How to Do In Vivo Coding in Qualitative Research

 
 

Ever stared at a stack of interview transcripts wondering how to make sense of it all? The insights are definitely in there, but making sense of everyone's experiences from that mess of data feels like climbing a mountain. The more transcripts you read, the hazier each unique voice becomes. Sometimes you catch yourself questioning if you're still capturing their true experiences or just what you think they meant.

This is where in vivo coding enters the picture. It’s not just as a methodology, but a way to preserve your participants' honest thoughts and experiences. Let’s go over when to use in vivo coding, how to implement it effectively, practical strategies for managing the challenges, and the best in vivo coding software for the job.

📘 New to qualitative coding?

It’s how you label and organize your data to find patterns and make meaning when you're dealing with dozens or hundreds of pages of transcripts. Take our free qualitative coding course or check out the coding guide for a full refresher course.

Capturing the authentic voice: Why words matter

When a cancer patient describes chemotherapy as "going into battle" or when an employee says they're "walking on eggshells" at work, these aren't just casual expressions. They're windows into deeply personal experiences, packed with emotional and cultural meaning that your codes might wash out or miss altogether.

If you replace "going into battle" with a code like "treatment difficulty," you can sometimes lose something personal in translation. When participants use metaphors, colloquialisms, or cultural literacy terms, they reveal how they make sense of their world. Recognizing this language is a great way to understand your research subjects' unique perspectives. 

In vivo coding, also called “verbatim coding,” keeps your participants' actual voices front and center. Instead of replacing their quirks and expressions with generic research terms, you use their exact words to uncover patterns and meaning in your data. Their language becomes your compass for navigating your big pile of interview transcripts.

🗣️ Words are a window to experience

Metaphors aren't just colorful speech—they're diagnostic tools. When a participant says they're "drowning in paperwork" rather than "busy," you've gained crucial insight into their emotional experience that standard coding might miss. Preserving these exact phrases can reveal patterns no predetermined code structure would uncover.

What is in vivo coding? Keeping it candid

Qualitative research is all about capturing human experiences. In vivo coding (from Latin meaning "within the living") is an elegantly simple yet powerful way to use your own participants' words to capture their experiences. Instead of imposing your terminology on your participants' experiences, you use their exact words and phrases as your codes. Here’s a quick video overview of the process:

 
 

In vivo coding is just one approach in your qualitative toolkit. It typically happens during initial coding when you're first breaking down your data. While other initial coding approaches might use researcher-created labels, in vivo coding specifically preserves participants' language. Many researchers start with in vivo codes to stay close to the data, then transition to axial or selective coding as they develop more abstract themes and connections.

Here’s a quick in vivo coding example:

  • When a participant says they feel like they're "always on call" in their remote work, you use "always on call" as your actual code

  • If urban gardeners repeatedly describe their gardens as their "little oasis," that phrase becomes your code

  • When employees talk about "dropping the ball" or their team being "like a family," these verbatim expressions become your coding framework

The beauty of in vivo coding is twofold: you stay true to your participants' lived experiences, and you preserve the nuanced language that might contain cultural insights you'd otherwise miss.

💡 Delve Coding Tip

In Delve’s coding software, you can highlight a participant's exact phrase and create an in vivo code with a single click. This streamlines the process of capturing authentic language without disrupting your analytical flow.

Delve Coding GIF

How it looks: In vivo coding example

Here’s an excerpt of a transcript from when we interviewed a researcher about their research analysis process. Parts of the transcript have been edited for brevity.  

🔍 In Vivo Coding Example

I think that's one of the fears with people doing interview research—the amount of data they’ll have to analyze. They go, ‘I'm gonna make such a mess and it's going to feel overwhelming1. I've just wasted so much time and I've got no results.’ ‘I've just increased my anxiety2 about what I have to do’ because I've made the analysis so massive. I guess the journey is about taking massive amounts of data and breaking it down. ‘You'll have so many little bits of information3 everywhere that you can use, re-arrange, and tidy up in the end.’

Extracted Codes:

1 Feel overwhelming

2 Increased anxiety

3 Bits of information

Notice how you’re pulling code names straight from the participant’s words. Instead of summarizing or rewording, you keep the original phrasing. You stay true to how it was expressed. This is especially useful when capturing emotions or key ideas, like "feel overwhelming" or "increased anxiety." Using in vivo codes helps preserve the natural way people describe their experiences, rather than reshaping their words to fit predefined categories.

When should I use in vivo coding?

Words carry meaning beyond their literal definitions, and in vivo coding helps capture that. It’s especially useful in research where language itself plays an important role in your work:

  • When studying language and identity – Helpful for discourse analysis, cultural studies, and research on how people construct meaning through words.

  • For cross-cultural or multilingual research – Preserves meaning by reducing miscommunications when interpreting participant language.

  • In specialized communities – Captures industry jargon, subculture slang, or professional terminology that might be lost in standard coding.

  • For sensitive topics – Lets participants frame the phenomenon you are studying to avoid misrepresenting or misunderstanding their experiences.

  • In grounded theory studies – Helps you build theories from the ground up, using your participants' language as building blocks rather than imposing existing frameworks.

  • In phenomenological research – Keeps you close to people's lived experiences by coding with their actual words, making your analysis more authentic and trustworthy.

  • During early thematic analysis – Gives you a rich foundation of authentic codes straight from your participants before you start grouping them into broader themes.

By using participants’ exact words as codes, you capture the emotional and cultural richness of their language without filtering it through your interpretations. You stay true to what they're really saying rather than imposing concepts or ideas that might miss the point.

Qualitative coding tools like Delve make this process simple, with one-click highlighting to create in vivo codes. Your participants' language stays intact, while your workflow remains smooth and efficient. No more manual tracking. Just seamless, accurate coding that preserves their authentic voices. 

Now that we've covered the basics, let's get into the actual hands-on process of making in vivo coding work.

Beating the bottlenecks: Managing the chaos of in vivo coding

While in vivo coding sounds straightforward as highlighting interesting quotes from your interview transcripts, you're working with dozens of transcripts and hundreds of unique participant expressions. Things can quickly spiral into chaos, especially if you're coding collaboratively with a team. 

Imagine you've conducted 25 interviews on workplace culture. One participant talks about "fighting fires all day," another mentions "putting out fires," and a third references "constant firefighting." These basically describe the same experience, but as separate in vivo codes, they fragment your analysis instead of building cohesive insights.

How you might consolidate these related expressions:

  1. Select the most representative phrase as your primary code. In this case, "fighting fires all day" might best capture the emotional weight and ongoing nature of the experience.

  2. Document your decision process in a memo, noting all variations: "Multiple participants used firefighting metaphors to describe reactive work environments. Variations included 'putting out fires' (Participant 8), 'constant firefighting' (Participant 12), and 'fighting fires all day' (Participant 3). Selected 'fighting fires all day' as primary code to preserve the time element and emotional intensity."

  3. Tag all similar expressions with this consolidated code, preserving the participants' original words in your excerpts for reference during analysis.

This is the usual bottleneck of in vivo coding: staying true to participants' words while still creating a manageable analytical framework that ties into the bigger picture. Without the right approach, a bulging list of codes can obscure patterns rather than reveal them to you. You need a system that allows you to quickly find, refine, and compare codes across transcripts, especially as patterns start to emerge.

🔹 Organizing In Vivo Codes with Delve

Using in vivo coding software like Delve helps you manage related expressions by letting you nest similar codes, merge variations when appropriate, and quickly retrieve your past coding decisions. As we’ll show next, you stay organized without sacrificing the authenticity that makes in vivo coding so valuable.

Delve Nesting Feature

Practical path: How to do in vivo coding effectively

Remember our researcher who was feeling overwhelmed with interview data? Their concerns about "making such a mess," having "increased anxiety," and dealing with "so many little bits of information" gave us perfect examples of in vivo codes. 

Now let's see how we could systematically develop these insights with Delve using a step-by-step process that won't leave you drowning in participant quotes or conflicting codes:

1. Prepare your data

Start by organizing your transcripts, field notes, or other qualitative materials. For our overwhelmed researcher example, this would mean getting all interview transcripts into a consistent format with proper labeling. Delve's clean, distraction-free interface makes this initial setup easy as a few clicks. You can easily import transcripts directly to your project and organize them logically from the start.

💡 Faster File Upload Tip

If you don't have .txt, .pdf, or .docx files, you can also copy and paste text from your transcript to add it manually.

2. First pass: Immersion without coding

Before assigning a single code, read through your materials completely. In our example, this would mean reading the entire interview to understand the researcher's overall experience with data analysis. As you read, jot down striking phrases like "feel overwhelming" without committing to them as codes yet. Anything you want to come back to later. This creates an upfront inventory of potential in vivo codes without locking you into decisions too early.

By reading and re-reading your data, you'll develop an intuitive sense for patterns and important language that might otherwise be missed if jumped right into coding. Think of it as developing an ear for your participants' unique voices before you start categorizing what they're saying. When you eventually do begin coding, you'll have a much richer understanding of the context that shapes each expression.

⏳ Take your time with immersion

This immersion phase might take longer than you expect, but don't rush it. Many researchers make the mistake of jumping straight into coding without fully absorbing their participants' worldviews. The time you invest in understanding the full narrative arc of each participant's experience pays dividends later.

3. Thoughtful coding: Quality over quantity

Now comes the actual coding. Remember that coding is inherently iterative – your codebook is a living document that captures your evolving understanding. What seemed meaningful in your first transcript might fade in importance by your fifth or sixth interview. When choosing "feeling overwhelmed" as your primary code rather than "drowning in data," document your reasoning in memos. These decision notes create a valuable record as you find new patterns and your analysis deepens.

Just as we highlighted "feel overwhelming," "increased anxiety," and "bits of information" in our earlier example, you'll want to:

  • Highlight phrases that capture significant ideas, experiences, or emotions

  • Use participants' exact words for your code names

  • Keep codes relatively short (2-5 words usually works best)

  • Look for recurring expressions across different participants

  • Be selective—not every colorful phrase needs to become a code

Delve's one-click coding and drag-and-drop nesting features simplify this entire workflow, letting you focus on bigger picture decisions rather than technical procedures for all your codes.

 
 

4. Code management: The critical bridge

Here's where many researchers struggle. If our example researcher interviewed multiple participants, they might encounter variations like "completely overwhelmed," "drowning in data," and "feel overwhelming." This is when you need to make thoughtful decisions about:

  • Which variant of an expression best captures the core meaning

  • How to group related in vivo codes without losing their authenticity

  • When to create categories that encompass multiple expressions

Expect to revise your coding structure multiple times throughout your iterative coding work. What starts as separate codes might eventually merge, or what seemed like a single concept might split into distinct categories upon deeper analysis. Delve's project search function lets you quickly find related expressions across all your transcripts, while its dynamic code organization features allow you to group similar codes without losing their original context.

 
 

5. Context preservation: The hidden challenge

Context matters. When a researcher mentions "increased anxiety," it makes a huge difference whether they're talking about data analysis or their entire work situation. Delve makes this easy by showing you the coded excerpt alongside the full transcript. This way, you always understand what your participants actually meant. You're not just looking at isolated quotes stripped of their surrounding conversation.

 
 

These steps work great on your own, but throw other researchers into the mix and things get a lot more tricky. Let's see how to keep everyone on the same page when you're doing in vivo coding as a team.

Collaborative coding: Doing in vivo coding as a team

What if you’re coding with 3, 4 or 5 other people? Your team members might pick up on different expressions based on what resonates with them personally. One person might code "feeling buried in work" while another codes the same passage as "workload overwhelm." These different interpretations add depth but can also create inconsistencies and inter-team confusion.

Your team needs to agree on which exact phrases to capture. Without predetermined codes to guide you through this inductive process, you'll want to build consensus about which expressions best represent key experiences. For remote teams, using a web-based platform like Delve lets everyone participate regardless of location or time zone.

Try having team members independently code the same transcript section before comparing results. This "split coding" approach reveals different interpretations and whether everyone identifies the exact key phrases. This process helps measure intercoder reliability, which measures how consistently your team codes data. Later, you can use "consensus coding" where the team reviews passages together and agrees on which exact words to capture.

This is where Delve's collaborative features truly shine:

  • Real-time code sharing lets everyone see newly created in vivo codes

  • The shared codebook ensures all team members work from the same definitions

  • Memo features allow researchers to discuss interpretations of ambiguous phrases

  • Code-merging capabilities help consolidate similar expressions without losing nuance

The result is a more consistent, rigorous analysis process, even when team members work remotely or asynchronously.

🔄 Align Your Team Coding

Schedule regular coding calibration meetings where everyone reviews the same transcript. Using split coding and consensus coding helps identify differences early, ensuring consistency—especially important for remote teams.

From Words to Insights: Synthesizing In Vivo Codes

Many researchers start with in vivo coding to capture participants' exact words, then transition to pattern coding or axial coding to identify broader themes. This approach keeps authentic language at the core of the analysis while making it easier to spot larger patterns across the data.

 
 

After grouping similar phrases like "fighting fires all day," "putting out fires," and "constant firefighting" into a single code in our earlier example, your next step is to look for related concepts.

As you continue analyzing transcripts, you'll notice other codes capturing the same reactive, high-pressure work environment—like "constant crisis mode" or "never-ending emergencies." Even without firefighting metaphors, these phrases describe the same experience.

Theme: Reactive Work Environment

This theme captures workplaces where employees are in constant crisis mode, reacting to urgent demands instead of planning strategically.

Code: "Fighting fires all day"

Definition: Describes employees constantly addressing urgent issues, leaving no time for proactive work.

Code: "Constant crisis mode"

Definition: Expresses the feeling of working in a state of nonstop high-pressure problem-solving.

Code: "Never-ending emergencies"

Definition: Reflects the perception that new problems arise as soon as old ones are resolved.

Reference: Delve Qualitative Data Analysis Tool

By consolidating these codes under a broader theme like "Reactive Work Environment," you maintain the nuance of participants' language while revealing larger patterns in your data.

🔎 Combining qualitative methods

Many researchers start with in vivo coding to capture authentic language, then transition to pattern coding or axial coding to build higher-level themes while keeping participants’ language at the core of their analysis.

Ready for more intuitive in vivo coding?

In vivo coding is challenging enough without wrestling with cumbersome tools or drowning in disorganized data. Delve offers in vivo coding software built by qualitative researchers who understand these challenges firsthand.

By streamlining the heavy-lifting parts of coding, Delve lets you focus on understanding your participants'. From capturing initial expressions like "fighting fires all day" to developing meaningful themes like "Reactive Work Environment," the right tools make all the difference in preserving authentic voices throughout your analysis.

Want to see how Delve can transform your in vivo coding process? Start your free 14-day trial today and experience the difference that purpose-built qualitative software can make.

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References

Cite This Article:

Coding Qualitative DataDaniel Politz