What makes writing more readable?
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Writing text that can be understood by as many people as possible seems like an obvious best practice. But from news media to legal guidance to academic research, the way we write often creates barriers to who can read it. Plain language—a style of writing that uses simplified sentences, everyday vocabulary, and clear structure—aims to remove those barriers.
Writing text that can be understood by as many people as possible seems like an obvious best practice. But from news media to legal guidance to academic research, the way we write often creates barriers to who can read it. Plain language—a style of writing that uses simplified sentences, everyday vocabulary, and clear structure—aims to remove those barriers.
You can see it in action right here! Click the text next to each paragraph to read it in plain language.
You can see it in action right here! Click the text next to each paragraph to read it in plain language.
You can also use the toggle on the top right to switch everything to plain language. Or use the “p” key on your keyboard.
You can also use the toggle on the top right to switch everything to plain language. Or use the “p” key on your keyboard.
Plain language is useful for everyone, but especially for those who are often denied the opportunity to engage with and comment on public writing. This includes the 20% of the population with learning disabilities, a number of the more than 7 million people in the US with intellectual disabilities (ID), readers for whom English is not a first language and people with limited access to education, among others.
Plain language is useful for everyone, but especially for those who are often denied the opportunity to engage with and comment on public writing. This includes the 20% of the population with learning disabilities, a number of the more than 7 million people in the US with intellectual disabilities (ID), readers for whom English is not a first language and people with limited access to education, among others.
These audiences are routinely excluded from public dialogues, including dialogues about themselves. People with disabilities are also often excluded from writing or sharing their own stories first-hand due to lower vocabulary skills, learning differences, and intellectual disabilities. For example, throughout much of US history, people with ID have had decisions made on their behalf based on the presumption that they do not and cannot understand. This, on top of discriminatory attitudes and stigma, has led to infantilization, institutionalization and eugenic sterilization.
These audiences are routinely excluded from public dialogues, including dialogues about themselves. People with disabilities are also often excluded from writing or sharing their own stories first-hand due to lower vocabulary skills, learning differences, and intellectual disabilities. For example, throughout much of US history, people with ID have had decisions made on their behalf based on the presumption that they do not and cannot understand. This, on top of discriminatory attitudes and stigma, has led to infantilization, institutionalization and eugenic sterilization.
Additionally, there is a tendency to censor content for these audiences rather than explain it, which can contribute to continued disparities, like the higher rate at which people with ID experience sexual violence than nondisabled people.
Additionally, there is a tendency to censor content for these audiences rather than explain it, which can contribute to continued disparities, like the higher rate at which people with ID experience sexual violence than nondisabled people.
The benefits of plain language aren’t limited to universally challenging texts like legal documents and tax forms. Even everyday writing, like news articles, can still pose a barrier for some readers.
The benefits of plain language aren’t limited to universally challenging texts like legal documents and tax forms. Even everyday writing, like news articles, can still pose a barrier for some readers.
How does a human assess readability?
Let’s walk through how Rebecca, an expert in plain language, translates a text to be more readable. We'll use an excerpt from her translation of a ProPublica article by Amy Silverman in the following example.
Let’s walk through how Rebecca, an expert in plain language, translates a text to be more readable. We'll use an excerpt from her translation of a ProPublica article by Amy Silverman in the following example.
Read the translation below. Click the highlights to see what Rebecca thinks.
Read the translation below. Click the highlights to see what Rebecca thinks.
Plain language translating: side-by-side comparison
ORIGINAL
Kyra is autistic and profoundly deaf. She was born premature at about 27 weeks, just a little over 2 pounds, which has impacted pretty much everything: eyesight, hearing, digestion, sleep patterns. A strong tremor in her hand makes it impossible for her to use American Sign Language. Her parents think she recognizes a couple dozen signs.
PLAIN LANGUAGE
Kyra is autistic and deaf. She was born early. She was very small when she was born. She has trouble seeing, hearing, eating and sleeping. Her hand shakes so she does not do sign language. Her parents think she knows some signs.
Click the highlighted text to see Rebecca's comments.
Click the highlighted text to see Rebecca's comments.
More about Rebecca’s translation process
When doing a plain language translation, my first step is always to do a close read of the original text. I identify the main points, the order information is presented, and any terms or concepts that I think will need to be defined or replaced. I always think to myself “what does this sentence/idea/concept assume the reader already knows?” There is so much implied in how we write, and plain language should aim to make the implicit more explicit.
When doing a plain language translation, my first step is always to do a close read of the original text. I identify the main points, the order information is presented, and any terms or concepts that I think will need to be defined or replaced. I always think to myself “what does this sentence/idea/concept assume the reader already knows?” There is so much implied in how we write, and plain language should aim to make the implicit more explicit.
Once I start translating, I typically take a paragraph-by-paragraph approach rather than sentence-by-sentence, because often I will need to re-order information, add definitions or examples, or reintroduce characters and ideas at the top of a new paragraph. Focusing too much on the sentence-level translation can mean losing sight of the bigger picture.
Once I start translating, I typically take a paragraph-by-paragraph approach rather than sentence-by-sentence, because often I will need to re-order information, add definitions or examples, or reintroduce characters and ideas at the top of a new paragraph. Focusing too much on the sentence-level translation can mean losing sight of the bigger picture.
How do algorithms try to assess readability?
As more people have recognized the practical value of plain language, researchers have sought to quantify the “plainness” of writing through readability formulas—mathematical models that assign numerical scores to text, indicating how understandable they are.
As more people have recognized the practical value of plain language, researchers have sought to quantify the “plainness” of writing through readability formulas—mathematical models that assign numerical scores to text, indicating how understandable they are.
Though most readability formulas were designed to offer rough difficulty estimates for specific groups of readers, their usage varies greatly, with the Agency for Healthcare Research and Quality warning that “these formulas are often interpreted and used in ways that go well beyond what they measure.”
Though most readability formulas were designed to offer rough difficulty estimates for specific groups of readers, their usage varies greatly, with the Agency for Healthcare Research and Quality warning that “these formulas are often interpreted and used in ways that go well beyond what they measure.”
Moreover, the simplicity of readability checkers has enabled their widespread adoption. Military engineers use them to help write technical documents. Governments and doctors use them to guide communication for a general audience. Schools and textbook manufacturers use them to tailor reading assignments to particular grade levels and students.
Moreover, the simplicity of readability checkers has enabled their widespread adoption. Military engineers use them to help write technical documents. Governments and doctors use them to guide communication for a general audience. Schools and textbook manufacturers use them to tailor reading assignments to particular grade levels and students.
To better understand how readability scores work—and how they can fail—let’s look at three representative examples.
To better understand how readability scores work—and how they can fail—let’s look at three representative examples.
Algorithm #1: Syllable Count
The Flesch-Kincaid Grade level formula looks, in part, at syllable count, based on the idea that words with fewer syllables are easier to understand.
The Flesch-Kincaid Grade level formula looks, in part, at syllable count, based on the idea that words with fewer syllables are easier to understand.
What happens if we only care about syllables
The two factors considered by Flesch–Kincaid are number of words per sentence and number of syllables per word. This is a short sentence with only two multi-syllable words (“over” and “lazy”), so the Flesch–Kincaid formula assigns it a low grade level.
More about this algorithm
The author Rudolf Flesch made a career as an early evangelist for plain language in the mid-20th century, promoting his namesake Flesch Reading-Ease Test and its cousin, the Flesch-Kincaid Grade Level Formula, developed in 1975 under contract with the U.S. Navy. It was calibrated on the reading comprehension scores of 531 enlisted Navy personnel, in order to measure readability in the specific context of technical communication. Today, perhaps thanks to its misleading name, the F-K Grade Level Formula is used in schools to estimate reading difficulty for children, overlooking the obvious fact that elementary school students and naval cadets may not agree on whether the same text is easy or difficult to understand.
The author Rudolf Flesch made a career as an early evangelist for plain language in the mid-20th century, promoting his namesake Flesch Reading-Ease Test and its cousin, the Flesch-Kincaid Grade Level Formula, developed in 1975 under contract with the U.S. Navy. It was calibrated on the reading comprehension scores of 531 enlisted Navy personnel, in order to measure readability in the specific context of technical communication. Today, perhaps thanks to its misleading name, the F-K Grade Level Formula is used in schools to estimate reading difficulty for children, overlooking the obvious fact that elementary school students and naval cadets may not agree on whether the same text is easy or difficult to understand.
Algorithm #2: Difficult words
The Dale-Chall Readability Formula considers the proportion of difficult words, where it deems a word “difficult” if it is not on a list of 3,000 words familiar to fourth-grade students.
The Dale-Chall Readability Formula considers the proportion of difficult words, where it deems a word “difficult” if it is not on a list of 3,000 words familiar to fourth-grade students.
One risk in the use of vocabulary lists is that the vocabulary of the readers surveyed to create them may not match the vocabulary of the intended audience. The original Dale-Chall list of “familiar words” was compiled in 1948 through a survey of U.S. fourth-graders, and even the most recent update to the list in 1995 retains obsolete words like “Negro” and “homely” while omitting “computer.”
One risk in the use of vocabulary lists is that the vocabulary of the readers surveyed to create them may not match the vocabulary of the intended audience. The original Dale-Chall list of “familiar words” was compiled in 1948 through a survey of U.S. fourth-graders, and even the most recent update to the list in 1995 retains obsolete words like “Negro” and “homely” while omitting “computer.”
What happens if we only care about “difficult” words
Dale–Chall considers average sentence length along with percentage of difficult words (PDW), where “difficult” words are words that don't appear on the Dale–Chall list. This is a short sentence made entirely of words on the Dale–Chall list, so its ASL score is low and its PDW score is zero, yielding a score of 0.45. Since the formula was calibrated on a group of 4th grade students, all scores below 5.0 are collapsed into a single readability category representing “4th grade and below.”
Algorithm #3: Algorithmic black boxes
More recently, US schools and textbook manufacturers have standardized their curricula on the Lexile Framework for Reading, a set of readability algorithms and vocabulary lists designed to automatically match students with appropriately difficult books. Publishers apply Lexile to their texts to rate their difficulty. A Lexile score of 210 indicates an easy-to-read book, while a score of 1730 indicates a very challenging one. A reading comprehension test assigns a corresponding reading score to each student, after which teachers pair students with texts that have comparable Lexile scores.
More recently, US schools and textbook manufacturers have standardized their curricula on the Lexile Framework for Reading, a set of readability algorithms and vocabulary lists designed to automatically match students with appropriately difficult books. Publishers apply Lexile to their texts to rate their difficulty. A Lexile score of 210 indicates an easy-to-read book, while a score of 1730 indicates a very challenging one. A reading comprehension test assigns a corresponding reading score to each student, after which teachers pair students with texts that have comparable Lexile scores.
The approach sounds simple enough, but critics have pointed out absurdities in Lexile's results. According to Lexile, The Grapes of Wrath (Lexile score: 680) is easier to understand than the Nancy Drew mystery Nancy's Mysterious Letter (score: 720), but neither of these is as challenging as The Library Mouse (score: 860), a 32-page illustrated children's book.
The approach sounds simple enough, but critics have pointed out absurdities in Lexile's results. According to Lexile, The Grapes of Wrath (Lexile score: 680) is easier to understand than the Nancy Drew mystery Nancy's Mysterious Letter (score: 720), but neither of these is as challenging as The Library Mouse (score: 860), a 32-page illustrated children's book.
How Lexile scores popular books for children
How exactly are Lexile scores calculated? Unfortunately for us, the Lexile Framework is the intellectual property of MetaMetrics, the private company that created it, so we can only guess at the secret recipe, but it's a pretty good bet that Lexile scores depend on a mixture of the same factors used in Flesch–Kincaid and other open-source readability measures.
How exactly are Lexile scores calculated? Unfortunately for us, the Lexile Framework is the intellectual property of MetaMetrics, the private company that created it, so we can only guess at the secret recipe, but it's a pretty good bet that Lexile scores depend on a mixture of the same factors used in Flesch–Kincaid and other open-source readability measures.
Formulas based on surface level text properties and word frequency have clear limitations. None of them consider how well organized the information is, or whether the sentences and paragraphs are coherent. None consider the role of grammatical tense. None account for the explanation of acronyms and jargon. None would balk at Jack Torrance's rambling and meaningless draft in The Shining, endlessly repeating “All work and no play makes Jack a dull boy.”
Formulas based on surface level text properties and word frequency have clear limitations. None of them consider how well organized the information is, or whether the sentences and paragraphs are coherent. None consider the role of grammatical tense. None account for the explanation of acronyms and jargon. None would balk at Jack Torrance's rambling and meaningless draft in The Shining, endlessly repeating “All work and no play makes Jack a dull boy.”
But proprietary tech like Lexile has some of the most disconcerting aspects of both worlds. As flawed as Flesch-Kincaid or Dale-Chall, but opaque and unexplainable. The main benefit of the F-K and D-C formulas (and other simple algorithms like Gunning-Fog and SMOG) is their transparency. A broken system locked in a black box can't even offer this.
But proprietary tech like Lexile has some of the most disconcerting aspects of both worlds. As flawed as Flesch-Kincaid or Dale-Chall, but opaque and unexplainable. The main benefit of the F-K and D-C formulas (and other simple algorithms like Gunning-Fog and SMOG) is their transparency. A broken system locked in a black box can't even offer this.
Where Do We Go From Here?
In recent decades, as disability activists have won more civil rights, both in the US and internationally, accessible writing has gained greater attention. And with this attention comes the very real possibility that plain language will be outsourced to blackbox technologies that are grounded in antiquated data.
In recent decades, as disability activists have won more civil rights, both in the US and internationally, accessible writing has gained greater attention. And with this attention comes the very real possibility that plain language will be outsourced to blackbox technologies that are grounded in antiquated data.
Technology alone isn’t the answer. Even the most thoughtful algorithms and robust data sets lack context. Ultimately, the effectiveness of plain language translations comes down to engagement with your audience. Engagement that doesn’t make assumptions about what the audience understands, but will instead ask them to find out. Engagement that’s willing to work directly with people with disabilities or limited access to education, and not through intermediaries. As disabled advocates and organizations led by disabled people have been saying all along: “Nothing about us without us.”
Technology alone isn’t the answer. Even the most thoughtful algorithms and robust data sets lack context. Ultimately, the effectiveness of plain language translations comes down to engagement with your audience. Engagement that doesn’t make assumptions about what the audience understands, but will instead ask them to find out. Engagement that’s willing to work directly with people with disabilities or limited access to education, and not through intermediaries. As disabled advocates and organizations led by disabled people have been saying all along: “Nothing about us without us.”
Authors' note
A lot of people helped us write this article.
Thank you to Zoe Gross and Leah Mapstead for being our expert readers. Zoe and Leah told us how to make the article better. Zoe is Director of Advocacy at the Autistic Self-Advocacy Network. Leah is a disability advocate and actor.
Thank you to:
- Rob Smith
- Michelle Pera-McGhee
- Matt Daniels
- Russell Samora
- The rest of the team at The Pudding
They made our article interactive. They helped brainstorm ideas. They told us how to make our writing better.
Thank you to Matt Hackert for making sure the article works on a screen reader. Matt leads the Center of Excellence in Nonvisual Accessibility at the National Federation of the Blind.
Thank you to Amy Silverman for helping come up with the idea for this article.
You can learn more about Kyra here.
You can learn more about how to write plain language and Easy Read here, here, and here.