Inductive, Deductive & Abductive Coding

Qualitative Coding Approaches Explained Simply (+ Examples)

By: Derek Jansen (MBA) | Expert Reviewer: Dr. Eunice Rautenbach | April 2024

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 unpack and explore the three overarching approaches to qualitative coding – inductive, deductive and hybrid – so that you can choose the best option for your project. 

What (exactly) is “qualitative coding”?

Simply put, qualitative coding is the process of categorising and labelling 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.

In practical terms, 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.

Imagine you have an interview transcript where a participant discusses their experience with a specific online learning platform. A segment of the transcript might read: “I found online classes challenging because I struggled with time management and staying motivated.” When it comes to coding this transcript, you might assign codes like “Time Management Challenges” and “Motivation Issues” to this specific passage. In other words, you’d be labelling and categorising snippets of text as you work your way through the transcript.

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 different coding structures and options in a separate post, so, for now, the key takeaway is that coding is about categorising and labeling data.

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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 unpack each of these.

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.

In practical terms, 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 deductive approach.

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.

This approach probably sounds a little rigid (and it is), but this top-down approach is useful when your research aims are more confirmatory in nature. In other words, the deductive approach can work well when your research aims involve testing a theory, rather than exploring an phenomena. For example, if you were undertaking a dissertation where you’re assessing the relevance of a specific motivation theory to a unique context, you might consider using the deductive approach.

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Hybrid (Abductive) Coding

Last but certainly not least, let’s look at hybrid coding, which is sometimes also referred to as abductive coding.

As the name suggests, hybrid coding combines the inductive and deductive approaches in an attempt to get the best of both worlds. With this approach, you might start with some predefined codes and then proceed to develop additional codes, based on the patterns you observe along the way. Naturally, the hybrid approach to coding offers a good deal of flexibility. This makes it particularly effective for studies that incorporate both exploratory and confirmatory research aims.

How to choose the right coding approach

As you can see, the right coding approach – inductive, deductive or hybrid – will depend largely on the nature of your research aims and research questions. If your aims are primarily exploratory and there’s not a large body of existing research regarding your topic, an inductive coding approach typically makes sense.

Conversely, if your research aims involve confirming or even contradicting an existing theory, a deductive approach would likely be better suited. So, as with all methodological choices, your coding approach needs to be informed, first and foremost, by your research aims.

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A quick about qualitative coding software…

It’s worth quickly mentioning that there are various software options available to assist with the coding process. Popular options include NVivo (not to be confused with in vivo coding), Delve, Atlas T.I. and MAXQDA. Now, while these tools can certainly assist in terms of managing the coding process, it’s important to understand that they are not essential, at least not for small datasets – which is commonly the case for student projects.

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.

Long story short, while there are software options available, don’t feel obligated to use them, unless your university specifically requires you to do so. On the flip side, be sure to check if your university has any 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!

While QDA tools can certainly help you manage the coding process, they are not essential, at least not for small to medium-sized datasets.

Key Takeaways

We’ve covered quite a bit of ground here, so let’s do a quick recap.

  1. Qualitative coding is the process of categorising and labelling textual data to lay the foundation your qualitative analysis.
  2. There are three overarching approaches to coding – inductive, deductive and hybrid.
  3. Your choice of approach needs to be informed by your research aims and research questions.

If you need a hand coding your qualitative data, be sure to check out private coaching, as well as our “done-for-you” qualitative coding service.

Psst… there’s more!

This post is an extract from our bestselling short course, Qualitative Research Bootcamp. If you want to work smart, you don't want to miss this.

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