Making sense of complex data—one connection at a time.
When researchers collect qualitative data, like interview transcripts, open-ended survey answers, or field notes, it often feels messy at first. There’s so much information, so many perspectives, and a variety of emotions, experiences, and opinions. That’s where coding comes in.
To make sense of this mountain of data, researchers start by doing open coding. In this early step, they break down the raw text into small parts and assign simple labels (or “codes”) to what they see. For example, if someone says, “I find it frustrating to use new technology,” a researcher might code it as frustration, tech anxiety, or learning curve. After doing this for a while, they end up with a long list of codes, sometimes dozens or even hundreds.
But having all these separate codes is just the beginning. At some point, the researcher needs to organize and connect them. That’s where axial coding comes in.
So, What is Axial Coding?
Axial coding is the process of finding connections between codes. Instead of treating each label as a separate idea, axial coding looks for patterns. It asks: Which codes are related? Can these ideas be grouped together? Is there a central concept that links them all?
Think of open coding as gathering puzzle pieces. Axial coding is when you start fitting those pieces together to see what the picture might be.
Let’s say you’ve coded several interview statements like:
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“I feel lost when no one explains the new system.”
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“My internet connection is terrible during meetings.”
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“There’s no proper training for using this app.”
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“Sometimes, students don’t even show up online.”
At first, these might just seem like separate problems. But through axial coding, you might start to see a theme: challenges in online learning. You begin grouping these codes into broader categories such as lack of support, technical issues, and student engagement problems. These categories become more meaningful than the individual codes alone.
Example of Axial Coding
Imagine you are researching teachers’ experiences during online learning. You conduct interviews and start open coding statements like these:
Interview quotes and open codes:
“I didn’t know how to use the new teaching platform.” → lack of training
“Sometimes, my internet connection drops during class.” → unstable internet
“Students rarely turn on their cameras.” → low student engagement
“I had to figure out everything on my own.” → no support
“It’s hard to keep students focused.” → distraction issues
At this point, you have five different codes. With axial coding, you now ask: What do these codes have in common? Are they connected? After analyzing, you notice that all five codes are related to challenges in online teaching. So you create a category called:
Category: Barriers to Effective Online Learning
Includes codes like:
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Lack of training
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Unstable internet
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Low student engagement
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No support
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Distraction issues
You’ve just used axial coding to connect smaller ideas into a more meaningful theme. This theme will help support your analysis and conclusions.
Why is it Called “Axial”?
The term “axial” comes from the word axis, like the central point of a wheel. In axial coding, researchers often choose a main idea (the axis), then link related codes or subcategories around it. Everything revolves around the central concept, making the structure of the data more coherent.
For example, the axis might be barriers to effective online teaching. Around that, you’d have connected ideas like poor internet access, insufficient training, and student motivation. Each one helps explain a different part of the main issue.
Why Axial Coding Matters
Axial coding turns a list of loose, scattered codes into a structured web of ideas. It helps you:
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See the bigger picture
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Understand relationships between different parts of your data
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Begin shaping your findings into a narrative or theory
In short, axial coding helps you go from “what people said” to “what it means.”
Let’s say you’re researching how teachers adapted to remote learning. After open coding, you’ve identified dozens of individual concerns—technical difficulties, emotional stress, communication problems, and so on.
Through axial coding, you might discover that many of these issues stem from a few key areas, such as lack of training, poor infrastructure, and student disengagement. These insights help guide your final analysis and may eventually support your conclusion, framework, or recommendations.
Axial coding can feel a bit abstract at first, but once you try it, it starts to make sense. It’s all about organizing and connecting the dots, so your research tells a clearer, more powerful story. Whether you’re a student learning qualitative methods or a researcher working through interview data, axial coding is a valuable tool to bring order and meaning to your work.