Inductive vs Deductive Coding

Qualitative coding is a topic that often leaves students feeling a little confused – but it doesn’t have to be! In this post, we’ll walk through the three overarching approaches to qualitative coding – inductive, deductive and hybrid – so that you can choose the best option for your project.

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What (exactly) is “qualitative coding”?

Simply put, qualitative coding is the process of categorizing and labeling textual data to lay the foundation for identifying themes, patterns, and ultimately, insights. In other words, it’s the first step toward qualitative data analysis.

Coding involves meticulously reading through a dataset – for example, interview transcripts, field notes, or documents – and assigning ‘codes’ to various excerpts from the text. These codes can be words, phrases, or short little summaries that capture the essence of each data segment. That probably sounds a bit fluffy and conceptual, so let’s look at a practical example.

Now, the exact pieces of text you decide to label and which specific codes you use will depend on the coding structure that you adopt, as well as your research aims and research questions. We explain the different coding techniques and structures in a separate post. For now, what matters is the choice you make before any of that: where your codes come from in the first place.

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The “big 3” coding approaches

Now that we’ve defined what we mean by qualitative coding, we can start to explore the three overarching approaches to coding – that is, inductive, deductive and hybrid (also called abductive) coding. Let’s look at each in turn.

Inductive coding

In simple terms, the inductive approach involves developing codes based on the data itself, as opposed to approaching the dataset with a pre-determined set of codes based on existing theory.

This means that you, as the researcher, will start the coding process with no preconceived codes or categories. Instead, you’ll read through each passage of text and allow the codes to emerge organically from the data, based on the patterns that you see.

In short, the inductive approach is bottom-up and iterative. This makes it ideal for exploratory research, especially when there is limited existing theory and understanding of a specific phenomenon. For example, if you were undertaking a study exploring how virtual reality affects the emotional well-being of elderly patients with limited mobility, you might consider using the inductive approach. You can also see more examples here.

Deductive coding

In contrast to inductive coding, the deductive approach uses an existing theory or theoretical framework as a basis for a pre-defined set of codes. This set of codes is developed in advance and is typically contained within something called a codebook.

In practical terms, deductive coding means that you’ll approach the data with a set of predefined codes and simply apply these codes to the data as you identify relevant passages or words. Importantly, with this approach, you don’t develop any new codes while coding – even if you see patterns in the data that aren’t represented by the existing code set.

A codebook is just a table that fixes what each code means before you start, so that you apply it the same way on transcript 1 and transcript 20.

This approach probably sounds a little rigid (and it is), but that is the point: it is useful when your research aims are confirmatory rather than exploratory. The deductive approach works well when you are testing a theory rather than exploring a phenomenon, and its discipline is what lets you say something defensible about whether the theory held up.

Hybrid (abductive) coding

Finally, let’s look at hybrid coding, which you will also see called abductive coding.

As the name suggests, hybrid coding combines the inductive and deductive approaches. You start with a small set of predefined codes and then add new ones as patterns emerge that your starting set does not capture. That flexibility makes it effective for studies with both exploratory and confirmatory aims, which describes most applied dissertation work.

In practice it runs like this. Build your starting codebook from the theory or framework, exactly as you would for deductive coding. Code your first two or three transcripts with it, and keep a separate list of anything interesting that has no home in the codebook. Once that second list stabilizes, promote the recurring items into codes, define them properly, and then re-code the earlier transcripts so every case has been through the same frame.

The discipline hybrid coding needs is recording where each code came from. Your methodology chapter has to say which codes were a priori and which emerged, and that is close to impossible to reconstruct after the fact. A column in your codebook marked “source: theory” or “source: data” costs nothing at the time and saves the write-up.

A note on “abductive”. The two words get used interchangeably in most dissertations, and if your advisor uses them that way, follow suit. Strictly, though, they describe different things.

Hybrid is about where your codes come from: some from theory, some from the data. Abduction is about what you do when the data surprises you. You notice a finding that existing theory cannot account for, then work back and forth between data and theory to build an explanation that fits. Hybrid is a coding procedure; abduction is a reasoning move aimed at producing new theory. If your study genuinely does the second, call it abductive and be ready to explain the anomaly you were reasoning from.

The same extract, coded both ways

Descriptions only get you so far. To make the difference concrete, here is a single interview extract run through both approaches, so you can see exactly where they diverge.

Notice what each pass costs you. The inductive pass captures “time management”, which self-determination theory has no slot for and which a deductive pass would simply discard. The deductive pass files the sentence under a construct the inductive pass never names, and it does so consistently across every transcript. Neither is more rigorous than the other; they answer different questions.

Which techniques belong to which approach

Inductive and deductive are families, not techniques. The specific method you use sits inside one of them, and knowing which is what stops a methodology chapter claiming one thing while the appendix shows another.

The data-driven family, where the codes come out of the text:

Inductive technique	What the code is
In vivo coding	The participant’s own words, lifted verbatim
Process coding	An action, usually named as a gerund: negotiating, avoiding, coping
Descriptive coding	The topic of the passage, in a word or a short noun phrase
Values coding	A value, attitude or belief the participant expresses
Emotion coding	The emotion stated outright, or reasonably inferred from the account
Initial (open) coding	A first, provisional label, refined or discarded on later passes

And the framework-driven family, where you bring the codes with you:

Deductive technique	What the code is
Structural coding	The research question that segment answers
Provisional coding	A code from a start list built out of prior research or theory
Hypothesis coding	A code written specifically to test a stated hypothesis
Protocol coding	A code from a standardized system supplied from outside your study
Attribute coding	A case descriptor fixed before fieldwork: site, role, cohort

Four of those have a full guide of their own here: in vivo, process and values coding on the inductive side, and structural coding on the deductive one. Initial coding is the first phase of grounded theory’s open, axial and selective sequence. Descriptive coding is covered inside the main coding guide rather than on a page of its own.

Two cautions on that split. It follows where the codes came from, not the technique’s name, so several of these run either way: descriptive coding is inductive when you name what you find and deductive when the topic list was agreed in advance. And a real study rarely uses one technique. Structurally coding a set of interviews to index them and then process coding inside each segment is a normal combination, not a muddle.

Our qualitative data coding guide walks through the individual techniques, and there are worked examples of several side by side.

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How to choose the right coding approach

The right approach depends on the nature of your research aims and research questions, not on which one sounds more rigorous. Side by side:

	Inductive	Deductive	Hybrid (abductive)
Where codes come from	The data	Existing theory or a prior codebook	Both: a starter codebook, extended from the data
Use it when	There is little existing theory and your aims are exploratory	You are testing or applying an established framework	Your aims are part exploratory, part confirmatory
Main risk	Code sprawl, and findings that are hard to compare across cases	Missing whatever the theory did not anticipate	Losing track of which codes came from where
Commonly paired with	Grounded theory, exploratory thematic analysis	Framework analysis, theory testing	Most applied dissertation work

If your aims are primarily exploratory and there is not a large body of existing research on your topic, inductive coding usually makes sense. If your aims involve confirming or contradicting an existing theory, deductive is better suited. If you are honest about your study being a bit of both, say so and use hybrid rather than forcing a purer-sounding choice you will have to defend.

George Washington University’s guide to the coding process sets out the same top-down and bottom-up distinction, along with what a codebook entry should contain.

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Do you need coding software?

Several software packages exist to help manage the coding process: NVivo (not to be confused with in vivo coding), Delve, ATLAS.ti and MAXQDA are the common ones. They are useful. They are not essential, at least not at the dataset sizes most student projects involve.

For the vast majority of projects, you can code your dataset using a simple word processor such as Microsoft Word or Google Docs. In fact, at Grad Coach, we code datasets for student projects every day using nothing more than Word and Excel. Taking a low-tech approach also helps you absorb and digest the data more deeply, as you naturally spend more time reading through it.

So do not feel obligated to use them unless your university requires it. Do check whether your university has restrictions in terms of what software you can use, especially anything AI-powered. You don’t want to run into a case of academic misconduct just because you used the wrong software!

Still have questions?

What is the difference between deductive and inductive coding?

Where the codes come from. Deductive coding starts with a codebook built from existing theory and applies it to the data; inductive coding starts with nothing and lets the codes emerge from what participants actually say. Deductive is consistent and comparable but blind to whatever the theory did not anticipate; inductive captures the unexpected but is harder to compare across cases.

Is coding inductive or deductive?

It can be either, and in practice a lot of research is both. The approach is a choice you make and then justify in your methodology chapter, not a property of coding itself. If you find yourself starting from a framework and then adding codes as you go, you are coding abductively, and it is better to name that than to describe your study as purely one or the other.

How do I remember which is which?

Deductive works down from theory to data, and both words start with a d. Inductive works up from the data. The other reliable test: ask whether you could write your code list before reading a single transcript. If yes, you are coding deductively.

Can I switch approaches partway through?

You can change your mind, but you cannot leave half your data coded one way and half the other. If you switch, re-code the transcripts you had already done so that every case has been through the same frame. Otherwise your findings reflect the order you happened to read things in.

Which approach do examiners prefer?

Neither. What examiners look for is that the approach fits your research aims and that you can explain why you chose it. A deductive study that tests a framework carefully is as defensible as an inductive one that builds something new. What does get questioned is an approach chosen by default, or a methodology chapter that describes one approach while the appendix clearly shows another.

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