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Summary

Maintainability
Test Coverage
---
title: "Looking at the different ways to test content"
date: 2016-04-19
authors:
- emileigh
- christine-cawthorne
tags:
- content design
- testing
- tools you can use
- gov.uk
excerpt: "We know good content when we see it, and we’re frustrated when we don’t. Keeping this in mind, are there ways that writers can quantify and measure their writing? We’ve looked at different tests you can run depending on the age of your audience."
description: "We know good content when we see it, and we’re frustrated when we don’t. Keeping this in mind, are there ways that writers can quantify and measure their writing? We’ve looked at different tests you can run depending on the age of your audience."
image: /assets/blog/content-testing/content-graph.jpg
---

*Emileigh is a content designer at [18F](https://18f.gsa.gov), and Christine is
a content strategist who works with the Government Digital Service (GDS)
in the UK. They met for the first time on a sweltering summer day in
D.C. A quick cup of (iced) coffee turned into a months-long,
transatlantic conversation on the different ways writers can test online
content to see whether it needs improving. This post was first
published on the [GDS
blog](https://gds.blog.gov.uk/2016/04/06/guest-post-looking-at-the-different-ways-to-test-content/).*

Much like [Supreme Court Justice Potter Stewart](https://en.wikipedia.org/wiki/I_know_it_when_I_see_it)’s famous saying, we know good content when we see it.

Good web content is clear. It’s actionable. Readers find what they’re
looking for — and they’re looking for a lot these days. People rely on
websites to conduct research, fill out tax forms, read the news.

We know good content when we see it, and we’re frustrated when we don’t.

Keeping this in mind, are there ways that writers can quantify and
measure their writing? We’ve looked at different tests you can run
depending on the age of your audience. Finding appropriate ways to test
our content helps us improve and find best practice patterns for
creating copy.

![A graph with comprehension and confusion at the top and bottom of the y-axis and failure and success on the left and right of the x axis. The desired spot for content is in the upper right quadrant between comprehension and success. ]({{site.baseurl}}/assets/blog/content-testing/content-graph.jpg)
*Christine’s graph of good content. The star marks
the sweet spot.*

Who you are writing for
-----------------------

Readers will come to your content with varying levels of knowledge and
education. Tell knowledgeable users what they know, and you’ll bore
them. Assume they know more than they do, and you’ll frustrate (or lose)
them. Whenever possible, keep things simple, short, and clear.

Here’s a good rule: If you’d like to reach a broad range of users, you
should strive to write at a middle-school level. A 2003 Department of
Education assessment showed average Americans read at a [seventh–eighth grade level](https://nces.ed.gov/pubs93/93275.pdf).

Testing your content depends on your audience
---------------------------------------------

When it comes to seeing if the content you’ve written is working, the
way you test it will depend on who you’ve written it for. We need to pay
particular attention to how we frame our usability sessions, because of
the stress associated with taking a “test.” It’s the content that’s
being tested (not the person), but this distinction needs to be made
clear.

Open-ended questions
--------------------

Asking open-ended questions — for example, “What does this mean to you?”
— is helpful when creating content for people with different cognitive
needs. Maybe they have learning difficulties or aren’t familiar with the
language. Additionally, if you’re writing content for children, using
open-ended questions is a good way to see if they understand the
information.

Using open-ended and task-orientated content questions allowed the Every
Kid in a Park project to keep pressure low and still measure how well
kids understood the website. For example, one question we asked was,
“How would you use this website to sign up for Every Kid in a Park?”

[![A screenshot of the Every Kid in a Park homepage]({{site.baseurl}}/assets/blog/every-kid-in-a-park/every-kid-in-a-park-homepage.jpg)](https://www.everykidinapark.gov)
*The Every Kid in a Park homepage reads at a 4.5 grade level and was
tested with kids through the University of Maryland’s HCI lab.*

Let people choose their own words
----------------------------------

When you ask people to self-identifying language, you allow them to have
a direct influence on the copy. This technique can be useful for
sensitive content. For example, GOV.UK has content on [what to do after
someone dies](https://www.gov.uk/after-a-death), not after someone
“passes away.”

Christine recently worked on [an app](http://www.mindofmyown.org.uk/)
that helps young people using social care services prepare for meetings.
The app aims to help them talk about their feelings; they can choose the
feelings that they’re experiencing, plus add their own.

In testing, target users were asked to list all the feelings they’d
experienced in the previous two weeks. The feelings listed by users in
testing were matched to the ones already in the app — plus the feelings
people had written into the “add your own” field.

![A grid of possible words such as OK, anxious, angry, and happy under the heading "How you feel right now"]({{site.baseurl}}/assets/blog/content-testing/choose-words.jpg)

The goal was to test whether the feelings chosen for the app were
representative and appropriate.

A/B testing
-----------

This kind of testing compares two versions of content to see which
performs better. It’s a good way to test how users connect with your
content. Maybe your site is easy to read and understand, but users
aren’t interacting with it in the way you hoped.

The [organ donation sign-up case
study](https://gds.blog.gov.uk/2014/03/18/organ-donor-register/) shows
how A/B testing works. Because this message appears after booking a
driving test, we know users will be over 17 years old.

The UK’s National Health Service (NHS) wrote eight variations of content
asking users to sign up as organ donors. For example:

-   Please join the NHS Organ Donor Register.
-   Please join the NHS Organ Donor Register. Three people die every day because there are not enough organ donors.
-   Please join the NHS Organ Donor Register. You could save or transform up to 9 lives as an organ donor.

The sign-up rate for each piece of content was measured and the most
successful was:

![A screenshot of the GOV.UK screen with "Please join the NHS Organ Donor Register" on a thank you page.]({{site.baseurl}}/assets/blog/content-testing/ab-test.jpg))

Each content variant the NHS tested used plain language and could be
easily understood. The A/B test showed which call to action was *most
effective* (though not why).

Cloze testing
-------------

For content about technical subjects — like finance, regulation and
health — [the Cloze
test](https://www.nngroup.com/articles/cloze-test-reading-comprehension/)
is ideal to help measure your readers’ comprehension..

In the Cloze test, participants look at a selection of text with certain
words removed. Then they fill in the blanks. You look at their
fill-in-the-blank answers to see how accurate they are to the original
text.

When creating a Cloze test, you can delete words using a formula (every
fifth word), or you can delete selectively (key words). You can accept
only exact answers, or you can accept synonyms. Sampling as many readers
as possible will give you better accuracy in your results.

When developing Cloze tests for [betaFEC](https://beta.fec.gov) — the Federal
Election Commission’s new web presence — Emileigh created Cloze tests
from passages of 150 words or more. She deleted every fifth word, and
she accepted synonyms as answers. She was hoping to get test scores of
50 percent or greater accuracy; in practice, FEC’s Cloze test scores ranged
from 65 percent to 98 percent, even better than she hoped.

![An example of a page from beta.fec.gov that used a Close test.]({{site.baseurl}}/assets/blog/content-testing/cloze-testing.jpg)
*This page from [beta.FEC.gov](https://beta.fec.gov) is one of several tested using a Cloze
test.*

Preference testing
------------------

[Christopher Trudeau](http://www.cooley.edu/faculty/trudeau.html),
professor at Thomas M. Cooley Law School in Michigan, did [research into
legal
communication](http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1843415)
to find out ‘to what degree do clients and potential clients prefer
plain language over traditional legal language’. He found that the more
complex the issue, the greater the reader’s preference for plain English
and that the more educated the person, the more specialist their
knowledge, the greater their preference is for plain English.

He employed a range of ways to test content for his research, including
A/B testing and asking respondents:

“Would you prefer this or this version”

then following up with:

“Why / why not?”

He then asked longer, qualitative question series:

“Have you ever read a document that was difficult to understand?”

“Did you persevere?”

“Why did you stop reading it?”

He named the method of using a mixture of A/B then qualitative questions
“preference testing.”

For checking whether people understand risks (in informed consent cases)
he says the best way to check comprehension is for the person who has
read the document to be asked follow-up questions, for example, “Based
on what you read in the document, can you explain the main risks to
me...”

This means a real person — not a computer — uses their judgment to
assess whether they understood the content.

Measuring what you’ve written
-----------------------------

Over time, organizations have developed reading scores and indexes for
measuring the “readability” of content. For example, the [Coleman-Liau
index](https://en.wikipedia.org/wiki/Coleman%E2%80%93Liau_index), the
[SMOG index](https://en.wikipedia.org/wiki/SMOG), and the [Gunning fog
index](https://en.wikipedia.org/wiki/Gunning_fog_index).

One that we find consistently suits our needs is the Flesch-Kincaid
grade level. Developed for the U.S. Navy, Flesch-Kincaid measures
sentence and word length. The more words in a sentence (and the more
syllables in those words), the higher the grade level.

Using these formulas will help you quickly estimate how difficult your
text is. It’s a clear metric that can help you advocate for plain
language. But, like every formula, Flesch-Kincaid misses the magic and
unpredictable nature of human interaction.

They also can’t help you figure out that “patience you must have my
young padawan,” is harder to read than “You must have patience, my young
padawan.”

We’d love to hear about your ways of testing content and comprehension.
Reach out to us on [Twitter @18F](https://twitter.com/18F/), or by [email](mailto:18f@gsa.gov).

Further reading
----------------

GDS’s research team put together [tips for testing your
words](https://userresearch.blog.gov.uk/2015/07/01/what-does-this-mean-tips-for-testing-your-words/)
when doing research.

A List Apart has a useful blog post on [testing
content](http://alistapart.com/article/testing-content).