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Qualitative Chart Chooser

Qualitative Chart Chooser

Hi.

Hey there.

Having a rough day?

I have something to make it a little brighter.

It’s my associate, Jennifer Lyons, with a qualitative chart chooser.

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Visualizing qualitative data is like making homemade risotto. You are standing over the stove (aka hunkered down with your computer), waiting patiently for the magic to happen. It’s slow and sweaty, but in the end SO worth it. There is a reason you can’t order risotto at McDonalds, and there is a reason you can’t display your qualitative findings in a nice neat dot plot. I am going to share some resources and ideas that will help give your audience a taste of your rich qualitative findings.

The reality is, most people are never going to be excited to read your text heavy 50-page report with no visuals. This is where data visualization can come in handy. Visualization is a great tool to get people interested and engaged with your story. The problem is, many of the qualitative visualizations I see are reports with endless callout quotes or ugly charts that were spit out of data analysis software. We can do better than this.  Let’s explore our options.

Take a peek at the qualitative chart chooser(s) we made. This is an attempt to organize different ways to show qualitative data. The truth is, this was hard. We have gone through many revisions. Like qualitative data, categorizing the options wasn’t a straightforward process. I am going to share with you the two best drafts we came up with. Here at Evergreen Data, we like to start with your story. Your message should be the foundation of all explanatory qualitative visualizations.

Draft One:

qualitative-chooser-1-0This is the draft we handed out during a qualitative panel at the American Evaluation Association conference. The different kinds of stories you tell with your data are in grey boxes on left. To the right are different chart options that help tell that story. This is all fine and dandy, but things are not as straightforward as they look. There are lots of overlap. For example:

Icons can be symbolic and help categorize themes.

theme-pic

They can also help show alignment with a goal or outcome.

goal-pic

Because of this overlap and my difficulty with fitting the visuals into boxes, we went in a different direction with the second draft.

Draft Two:

This one is two pages. The first page has the options categorized by visualization type. Because this doesn’t go into detail on the story each visual tells, we included a matrix that does. The matrix shows the interconnectedness and complexity of visualizing qualitative data.

qualitative-chooser-2-0_page_1 qualitative-chooser-2-0_page_2

Let’s look at an example of when the chart chooser can be helpful. I am evaluating the factors that impact work culture at a local community healthcare center. Based off the key informant interviews, I found two themes: cross collaboration and connectedness. The client is particularly interested in differences between staff. During data analysis, I found that within these themes, management staff saw collaboration as the most important factor influencing work culture. Program level staff, on the other hand, found connectedness to be most important. What is the best way to display this difference? A spectrum display is a good fit because it helps show themes by quantifying individual cases centered around a mutually exclusive variable.

Stuart Henderson does a great job describing and analyzing this visualization in his article “Visualizing Qualitative Data in Evaluation Research” in AEA’s journal New Directions for Evaluation. A spectrum display compares the relationship between qualitative cases and themes. You must have a mutually exclusive variable. In the example below, 12 key informant interviews were conducted with staff. The mutually exclusive variable in this display is staff type. The two themes are displayed at the bottom of the spectrum. Each case is coded on the presence of that theme.

spectrum-1-pic

At first, this display comes off complex and overwhelming. It is hard to tell that my main point is to show differences between staff perception. Choosing the best visual for your message is just the first step. We can apply data visualization techniques to transform our visual into something useful. After breaking the visual into small multiples, using strong titles and color coding, and adding quotes, the message becomes much clearer.

spectrum-2-pic

My biggest piece of advice is to stay true to the data. Whenever possible, link a visual with quotes and narrative that help provide evidence and context to your main point.

We are sharing these drafts with you because the qualitative chart chooser is a living document. We want your feedback! What do you like? What would make it better? Let us know in the comments section below.

15 thoughts on “Qualitative Chart Chooser
  1. Sena says:

    This was a pleasant surprise, especially today! Will you be providing links or how-to’s for some/each/most of these charts? Because I’ll be honest, I’ve never used most of these and am not entirely sure how they work haha.

  2. Jane Davidson says:

    This is seriously, *seriously* awesome – well done, both of you!! 🙂

    • Stephanie Evergreen says:

      Feedback absolutely welcome. Now or when you begin using it. If something doesn’t fit right or needs to be added, please let us know. It’s a work in progress but too good not to share as is.

      • Eric Payne says:

        Thanks for the visualization ideas.
        Please unstick your gears in the cross-collaboration icon. As drawn with 3 interlocking gears, they represent a non-functioning machine. (I know that’s how a lot of collaboration efforts actually work, but thought you would want to represent a more idealized scenario.)

  3. Rasha says:

    This is extremely helpful. I’ve always wondered how to make qual data jump out. It would be exciting to use it for an infographic like sheet.

  4. Kamakana says:

    This is awesome!!! We are currently working on a report that also includes qualitative. This is a great start and the spectrum display looks really interesting.

  5. Alicia Moag-Stahlberg says:

    Stephanie and Jennifer, Thank you for sharing this – you are so generous and very clever. These charts and the many tips you provide in your blogs have helped me so much. It is hard for me to come up with a visual to display data and this chart chooser will really help.

  6. Jen Akuna says:

    “Visualizing qualitative data is like making homemade risotto. ” Thank you for this 🙂

  7. Mike Miller says:

    I’m pretty amazed at the before and after in this post. The difference between the initial visual, which I can barely make sense of, and the small multiple follow-up is astounding because the story really jumps out. I like the use of small multiples here. That is such an awesome ninja trick.

  8. Lydia Schuck says:

    Hi Stephanie, I always appreciate your resources. I work in a setting with many blind people and sighted people together. Sighted people always love the graphics, but I want the vis to make sense to blind colleagues, too. It has to be fairly linear to best represent the information and to work well with screen readers. Your thoughts.

    • Stephanie Evergreen says:

      It’s a good question, Lydia. My best understanding at the moment is to be sure to include strong alt text behind the graphic so screen readers can pick it up and describe the image to a reader with blindness or low vision.

  9. Margaret Roller says:

    Thank you Jennifer & Stephanie. Visualizing qualitative data is a real challenge and I love your ideas. As far as the spectrum display example you give, I would do something a bit different. Because I am careful to not report numbers (or convey counts/numbers in any way) in my qualitative reporting, I would use solid color in place of the “dots,” with the length of the arc giving the sense of weight for each theme for each participant segment. This could be a side-by-side display depicting each theme (as you have it) or each segment.

  10. Rudy Owens says:

    A superb post. Kudos! Everyone in the fields of public health and polling should Tweet this one out today.

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