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The following is a toggle that allows you to change whether the text is standard or translated into plain language. Press the p key at any time to switch between standard and plain language versions of the article. If you are using a screen reader, you will need to disable your the quick navigation keyboard commands in order for the P key to function.
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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 languageâa style of writing that uses simplified sentences, everyday vocabulary, and clear structureâaims 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 languageâa style of writing that uses simplified sentences, everyday vocabulary, and clear structureâaims to remove those barriers.
Good writing is easy to read. But a lot of writing is hard to read. Some people canât read hard writing. Plain language fixes this problem. It makes writing easy to read for more people.
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 âpâ key on your keyboard.
You can also use the toggle on the top right to switch everything to plain language. Or use the âpâ 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.
20% of the population with learning
disabilities
million people in the US
Plain language is helpful for everyone. But it is really good for people who may find other kinds of writing hard to read. That includes:
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.
on their behalf
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.
made on their behalf
institutionalization and eugenic
sterilization
These groups of people get left out of the conversation a lot. Even when the conversation is about them.
For example, many people think people with ID donât understand how to make choices for themselves. Nondisabled people make choices for them. These choices sometimes hurt people with ID. Throughout history, many people with ID have been:
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.
Writers will censor writing for these groups. To censor something means to take out information the writer thinks is not appropriate. Taking out information can make some problems worse.
For example, people with ID experience sexual violence more than nondisabled people. But some writers think people with ID should not read about sex or sexual violence. So, readers donât have all the information they need.
The benefits of plain language arenât 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 arenât 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.
Some kinds of writing are hard for everyone to read, like tax forms. But everyday writing, like the news, can be hard to read too.
Letâs 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.
article
Letâs 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.
article
Here is an example for how to make writing easier to read. Rebecca wrote this example. She is an expert in plain language. This quote is from a news article. Amy Silverman wrote it for ProPublica. Rebecca wrote it in plain language.
Read the translation below. Click the highlights to see what Rebecca thinks.
Read the translation below. Click the highlights to see what Rebecca thinks.
An example of translating text from standard to plain language where you can select Rebecca's comments to learn more about her translation process.
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.
Rebecca's comments
Click the highlighted text to see Rebecca's comments.
Click the highlighted text to see Rebecca's comments.
Click the highlighted text to see Rebecca's comments.
More about Rebeccaâs 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 âwhat does this sentence/idea/concept assume the reader already knows?â 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 âwhat does this sentence/idea/concept assume the reader already knows?â There is so much implied in how we write, and plain language should aim to make the implicit more explicit.
My first step is to read the whole paragraph. I look for:
A lot of writing assumes the reader already knows something about the topic. Plain language should not assume that. I will explain things fully.
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.
When I write something in plain language, I donât take every sentence from the original writing. I look at the big ideas. I:
As more people have recognized the practical value of plain language, researchers have sought to quantify the âplainnessâ of writing through readability formulasâmathematical 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 âplainnessâ of writing through readability formulasâmathematical models that assign numerical scores to text, indicating how understandable they are.
Researchers try to measure how easy something is to read. They use math to give the writing a score. The score tells us how easy something is to read. These are called âreadability scores.â
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 âthese formulas are often interpreted and used in ways that go well beyond what they measure.â
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 âthese formulas are often interpreted and used in ways that go well beyond what they measure.â
Readability scores just give us an âestimateâ about how hard something is to read. That means the scores are not perfect. But they give us a good idea about how hard it might be for different groups of people to read something.
But the Agency for Healthcare Research and Quality said, âthese formulas are often interpreted and used in ways that go well beyond what they measure.â This quote means people use these scores in ways they were not made to be used.
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.
Readability scores are easy to understand. Many people use these scores to help them write. Some groups that use readability scores are:
To better understand how readability scores workâand how they can failâletâs look at three representative examples.
To better understand how readability scores workâand how they can failâletâs look at three representative examples.
Letâs look at three examples of readability scores. They can help us understand how these scores work and how they can fail.
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.
The Flesch-Kincaid formula measures two things:
The formula says the shorter the words and sentence, the easier it is to read.
An interactive showing what the Flesch-Kincaid algorithm considers a easy, medium, and hard sentence. The algorithm deems sentences with lower syllable counts to be easier, so when long multisyllabic words are added (even if they are easy words), the algorithm says it's a hard sentence. If we add short but obscure words, the algorithm thinks it's an easier sentence. We see that the Flesch-Kincaid algorithm isn't able to handle much complexity when it comes to assessing readability.
What happens if we only care about syllables
The below text is at a 2.34 (2nd grade) grade reading level according to Flesch-Kincaid
The quick brown fox jumped over the lazy dog.
The dun fox cleared that slouch of a dog at full tilt.
The quick brown fox jumped over the lazy dog.
The wonderful, beautiful fox jumped over the unbelievably lazy dog.
The two factors considered by FleschâKincaid are number of words per sentence and number of syllables per word. This is a short sentence with only two multi-syllable words (âoverâ and âlazyâ), so the FleschâKincaid 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.
Rudolf Flesch was an author. He lived in the 1900s. He thought plain language was very important. He made two scores to measure how easy something was to read:
Rudolf Flesch created the Flesch-Kincaid Grade Level Formula when he was working for the Navy. He measured how well 531 people in the Navy could read technical communications. Technical communications have special information that people in the Navy need to do their jobs.
Now we use the Flesch-Kincaid Grade Level Formula to measure readings for children. But the score has never been tested with children.
The Dale-Chall Readability Formula considers the proportion of difficult words, where it deems a word âdifficultâ 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 âdifficultâ if it is not on a list of 3,000 words familiar to fourth-grade students.
Dale-Chall is another readability score. It measures two things:
Dale-Chall uses a list of 3,000 easy words. Dale-Chall says these are words most 4th graders know. Any other word is a hard word.
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 âfamiliar wordsâ 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 âNegroâ and âhomelyâ while omitting âcomputer.â
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 âfamiliar wordsâ 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 âNegroâ and âhomelyâ while omitting âcomputer.â
But not everyone knows the same words. Dale-Chall made the first easy word list in 1948. They updated the list in 1995.
We donât use the same words now that we did back then. The list has words most people donât use now, like âNegroâ and âhomely.â And it doesnât have words a lot of people use now, like âcomputer.â
An interactive showing what the Dale-Chall algorithm considers a easy, medium, and hard sentence. Dale-Chall considers average sentence length along with how many difficult words are used, where âdifficultâ words are words that don't appear on the Dale-Chall list. If we add words that are unfamiliar (like dinosaur or dude) the algorithm says it's a difficult sentence. If we simply add a sentence that just contains the exclamation "Yes!", that lowers the average sentence length and makes the algorithm say it's an easier sentence. We see that the Dale-Chall algorithm isn't able to handle much complexity when it comes to assessing readability.
What happens if we only care about âdifficultâ words
The below text is at a 0.45 (4th grade or below) grade reading level according to Dale-Chall
The quick brown fox jumped over the lazy dog.
Yes! The quick brown fox jumped over the lazy dog.
The quick brown fox jumped over the lazy dog.
The quick brown dinosaur jumped over the lazy dude.
DaleâChall considers average sentence length along with percentage of difficult words (PDW), where âdifficultâ words are words that don't appear on the DaleâChall list. This is a short sentence made entirely of words on the DaleâChall 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 â4th grade and below.â
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.
Lexile Framework for Reading is another formula to measure how easy something is to read. Schools and textbook writers use Lexile. Lexile helps match students with books they are able to read.
Here is how it works:
read.
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.
But the scores Lexile gives books donât always make sense. Hereâs an example. Lexile says:
of Wrath is a book for adults. It is over 400 pages long and has many complex themes and ideas.
than The Grapes of Wrath. Nancy Drew books are made for preteens. Most people would consider it much easier to read than The Grapes of Wrath.
Grapes of Wrath. The Library Mouse is a picture book for children.
It does not make sense that a childrenâs picture book would be harder to read than a book for adults.
Images of three book covers arranged by how difficult the Lexile algorithm thinks they are. It says Grapes of Wrath is the easiest, followed by Nancy's Mysterious Letter, and the hardest is The Library Mouse, a 32-page illustrated children's book. This doesn't make much sense, most people would say that Grapes of Wrath is much more difficult than a simple children's book.
680
(5th grade)
720
(7th grade)
860
(10th grade)
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 FleschâKincaid 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 FleschâKincaid and other open-source readability measures.
We donât know how Lexile decides how hard each book is. MetaMetrics is the company that made Lexile. They do not share what math they do to get their readability scores. But it is probably close to the other formulas we looked at. We know Lexile looks at:
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 âAll work and no play makes Jack a dull boy.â
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 âAll work and no play makes Jack a dull boy.â
None of these formulas are perfect. They donât measure:
These formulas would have no problem with the scene in The Shining where Jack Torrance writes âAll work and no play makes Jack a dull boy,â over and over again. They do not check if the writing means anything.
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.
At least we know how Flesch-Kincaid and Dale-Chall work. They are not perfect but we can explain them. We donât even know what Lexile measures.
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:
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.
