Practical Guide to Grounded Theory Research

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Let’s say you’re trying to understand an under-explored topic. Maybe you want to study how remote workers find work-life balance, or figure out what drives healthcare workers to stay resilient during a crisis. You don’t have a neat hypothesis and it doesn’t seem to fit into other studies. You’re not even sure what the right questions are. This is exactly the kind of research grounded theory was built for.

Grounded theory is a research methodology where data collection and analysis happen together in cycles. Unlike traditional research that collects all data up front, then analyzes it, grounded theory uses each round of analysis to determine what data to collect next. This iterative loop continues until you've built a theory that explains the underlying process.

Whether you’re a graduate student tackling your first thesis or a researcher exploring an understudied problem, this guide walks through what grounded theory is, how it works, and how qualitative coding tools like Delve can simplify the many moving parts without reducing the depth of your analysis.

📖 Develop your theory as insights unfold

Grounded theory is an end-to-end qualitative research process designed to build theories from real-world data. You start with questions, not data, cycling between data collection and analysis to build a coherent theory.

🎬 Watch – 5-minute grounded theory explainer

🧠 Quick grounded theory example

You want to understand how remote workers set work-life boundaries without the structure of an office. Some thrive at home, others feel overwhelmed. Why does it work for some but not others? Grounded theory helps uncover the process behind those differences—not just describe what’s happening.

Building from the ground up: What is grounded theory?

Grounded theory asks you to collect data and analyze it in cycles to build a theory. Think of it like a snowball gathering mass as it rolls downhill. You start small with questions or broad initial observations, but each cycle of analysis accumulates new layers of insight until you have a robust theory grounded in real experiences.

Through each cycle of data collection and analysis, you're inductively building theory by comparing new insights with what you've already seen. Your codebook and memos evolve with each cycle. This constant comparison process takes you from the concrete to the abstract. You start with specific observations – what individual participants say and do – then step up through increasingly general explanations. Individual quotes become codes, codes become categories, and categories become theory to explain it all.

Unlike other qualitative research approaches that separate data collection from analysis, grounded theory blends both in real time. You might conduct a few interviews, analyze them, then decide who to interview next through theoretical sampling based on what you find. Maybe parents instead of single workers, or managers instead of individual contributors. Don’t aim for drastic changes between comparisons. Usually, you’ll make small adjustments and minor additions as you unearth new patterns and your theory snowballs.

These loops continue until constant comparison with new data just confirms what you already know, a key inflection point in your research called theoretical saturation. We'll show how all these pieces fit together throughout as we go.

The key is that grounded theory is flexible but not totally unstructured. You begin with broad questions and then compare, refine, and deepen your understanding through repeated data collection and analysis. While you’ll need to start with questions that guide your first round of interviews, the richness of your results comes from the consistency of your comparisons. This extra depth of engagement with your data is what makes grounded theory so rewarding yet demanding.

🔄 Grounded theory doesn’t follow a straight line

You move in cycles: gather data, analyze it, then go back to the field with sharper questions. It’s a powerful approach, but can get messy fast. Tools like Delve help you stay organized across rounds of coding, memoing, and theory-building.

Without a map: Deciding when to use grounded theory

Grounded theory is a powerful but intensive research process, asking you to collect, analyze, and adjust your data comparisons over iterative cycles. So when is that level of effort worth your limited time and resources?

Grounded theory really shines (and is designed for) when you're exploring uncharted territory where existing theories don't really provide adequate explanations. It's particularly valuable when:

You're studying a topic with little prior research
You want your analysis to evolve based on what participants tell you
You’re focused on process: how something unfolds, changes, or adapts
Your questions are more about "how" and "why" than just "what"

What sets grounded theory apart from other methods is that it isn't just coding and theme-building. Many approaches do these things. The difference is the process itself, looping between questions, data, coding, comparisons, and eventually theory. This inherent cyclicality reshapes your research as you go.

🎓 Real-world grounded theory

In their study on how nurses adapted to work during the pandemic, Nowell et al. described how clinical nurses “safeguarded patients and themselves” by navigating constantly shifting rules and risks. That core concept was rooted in dozens of categories, like boundary-setting, risk management, and emotional fatigue, all built from interview data.

When not to use grounded theory

Grounded theory isn’t always the right fit. If you’re working within an established framework or analyzing a fixed dataset, you may not need to build a new theory.

For example, inductive thematic analysis works well for identifying patterns in predetermined sets of qualitative data, while inductive content analysis is better for coding huge amounts of structured or mixed-format sources without aiming for a unified theory.

The difference is that grounded theory doesn't just look for patterns, it builds explanations. While you can do many other qualitative methods inductively, the final results often stop at describing what's there. Grounded theory keeps going, using constant comparison to guide who you talk to next and what questions to ask, until you’ve refined an integrated theory.

This exploratory, cyclical and bottom-up structure is what makes grounded theory so effective for studying evolving processes, shifting behaviors, or underexplored issues. And to understand why it works the way it does, it helps to look at where it came from.

📘 Grounded theory vs. thematic analysis

Thematic analysis identifies themes through coding but usually stops short of building a full explanation. The difference is more than just a way to code data or identify themes. It's a full methodology that shapes how you collect data, analyze it, and build theory through constant comparison. Read the full breakdown of thematic analysis vs grounded theory.

Where grounded theory came from (and why it matters)

Grounded theory's evolution explains both why constant comparison became so central and why you might encounter different approaches depending on who you ask.

Back in the 1960s, sociologists Barney Glaser and Anselm Strauss were frustrated by rigid research methods that started with grand theories and tested them downward. Their revolutionary idea? Build theory directly from real-world data using systematic comparison instead. Their 1967 book The Discovery of Grounded Theory challenged the dominant "hypothesis first" paradigm by showing how constant comparison of interview data could stack up new explanations from the ground up.

Over the following decades, grounded theory branched into different schools of thought with slight variations:

Glaser promoted a flexible, data-led approach emphasizing constant comparison without rigid structure
Strauss and Corbin introduced a more structured coding process with three specific phases
Charmaz added a constructivist view, acknowledging the researcher's central role in creating meaning through comparison

This explains why simply saying "I'm using grounded theory" isn't enough sometimes. Many researchers (and professors) will want to know which version guides your comparative analysis.

📚 For further background

Glaser rejected structured stages and emphasized theory “emerging” from data through open comparison. Strauss and Corbin argued that structure makes analysis more transparent and replicable. Charmaz, in turn, brought more attention to the researcher's central role in shaping meaning, making the coding process more reflexive. Read more on the history of grounded theory.

Constant comparison is the crux

Despite the different approaches we just explored, all grounded theory shares a fundamental principle: using constant comparison method to build new theories from scratch. To recap, this is where you compare new data to previous data, data to codes, codes to categories, and categories to each other, slowly moving up levels of abstraction.

How constant comparison works in practice:

Let’s say you interview your first participant and start noticing patterns in what they tell you. Then you interview a second person and immediately start comparing:

Is this the same experience?
Is this different?
How does this relate to what I've already seen?

This comparative process does several things at once. It helps you identify patterns, guides your next interviews and questions, shows when codes need refining, and pushes you from individual experiences toward abstract explanations. Through ongoing comparisons, you'll start noticing three key patterns:

Expansion: New data adds fresh dimensions to existing categories. A remote worker mentions 'switching off work mode like a light switch,' which expands your 'mental barriers' code to include more active boundary regulation strategies.
Contradiction: New data challenges your categories or assumptions when compared, leading to theoretical breakthroughs. Maybe a remote worker describes feeling "more connected to family" during boundary struggles, forcing you to reconsider whether boundary challenges always isolate.
Confirmation: New data supports existing patterns through comparison. When multiple participants describe similar "crisis routines," you're seeing your categories solidify.

💡 What constant comparison looks like in practice

You're studying how people adapt to remote work. You notice several participants mentioning building "psychological boundaries" between work and home. Here's how constant comparison would work:

Compare excerpt A with excerpt B: Both mention creating boundaries, but are these the same type of boundaries?

Compare excerpt C with your "psychological boundaries" code: This participant talks about "mental compartments." Does this fit your existing code or suggest something different?

Compare across multiple participants: You see this boundary-building pattern in 6 out of 8 interviews. Is this pattern consistent?

When comparisons expand, contradict or confirm each other, they point toward deeper theoretical insights about the conditions shaping participant responses. In the example above, maybe you start to see that timing, household composition, and job demands all influence how people experience remote work boundaries.

The most important part of constant comparison is that you actively contrast each new piece of data against what you've already found to build a better understanding of the underlying process. As you add more codes, data, and memos, Delve helps by keeping your growing analysis organized and searchable.

And as we’ll show next, coding techniques like open, axial, and selective coding help organize these comparisons (especially for first-timers). But they're all just tools within the broader constant comparison process, which is the true lynchpin of grounded theory research (no matter who you ask).

📚 Three styles, one purpose

Strauss and Corbin lay out open, axial, and selective coding for theory-building in stages. Charmaz encourages a more fluid, constructivist approach, refining codes as you engage with your data through reflexive memos. Glaser, even looser, emphasizes constant comparison and theoretical sensitivity without formal steps. Comparison is the thread that holds it all together.

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Open, axial, selective coding: Beginner-friendly constant comparison

Whether you follow formal steps or stay flexible, you're always using comparison to make sense of your data. With these three steps, each level moves you up a layer of abstraction: open coding takes concrete observations and creates descriptive codes, axial coding abstracts multiple codes into broader categories that explain relationships, and selective coding finds the central concept that explains the underlying process – your theory.

Unlike methods that simply group codes into themes, grounded theory builds explanatory relationships from scratch. Open coding creates initial codes from data comparison. Axial coding creates categories by comparing codes and identifying how they connect. Selective coding identifies the core category by comparing categories with each other.

For newcomers to grounded theory, open, axial, and selective coding offer one popular structured approach for managing comparisons, though some researchers prefer more fluid constant comparison methods.

📖 Grounded theory terminology

Term	Created through	How it fits
Code	Open coding	Initial labels created by comparing data
Category (aka axial code)	Axial coding	Formed by comparing codes and linking related ideas
Core category	Selective coding	Unifies categories under a central concept

Visual flow of comparison:

Raw data → Codes → Categories → Core category

The open, axial, and selective coding process

With open (or initial) coding, you compare data excerpts with each other. As you do, you naturally start clustering similar ideas into your codes. After more interviews, you continue comparing those interviews with each other and the codes you're developing. These comparisons label and organize your raw data.

After some rounds of open coding, you start comparing your codes with each other through axial coding. As you compare codes you see how they relate and connect. These connections are your categories (also called axial codes), as they form the axis around a number of different ideas. These comparisons clarify broader relationships.

During the selective coding phase you step up one more level of abstraction, and begin to compare categories (axial codes) with each other. In comparing these categories, you will develop your core category that explains your entire phenomenon. Beyond just another category. you discover the theoretical core that shows how and why everything else happens – your grounded theory.

While these steps help newcomers visualize how to move from codes to categories to core categories, they're all really just manifestations of the core comparative method that makes grounded theory so unique. As it takes a lot of organization to manage the process, features like Delve's nesting feature make it easy to drag and drop codes and categories into hierarchies without losing track of your analytical decisions.

While the progression looks linear, remember that grounded theory is fundamentally cyclical. You're constantly moving between data collection and comparison, making updates as your theory gathers mass.

🧩 Mirroring natural learning

Like doing a puzzle, you start by sorting pieces of data into rough groups (open coding), then connect related sections (axial coding), until everything fits around one central image (selective coding). Each new piece opens up new possibilities for the next connections.

Connecting the dots of grounded theory

Once you understand how constant comparison drives coding, the other key concepts of grounded theory start to make more sense. Thinking of them as interconnected parts of the same system rather than separate techniques makes the process feel a lot less like a maze and more like a manageable system:

Constant comparison method - This is the analytical backbone of grounded theory. You're continually comparing new data to previous data, data to codes, and codes to categories. These comparisons reveal patterns, seek out contradictions, and push your analysis toward abstraction.
Theoretical sampling - Instead of mapping out all your participants and interview questions in advance, you let your insights determine who to talk to next. Each round of sampling builds on what you’ve already learned, helping you compare ideas, explore gaps, or expand categories.
Theoretical saturation - You stop sampling when comparing new interviews or observations no longer tell you anything new. Everything you’re hearing confirms codes and categories you’ve already built. When constant comparison keeps turning up the same answers, you’ve likely reached saturation.

Think of it like this: Qualitative coding isn’t just labeling data but sets you up to compare. Comparison shows you who to talk to next in theoretical sampling. Memos track how your understanding changes over time. This builds theory from the inside out until new data just confirms what you already know. With Delve, you can write memos directly in context and trace how your theory evolves without losing ideas or bouncing between tools.

With the need-to-knows aside, let's walk through how grounded theory actually works in practice.

📝 Aha moments in memoing

Treat memos as a personal dialogue with the data. Some memos read like informal “aha moments,” others like analytic essays. The key is to write them regularly. For example, after coding an interview about a COVID-19 workplace study, you might write a memo noting, “Interesting: both Alice (engineer) and Bob (manager) mention ‘Zoom fatigue’ affecting team morale. Could this be part of a larger category of communication challenges?” Capturing these thoughts will help immensely when you weave these concepts into a coherent theory later.

Cycles, not steps: How to do grounded theory guide (the right way)

This guide walks through sampling, coding, memoing, and comparison work together as you cycle between data collection and analysis. The approach we’ll outline is an overview of common steps and best practices, synthesized from classic sources like Glaser, Strauss, Corbin and Charmaz.

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Core cyclical process for grounded theory:

Formulate open-ended research questions
Begin theoretical sampling and data collection
Start constant comparison by comparing your data with each other to form your initial codes with your first codes (open coding)

Continue theoretically sampling and open coding until ready to graduate to axial coding

Compare codes with codes to build categories (axial coding)

Continue theoretically sampling and axial coding until ready to graduate to selective coding

Compare categories with categories to build core categories (selective coding)

Continue theoretically sampling and selective coding until you reach theoretical saturation

Define and explain core category in your write-up

⚠️ Reminder

The process below looks sequential on the page, but grounded theory is inherently cyclical. Think of these as guideposts rather than rigid rules, moving back and forth between them as your understanding develops.

🟢 Formulate open-ended research question

Your research question sets the direction of your research without locking you into a predetermined path. Unlike hypothesis-driven research, grounded theory questions should be broad and exploratory, focusing on processes, experiences, or how people navigate situations.

Strong grounded theory questions:

"How do remote workers establish work-life boundaries?"
"What process do parents use to manage competing demands while working from home?"
"How do people adapt their daily routines when home becomes their office?"

Notice how these focus on "how" and "why" rather than "what." You're not testing existing theories – you're exploring territory where new explanations might emerge through systematic comparison of what you discover.

🔁 Begin theoretical sampling and data collection

Start by recruiting a small group of participants based loosely on your initial questions. You're not trying to represent the entire population, like in survey research. Just start with people who can answer your questions. The goal is to learn from their experiences and use those insights to guide who you talk to next.

As we covered earlier, comparisons will create three key patterns: expansion (new data adds dimensions), contradiction (challenges that lead to breakthroughs), and confirmation (supporting existing patterns). Maybe you begin by interviewing five remote workers about boundary-setting, or three healthcare workers about stress management. After each interview, transcribe them to get them ready for analysis.

With each round of data analysis, collection, and constant comparison, theoretical sampling asks you to take what you've learned to decide who to interview next – and what questions to ask them.

🔍 Example of theoretical sampling in action

Round 1	Interview 5 remote workers about work-life boundaries. Through analysis, you notice parents describe completely different challenges than single workers.
Round 2	Interview working parents specifically about their boundary strategies during different times of day.
Round 3	After discovering that emergency childcare creates unique boundary challenges, you decide to interview parents who manage remote work during school closures.

With Delve, you can upload multiple transcripts as text, Word, or PDF, then code directly without needing to juggle multiple tools in the next steps. You can also tag participant roles or attributes to stay organized and filter as your sample evolves. This keeps everything in one place as your dataset grows and evolves.

🆚 Start constant comparison with your first codes (open coding)

Once you have transcripts, the constant comparison method continues leading the way. This phase strips raw data into chunks you can then compare. You'll break transcripts into meaningful excerpts, but the key is comparing each excerpt with others. The juxtapositions naturally lead to your open (or initial) codes.

If a remote worker says, "I had to create mental boundaries between work and family time," and another mentions, "I built these invisible walls around my home office," constant comparison uncovers a pattern worth a closer look. This becomes a code like "boundary creation." Not because you planned it, but because comparison led you to that realization.

✍️ Get your first ideas down quickly

During open coding, you don’t need perfect labels. Just focus on capturing key actions, patterns, or problems as they show up in the data:

"boundary creation"
"space separation"
"routine establishment"
"interruption management"
"family negotiations"

You're not building categories (or axial codes) through comparison yet, you're gathering "less raw" material from your initial set of unstructured data. You can code line by line, sentence by sentence, or in larger chunks, depending on your research goals and how much time you have.

If you're aiming for deep, detailed analysis, go granular with line by line coding. If you're working with many transcripts or limited time, it's okay to start broader and refine through later comparative work. Our qualitative coding guide covers these approaches in more detail.

🔁 Recap – What constant comparison looks like in practice

Compare excerpt A with excerpt B: Are these similar experiences?
Compare excerpt C with your emerging "space separation" code: Does this fit or suggest something different?
Compare across multiple participants: Is this pattern holding up?

Strauss and Corbin would call this phase "open coding," but others like Glaser would say it's really just constant comparison applied to raw data. The main goal is you're comparing data with data to identify meaningful patterns.

In Delve, just highlight the relevant section and tag it with one or more codes. The interface is designed for line-by-line coding and lets you apply multiple codes to the same excerpt. It even auto-selects the sentence when you click, which makes coding and comparison fast and precise.

🔁 Continue sampling and open coding until graduating to axial coding

After analyzing your first set of data with open coding, you return to sampling with sharper questions based on your comparative insights. The codes you are seeing point toward gaps that need exploring or patterns that need testing with new participants. You're not just collecting more data at random but strategically building on what comparison has already revealed. This back and forth sampling and analysis process takes time and patience, but each cycle should feel more focused than the last.

🆚 Compare codes with codes to build categories (axial coding)

Eventually, codes will start to cluster and overlap. As they multiply, constant comparison shifts focus to compare codes with other codes. When you compare "boundary creation," "space separation," and "routine establishment," you might see they all relate to how people manage physical and temporal boundaries during remote work.

As you go through this refinement process, ask yourself:

What causes or influences this issue?
What actions do people take?
What are the outcomes?

Your comparisons naturally lead to broader categories and reveal relationships between them. Again, some researchers call this "axial coding," but it's still the same constant comparison method. You just apply it to codes instead of raw data. You're looking for the "axes" or features that link your codes together.

As your comparisons deepen, certain clusters will stand out. You might find that your boundary codes don't just cluster together, but relate to other code clusters in specific ways. Maybe "space separation" (physical boundaries) connects to "family negotiations" (social boundaries) under certain conditions, creating larger conceptual relationships built around something like "boundary management strategies.”

📝 Write memos to track your comparative thinking

Comparison memos: "When I compare 'boundary creation' across managers and individual contributors, both describe similar challenges, but managers seem more systematic about time boundaries."
Category memos: "Comparing all these boundary codes, I'm seeing a pattern of 'adaptive boundary management.' People don't just set boundaries once, they continuously adjust them."
Relationship memos: "Comparing how 'routine establishment' and 'family negotiations' interact, it seems like successful boundary management requires both individual strategies AND household coordination."
Reflexive memos: "My own experience with remote work might be making me see boundary strategies where participants describe something different."

The constant comparison method works whether you organize these comparisons into formal "axial coding" phases or keep the process more fluid. At this stage, comparison through methods like pattern coding reveal both categories and their relationships.

With Delve, you write and pin memos directly to your codes. Over time, those memos help you notice contradictions, when a concept is becoming more defined, or when it’s time to rework a category. They become the breadcrumbs that drive your theory forward.

🔍 Stay reflexive during comparison

Your background shapes how you see patterns in the data. When comparing codes like "boundary creation" across interviews, ask yourself: Am I seeing this pattern because it's really there, or because it matches my own remote work experience? Regular reflexive memos help you stay aware of your perspective. Learn more about practicing reflexivity in qualitative research.

🔁 Continue sampling and axial coding until graduating to selective coding

Keep cycling between data collection and analysis, using constant comparison to test and refine your emerging categories. Each new interview gets compared against your existing understanding. Through ongoing comparisons, you look for more expansion, contradiction, and confirmation patterns.

While you’re not there yet, these comparisons continue until you reach theoretical saturation, where new data just confirms what you already know. You'll know you're getting close when interviews start feeling predictable, with participants describing smaller and smaller variations of patterns you've already captured.

With Delve, you can track this evolution by filter snippets by participant and code to see how each category develops across interviews, making it easier to spot when patterns are becoming stable.

🆚 Compare categories with categories to build core categories (selective coding)

Once your categories feel solid enough through repeated comparison, you'll start seeing how they connect at even higher levels. This is where the general idea of selective coding comes in. You’re now comparing categories with each other to find the central thread that pulls your theory together. (See earlier callout for comparison breakdown.)

Here, you’re focused on:

Anchoring your analysis around that core category
Showing how the other categories support or explain it
Refining your memos to articulate those links clearly

By comparing categories like "Space management," "Time boundaries," and "Family communication patterns," you discover the core process that connects everything. "Creating stability through flexible boundaries" isn't just another category. It's your theory because it explains the underlying process of how and why people manage boundaries this way, not just what they do.

🛠️ Delve Tip

Use the snippets view to see all excerpts tagged under a code or category. It’s a quick way to assess whether a concept is well-supported or still evolving.

But you're still not done with constant comparison. A natural question at this point: “Do I need to wait for complete theoretical saturation before moving into selective coding?” The short answer is you'll need to keep sampling and comparing new data against this core category. You’ll know you’re nearing saturation when constant comparison keeps confirming your core category rather than expanding or challenging it.

These steps often overlap so just do your best! As your central concept crystalizes, you can start shaping your theory around it, while still checking that categories are holding up to new data.

🎯 From core category to theory

Your core category becomes theory when it explains the conditions under which something happens, the strategies people use, and the outcomes that result. It moves beyond describing what you found to explaining the process that drives it.

Think: conditions → strategies → outcomes.
If your explanation answers how and why patterns unfold across participants, you're building theory.

🛑 Continue collecting and comparing until theoretical saturation

You stop sampling when comparing new interviews or observations no longer tell you anything new. Everything you're hearing confirms codes, categories and core categories you've already built. When constant comparison keeps turning up the same answers, you've likely reached theoretical saturation.

📌 Are we there yet?

Saturation doesn't mean no new data. It means new data isn’t providing fresh insights. Just variations on the same themes rather than discovering new ones.

📝 Define and explain core category in your write up

Your final theory emerges from synthesizing all your comparative work. From your first interview comparisons through your final category relationships, your grounded theory write up should explain:

The core process: What's really happening beneath the surface
Conditions: When and under what circumstances this process occurs
Strategies: How people navigate or respond to the process
Outcomes: What results from different approaches

As you commit to a central storyline, write one final round of memos reflecting on why this concept holds together – and how your own interpretation may have shaped the connections.

This is where your comparative analysis becomes a framework that others can apply. It might predict that boundary reconstruction works better than boundary enforcement during periods of uncertainty. Or explain why some remote workers thrive while others struggle – based on their approach to flexibility versus control. "Creating stability through flexible boundaries" doesn't just say what you found. It shows the underlying process that connects how "Space management" relates to "Time boundaries" and "Family communication patterns" grounded in the systematic comparisons you've made.

This cyclical approach builds robust theories, but juggling theoretical sampling, coding, and memo-writing can feel chaotic. Here are the most common challenges and practical solutions.

🧠 What it looks like in practice

If your core category is "Creating stability through flexible boundaries," your finished grounded theory might explain:

Core process	People don't set fixed work-life boundaries—they continuously adjust them based on changing circumstances
Conditions	This happens when traditional boundaries (like office walls or set schedules) become unreliable
Strategies	Workers use flexible tactics like spatial cues, time blocking, and family negotiations rather than rigid rules
Outcomes	Successful boundary flexibility creates stability without sacrificing adaptability

Challenges and best practices in grounded theory

Grounded theory keeps you honest. If an idea can’t hold up under constant comparison, it doesn’t make it into the theory. That’s the selling point but also adds a new layer of complexity. With no set structure to start from, everything is built from the ground up, which can get messy fast. Especially for research teams.

Here are the most common challenges researchers face and how to avoid them:

→→ Data constantly grows and evolves over time

You’re going to finish with a lot more data than you start off with in grounded theory. Unlike other methods where you might have a fixed coding framework, grounded theory means your organizational system is always shifting. As constant comparison reveals new connections, you need to reorganize codes, merge categories, and restructure your analysis multiple times throughout the process.

Solution: Delve's drag-and-drop nesting feature works hand-n-hand with grounded theory’s fluid structure. As you discover new connections through comparison, you can easily drag and drop codes into developing categories without losing your analysis trail. The visual hierarchy helps you see how your theory is building from the ground up through systematic comparison.

→ Knowing when to switch between coding approaches

There's no magic number of codes that signals when to transition from open to axial coding to selective coding through comparison. Instead, look for signs that you're starting to see patterns and relationships in your data. When you find yourself repeatedly using similar codes or grouping categories together mentally through comparison, it's time to formalize those connections through axial coding.

Solution: Use memos in Delve to capture early patterns as they form through comparison. When multiple codes start overlapping or showing up frequently in your codebook, use nested codes to begin structuring your categories. As those categories strengthen, these tools make it easier to find the central thread that ties them together.

→ Coordinating your constant comparison

One of the trickiest parts of grounded theory is knowing when your categories are fully developed: when you’ve compared enough, and it’s time to stop collecting data. Because grounded theory doesn't offer a checklist, it’s easy to second-guess yourself. Are you done? Or did you miss something?

That’s where the constant comparison method becomes your guide. As you compare data with other data, codes with codes, and categories with categories, patterns start to settle. Eventually, you notice that new data isn’t shifting your categories much anymore. That’s the signal you’re approaching saturation.

Solution: Delve’s AI assistant can support your constant comparison work in real time. You can ask it to summarize your codes, reflect on possible connections, or surface patterns that might signal saturation. Pair that with the co-occurrence matrix to compare how your codes relate, and use the code page view to quickly scan all excerpts under a concept. These features keep your theory development grounded without losing momentum or duplicating effort.

→ Balancing creativity and rigor

Beyond dealing with a growing data set, grounded theory requires both structure and intuition. Too much structure, and you miss opportunities for discovery. Too little, and your analysis loses focus. Striking that balance is especially challenging when you’re building everything from the ground up.

Solution: Keep your tools from getting in the way of this creative process. When you're coding, you need to capture insights as they emerge without wrestling with interfaces. The moment you have to fight with menus or navigate complex workflows, you lose the flow state that's essential for discovery. A streamlined coding experience lets you maintain that delicate balance between structure and intuition, keeping you engaged with your data rather than distracted by the mechanics of analysis.

→ Collaborate from anywhere without confusion

When multiple researchers work together on grounded theory analysis, maintaining consistency in coding and interpretation can be challenging. It’s hard enough to build a theory from scratch but doing it with a handful of others presents extra hurdles. Especially when they’re in different places. Without clear communication and coordination, your team might develop divergent interpretations of the data.

Solution: Delve supports the free-form nature of grounded theory collaboration for teams. You can code simultaneously, discuss comparative decisions through memos, and see who coded what. The coding comparison view lets you see how different teammates interpreted the same transcripts. You can also have conversations through memos and track who made which changes and when they happened.

Final decision: The best tool for grounded theory

Many researchers select grounded theory specifically for its theory-building capabilities. However, it demands more time and methodological rigor than other qualitative approaches. This is where coding tools like Delve help manage coding complexity as your project files swell over time.

While you can technically do grounded theory with sticky notes, spreadsheets, and word processors, it’s not ideal for large projects or teams. They can handle basic coding, but they struggle with the nested hierarchies, constant reorganization, and collaborative comparison that makes grounded theory effective.

As your codes evolve through comparison and your categories develop, you need tools that evolve with your thinking. Things get lost and communication can be splintered.

What grounded theory actually needs

The methodology requires tools that support:

Seamless reorganization as constant comparison reveals new connections
Collaborative comparison when working with research teams
Integrated memo-writing to track comparative insights as they develop
Flexible data handling as your sampling evolves through theoretical sampling
Clear audit trails showing how comparisons led to theoretical insights

How Delve answers the call:

Code transcripts and organize excerpts visually
Write and link memos to specific data points
Leverage AI assistant for initial coding suggestions and brainstorming
Compare codes with a co-occurrence matrix
Track saturation and theory development

Whether you're building a full theory or doing a smaller grounded theory approach, Delve can help you stay focused on insight instead of all the grunt work and logistics.

Build grounded theory without losing your way

Grounded theory is a way to turn messy, real-world data into meaningful explanations. Throughout this guide, we've seen how constant comparison drives every aspect of the broader process, from initial coding decisions to recognizing theoretical saturation. Comparisons are what makes the methodology unique, but it's also what makes it organizationally demanding, especially when working with teams or large datasets.

With tools like Delve, managing codes, retrieving data, and organizing your analysis gets a lot simpler, so you can focus on the heavier intellectual work of theory building. As you try to help your readers understand how and why things happen the way they do, you’ll thank yourself for the extra set of hands.

Ready to start your grounded theory journey? Try Delve free for 14 days and discover how our intuitive software can simplify (and shorten) your research process.

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“Delve was easy to use. I used it for my doctoral research and it helped me to organize my interview information into codes and categories.” – David S. Read more stories

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