Abductive Coding in Qualitative Research: A Practical Guide
Abductive coding is built for times when your research data is telling a different story than pre-existing theories predicted.
Say you're studying how doctors adopt new medical technologies. While you begin coding interview transcripts based on published theories, doctors describe creative workarounds your framework can't quite explain. One doctor adapted the electronic record system to document rare conditions it wasn't designed for. Another describes an unofficial network where staff share productivity tips for new tech and tools.
Do you stick with your pre-determined deductive codes and miss these rich insights, or switch to inductive coding and build entirely new codes from the ground up?
Abductive coding offers that middle path. You start with established theories while staying open to new, data-driven insights. This guide will show you how abductive coding works in practice, when to use it, and how coding tools like Delve can help manage the inherently messy process.
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What is abductive coding in qualitative research?
Abductive coding lets you develop new theories by building on existing ones. Named after Peirce's concept of abduction (finding the simplest explanation when faced with incomplete information), it combines the strengths of both deductive and inductive approaches. You start with existing theoretical concepts but modify them or create new ones based on what your data tells you.
Going back to our doctor example, with deductive coding, you'd start with existing theories about technology adoption and look for evidence of it in your interview transcripts. With inductive coding, you'd set existing theories aside and build understanding purely from what doctors tell you in interviews.
But with abductive coding, you get to do both. You might start with technology adoption concepts, but when doctors repeatedly mention those "custom workarounds" for system limitations (something existing theories don't address), you add this new code. You’re learning something new about how doctors adapt to medical technology that traditional theories missed.
Use Delve to create, label, and organize your codes with just a few clicks. It’s easy to stay structured while exploring new insights in your data.
This abductive coding process involves constantly refining your codebook as you discover new patterns, which is why researchers often use qualitative coding tools like Delve. It’s easy to add new codes (and memos) as you go, helping you manage the natural messiness of switching between theory and data-driven insights.
You can choose between deductive, inductive, or abductive coding depending on how much you want to rely on existing theories versus discovering new patterns.
Deductive coding (Top-Down): You start with established theories and look for supporting evidence in your data. This is structured but may overlook unexpected insights.
Inductive coding (Bottom-Up): You find new patterns directly in your data, free from pre-existing theories. This is open-ended but can be harder to organize into broader frameworks.
Abductive coding (A Middle Path): You begin with existing concepts but refine or expand them based on what your data reveals, bridging theory and new insights.
How abductive coding is different and when to use it
Let’s zoom in on this concept of the middle path. Unlike deductive coding (where you're stuck with existing theories) or inductive coding (where you start from scratch), abductive coding lets you move freely between the two as you learn more from your data.
Here's how it tends to work: You start with some theories you think might apply, but as you dig into your interviews or field notes, you notice something unexpected. Instead of ignoring it because it doesn't fit your existing theory, you create a new code and follow where it leads. This back-and-forth journey between what you know and discovery is what makes abductive coding so powerful and productive.
Here’s a simple side by side comparison:
| Approach | Starting Point | Process | Outcome |
|---|---|---|---|
| Deductive | Existing theory | Apply predetermined codes to data | Test or extend existing theory |
| Inductive | Raw data | Develop codes based solely on what data shows you | Generate entirely new theory |
| Abductive | Existing theory and raw data | Flexible iteration between theory and data | Refine existing theory or develop new explanations |
The table shows how abductive coding gives you the best of both worlds – you can start with existing theories while staying open to what your data is telling you. This back-and-forth approach helps you either improve existing frameworks or develop entirely new explanations for what you're seeing.
Use abductive coding when:
You have theories that explain part of what you're seeing but keep hitting gaps
You're pulling from different fields and need to connect various theories
You're studying something complex, like cultural practices or social movements
You want to both test what we already know and discover something new
The catch? It can get messy. As you bounce between theory and data, codes can multiply quickly. You'll find yourself constantly merging similar ideas, splitting concepts that seemed the same but aren't, and reorganizing your entire framework. Delve saves you time by tracking relationships between codes, avoiding duplicate concepts, and keeping your analysis organized as it grows. It’s also easy to write memos to track codes as they evolve.
Writing analytical memos and keeping a reflexive journal helps you track your thinking as you move between theory and data. This documentation clarifies your insights and keeps your interpretations grounded.
Learn more about analytical memos and reflexive analysis.
When NOT to use abductive coding
Not every project needs abductive coding. If your study requires strict hypothesis testing (like applying Maslow's hierarchy of needs to workplace satisfaction), stick with deductive coding for a more streamlined process. But if you're exploring completely uncharted territory with no relevant theories to draw from, pure inductive coding gives you the freedom to discover patterns without preconceptions.
Choose abductive coding when your research falls somewhere in between—when you have some theories to guide you but expect to discover things those theories don't fully explain.
With when to use (and when not to use) abductive coding aside, let's see how it works in practice by returning to our example about technology adoption in classrooms.
Abductive coding example: How doctors adopt new tech
Let's see how abductive coding might play out in our study on how doctors adapt to new medical technologies. You start off using existing theories about technology adoption and change management (the deductive component). Your initial codebook includes concepts like "perceived usefulness," "technology acceptance," and "institutional support." These codes are based on established models like the Technology Acceptance Model (TAM).
As you code more interview transcripts, you notice something unexpected that doesn't fit these theoretical codes: doctors repeatedly mention developing their own solutions when systems fail or don't meet clinical needs. There might be something more there so you write a memo. Your initial theoretical framework doesn't explain this pattern, so you create a new inductive code called "creative workarounds" to capture this nascent theme.
To explore the relationship between your original theoretical codes and this new one, use Delve’s filtering feature to find snippets where “creative workarounds” overlap with “institutional support.”
The results reveal an interesting pattern: Doctors in hospitals with rigid IT policies develop more elaborate workarounds. They create shared troubleshooting documents, mobile-based alternatives to failing systems, and informal networks to share solutions for documenting rare conditions that standard templates don't accommodate.
Learning that restrictive technology policies actually increase unofficial adaptation strategies is abductive coding in action. You've extended existing theory by discovering how institutional context shapes physician workarounds, something neither purely deductive nor inductive approaches had captured.
Having guidelines for when to explore new codes versus when to stick to your theoretical lens helps keep your analysis structured yet flexible—exactly the strength of abductive coding.
The abductive coding process: A step-by-step guide
Now that we've seen how abductive coding worked in our classroom technology example, let's walk through the process step-by-step so you can start applying it in your own research:
1. Familiarize yourself with your data
Read through interview transcripts, field notes, or other qualitative materials multiple times to develop a deep understanding of the content. Remember, abductive coding is an iterative process. You want to read your data a few times to get to know it. You will go through all these steps in a circular motion, more than a linear one.
If you do this manually, you'll need to juggle notes, highlight key passages, and track emerging patterns on your own, often across multiple documents. With Delve, you can upload multiple transcripts in a few clicks. This makes it easy to highlight, tag, and organize key insights in one place so you can connect findings without losing the plot.
During this phase:
Take first-impression notes on initial impressions
Mark passages that seem significant
Begin thinking about how existing theories might relate to what you're seeing
Note anything that seems surprising or doesn't fit your expectations
Focusing on familiarization helps you develop a feel for your data before formal coding begins. In our classroom technology example, this involved reading through teacher interviews and noting mentions of unexpected adaptation strategies several times to get a strong feel for the data.
The value of abductive coding lies in spotting unexpected patterns, so be open to surprises that don't fit your initial assumptions.
2. Create your abductive codebook
Now you'll develop your initial coding framework, which combines both deductive and inductive elements.
→ 2a. Create a deductive codebook based on existing theories
Start by selecting theories or concepts that seem relevant to your research questions. Develop an initial set of codes based on these theories and add them to your codebook.
For example, in our classroom technology study, you started with codes from the Technology Acceptance Model like "perceived usefulness" and "ease of use." This first batch of codes provides structure and keeps your analysis focused as you add new information and initial ideas stretch at the seams.
Delve's tagging system makes it easy to distinguish between your deductive codes (from theory) and inductive codes (from data). Use structured naming conventions—such as
D_ for deductive codes and I_ for inductive codes—to track where each code originated. This helps maintain clarity as your analysis evolves and in your final write-up.
Learn more about using tags in Delve.
→ 2b. Create inductive codes based on your data
After developing your deductive framework, revisit your data with fresh eyes. Now you’re looking for patterns, themes, or concepts that don't fit neatly into your initial codes.
As you find these elements, create new inductive codes to capture them. In our classroom example, this is where you created "creative workarounds" from teachers' actual experiences rather than existing theories.
Write analytical memos about why you created each new code. Delve lets you attach these memos and reflections directly to codes, creating a record of your thinking that you can lean on later when synthesizing your findings into a write up.
Write memos to explain why you created each new code and what you think it represents. Delve lets you attach memos directly to your codes, so your insights are always documented and easy to revisit when building themes or writing up your analysis.
→ 2c. Code your full dataset using your abductive codebook
Once you've developed both deductive and inductive codes, apply them to your entire dataset. Be prepared to add more inductive codes as you continue analyzing your data.
The result is an abductive codebook that combines theory-driven and data-driven elements. For our classroom technology researchers, this meant having traditional technology adoption concepts alongside newly discovered themes about teacher adaptability and resilience.
If you’re doing collaborative coding as a team, keeping everyone aligned on how your code is changing is critical. Delve’s collaborative features let your team members review, refine, and discuss codes in real time.
After coding your full dataset, the next step is identifying broader themes that capture the deeper meaning behind your codes. Themes help synthesize patterns across your data, moving from isolated observations to meaningful insights. Learn more about deriving themes.
3. Filter your data to find related excerpts
After coding your data, you need to find a way to filter your data. You’re looking to identify patterns and relationships across your codes, show you how different concepts connect and overlap.
If you're using pen and paper or tools like word processors, you can manually highlight key excerpts and create a system for tagging overlapping concepts. This might involve color-coding sections, using tables to track where different codes appear together, or keeping a separate document to list connections as you notice them. Delve filters let you search by key variables to show text snippets that contain multiple codes. For example, in our classroom study, researchers filtered to see where "institutional support" and "creative workarounds" codes overlapped.
Filtering your data shows concepts interacting in unexpected ways. Teachers in schools with rigid IT policies develop elaborate workarounds, creating shared troubleshooting documents, smartphone-based alternatives to failing technology, and informal training networks that fill gaps left by official training.
4. Conduct In-Depth Abductive Analysis
The final step involves two key phases:
→ 4a. Inductive coding your filtered data
Once you've used filters to surface key patterns, it's time to dig deeper. Conduct another round of inductive coding, refining or expanding categories based on how these patterns connect. This deep analysis helps you uncover nuances and relationships that might have missed before.
Looking back at our classroom example, you might have discovered that the nature of workarounds varied significantly based on leadership styles when examining excerpts where "institutional support" and "creative workarounds" overlap. Even at this stage, you’re still trying to be as rigorous and thorough as possible to fine-tune your findings. Delve filters make it easy to track where ideas and codes intersect.
Instead of manually scanning through documents to track where ideas connect, Delve’s project search feature makes it easy to make connections. With a quick search, you can pull up all excerpts containing a particular concept or phrase, helping you efficiently refine and validate your analysis.
Use Delve’s smart search to instantly locate quotes, codes, or keywords across all your transcripts. No more digging through documents—just fast, focused searching to keep your analysis moving.
→ 4b. Develop your theoretical explanation
Finally, synthesize your findings into a coherent theoretical explanation. This explanation should:
Address your research questions
Show how existing theories apply to your data
Incorporate the new elements you discovered
Explain anomalies or surprises in your data
If you encounter anomalies in your data, try generating multiple possible explanations before settling on one. Use memos to track your reasoning as you go. This helps you avoid premature closure and keeps your analysis flexible and thoughtful.
Your final theory might extend existing frameworks, combine multiple theories in novel ways, or present entirely new concepts based on your findings. Here, you developed a new framework of "adaptive technology implementation" that accounted for the role of teacher-developed workarounds in successful technology adoption.
If your initial theory doesn't fully explain your data, return to earlier steps and refine your approach. Tools like Delve can help manage this back and forth by organizing your evolving codebook, tracking relationships between concepts, and making it easy to filter and visualize patterns as they emerge.
Stress-free abductive coding—with Delve
The iterative nature of abductive coding can be time-intensive, especially when managing large datasets. Qualitative data analysis software like Delve can help overcome these challenges by:
Organizing codes and memos in a centralized, accessible system
Providing powerful filtering capabilities to identify patterns and relationships
Offering intuitive visualization tools to help see connections between concepts
Supporting collaborative analysis when working with research teams
Creating an audit trail of your analytical decisions
Customers love Delve's cloud-based platform for abductive coding, allowing you to flexibly move between deductive and inductive approaches as your analysis evolves.
Final decision: Is abductive coding right for your research?
Abductive coding is a balanced approach to qualitative analysis that can help you navigate the middle ground between testing existing theories and discovering new insights.
This method works best when:
You have some theoretical foundation but remain open to surprises
Your research phenomena are complex and multifaceted
You want to both build on existing knowledge and generate new understanding
You're comfortable with an iterative, flexible analytical process
By balancing structure and exploration, abductive coding helps you uncover deeper meaning in your data. And with the right tools, like Delve, you can streamline the process.
Ready to dig into abductive coding? Start your free 14-day trial of Delve today and experience how the right tools can make abductive coding more efficient and effective.
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References
Vila-Henninger, L., Dupuy, C., Van Ingelgom, V., Caprioli, M., Teuber, F., Pennetreau, D., Bussi, M., & Le Gall, C. (2022). Abductive Coding: Theory Building and Qualitative (Re)Analysis. Sociological Methods & Research. https://doi.org/10.1177/00491241211067508
Timmermans Stefan, Tavory Iddo. 2012. Theory Construction in Qualitative Research From Grounded Theory to Abductive Analysis. Sociological Theory 30(3):167-86.
Douven, Igor, "Abduction", The Stanford Encyclopedia of Philosophy (Summer 2021 Edition), Edward N. Zalta (ed.), URL = <https://plato.stanford.edu/archives/sum2021/entries/abduction/>.
Rahimi, B., Nadri, H., Lotfnezhad Afshar, H., & Timpka, T. (2018). A Systematic Review of the Technology Acceptance Model in Health Informatics. Applied clinical informatics, 9(3), 604–634. https://doi.org/10.1055/s-0038-1668091
Cite This Article
Delve, Ho, L., & Limpaecher, A. (2025c, March 31). Abductive Coding in Qualitative Analysis https://delvetool.com/blog/abductive-coding