are used legitimately, most participants don’t miss this information?

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Let us know by leaving a comment!