The design basics of charts & data visualization

How do I effectively display research data?

Do you often ask yourself this question? Design and data visualization needs to be easy for readers to understand and interpret. Here’s a how-to guide.

Creating charts

When you’re making charts, stick with specific design choices to maintain consistency throughout. Contrasting colors for positive, neutral, and negative responses help to visually process information. These charts can be produced quite simply on programs like Excel or Google forms. Or you can make them in Adobe Illustrator and InDesign.

One: Small-multiple pie charts

Pie charts are like evil minions. They are tough to read and are interpreted through the reader’s ability to decrypt quantitative information. You compare angles and area, which may be inaccurate. So, we use the next best thing: small-multiple pie charts

In the case of small-multiple pie charts, each pie shows the relative frequency of each response category. Color codes are effective when used with a key. A simple contrast between colors is crucial. This way it is easier to compare the feedback responses within each pie than among different pies.

Two: Grouped bar chart

Google Forms are popular. A non-statistician can evaluate responses with the grouped bar chart the G-Forms produce. It colors each line differently.

Studies show that it is easier for readers to compare length and position (like on a bar or line chart) than area and angle (that you find in pie or donut chart). Like Stephen Few puts it,‘Save the pies for dessert.’

Grouped bars can be overwhelming and need more visual processing time. The bars are segmented by session and you run the risk of the reader skipping ahead to more appealing representations.

Three: Small-multiple bar charts

Small-multiple bar charts on the other hand allows the reader enough visual time to register the information. The average reader is better at reading length and position in smaller bytes.

Four: Small-multiple waffle charts

Waffle charts are usually a grid of 100 squares (10 x 10). They show the relative frequency of data. A more modern and non-traditional way to show the part-to-whole relationship. Almost like the fondly used pie.

The gradual gradient of colored squares is gentle on the eye. Having categories and values directly next to each section helps this. And the reader gets a fairly accurate overall picture of the data and responses.

Five: Large number and text

The large number and text option like you see on info-graphics is a great way to break up a lengthy, wordy report filled with too many bullet points.

Six: Stacked bar chart

The stacked bar chart and the diverging stacked bar chart are perhaps most effective to demonstrate the Likert scale. A stacked bar chart belongs to the part-to-whole group of charts and shows the whole range of responses proportionately.

Seven: Diverging stacked bar chart

The diverging stacked bar chart is essentially the same as a stacked bar chart. But they are separated with a thick visual baseline. In this chart I chose to categorize “neutral” as a negative result. There is no universal rule on when to use which. This entirely depends on who you are speaking to and what message you need to convey.

Tips to represent visual data better

• Use a variety of interactive graphs, including pie, bar, area, line or column
• Create custom data views including only the questions, tabulations, filters and charts that you want
• Pie charts and small multiple pie charts help quickly represent how responses vary among different groups
• Bar graphs make quick comparisons
• Line graphs show changes over time
• Create word clouds to visually communicate open-ended text responses
• Customize colors and fonts to create brand continuity

Collateral elements to consider

Other collateral things that you might need to think about:

• The medium you are designing for. You need to experiment with different options for different content forms like reports, social media content, decks, website, and print handouts.
• The space ratio between content and charts.
• Your audience target.
• The kind of charts that they can easily read.
• The colors you use to distinguish between positive and negative categories.
• Your brand’s colors.
• The message you want to drive home through these charts.

There are multiple ways to represent research scales. Besides, the chart follows your research, not the other way around. There are no binary answers to data visualization. Inspiration can come from any of the options. 

Ready to act on your research goals?