Category: Plain language

Karen Schriver—Plain by design: Evidence-based plain language (PLAIN 2013)

We may be good at the how of plain language, but the why can be more elusive. To fill in that missing chunk of the puzzle, information design expert Karen Schriver has scoured the empirical research on writing and design published between 1980 and 2010. She gave the PLAIN 2013 audience an eye-opening overview of her extensive, cross-disciplinary review, debunking some long-held myths in some instances and reaffirming our practices in others.

Audiences, readers, and users

In the 1980s, we classified readers and users as experts versus novices, a distinction that continues to haunt the plain language community because some people assume that we “dumb down” content for lower-level readers. Later on we added a category of intermediate readers, but Schriver notes that we have to refine our audience models.

What we thought

A good reader is always a good reader.

What the research shows

Reading ability depends on a huge number of variables, including task, context, and motivation. Someone’s tech savvy, physical ability, and even assumptions, feelings, and beliefs can influence how well they read.

Nominalizations

What we thought

Processing nominalizations (versus their equivalent verbs or adjectives) takes extra time.

What the research shows

It’s true, in general, that most nominalizations do “chew up working memory,” as Schriver described, because readers have to backtrack and reanalyze them. However, readers have little trouble when nominalizations appear in the subject position of a sentence and refer to an idea in the previous sentence.

Conditionals

What we thought

Conditionals (if, then; unless, then; when, then) break up text and help readers understand.

What the research shows

A sentence with several conditionals are hard for people to process, particularly if they appear at the start. Leave them till the end or, better yet, use a table.

Lists

What we thought

Lists help readers understand and remember, and we should use as many lists as possible.

What the research shows

Lists can be unhelpful if they’re not semantically grouped. If an entire document consists of lists, we can lose important hierarchical cues that tell us what content to prioritize.

Text density

What we thought

A dense text is hard to understand.

What the research shows

It’s true! But there’s a nuance: we’re used to thinking about verbal density, which turns readers off after they begin reading. Text that is dense visually can make people disengage before reading even begins.

Serif versus sans-serif

What we thought

For print materials, serif type is better than sans-serif. Sans-serif is better for on-screen reading.

What the research shows

When resolution is excellent, as it is on most screens and devices nowadays, serif and sans-serif are equally legible and easy to read. Factors that are more important to readability include line length, contrast, and leading.

Layout and design

What we thought

Layouts that people prefer are better.

What the research shows

We prefer what we’re used to, not necessarily what makes us perform better. This point highlights why user testing is so important.

Impressions and opinions

We thought

It takes sustained reading to get an impression of the content.

What the research shows

It takes only 50 millseconds for a reader to form an opinion, and that first impression tends to persist.

Technology

What we thought

Content is content, regardless of medium.

What the research shows

Reader engagement is mediated by the technologies used to display the content.

Teamwork in writing and design

What we thought

Writing and design are largely solitary pursuits.

What the research shows

Today, both are highly collaborative. We now have an emphasis on editing and revision rather than on creation.

***

Evidence-based plain language helps us understand the reasons behind our principles and practices, allowing us to go beyond intuition in improving our work and developing expertise. We can also offer up this body of research to support our arguments for plain language and convince clients that our work is important and effective. What Schriver would like to see (and what the plain language community clearly needs) is a repository for this invaluable research.

Isabelle Boucher—Plain language is a service to members (PLAIN 2013)

Isabelle Boucher, education officer with the Canadian Union of Public Employees (CUPE), made a strong case at her PLAIN 2013 plenary session for using plain language in union documentation and union activities. “Unions aren’t just about collective agreements,” she said. “They’re about social justice.” Plain language allows members to understand their rights and

  • is inclusive—not all union members have the same education and literacy skills
  • is democratic—union members who understand what’s going on are more likely to vote
  • encourages participation—members are more likely to speak up and take on roles within the union if they feel like part of the process
  • creates safe and healthy workplaces—workers can follow safety procedures only if they understand them.

Boucher outlined the work of CUPE’s literacy working group—which  develops programs and gives feedback on literacy tools—since it was created in 2000. In 2001–2002, the literacy program offered clear language training for CUPE staff across Canada.

At its general meetings in 2005 and 2007, CUPE accepted both traditional resolutions

Whereas…
Be it resolved that…

as well as plain language resolutions

CUPE National will…
Because…

It announced that in 2009 it would accept only the plain language version. The literacy program knew it was on the right track when, in 2007, virtually all of the resolutions submitted used the plain wording.

In 2011, CUPE rewrote its constitution in plain language, and in 2013, it began revising its model bylaws.

More information about CUPE’s clear language initiatives can be found here.

Sarah Stacy-Baynes and Anne-Marie Chisnall—User testing: health booklets that work for people (PLAIN 2013)

Sarah Stacy-Baynes is the national information manager at the Cancer Society of New Zealand, and she teamed up with plain language specialist Anne-Marie Chisnall of Write Limited to work on twenty booklets for cancer patients and their families, covering topics from types of cancer and types of treatments to living well with cancer and managing symptoms and side effects. These booklets offer clear, evidence-based information to a general audience, and to ensure that they remain accurate, relevant, and useful, Stacy-Baynes and her team put each of them through a review process once every four to five years. Oncologists, nurses, and other health practitioners are consulted to ensure the content is up to date, and the team actively solicits user feedback. Not only does each booklet contain a feedback form at the back, which asks such questions as “Did you find this booklet helpful?”, “Did you find this booklet easy to understand?”, and “Did you have any questions not answered in the booklet?”, but it also undergoes rigorous user testing before publication or republication.

For these booklets Stacy-Baynes and Chisnall performed think-aloud testing, a method in which test subjects receive a document they’ve never seen before and talk about it as they go through it. Each testing session includes a tester, user, and note taker, and it may be recorded (video or audio). The tester uses a script to work with the participants, and they make sure the users are informed about confidentiality and other ethical issues.

The tester and user go through a warm-up exercise first, on an unrelated item such as a menu. Once testing begins, the tester can reflect or repeat what the user says but can’t directly ask the user any questions. However, the tester can coach the user to make sure he or she has thought about the visuals, for example. A video of a sample testing session can be found here.

To recruit testers, Stacy-Baynes and her team asked for volunteers at the cancer society and through Cancer Connect, a peer support group for cancer patients and their families. As a result, their pool of testers was a representative sample of their target audience. They also made sure to recruit Māori participants: each booklet is bilingual—in English and Te Reo Māori—a particularly important feature because, as Stacy-Baynes noted, of all ethnicities of women, Māori women have the highest incidence of lung cancer. The team found that testing five participants for each booklet gave them plenty of useful feedback.

They discovered through their testing that users

  • preferred that the cover image not be of a person—they and their families didn’t want to see pictures of sick people when the booklet was out on their coffee table (the booklets now feature photos of plants)
  • wanted to see pictures of the treatment facilities
  • wanted to see pictures of Māori
  • preferred simplified diagrams rather than detailed medical illustrations.

Stacy-Baynes did encounter problems during the project—for example, some clinicians reviewing the material were sometimes determined to include some content regardless of whether the readers wanted it. Keeping team members motivated and interested through the long process of testing and redrafting was also challenging, but the team gained a lot of information from user testing that they wouldn’t otherwise have found.

Greg Moriarty and Justine Cooper—Persuading clients that plain language works (PLAIN 2013)

Those of us who work with plain language have no doubts about its value, but how do we convince clients that creating plain language communications is worth the investment?

We tend to default to telling potential clients about the consequences of implementing plain language—for example, that they’ll get fewer calls from confused customers or that they’ll increase compliance. These types of arguments can be quite persuasive, but Greg Moriarty and Justine Cooper from the Plain English Foundation in Australia reminded us about other types of proof that we can add to our arsenal.

Example arguments

When putting together proposals, use case studies—preferably examples from comparable organizations. (Joe Kimble’s Writing for Dollars, Writing to Please is a good source of case studies.) Seeing concrete numbers of money and time saved can help potential clients recognize the value of plain language.

Authority arguments

Government commitments to clear communication, such as the Plain Writing Act in the U.S. or Health Canada’s Plain Language Labelling Initiative, offer authoritative reasons to adopt plain language and are hard for clients to argue with. When writing proposals, establish your own authority, supporting your arguments with research or precedent. (Karen Schriver has compiled an impressive body of research related to various facets of information design.) Finally, don’t underestimate an appeal to principle: that plain language is ethical and leads to more transparency and accountability can be a strong motivator for clients to sign on.

***

Moriarty and Cooper recommend using all three types of arguments whenever you pitch plain language to a prospective client. Your primary arguments should be based on consequences: outlining the benefits of plain language, showing cause and effect and appealing to the power of possibility, is one of the most powerful ways to make your case. Support these with examples, followed by authority arguments. Consider the context and tailor your arguments to the client; the more specific you can be, the better.

Peter Levesque—What is the role of plain language in knowledge mobilization? (PLAIN 2013)

How do we measure the value of research?

Productivity in academia is still largely measured using the “publish or perish” model: the longer your list of publications, the more quickly you rise through the ranks in the ivory towers.

But what happens to the research after it’s out there? In an ideal world, industry, government, and community groups put it to use, and other researchers build on it to make more discoveries. “If we put billions of dollars into research, shouldn’t we get billions of dollars’ worth of value?” asked Peter Levesque, director of the Institute for Knowledge Mobilization. “We couldn’t answer that question,” he told us, “because we didn’t know what we were getting.”

Part of the disconnect comes from the data explosion of the information age. For example, in 1945, fewer than five hundred articles were published in geology—it was possible for a geologist to know everything in his or her field. Today, thousands of geology articles are published each year, and there’s no way a researcher can keep on top of all of the research. Levesque gave another example: a family doctor would have to read seven research articles a day, every day of the year, to keep up with the latest discoveries. Physicians don’t know the latest research simply because they can’t.

Disseminating research via the traditional passive push of journals and conferences isn’t effectively getting the knowledge into the hands of people who can use it. The growing complexity and interdisciplinarity of research is also demanding a shift in thinking. This recognition—that knowing isn’t the same thing as doing—is one of the foundations of knowledge mobilization, which advocates not only a push of knowledge but an active pull and exchange, creating linkages between communities and encouraging joint production. Knowledge mobilization, admits Levesque, is a term wrapped up in jargon: there are over ninety terms used to describe essentially the same concept, including knowledge translation and knowledge transfer, but they all boil down to “making what we know ready to be put into service and action to create new value and benefits.” Knowledge mobilization is still a system under construction, said Levesque, but it is a promise of improvement. By getting the latest evidence into the hands of those who develop our policies, programs, and procedures, we can improve—and in some cases save—lives.

So where does plain language come in? Well, if the evidence we’re using is incomprehensible, it’s effectively useless. Solutions in complex systems require input from several sources of knowledge, and specialized language creates barriers to interdisciplinary communication. Because academics aren’t used to communicating in plain language, they need plain language practitioners to translate the knowledge for them or to consult them on knowledge mobilization projects.

A practical example of knowledge mobilization in action is ResearchImpact, where universities from across Canada have posted over four hundred plain language summaries of research. Another example is the Cochrane Collaboration, the world’s largest organization that makes current medical research, including systematic reviews and meta-analyses, available to all government, industry, community, and academic stakeholders. The Knowledge Transfer and Exchange Community of Practice aims to connect the practitioners of knowledge transfer from across the country.

Levesque advocates continuing the conversation between the plain language and knowledge mobilization communities to strengthen the links between the groups. One opportunity to do so is the Canadian Knowledge Mobilization Forum, set for June 9, 2014, in Saskatoon.

Karine Nicolay—IC Clear update (PLAIN 2013)

I posted about IC Clear earlier, when Katherine McManus spoke at the EAC-BC meeting about the clear communication certificate program. Project coordinator Karine Nicolay gave PLAIN 2013 an update:

IC Clear is supported by the European Commission’s Clear Writing Campaign and fills a gap in education and training. Stockholm University, one of the program’s associate partners, has had a language consultancy program (broader than just plain language) for thirty years, but it is only in Swedish. IC Clear has been able to draw on SU’s expertise to develop its program.

Demand for clear communication professions will grow, said Nicolay, because in many countries the illiteracy rate is high and government recognition of people’s right to understand means there will be more forthcoming legislation requiring clear communication.

Neil James—What’s in a name? The future for plain language in a converging communications profession (PLAIN 2013)

What’s in a name?

Neil James, executive director of the Plain English Foundation in Australia, asked us this question and did a bit of crystal-ball gazing at his plenary session at PLAIN 2013.

Plain language practitioners go by many titles—editor, technical communicator, business writer, and information designer, among others. Historically, we specialized in different types of documents—the plain language professionals worked on government and legal documents, the technical writers on engineering and technical documents, editors on books and magazines, etc.—and as a result, we found ourselves in institutional silos. For example, members of the Society for Technical Communication don’t often get a chance to exchange ideas with members of the International Institute for Information Design, and the Usability Professionals Association rarely talks to the International Association of Business Communicators. This fragmentation has hurt us, said James: “By being fragmented, we have allowed the organizations that we work for to downplay our importance. And what we do is damn important.”

Can we unite? Our differences are small, lying in the types of documents we work on and the extent of our intervention; we are on a spectrum of communication, not in silos. James offered this quote from Ginny Redish:

We really are all about the user experience. My definition of usability is identical to my definition of Plain Language, my definition of reader-focused writing, my definition of document design … We’re here to make the product work for people.

Plain language, as a profession, is pretty young, and it has evolved rapidly over the past few decades, expanding its focus from clear language to document structure and design and, more recently, to user testing. Because of communication convergence—the tendency for different communication fields over time to apply a common range of methods—it makes sense for us to consider converging as well. We face pressure from various fronts:

  • Technology: For example, desktop publishing has allowed us to collapse the roles of writer, editor, typesetter, designer, and proofreader into one
  • Information age: “Most of what’s on the Internet,” said James, “let’s face it, is crap.” Users will need us to mediate that vast ocean of information to get what they need.
  • State sanction: The U.S. Plain Writing Act of 2010 is an example, and many other governments have recognized the need for clear communication with citizens.
  • Self-interest: By banding together, we can share resources, raise our profile, and maybe even raise our income.

What lies ahead for us? James proposes this plan:

  • Step 1: Start a dialogue—within each field and between fields.
  • Step 2: Do some research to nail down what we each do and what skills we have.
  • Step 3: Put the pieces together and consider federation or mergers.
  • Step 4: Engage stakeholders, working with academics to unify research and theory, working with industry to map out the benefits of what we do, and working with government for legislative support.
  • Step 5: Pick a name.

James predicts that we will eventually settle on “clear communication,” which captures the breadth of our work more comprehensively than any term that refers specifically to language or design. By uniting under a common name, we’ll be better able to push for formal standards and professional recognition.

Joe Kimble—Wild and crazy tales from a decade of drafting U.S. Federal Court Rules (PLAIN 2013)

Joe Kimble, a professor at the Thomas M. Cooley Law School and the editor-in-chief of the Scribes Journal of Legal Writing, is a stalwart of the plain legal language movement. His book Writing for Dollars, Writing to Please is an invaluable reference for any plain language practitioner.

Starting in 1999, he led a decade-long project to redraft the Federal Rules of Civil Procedure and the Federal Rules of Evidence. At the PLAIN 2013 opening reception, Kimble shared some stories from that experience.

We all know that legalese is cumbersome to read, but its much more serious problem is that it leads to ambiguity. “Legalese is not precise,” said Kimble. “It’s pseudo-precise. It only seems precise.” Using before-and-after examples from the U.S. Federal Court Rules, Kimble showed how complex legal language results in

  • semantic ambiguity—when a word or phrase has more than one meaning
  • syntactic ambiguity—when the structure of the sentence gives rise to more than one meaning
  • contextual ambiguity—when inconsistencies or internal contradictions raise questions about which alternative should prevail

Ambiguity, Kimble was careful to point out, isn’t the same thing as vagueness, which presents uncertainty at the very margins of applying a term. Vagueness is unavoidable in legal drafting; the goal is to arrive at the right degree of vagueness.

Semantic ambiguity

A convenient example of semantic ambiguity in legalese is “shall”—does it mean “must,” “may,” “will,” or “should”? Kimble worked to eliminate all five hundred instances of “shall” from the Federal Court Rules and succeeded, until one “rose from the grave,” as he put it. Deciding the meaning of “shall” is a substantive call, and in Rule 56 of the Civil Rules, the “shall” had been changed to “should” during the restyling. Later, a debate flared up over whether it should have been changed to “must.” Rather than deciding the issue, the advisory committee resurrected the “shall,” while acknowledging in their report that it is “inherently ambiguous.”

Syntactic ambiguity

At the heart of many syntactically ambiguous sentences is the lack of a clear antecedent for a modifier or a pronoun. The committees working on the Court Rules often raised the concern that Kimble’s changes might alter the meaning, to which Kimble once responded, “It’s odd to worry about changing meaning when nobody seems to know what the meaning is.” In several of those cases, the committees decided to “keep it fuzzy” because the original language didn’t indicate which interpretation was the right one; that decision would be left to the courts.

Contextual ambiguity

Contextual ambiguity is particularly troublesome: are inconsistencies deliberate, or are they the result of sloppy drafting? Kimble’s examples show that “Most lawyers, no matter how skilled and experienced, are not good drafters.”

***

Of course, beyond untangling ambiguity, Kimble also worked on cutting wordiness. Why write, “the court may, in its discretion” when “may” implies “in its discretion”? For comparison, whereas the old Civil Rules had 45,500 words, the new rules have 39,280 (14% less). The new rules have 45 fewer cross-references and have more than twice as many headings as the old rules. The difference in the readability of the original and plain language versions is stark. An example (from the Evidence Rules):

Before

Evidence of the beliefs or opinions of a witness on matters of religion is not admissible for the purpose of showing that by reason of their nature the witness’ credibility is impaired or enhanced.

After

Evidence of a witness’s religious beliefs or opinions is not admissible to attack or support the witness’s credibility.

The process that worked well for Kimble and his team was to have a plain language expert write the first draft; that version persisted unless it created a substantive change. This approach was more effective and efficient than having a plain language expert edit a document after the fact.

Upcoming posts: PLAIN 2013 and EAC-BC

I feel privileged to have been a part of the inspiring PLAIN 2013 conference over the weekend, which brought together clear communication representatives from nineteen countries and had us talking about everything from legalese to health literacy to usability testing, among many other fascinating topics. Huge congratulations to Cheryl Stephens and her team for putting on such a terrific event.

A major takeaway for me is that opportunities abound for editors and other communication specialists. After years of toiling in the book industry, whose traditional model has teetered on the brink for as long as I’ve been involved, I’ve found renewed optimism in my profession after attending this conference. Recognition by not only government but also the private and academic sectors that clear communication is essential in our age of information overload means there is so much work out there for plain language practitioners and trainers, and the fact that a lot of what we do is rooted in social justice and the belief that citizens, union members, and consumers have the right to understand our laws, regulations, and contracts is hugely affirming and provides an extra bit of motivation to keep doing what we do.

As usual, I’ll be summarizing the sessions I attended, but, as usual, the process will probably take me a few weeks. Interrupting the PLAIN entries will be one about tomorrow’s important EAC-BC meeting, where Maureen Nicholson will tell us about potential changes to the Editors’ Association of Canada’s governance structure and ask members for their input. A primer on what EAC’s governance task force has been up to is here.

Writing in plain language—an Information Mapping webinar

David Singer of Information Mapping hosted a free webinar about writing in plain language. Much of the second half of the session was devoted to the Information Mapping method, covered in the Introduction to Information Mapping webinar that I wrote about earlier, but the first half focused on plain language itself.

Plain language defined

What is plain language? The Center for Plain Language in Washington, DC, uses the following definition:

A communication is in plain language if the people who are the audience for that communication can quickly and easily

  • find what they need
  • understand what they find, and
  • act appropriately on that understanding.

Singer likes this definition, noting that there’s no mention of “dumbing down” the information, which is not what plain language is about.

Plain Writing Act

On October 13, 2010, President Obama signed the Plain Writing Act into law: “The purpose of this Act is to improve the effectiveness and accountability of Federal agencies to the public by promoting clear Government communication that the public can understand and use.” Interestingly, regulations were exempt from this requirement, although there’s since been a push to have regulations given in plain language as well.

Have the agencies made progress? Although some agencies have made an effort to implement plain language principles, the new law hasn’t made that much progress since it came into effect in 2011. The Center for Plain Language issued a report card in 2012 and found that out of the twelve agencies they looked at, only four scored a B or higher in complying with the basic requirements of the act. The Department of Homeland Security scored a D, and the Veterans Affairs Department scored an F.

Why were they having so much trouble?

  • The agencies were dealing with an unfunded mandate. Although the Plain Writing Act was signed into law, the agencies had no budget allowances to implement the training and changes to government documentation.
  • There was no specific yardstick to measure success. How do you define “clearer” or “easier to understand”?
  • There were no consequences for non-compliance.
  • There were no clear plans for implementation.

The effort to implement plain language faces a lot of barriers, including the fact that initial enthusiasm about the idea can fade and there is a lot of resistance to change. Technical folks may not believe that their communications can be made simpler or clearer, and attorneys and security people may not want their language to be easy to understand.

Telling people to use personal pronouns, active voice, and shorter sentences isn’t enough, argues Singer. You need a systematic method based on sound principles and a clear plan for implementation to work.

The Information Mapping method

Most of the challenges, says Singer, don’t involve grammar. Plain language’s chief concerns are about making complex information clear and accessible; writing for different audiences (how do you create a single document that meets the needs of many groups of people?); organizing large amounts of information; working with a team of writers (managing different styles, etc.); keeping up with changes; and finding a way to reuse content. Singer suggested the Information Mapping method as a way to achieve these objectives.

Some of the principles behind Information Mapping—chunking, relevance, and labelling—were covered in the Introduction to Information Mapping webinar. The method also has three other principles—consistency, integrated graphics, and accessible detail—which the Information Mapping crew covers only in the training sessions and not in these free webinars.

Singer presented case studies to show the benefits of applying the Information Mapping to business communication. In general, Information Mapping has found that its method leads to a

  • 32% increase in retrieval accuracy
  • 38% increase in usage of the documentation
  • 83% increase in initial learning during training
  • 90% decrease in questions to the supervisor
  • 83 % decrease in the time for a first draft
  • 75% decrease in the time to review the documentation
  • 54% decrease in error rates
  • 50% decrease in reading time
  • 30% decrease in the word count

By implementing a concrete plain language plan, such as the Information Mapping method, you may see the following benefits:

  • revenue growth—by reducing the time it takes to create content and shortening the time for products and their documentation to make it to market
  • cost reduction—by capturing employee knowledge, increasing operational efficiency, reducing support calls, and decreasing translation costs (owing to lower word counts and clearer content)
  • risk mitigation—by increasing safety and compliance

Resources on plain language

For more information about plain language, visit:

(or come to PLAIN 2013!)

An archive of this webinar, as well as more information about the Information Mapping system and training, can be found on the Information Mapping website.