guest post by Jennifer Lyons
When we debuted our Qualitative Chart Chooser last November, we promised to dive into detail on specific visualizations, so let’s kick it off by discussing how and when to use one of the most derided charts of all: the gauge diagram. I know, I know… I am sure you are tired of them by now after being stressed out by the New York Times’s jittering gauge showing the live presidential forecast. Most of the time I think gauge diagrams are useless. I am going to take a different spin on this controversial chart type by introducing two scenarios where I think they can be useful for qualitative data.
First, let’s start by covering why people hate these diagrams so much. They take up a lot of space on a dashboard and traditionally only show a single number. Gauge charts don’t align with lessons learned from Cleveland’s studies of graphical interpretation and precision. People are really bad at measuring and comparing angles, so why not use something easier for people to understand like a bar graph? Gauge diagrams are simplistic, waste valuable space, and lack context. Look at the difference below between visualizing satisfaction interview data with gauge diagrams compared to bullet charts. It is apparent that if you were putting this data in a dashboard, using the bullet chart both conserves space and allows for better data comparison.
With all that said, SOMETIMES they can be a good choice. Let’s talk about two scenarios when I think they can be useful.
Scenario One: Visualizing the sweet spot
When you are visualizing a measure where the sweet spot is in the middle, a gauge diagram can be a great choice. For example, I am visualizing employees’ rating of their boss’s level of involvement in projects. Having a low score would mean their boss wasn’t involved enough; whereas having a high score would mean the boss was too involved and micro-managing. The sweet spot is in the middle. A gauge diagram is one of the only charts that can visualize this quality effectively. People hold a lot of assumptions when reading data from other graphs about slope and rate of change. Typically a high value is good or low value is good. Rarely are we asked to visualize the sweet spot and when we are, the gauge diagram is a solid choice.
Scenario Two: When a big, simple visual breaks up qualitative narrative
There are so many nuances to qualitative data that provide an opportunity for our audience to really get a deep understanding. This means that qualitative reports are often bogged down by long narrative. This is the perfect situation where we need a big visual to balance out all of those pages and pages of words. Sometimes we need a big, simple visual to give people’s eyes and brains a break.
Let’s say you are reporting client satisfaction across programs at your non-profit. You did some interviews with clients and categorized their overall sentiment and responses into three categories: not satisfied, satisfied, and very satisfied. You are giving the board a short qualitative report, but you want some simple way to get across what program is and isn’t working so they can allocate resources accordingly. The very thing most people don’t like about the gauge diagram – it’s waste of space – becomes an advantage when working with narrative-heavy context. Yes, gauge diagrams may not be efficient on a data dashboard – but this isn’t a data dashboard. Take a look at the difference between these two reports.
Report 1 uses the space-efficient bullet graph:
Report 2 capitalizes on the large gauge visual to break up all the narrative:
- Make gauge diagrams simple with no tick marks, extra decorative elements, or unnecessary clutter. Never put more than one dial in a diagram.
- Add a strong title for more bang for your buck.
- Add some context and raw data to back up your qualitative categorization.
- Use color intentionally to emphasize your point.
These two scenarios (with some suggested guidance) offer opportunities where a gauge diagram effectively visualizes qualitative data. Want to learn how to make one? We used this tutorial, which is based in Excel.
We are committed to contributing to the ongoing discussion and development of qualitative data visualization. The Qualitative Chart Chooser is still a living document. Keep us updated on what is working and what isn’t. I promise to continue to dive into detail on more qualitative visualizations in the months ahead.
You can find a lot more step-by-step instruction on how to make awesome visuals in my Evergreen Data Visualization Academy. Video tutorials, worksheets, templates, fun, and community. Excel, Tableau, and R. Come join us.