In Vivo Coding 101 🖍️

A Plain-Language Explainer (With Practical 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 that way! In this video, we’ll explore a popular coding technique called in vivo coding. We’ll unpack what it is, which types of projects it’s well-suited to, and how to approach it, along with loads of practical examples. 

First things, first – let’s talk coding.

At the most basic level, qualitative coding is the process of labelling and categorising textual data. The coding process is foundational because it sets the scene for you to start identifying themes and patterns within your data, and ultimately, extracting insights from it. In other words, coding is the first step in the broader qualitative analysis process.

Now, when it comes to qualitative coding, there are three different approaches you can take – inductive, deductive and hybrid.

Going the inductive route means that you’ll allow your codes to emerge from the dataset itself, based on the patterns you encounter as you review it. Conversely, going the deductive route means that you’ll approach the dataset with a pre-determined set of codes, based on an existing theory or theoretical framework. Last but certainly not least, taking a hybrid (or abductive) approach would involve blending the two approaches to get the best of both worlds.

Which coding approach to take will depend on your specific research aims and research questions. We’re not going to cover that decision-making process here, but if you’d like to learn more, be sure to check out this blog post.

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Now, in vivo coding…

Now that we’ve defined what coding is and what the three approaches are, we can explore in vivo coding specifically. So, what exactly do we mean by “in vivo coding”?

The term “in vivo” originates from Latin, where it means “within the living”. More practically though, it refers to the act of studying something in its natural environment. So, within the context of coding, in vivo refers to a technique where you use the participants’ own words as your codes, as opposed to creating codes based on your interpretation of their words.

Let’s look at a practical example of in vivo coding.

Imagine you’re undertaking a study where you’re exploring people’s experiences of urban gardening. While interviewing participants, you might find that participants regularly refer to their garden as their ‘little oasis’. In this case, you could use the term ‘little oasis’ as an actual code. In other words, you’d be using your participants’ terminology (as opposed to your own terminology) as your codes.

Now, you’re probably wondering why someone would take such a stripped-down approach to coding, right?

Well, one benefit of in vivo coding is that it helps you avoid inferring meaning by staying as close to the original phrases as possible. This is particularly useful for studies where the subtleties of language and the specific expressions that people use are central to the research aims. That probably sounds a bit fluffy, so let’s look at a practical example.

Imagine a study where you’re exploring cancer patients’ perceptions of their treatment journey. In this case, participants might describe their chemotherapy sessions as “going into battle”. This expression is rich with connotations of struggle, resistance, and resilience. In other words, this specific choice of words reveals the patients’ perspective on their treatment, but also frames it within a context of conflict and endurance. By using this term verbatim as a code, all of this richness can be carried through to the analysis without risk of dilution.

In vivo coding can also be useful when your data are derived from participants who speak different languages or come from different cultures, as it reduces the risk of you interpreting the data through your own cultural lens.

For example, English speakers typically view the future as in front of them and the past as behind them. However, this isn’t true for all cultures. Speakers of Aymara in the Bolivian Andes view the past as in front of them and the future as behind them. In a situation like this, in vivo coding would help avoid misinterpretation due to this subtle but significant cultural difference.

To sum up then, in vivo coding helps ensure that your analysis is deeply rooted in the actual perspectives of the participants by using their exact words or phrases as codes. Simple but powerful 🙂

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How to “do” in vivo coding

Just like any type of inductive coding, the first step of the in vivo coding process is to gather and prepare your dataset. This may consist of interview transcripts, field notes, or even secondary data such as organisational documents or reports.

From there, you’ll start with an initial read-through to familiarise yourself with the dataset. This read-through will help you to understand the overall content and context of the dataset as a whole (as opposed to getting tunnel vision on one or two portions of the dataset). From there, you can note down any initial patterns that you notice and start making a list of in vivo codes, based on these patterns. Remember, these codes need to be lifted directly from the text, verbatim.

Once you’ve built a preliminary list of codes, you can start your second reading and begin applying those codes to your dataset. As you do this, you’ll likely notice new potential codes emerging. If so, you can add these to your list and apply them to your dataset accordingly. This will usually mean cycling back and forth through your dataset to ensure that you code all the data consistently.

Once you’re comfortable that you’ve applied your codes consistently, you can start grouping them into meaningful categories – this process is called code categorisation. Importantly, these categories should reflect broader patterns that are relevant to your research aims.

Let’s look at an example to see what this might look like in practice.

Imagine you’re coding interview transcripts from employees discussing their experiences and feelings about their workplace culture.

In doing so, you could group codes such as “Walking on eggshells”, “Fighting fires” and “Dropping the ball” under a category titled “Emotional climate”.

Similarly, you could categorise codes such as “Like a family” and “Supporting each other” under the title of “Support and relationships”.

Now, it’s useful to point out that there are many different ways in which you could categorise any set of data. In other words, it’s possible to identify a range of different threads that run through a dataset, depending on your perspective. So, what’s important at this stage is to develop categories that have a clear link to your research aims and research questions. For example, if you aim to explore emotional states, you could categorise your codes based on broad emotional states such as anger, fear, surprise or disgust.

Once you’ve applied your codes and categorised them into logical groups, your dataset should, in principle, be ready for analysis. This might take the form of something like thematic analysis or content analysis, depending on your specific research aims. In the video below, we explore some of the most popular qualitative analysis options. 

Key Takeaways

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

  1. In vivo coding is an inductive coding technique that uses verbatim extracts from the dataset as codes.
  2. It is well suited to studies where the subtleties of language and the specific expressions people use are central to the research aims. It’s also useful when samples span across multiple languages and cultures. 
  3. Undertaking in vivo coding involves an initial read-through, followed by code development, application and categorisation.
  4. In vivo coding can be combined with other coding techniques for a more multi-dimensional analysis. 

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.

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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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