I’ve always thought plain English (or any other language!) is to content what usability is to design. It just makes things easier to read, to understand and to act on. It means not trying as hard to be comprehensive as comprehensible.

A quick test for plain English is whether you can understand something after just one reading. This rule of thumb guides all our communication: on our website, in our usability products, even our emails.

Plain English doesn’t mean boring, dumbed down or even everyday English. We are often dealing with complex information and that makes the challenge even greater, and more rewarding. Turning technical specs or instructions into plain English can sometimes feel like translating from one language to another. You’ll know you’ve nailed it when that sentence you’ve laboured over finally feels effortless.

If I was to offer just one tip for making your writing crystal clear it would be:

1. Read your writing aloud
   You’ll stumble over anything that doesn’t need to be there.

And then if you begged me for another pearl of wisdom it would be:

2. Put it down for a few days
   and then go back to tip #1

Of course plain English wouldn’t have the universal profile it does today without the schadenfreud factor. Every year crimes against clarity are celebrated for their ability to confound as much as perfect specimens are held up as shining examples.

After you’ve checked out the winners in the WriteMark Plain English Awards be horrified at the ‘brainstrains’ and the Plain English UK Golden Bull awards.

My personal favourite is from the Canadian Emergency Medical Service news report after a man had been attacked and lost part of his ear. They reported it like this:

‘He was missing a body part to the side of his head due to the assault. Luckily he was [in] stable and non-life-threatening [condition].’

Sometimes you need to know how not to do something before you can learn to do it well.

This means you no longer have to save your survey manually because it will be saved automatically each time you make a change. You can still hit the save button too if it makes you feel good!

Why autosave? Two reasons:

1. We want to save you time wherever we can. The less time you spend saving the more time you save. Of course we’re gambling that everything you do is worth saving…
2. There were a couple of places in the app where it wasn’t very clear whether or not your changes had been saved. Adding new tasks in Treejack or Chalkmark for example. It felt like you’d completely added a task to your survey, and it looked that way, but you had to press the ‘Save’ button. Why!? Why would we assume you don’t want that task until you explicitly say so?! Personally, I feel much better now that we assume you want to keep everything unless you explicitly press ‘Delete’.

In short, we are always working to improve the user experience for our users, just like you are doing for yours by using our usability tools.

But how will I know if my survey has been saved?

Once you’ve made a change to your survey (and click outside the changed text field), we will automatically perform the save for you. While a save is occurring a small spinner will appear on the ‘Save’ button and it will be temporarily disabled. Once the survey has been saved a message will appear underneath the button saying “Save successful”. How appropriate

Let’s start with a quick rundown on the major differences in outcomes from moderated and unmoderated card sorting.

Remote & Unmoderated Card Sorting (Online):

• Unlimited scale. You can have as many participants as required to get the answer you need.
• Much closer to “fire & forget”. Set up a study, fire it out to potential participants, enjoy the afternoon in the sun.
• Relatively cheap. Compared to the cost of having a facilitator, note taker, clients on site, reception, coffees, compensation… remote testing is clearly cheaper to conduct.
• It can be difficult to know why things happen. Qualitative results are not nearly as apparent because participants are not facilitated, moderated, steered and often not recorded. You don’t get to hear them thinking out loud or discussing decisions.
• Great for gathering quantitative results. If you have a hunch of your own or as a result of qualitative tests then remote and unmoderated user testing is a great way to back it up with some numbers.

In-Person & Moderated Card Sorting (Offline):

• Limited scale. You can only bring in as many participants as you can afford in terms of time and budget.
• Relatively heavy investment per participant. Each participant will have associated costs and will create work for you. (I’m not saying it isn’t worthwhile, it generally is, I’m just pointing out the differences)
• Great for gathering qualitative results. This is where you get insight into how people feel about what they’re doing or saying in the study.
• It is usually too expensive to get quantitative results from moderated testing. Yes, you will undoubtedly uncover most of the problems and convince yourself that something must be done, but many situations call for more.

So what should you do?

I recommend that people conduct from 1 to 5 offline, in-person and moderated card sorts to get a good understanding for themselves of how other people would organise their content and the rationale for it. Then I suggest that people conduct an online study using OptimalSort to put some numbers behind the hunches. By the way, I don’t mean to belittle any professional observations by calling them hunches, I’m just making the point that however duly convinced you might be it is usually not unreasonable for a stakeholder to want more data if a change will impact thousands or potentially millions of other people (or dollars for that matter).

If you are fortunate enough to have crystal clear direction from your qualitative research to propose an immediate way forward then I suggest you could skip the online card sort and move directly to validating your proposed new information architecture using tree testing. Either way you should be validating your chosen labels and content hierarchy using Treejack after a card sort.

We believe there is so much value in both qualitative and quantitative research techniques that we want you to do both. To assist you with this we have recently implemented an important change to OptimalSort: You can now print your OptimalSort cards (from a generated PDF) for moderated and in-person paper based card sorting and easily get the results back into OptimalSort for analysis alongside your quantitative research data. Hooray!

Step 1: Print the cards

OptimalSort's printed cards are printed with crop marks for easy cutting

Step 2: Sort the cards

Please don't analyse this card sort. I just laid them out to look pretty.

Step 3: Scan the groups back into OptimalSort

OptimalSort works with common barcode scanners so that you can quickly get your results into the tool for analysis

I’d love to know what you think of this new feature and whether it will be useful to you in your own card sorts. It certainly beats trying to moderate card sorts around a screen or retrospectively entering participant sorts by doing multiple sorts yourself (you know who you are!).

Treejack's Pietree Visualising Tree Test Results

A couple of months ago now I presented this poster (at a monstrous 4′ square) at Euro IA in Prague to an excitable group of enthusiastic Information Architects, Content Strategists and UX Designers. I believe it was just prior to the piano sing-a-long in the hotel lobby!

For some reason we’ve never officially announced Treejack’s Pietree, probably because it has always been somewhat of an experiment. So far though the experiment has been a wild success. People love it. Even Dave O’Brien, the guy who [loves spreadsheets, and] built the first prototype of Treejack for his own use now uses the Pietree as his primary method for understanding his study results.

In short, Treejack is our online user research tool for validating proposed information structures and labels. Usability testing a taxonomy you might say. The Pietree shows you where your participants went in your site IA in response to the task you set them, and what choice they made at each point along the way.

You can choose to view it in vertical format rather than radial if you’d like to, and you can hide all the nodes that got no visits for this task.

Of course, being a baby there are still some issues with the Pietree visualisation, like scalability for example. Deep trees are sometimes both slow to render (in animated browser based SVG) and too large to view effectively onscreen. However we’ve got some more enhancements in the pipes to help out with these issues (and see the PPS below).

If you’ve got a tree test coming up, or you’ve got some old results in your account I highly recommend you check out the Pietree, and please let us know what you think of it.

PS. I thoroughly recommend the Euro IA conference, it is a fantastic mix of culture and opinions and a lot of fun to boot.

PPS. Did you hear when I said it is an SVG?! Do you know how awesome that is? It mean you can view source (until we add a special Download As File feature), copy the SVG part into a text file and open that file in Illustrator or Inkscape and print it out on a large format printer! THAT is how awesome it is. THAT is how I made the poster

1. You can now optionally choose to view your image in greyscale with the heatmap overlaid. This can be very helpful if your screenshot or mockup is particularly colourful causing the heatmap to get a little lost.
2. Individual dots on the heatmap are no longer elliptical, but instead they are now circular as they should be!
3. The colours are much hotter than they were previously. Significantly less application of lavender.
4. Your heatmaps are now available as a downloadable PDF. Long time friends will remember that this feature used to be there but got dropped in the midst of our major platform upgrade last year. It has returned. Oh joy!

Wireframe Heatmap from a Chalkmark Usability Study

1. Current methods for Card Sort Analysis are essentially qualitative. Although this is very useful, there are times when it is desirable to use a larger data set. Quantitative Card Sort Analysis using current methods is difficult, or damn near impossible with hundreds or thousands of results.
2. Current visualizations for presenting Card Sort Analysis (dendrograms and similarity matrices) are not very helpful at showing alternate popular mental models that might come through in the raw data. Understanding alternate models can help you decide what to put in a sidebar or footer (for example) or provide valuable insights for second tier navigation or even copy writing. Traditionally you would need to wade through a spreadsheet to uncover these insights.

Participant Centric Information Architecture Analysis

In short, we test each card sort result against all the others and come up with an “acceptability score” which represents the degree to which each participant agrees with the other results. In this way we can establish which particular results is most acceptable to the population, and from there, we can answer the question: “Of those who do not agree with this particular IA, how would they prefer to group the cards?”.

We have already developed a working prototype of our Participant Centric Analysis Method and hope to integrate the new visualization into OptimalSort in the near future. We’d love to hear any feedback you might have on this new method.

Download the poster (PDF)

Part 2 will be ready in a couple of days is now available!

 
WHO’S DAVE?
Dave O’Brien designed the first version of Treejack to make it easy to run tree tests online. He’s a senior consultant at Optimal Usability, New Zealand’s leading usability company, and has been deep into usability and design for 15 years.

WHAT’S THE WEBINAR ABOUT?
Tree testing is a great way to quickly validate your Information Architecture (IA) and site navigation ideas. This webinar is about how to get up and running with Treejack quickly and avoid the most common mistakes. You’ll also learn how to get more out of your tree tests using a few of the more advanced features of Treejack, particularly in Part 2.

You can download the files used in the webinar here:

• Tree test worksheet template (excel)
• Tree test planning questions (word)

The agenda for the webinar is:

1. Quick Treejack tour
2. What is tree testing?
3. Planning a tree test
4. Setting up Treejack
5. Running a test
6. High-level results analysis
7. Detailed results analysis
8. Lessons Learned
9. Q&A

Card sorting is an effective technique for teasing out the important distinctions in our content inventory. Conducting card sorts is also a great way to gather insights about the nature of the content and your users’ mental models. I like to think of it as an opportunity to ‘load up your brain’ with the information you’ll need to design a well-informed IA. Sam Ng has called it ‘eye-balling’ the data Card sorting produces much more than just a ballpark in which to throw around ideas. However, as you move toward a final candidate for your site structure, you’re entering territory that card sorting simply wasn’t designed for.

When designing an Information Architecture, we start with a collection of loosely related content and work tirelessly to create an information structure that ‘works’ for as many of our users as possible. What we need is a simple way to validate our ideas so we can use our concepts developed through card sorting and refine them based on research and testing. We need a way to find out if our IA is actually going to work.

What card sorting achieves

Structuring information in a way that makes sense to anybody is not easy, let alone designing for everybody – often thousands of users from different perspectives. Even in simple examples, differences in perception and the effects of personal experience will manifest as disagreements about the nature of content and the interpretation of labels.

Card sorting guides the process of determining ‘what should go together.’ Or as I like to say: ‘what should probably go together… maybe.’ Results from a card sort usually require substantial massaging to form an Information Architecture (IA) and that IA still needs to be proven to work.

Picking up where card sorting leaves off

Users process information differently when performing a seek task as opposed to a sort task. Users process information differently when performing a sort task as opposed to a seek task. When in sort mode we are deeply evaluative, applying considerable effort to organize ideas in a coherent manner. In seek mode, we skim through content, readily discarding information we don’t need and selecting quickly when we think we’ve found something – a pretty close approximation of our web browsing habits!

So we take our card sorting insights from our sort mode respondents, and test the resulting draft IA against some ferocious seek mode users.

Tree testing

We’ve established a simple incompatibility between generative IA techniques like card sorting and the end goal of findable content on your website. With this in mind, tree testing aims to get as close as possible to the actual experience of navigating a website while remaining ‘pure’ about testing the IA in isolation.

From Wikipedia:
“Tree testing is a usability technique for evaluating the findability of topics in a website. It is also known as reverse card sorting or card-based classification. Tree testing is done on a simplified text version of your site structure. This ensures that the structure is evaluated in isolation, nullifying the effects of navigational aids, visual design, and other factors.”

Participants are given a task and set about traversing the IA to look for it. Every step they take is recorded for your analytical pleasure. Did they find the right page? Did they take any wrong turns? How long did it take them? I want every detail!

This provides a wealth of information that we can use to pinpoint problem areas in the IA and identify what the problems are likely to be. Tree test analysis is still a human-intensive process, but the data is decidedly more conclusive and easier to interpret when compared to card sorting. The ability to deliver a conclusive test result is as valuable to the IA design process as it is to overcoming project politics. For example:

“When asked to download a purchase order form, forty percent of participants incorrectly set out within the products and services section. Although some of those participants found the correct destination eventually, fifteen percent of the total participants never found the form.”

Unlike full usability testing, tree testing only deals with the IA. This streamlines IA development, as iterative refinement can be done rapidly and with minimal cost. By testing and refining findability early in the project, it is possible to avoid costly late changes that are likely to affect design, content management and copy writing teams. That is, if you are able to push late changes through at all.

Getting started with tree testing

This advice draws upon our experience with client projects and with helping Treejack users around the world to get the most from their tree studies.

One: Task authoring matters. A lot. Don’t ask your participants to “Find XYZ” twelve times in a row. You’ll see the boredom reflected in your results: a high skip rate and plenty of non-sequitur responses. Mix it up a little and create real-world scenarios. If necessary, ask your participants to “imagine” or “suppose” that they are coming at it from a certain perspective. Never use the same language in your task description as a label in your IA. As an example, if you ask participants to investigate a certain variety of your company’s provided services, any label with the word services in it will experience undue attention. Think of another way to phrase the task.

Two: Don’t bother testing your entire IA. Focus on the parts that matter and that you think are worth worrying about. If you write a task to test your “Contact Us” page, you’ve just wasted the precious attention of your participant, which could’ve been used to test something peculiar to your site. The world is very familiar with common navigation metaphors and its not worth your time to verify that hypothesis. This advice also goes for loading up your tree (the IA itself). Use discretion here, but in most cases you can probably leave out the really common ‘boilerplate’ navigation items.

Three: This isn’t a marathon. Ask your participants to complete ten to fifteen tasks. You might have thirty or more tasks in your overall survey, but for each survey participant you’ll want to keep the workload humane and display a subset to each participant. We recommend collecting 40 or more responses to each task. This means for 30 tasks displayed at 10 per user you will need 120 participants to complete your survey.

Four: Ask questions! We’re always here to help. Email support@optimalworkshop.com

In the lead up to xmas I got lazy with my timesheets. I just don’t even think of them anymore, apparently for weeks at a time.. [sorry Michelle]

BUT all this is behind me. To establish a good habit it seems that I just needed a simple poke in the ribs every 15 mins. When I’m going ok with that I’ll just reduce it to every 30 mins and then every hour and so on. So here’s what I’ve done:

1. If you’re on OSX install Growl and GrowlNotify (from the Extras folder in the .dmg), or
   if you’re on Linux install Gnotify
2. Next, open a terminal and type: crontab -e to open your list of automated recurring tasks using your default commandline text editor.
3. Add the following line to list (which may be empty) of cron jobs:
   */15 8-18 * * mon,tue,wed,thu,fri /usr/local/bin/growlnotify -t "Update your timesheet" -m "and drink some water"
   (substituting gnotify if you’re on Linux)
   If your default text editor is vi then you can press i to enter “insert mode”, then paste the copied line above, press esc to exit insert mode and shift-z-z to save and exit vi.

This will create a recurring reminder every 15 minutes from 8am to 6pm on weekdays to remind you to update your timesheet and drink some water. Two good habits I need to get into. The notification is not “sticky”, so it won’t hang around or require you to acknowledge it. It just pops up for a second or so and then goes away (unless you have some overriding Growl preference).

Belated Happy New Year!