Highlight the Humanity of Your Main Characters

Dissertation advice about data usually boils down to “let the data speak for itself” and then people wonder why their reviewers aren’t getting the point. The whole trick is that people connect with people, not raw data. 

Most conversations about data are abstract and disconnected from real-life consequences. But audiences prefer a humanized figure they can empathize with, which makes them necessary for storytelling to work. Missing characters are a common problem in dissertations, which are typically written around the numbers rather than why the numbers matter.

To avoid confusion, we'll use "character" as a catch-all for what is interesting about your data, not a literal person. A character can be a persona, a research question, or a population demographic. What makes a character work is that it has stakes. You can explain what your character cares about and why, giving it humanity and making your audience care.

Here's an example. Say your data shows that first-generation college students attend office hours less often than their peers. That's a finding. Now to make the character. The character is the first-gen student sitting outside a professor's open door, nervous because she’s taking up the professor’s valuable time. She came straight out of the thousands of rows of data, giving them a face.

You don't need to make just one character, either. A dissertation with multiple research questions is really an ensemble. If your sample includes multiple demographics, each one can become a character. From there, you can show how the characters interact and how those interactions shift based on what your data shows. Our first-gen student's story means something different next to the classmate whose parents told her to email professors early and often. Same door, different distance, different expectations.

Once you have a character, the results section gets easier to write. Imagine explaining your data to this character, who doesn't yet know why they should care. What matters to them? Why should they care about these statistics? What do these findings mean for their life? Writing this way makes it much easier for reviewers to understand your data, instead of feeling like they have to analyze it for you.

Some questions to ask while fleshing out your main character: How do you want to present them to your audience? What are their concerns? Which parts of your data matter most to them? Charts and graphs are extremely helpful here. A good visualization is often the first place your character becomes visible, because it shows you where their story lives in the data.

At Data in Plain English, we help you find your characters and support them in every chapter, not just Results. Clean, well-visualized data makes characters easier to see and easier to write for. And we make sure you understand your analysis well enough to explain it in your own words.