Open, Axial, and Selective Coding in Qualitative Research: A Practical Guide

This is part of our Essential Guide to Coding Qualitative Data | Start a Free Trial of Delve | Take Our Free Online Qualitative Data Analysis Course

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Open, axial, and selective coding offer a three-pronged strategy that guides your journey from a labyrinth of raw data to coherent research gold.

Open, axial, and selective coding are interconnected steps in qualitative data analysis. A cornerstone of grounded theory, you use these coding methods at different stages of your analysis to progressively crack the code buried in your mountain of transcripts and field notes.

Let’s walk through open, axial and selective coding, showing how they manage the overwhelming process of qualitative research. We’ll also show how Delve’s computer assisted qualitative data analysis software (CAQDAS) helps avoid many of the usual sticking points along the way.

The Building Blocks of Open, Axial, and Selective Coding

Before diving into the specifics of these three methods, let’s quickly cover the concept of coding itself. A code in qualitative analysis is a label or tag you apply to meaningful data points, such as excerpts from interview transcripts or observational research.

For example, if you were doing a study about workplace stress, you might assign the code “Long Hours” to the following transcript excerpt:

These individual codes are the foundation for your broader analytical work ahead. However, “tagging” your data only takes you so far and can get unmanageable without a plan. While you have many qualitative coding techniques at your disposal, open, axial and selective coding offer a framework for managing, organizing, and thinking about your code in a more strategic way.

Rather than drowning in endless codes and tangents, this triad of coding methods paves the path from your initial observations to overarching theoretical breakthroughs.

Connecting the Dots for Your Grounded Theory

Learn more about grounded theory and qualitative analysis on Delve’s YouTube channel.

As the backbone coding in grounded theory, open, axial, and selective coding help you unearth new theories from your data. Instead of using them to confirm existing information, these inductive coding methods help you discover new theories buried in interviews and other data. There is no roadmap. You’re creating one along the way.

Picture it like mining for gold. Each method plays a crucial role:

• Open Coding: You’re breaking ground, examining every piece of data for potential.
• Axial Coding: You’re tracing gold streaks, connecting related data into meaningful patterns.
• Selective Coding: You’re following the richest vein to strike gold–your core theory.

While you’re steadily moving toward your final theory, remember that grounded theory is iterative. You’ll cycle back and forth through these stages, but dive deeper with each pass. You continue doing this until new information stops adding new insights to your study – a spot called “theoretical saturation.” It’s a time-intensive process, but tools like Delve are built for digging deep into your data (in less time).

When to Use Open Coding, Axial Coding, and Selective Coding

• You want to follow a grounded theory method of qualitative analysis
• You want to derive new theories or concepts from your data
• You don’t want preconceived theories to determine the outcome of your research
• You are conducting exploratory research where you want to generate new concepts and ideas

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Open, Axial, and Selective Coding: A Grounded Theory Coding System

Keeping that brief overview in mind, let’s take a closer look at what goes into (and separates) open, axial, and selective coding based on the formative work of Corbin and Strauss (1990).

Open Coding: Finding New Theoretical Possibilities

What is open coding in qualitative research❓ Open coding is your first step in making sense of your data. It’s about breaking down piles of data into manageable pieces and starting to identify salient patterns that really stick out to you.

Corbin and Strauss paint the process as digging up “concepts, properties, and dimensions within the data” (p. 12). The goal is to “open” you up to these new theoretical possibilities right when starting off with your data, not simply to organize it. Writing notes or memos helps you track these ideas and connections as you create them.

So, at this initial stage in your analysis, open coding (also called initial coding) helps you identify fresh patterns as you first embark on your larger pattern-seeking mission.

What You’ll Do in Open Coding:

1. Read through your data carefully.
2. Break the text into small, meaningful segments (these could be words, phrases, or sentences).
3. Assign a code (a short descriptive label with a clear definition) to each segment.
   • Suggested: Record memos and attach them to your codes using a tool like Delve.

The purpose of breaking up your data and labeling them with codes is to continuously compare and contrast similar events in your data. This pattern-seeking process, called the constant comparison method, forces you out of preconceived ideas about your subjects. By systematically comparing each piece of coded data against others, you begin to see connections that might otherwise remain hidden.

OPEN CODING EXAMPLE

Let’s say you’re researching work-life balance in the relatively new phenomenon of remote working environments. Here’s an snippet from an interview with Natasha, a respondent in your study:

Axial Coding: Mapping Out Connections

What is axial coding in qualitative research❓ After breaking everything down through open coding, axial coding helps you rebuild it in meaningful ways to explain something new about it all. Axial coding is where you start seeing how different pieces of your data connect and influence each other – like pieces in a puzzle. These connections form broader categories, or themes, that help explain what’s really going on in your data.

Think of categories as the major plot points in your research story. They become the ‘axes’ or central points that help organize your understanding and link ideas together.

What You’ll Do in Axial Coding:

1. Review your open codes.
2. Look for connections or relationships between codes.
3. Group related codes into broader categories.
4. Consider how these categories might be linked.

This step is about moving you from describing what you see to understanding how it all works together. You’re tracing gold dust back to the real pay dirt – where your core theory waits to be unearthed.

Instead of just having a bunch of disjointed pieces, you’re building the bigger picture of how all the pieces interact, group together, and influence each other.

AXIAL CODING EXAMPLE

Continuing with our work-life balance example, you might start seeing deeper patterns in your initial codes. Here’s how you could cluster those earlier codes, along with additional related ones for the purpose of this example, into more robust categories:

These categories build upon initial open coding insights while fleshing out deeper patterns. The first category captures the various impacts of constant connectivity, while the second explores both reactive and proactive boundary-setting strategies.

Through axial coding, you begin to see how these categories might interact – perhaps how increasing costs of connectivity drive more sophisticated boundary management techniques, or how weak boundaries intensify connectivity costs.

The challenge of axial coding is that it requires you to think abstractly about your data and identify sometimes subtle relationships. Nesting is a useful approach here, where top-level categories cluster your open codes together in meaningful ways. Creating this hierarchy helps clarify the relationships between them.

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Selective Coding: Identifying the Core Concept

What is selective coding in qualitative research❓ Selective coding is where everything comes together. After breaking down your data and finding connections, this final stage helps you identify the central theme that unifies all your categories. This core theme becomes the foundation of your theory and your main explanation about what’s really happening in your research. Here’s where you zoom way out and try to see the forest for the trees.

What You’ll Do in Selective Coding:

1. Review your categories from axial coding.
2. Spot the central category that keeps appearing and connects to others
3. Link all other categories to this core theme
4. Refine your categories and their relationships.

Landing on your core theme should feel like you’ve struck gold. It’s your that “aha” moment when everything clicks into place like your final puzzle piece. Through constant comparison, you’ll build your theory from the ground up. When you can explain your grounded theory in a few strong sentences, you’ll know you’re on the right track.

SELECTIVE CODING EXAMPLE

Returning to our work-life balance study, careful analysis reveals a core category that ties everything together: “The Quiet Erosion of Self in the Age of Unchecked Work.” This theme ties together the impact of constant digital availability with individual efforts at boundary-setting, revealing a cumulative effect on personal identity.

Through selective coding, you’ve developed a powerful insight: Digital presence and boundary management create a ‘quiet’ erosion of identity in remote work - quiet because it’s gradual, unnoticed, yet relentlessly systematic. What began as a hypothesis about workplace boundaries becomes, through your analysis and coding, evidence of a subtle but persistent wearing away of self.

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Step-by-Step Guide: Open, Axial, and Selective Coding

FIRST READ-THROUGH

• Read your transcript or field notes without coding, just to get familiar
• Use Delve’s memo feature to jot down initial thoughts and potential patterns
• Highlight any quotes that jump out as particularly meaningful
• Here is the uncoded segment we’ll focus on

START LINE-BY-LINE CODING

• Begin Line-by-line coding to avoid jumping to conclusions about larger sections
• Work through small chunks (2-3 sentences) at a time to stay focused and catch key details
• Try to create codes that capture the meaning and action, not just obvious, surface-level descriptions

BUILD YOUR INITIAL CODE LIST

• Keep codes as simple and clear as possible
• Write codes as actions (e.g., “Boundary Setting” rather than just “Boundaries”)
• Use Delve to create your codebook and track code definitions and examples
• Record why you created each code using memos, which tracks your thought process and allows you to view their evolution
• Review your codes after every 2-3 transcripts to check for overlap. This helps you apply codes consistently and prevents redundant codes from bottlenecking.
• As you refine your codes, you’ll start to see which ones are capturing the most relevant themes across multiple interviews.

PHASE II - AXIAL CODING (FINDING CONNECTIONS)

GROUP SIMILAR CODES

• Look for codes that seem to be talking about the same bigger idea
• Create categories that capture these related codes
• Use Delve’s nesting feature to organize codes within categories

MAP OUT RELATIONSHIPS

• Create hierarchies to identify higher-level concepts vs. subcategories
• Draw simple diagrams showing how categories might connect
• Use data visualization tools, like those included with Delve software
• Look for cause-and-effect relationships
• Note which categories frequently appear together

REFINE YOUR CATEGORIES

• Write clear definitions for each category
• Re-check if all your codes fit well within their categorical hierarchies
• Be willing to iterate, rearrange, or rename as needed

PHASE III: SELECTIVE CODING (FINDING YOUR CORE STORY)

IDENTIFY POTENTIAL CATEGORIES

• Look for categories that are linked to many others or appear frequently in your data
• Test different categories as the potential “core category” or “main story” to see which one explains the most and fits best within the hierarchical structure
• You want the one explains the most about your data in a way that you think you can clearly articulate

TEST YOUR CORE CATEGORY

• Go back to your raw data with your potential core category in mind
• See if it helps explain most of what you’re seeing
• Check if other categories fit naturally around it

REFINE YOUR THEORY OR THESIS

• Continue memoing about how other categories relate to your core category
• Refine your categories and codes based on how well they fit with your core concept
• Look for gaps in your explanation, poking and prodding for any weak links
• Go back to your data if needed to shore up any missing pieces or broken logic

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Overcoming Challenges in Open, Axial and Selective Coding

These three coding steps piece together your roadmap to grounded theory. But with such a large data with many moving parts, you might hit a few bumps along the way. Let’s look at some common challenges of open, axial, and selective coding - and how Delve centralizes and streamlines the entire process:

1. Creating and Managing Codes: As your pile of data grows, it often gets overwhelming to keep track of all your codes and categories. The answer to this problem (and the foundation of all three of these coding phases) is creating, organizing, and clustering codes effectively.

• Solution: Delve’s intuitive interface makes it easy to create, edit, filter, search, and organize codes, with features specifically designed for managing huge amounts of qualitative data more effectively and efficiently.

2. Team Consistency and Collaboration: Maintaining consistent coding across multiple researchers while coordinating analysis can be challenging, especially when working remotely.

• Solution: Delve’s shared codebook and real-time collaboration features make it easy for teams to work together (from anywhere with an internet connection) in a web-based project file, with standardized code definitions and instant updates for all users. You can also hide the coding work of others or compare them side by side.

3. Tracing Insights Back to Data: It’s crucial to maintain a clear link between your developing theory and the original data.

• Solution: Delve’s backlinking feature ensures every insight is traceable back to the original data source, maintaining transparency and rigor in your analysis.

4. Identifying Subtle Relationships: Some connections between codes and categories may not be immediately obvious.

• Solution: With Delve, the Co-occurrence Matrix feature visually represents relationships between codes that help you spot patterns you might otherwise miss. While repeating ideas or codes can’t give you definitive information, it can lead to surprising patterns you hadn’t noticed before.

Delve’s AI assistant pairs well with the matrix. You can ask it why it thinks certain codes keep appearing together and whether they point toward a shared category. You should always take AI responses with a grain of salt, but you may learn something from having a backboard to bounce ideas.

Delve: The Best Tool for Open, Axial, and Selective Coding

Created by qualitative researchers for qualitative researchers, Delve is designed for managing this complex coding process with fewer headaches and deeper insights.

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Wrapping Up: From Unstructured Data to Unified Theory

The journey from open to axial to selective coding takes you from raw qualitative data to the golden nugget of your final grounded theory. It’s more than just organizing information – it’s about immersing yourself in your data to discover patterns and generate innovative perspectives about our world.

Each coding stage builds on the last, guiding you from initial observations to a cohesive, data-grounded theory. While challenging and time-consuming, tools like Delve can streamline this journey, allowing you to focus on developing nuanced understanding and meaningful insights right from the start.

Now, with practice and the right tools, you’re well-equipped to deep dive into grounded theory research!

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Other Ways to Analyze Qualitative Data

Open coding, axial coding, and selective coding for grounded theory is just one approach of many ways to analyze qualitative research. To read more about other types of coding, read our Essential Guide to Coding Qualitative Data.

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References

• Corbin, J., & Strauss, A. (1990). Grounded theory research: Procedures, canons, and evaluative criteria. Qualitative Sociology, 13, 3-21.
• Saldana, Johnny (2009). The Coding Manual for Qualitative Researchers. Thousand Oaks, California: Sage.

Cite this blog post:

• Delve, Ho, L., & Limpaecher, A. (2024, October 29). Open, Axial, and Selective Coding in Qualitative Research: A Practical Guide. Practical Guide to Grounded Theory. https://delvetool.com/blog/openaxialselective