If you’re doing qualitative research, you’re probably coding your data. But coding isn’t just a step you complete before the “real” analysis begins — it is a fundamental part of the analysis. It’s where you begin to make sense of what participants are saying, how they’re saying it, and how that connects to your research questions.
But here’s the thing: not all coding looks the same. How we code is shaped by the kind of research we’re doing, what we believe about knowledge and meaning, and the practical decisions we face in real projects.
What Shapes the Way We Code?
There’s no one-size-fits-all approach to coding. Your approach will reflect:
- Your theoretical perspective: Are you aiming to capture what’s “really there” in the data, or are you interpreting how people make meaning?
- Your methodology: Are you building theory, understanding experience, or examining language?
- Your research questions and aims: What are you hoping to find out, and what kinds of meaning are you attuned to?
These aren’t just academic choices. They guide what you notice, how you label it, and what you do with those labels.
A Closer Look: Coding Across Different TA Approaches
Even within Thematic Analysis, coding looks quite different depending on the approach you’re using. Here’s a quick comparison:
| Coding Reliability TA | Codebook TA | Reflexive TA | |
|---|---|---|---|
| Structure | Follows a fixed codebook | Uses a shared, evolving codebook | Organic, iterative and flexible approach. |
| Codes | Structured code book is developed and applied to the data. Codebook enables researcher to ‘find’ the codes in the data | Structured code book is developed, but can evolve as researcher(s) work through the coding process | No codebook — codes are created during analysis |
| How themes are built | From pre-set codes | From a defined set of codes and their meanings | From the researcher’s interpretive engagement with the data |
| Coding logic | Usually deductive — testing predefined ideas | Can be inductive or deductive, but within a structure | Inductive and/or deductive, but always interpretive |
| Goal | Consistency and accuracy across coders | Shared coding within teams, good for applied or team-based research | Reflexivity, subjectivity, and analytic depth |
Each of these approaches is valid. Which one you use depends on your paradigm, your team, and the kind of knowledge you’re trying to generate.
Inductive and Deductive Coding — You Don’t Have to Choose One
You may have heard that you need to choose between inductive and deductive coding. That’s not really how it works.
- Inductive coding means you’re developing codes based on what you see in the data. You don’t come in with a full set of categories — you let them emerge through close attention.
- Deductive coding means you start with ideas already in mind — maybe drawn from theory, previous research, or your evaluation framework — and you look for them in the data.
Most of us do a bit of both. You might begin with some deductive codes based on your research aims, but then notice things that call for new, inductive codes. That’s not sloppy — it’s responsive.
The important thing is to be clear about where your codes are coming from and why you’re using them.
Descriptive vs Interpretive Coding — What Are You Actually Doing?
Separate from whether your coding is inductive or deductive is the question of what your codes are doing.
- Descriptive coding stays close to the data. You label what people said or did, often using their own language.
→ For example: “working from home,” “team conflict,” “seeking help.” - Interpretive coding goes further. You’re trying to understand what the data suggests or implies — what it might mean in context.
→ For example: “navigating exclusion,” “managing emotional risk,” “performing competence.”
You can do descriptive or interpretive coding in either an inductive or deductive way. They’re different dimensions. What matters is being aware of what kind of coding you’re doing, and making sure it fits your methodology and analytic goals.
Always Ground Your Codes
No matter your coding approach, one thing stays the same:
You need to be able to show where the evidence for a code comes from.
This doesn’t mean you have to quote it every time. But you should be able to explain what in the data made you choose that code. This is especially important when coding implicit meaning.
Ask yourself:
- What makes me think this is happening here?
- Does this interpretation fit the participant’s language and context?
- Can I explain this decision clearly to someone else?
Interpretation is part of qualitative work — but it needs to be careful, and it needs to be grounded.
You’re Interpreting Someone Else’s Interpretation
One final reminder: when we code qualitative data, we’re not working with cold, hard facts. We’re working with people’s accounts — their stories, explanations, and reflections. These are already interpretations.
So when you code, you’re not asking, “Is this true?”
You’re asking, “How is this person making sense of their world — and how does that relate to my research focus?”
You don’t need to agree with your participants. But you do need to respect the meaning they’re trying to express — and understand your role in interpreting it.
In Summary
- Coding is shaped by your theoretical stance, methodology, and research aims
- Different TA approaches treat coding differently — structure and flexibility vary
- Inductive/deductive = where codes come from
- Descriptive/interpretive = what codes do
- Ground all coding decisions in evidence from the data
- Your job is to interpret meaning, not extract facts
This is what makes qualitative coding powerful — and why doing it thoughtfully matters.
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