Grounded Theory in Research: Types, Steps, Examples

Grounded theory is a systematic qualitative research methodology used to generate theories from empirical data rather than beginning with predetermined hypotheses.

Unlike many research methods that start with a theoretical framework, grounded theory allows concepts and explanations to emerge inductively through repeated interaction with data.

 

Why Use Grounded Theory?

Grounded theory is particularly valuable when:

• Existing theories inadequately explain a phenomenon.
• Researchers seek to understand complex social processes.
• Little prior research exists.
• New conceptual frameworks need to be developed.
• The research aims to explain “how” or “why” people behave in certain ways.

Schools of Grounded Theory

The three most widely recognized approaches are Classic (Glaserian) Grounded Theory, Straussian Grounded Theory, and Constructivist Grounded Theory.

Type	Key Proponents	Primary Focus	View of the Researcher
Classic (Glaserian) Grounded Theory	Barney Glaser	Allow theory to emerge naturally from data	Researcher strives to remain as neutral as possible
Straussian Grounded Theory	Anselm Strauss and Juliet Corbin	Systematic coding and structured analysis	Researcher actively organizes and interprets data
Constructivist Grounded Theory	Kathy Charmaz	Co-construction of meaning between researcher and participants	Researcher’s perspectives and interactions shape the analysis

Which School Should You Choose?

The choice depends on your research objectives and philosophical orientation:

• Choose Classic (Glaserian) Grounded Theory if your goal is to let concepts emerge with minimal preconceptions and you are comfortable with a less prescriptive methodology.
• Choose Straussian Grounded Theory if you prefer a clearly defined coding process and a systematic framework for linking categories into a theory.
• Choose Constructivist Grounded Theory if your research focuses on subjective experiences and you wish to explicitly acknowledge the role of researcher interpretation in generating knowledge.

Regardless of the approach selected, the core principles of grounded theory remain the same: iterative data collection, constant comparison, memo writing, theoretical sampling, and the development of theory that is firmly grounded in empirical evidence.

 

Key Principles of Grounded Theory

1. Simultaneous Data Collection and Analysis

Researchers do not wait until all data are collected.

Instead they

1. Collect initial data.
2. Analyze immediately.
3. Identify emerging concepts.
4. Collect additional targeted data.
5. Refine categories continuously.

 

2. Constant Comparative Method

Every new piece of information is compared against:

• Previous interviews
• Existing codes
• Emerging categories
• Developing theoretical explanations

This iterative comparison strengthens conceptual understanding.

 

3. Theoretical Sampling

Participants are selected based on the evolving theory rather than predetermined demographic quotas.

For example:

Initial Finding	Subsequent Sampling Decision
Junior nurses rely heavily on peers.	Recruit senior nurses for comparison.
Patients mention family influence.	Interview caregivers and spouses.
Remote workers discuss isolation.	Recruit fully remote and hybrid employees.

 

4. Emergent Theory

Rather than confirming an existing model, researchers allow explanations to arise naturally from observed patterns.

The final theory should explain:

• Relationships
• Processes
• Conditions
• Consequences
• Contexts

 

5. Memo Writing

Throughout analysis, researchers document:

• Coding decisions
• Emerging hypotheses
• Questions
• Relationships between concepts
• Theoretical reflections

Memo writing creates an audit trail and supports theory development.

 

The Grounded Theory Research Process

Step 1: Define the Research Problem

Researchers identify:

• A broad area of interest
• An underexplored phenomenon
• An open-ended research question

Avoid highly restrictive hypotheses.

 

Step 2: Collect Initial Data

Possible methods include:

• Semi-structured interviews
• Focus groups
• Participant observation
• Field notes
• Diaries
• Documents
• Online discussions

 

Step 3: Conduct Initial Coding

Initial coding is the first stage of data analysis, where the researcher examines the data line by line and assigns short labels, i.e., codes, to meaningful segments of text. The objective is to remain as close as possible to the participants’ own words and avoid imposing preconceived interpretations.

Researchers often ask questions such as:

• What is happening here?
• What action or process is being described?
• What concern is the participant expressing?
• How are they responding to the situation?

For example, consider the interview excerpt:

“Whenever I encounter a difficult patient, I ask a senior nurse to review my treatment plan before proceeding.”

Possible initial codes might include:

• Seeking expert guidance
• Managing uncertainty
• Building clinical confidence
• Relying on mentorship

At this stage, researchers typically generate dozens or even hundreds of codes across multiple interviews. These codes provide the foundation for identifying broader patterns later in the analysis.

Step 4: Conduct Focused Coding

Once a substantial number of initial codes have been created, the researcher begins focused coding: selecting the most significant or frequently occurring codes and using them to organize larger sections of data. Rather than analyzing every line independently, the goal is to synthesize and refine concepts.

For example, the following initial codes:

• Asking senior colleagues for advice
• Consulting experienced coworkers
• Seeking supervisor approval
• Requesting second opinions

may all be grouped under the broader category Professional Support-Seeking.

Similarly:

• Feeling nervous before procedures
• Worrying about making mistakes
• Fear of harming patients

might become the category Managing Professional Anxiety.

Focused coding helps reduce analytical complexity while preserving the richness of the data. It also begins to reveal recurring social processes that may ultimately contribute to the emerging theory.

 

Step 5: Explore Relationships Through Axial Coding

In many grounded theory traditions, particularly the Straussian approach, the next stage involves axial coding, where researchers examine how categories relate to one another. Instead of viewing categories as isolated concepts, they investigate possible causal links, conditions, strategies, and consequences.

For example, a study on novice nurses may identify these categories:

• Professional support-seeking
• Clinical confidence
• Independent decision-making

During axial coding, the researcher may observe that seeking professional support helps build confidence, which in turn enables more independent decision-making. These relationships can be represented in diagrams or conceptual maps to clarify the developing theory.

Researchers also explore contextual factors by asking:

• Under what conditions does this occur?
• What triggers this behavior?
• What are the outcomes?
• What influences variation across participants?

By connecting categories into a coherent structure, axial coding moves the analysis beyond description toward explanation.

 

Step 6: Use Theoretical Sampling to Refine Emerging Ideas

Unlike purposive sampling, which is planned before data collection begins, theoretical sampling is driven by the emerging analysis. Researchers deliberately seek participants, settings, or documents that can elaborate, challenge, or refine developing categories.

Suppose a study initially interviews junior software engineers and discovers that mentorship strongly influences career development. The researcher may then intentionally recruit senior engineers, engineering managers, or formal mentors to better understand how mentorship evolves across career stages.

Theoretical sampling may also involve investigating contradictory cases. If most participants describe remote work as improving productivity but a few report the opposite, additional participants with different working arrangements may be recruited to explain the variation.

The process continues until categories are sufficiently developed and additional data no longer provide substantial new insights.

 

Step 7: Determine Whether Theoretical Saturation Has Been Reached

Theoretical saturation occurs when additional data fail to produce meaningful new concepts or significantly modify existing categories. Importantly, saturation is not based on reaching a predetermined sample size but on the depth and completeness of the analysis.

Researchers should evaluate questions such as:

• Are new interviews generating genuinely new codes?
• Are the properties of each category fully understood?
• Have relationships between categories been adequately explained?
• Are exceptions and variations already accounted for?

For example, after interviewing 30 doctoral students about research supervision, the researcher may find that recent interviews simply reinforce previously identified themes—such as feedback quality, mentor availability, and peer collaboration—without introducing new conceptual dimensions.

Although no universal number of participants guarantees saturation, documenting how and why saturation was judged strengthens the transparency and credibility of the study.

 

Step 8: Develop and Refine the Final Theory

The final stage involves integrating all categories into a coherent explanatory framework that accounts for the phenomenon under investigation. Rather than presenting disconnected themes, the researcher constructs a theory that explains how a particular process unfolds and why it occurs.

For example, a grounded theory study on entrepreneurial recovery after business failure might conclude that recovery progresses through four interconnected stages:

1. Emotional processing
2. Seeking social support
3. Reframing failure as learning
4. Strategic re-engagement with new ventures

These stages would be supported by interview data, linked through analytical memos, and connected through theoretical coding. The resulting theory should explain relationships among concepts while remaining firmly grounded in empirical evidence.

A well-developed grounded theory is not intended to be universally applicable but should provide a robust conceptual model that can inform future research, policy, or professional practice in similar contexts.

 

Coding in Grounded Theory

Types of Coding

Coding Stage	Purpose
Initial Coding	Break data into small conceptual units.
Focused Coding	Identify significant recurring patterns.
Axial Coding	Connect categories and subcategories.
Theoretical Coding	Integrate concepts into a complete theory.

 

Example Coding Progression

Raw Data	Initial Code	Category	Theory Component
“I ask coworkers before making decisions.”	Seeking peer advice	Professional support	Collaborative adaptation
“Experience reduces panic.”	Growing confidence	Skill development	Confidence-building process
“Mistakes teach valuable lessons.”	Learning from failure	Experiential learning	Adaptive expertise

 

What is the Constant Comparative Method?

The Constant Comparative Method is the core analytical strategy used in grounded theory research. The method works through continuous, iterative comparison at multiple levels of analysis. As researchers collect data, they simultaneously analyze it, comparing each new piece of data against data already collected. This happens in several stages:

• Open coding: Data is broken into discrete incidents, events, or ideas, which are compared against each other to identify similarities and differences, generating initial categories.
• Category comparison: New incidents are compared to existing categories to refine their properties, boundaries, and dimensions, while also looking for variations within categories.
• Theoretical sampling: Based on emerging categories, researchers deliberately seek out new data sources that can challenge, extend, or saturate the developing categories, rather than sampling randomly or for representativeness.
• Integration: Categories are compared with one another to identify relationships, eventually integrating into a coherent theoretical framework.

This process continues until you reach “theoretical saturation”: the point where new data no longer reveals new properties or relationships within the categories.

The method’s power lies in its recursive nature: comparison never stops at a single pass. Data, codes, categories, and emerging theory are constantly cross-checked against one another, ensuring the resulting theory remains tightly grounded in the empirical data rather than imposed by the researcher’s preconceptions.

In short, researchers compare:

• Participant with participant
• Interview with interview
• Incident with incident
• Code with code
• Category with category

This ongoing comparison helps refine concepts and eliminate inconsistencies.

 

Memo Writing Best Practices

Memo writing is central to grounded theory, serving as the bridge between raw data and emerging theory. Best practices include:

• Write memos continuously, from the start. Begin memoing during initial coding, not after data collection ends—memos should grow alongside the analysis, not follow it.
• Prioritize ideas over data accuracy. Memos are a space for free theoretical thinking; capture insights, hunches, and connections as they occur, even if rough or tentative.
• Date and label every memo. Track when it was written and which codes or categories it relates to, so the analytical trail remains visible and revisitable.
• Use memos to define and refine categories. Explore a category’s properties, dimensions, and boundaries, and note when and how it varies across the data.
• Compare incidents and categories within memos. Memos are where constant comparison becomes explicit—write out how new data confirms, contradicts, or extends prior thinking.
• Avoid premature closure. Treat early memos as provisional; revise them as understanding deepens rather than treating first impressions as final.
• Separate memos from raw data and literature notes. Keep them distinct so theoretical reasoning isn’t conflated with description or external citations.
• Sort and integrate memos later. As categories mature, sorting memos by theme helps reveal relationships and structure for the final theoretical write-up.
• Write freely, without worrying about style. Memos are working documents, not polished prose—clarity of thought matters more than grammar.
• Use memos to guide theoretical sampling. Let gaps or questions identified in memos inform where to collect data next.

 

 

 

Strengths of Grounded Theory

• Generates new theories. Rather than testing existing hypotheses, grounded theory builds theory directly from data, making it ideal for exploring phenomena where established frameworks are inadequate or absent.
• Highly flexible methodology. The approach can be adapted across disciplines (e.g., sociology, nursing, education) and accommodates diverse data sources, including interviews, observations, and documents.
• Well suited for emerging research areas. When little prior research exists on a topic, grounded theory allows researchers to explore it without being constrained by preconceived categories or theoretical assumptions.
• Captures complex social processes. The method is particularly effective at illuminating how people experience, interpret, and navigate social interactions, transitions, and processes over time.
• Encourages close engagement with participants. Through iterative data collection, researchers develop a deep, nuanced understanding of participants’ perspectives, often surfacing insights that surveys or fixed instruments would miss.
• Integrates data collection with analysis. The simultaneous, iterative nature of collection and analysis allows researchers to follow emerging leads and refine focus as understanding develops, rather than waiting until all data is gathered.
• Produces practically relevant explanations. Because theory emerges from real-world data rather than abstract assumptions, the resulting frameworks tend to be directly applicable to practice, policy, and intervention design.
• Provides a systematic yet adaptable structure. Unlike purely inductive approaches, grounded theory offers concrete analytical tools (coding, memoing, theoretical sampling) that give researchers methodological rigor while still allowing flexibility in how these tools are applied.

Limitations of Grounded Theory

Challenge	Explanation
Time-intensive	Coding and iterative sampling require substantial effort.
Large data volume	Managing extensive qualitative data can be difficult.
Researcher subjectivity	Interpretation influences category development.
Achieving saturation	Determining saturation is not always straightforward.
Methodological complexity	Novice researchers may struggle with coding procedures.

 

Grounded Theory vs Other Qualitative Methods

Feature	Grounded Theory	Phenomenology	Ethnography	Case Study
Primary Goal	Generate theory	Understand lived experience	Study culture	Explore a bounded case
Main Outcome	Explanatory model	Essence of experience	Cultural description	In-depth case understanding
Sampling	Theoretical	Purposeful	Purposeful	Case selection
Analysis	Iterative coding	Thematic interpretation	Cultural interpretation	Multiple evidence synthesis
Theory Development	Central objective	Secondary	Optional	Optional

 

Common Mistakes in Grounded Theory

Starting with a Fixed Hypothesis

Grounded theory is intended to generate theory from data, not to confirm an existing hypothesis. Entering the study with a rigid expectation about the findings can bias data collection and analysis.

How to avoid it:

• Begin with broad, open-ended research questions.
• Stay receptive to unexpected findings.
• Allow categories and concepts to emerge inductively from the data.

 

Delaying Data Analysis Until Collection Is Complete

Unlike many other research methods, grounded theory requires simultaneous data collection and analysis. Waiting until all interviews or observations are completed can prevent the researcher from refining questions or pursuing promising new directions.

How to avoid it:

• Analyze data immediately after collection.
• Revise interview guides based on emerging insights.
• Use early findings to inform subsequent sampling and questioning.

 

Neglecting Memo Writing

Memo writing is a cornerstone of grounded theory because it captures the researcher’s evolving interpretations and theoretical ideas. Without memos, valuable insights may be forgotten or remain underdeveloped.

How to avoid it:

• Write memos regularly throughout the project.
• Record questions, hypotheses, and conceptual links as they arise.
• Revisit and revise memos during later stages of analysis.

 

Treating Coding as Simple Labeling

Coding in grounded theory is more than assigning descriptive tags to text. The goal is to identify underlying concepts, relationships, and processes that contribute to theory development.

How to avoid it:

• Focus on the meaning behind participants’ words.
• Compare incidents across interviews to refine concepts.
• Progress from descriptive codes to higher-level categories.

 

Stopping Data Collection Too Early

Ending recruitment before achieving theoretical saturation can leave important categories incomplete or poorly understood.

How to avoid it:

• Continue collecting data until categories are well developed.
• Look for diminishing returns in new interviews.
• Confirm that additional data no longer contribute meaningful conceptual insights.

 

Failing to Use Theoretical Sampling

Many beginners continue recruiting participants according to their original sampling plan instead of allowing emerging findings to guide subsequent data collection.

How to avoid it:

• Let emerging categories determine who should be sampled next.
• Seek participants who can clarify, challenge, or expand developing concepts.
• Be prepared to modify recruitment strategies throughout the study.

 

Ignoring Contradictory or Negative Cases

Researchers may focus only on data that reinforce their emerging theory while overlooking participants whose experiences do not fit the pattern. This can weaken the credibility and explanatory power of the final theory.

How to avoid it:

• Actively search for cases that challenge preliminary conclusions.
• Examine why certain participants differ from the majority.
• Revise categories and theoretical explanations to account for variation where appropriate.

 

Forcing Data to Fit Existing Theories

Researchers who are overly influenced by prior literature may unconsciously shape their analysis to align with established frameworks rather than allowing new explanations to emerge.

How to avoid it:

• Use the literature to inform rather than dictate analysis.
• Remain open to concepts that diverge from established theories.
• Allow the final explanatory model to be grounded primarily in the empirical data collected during the study.

 

Frequently Asked Questions (FAQs)

What is the main purpose of grounded theory?

Grounded theory aims to develop a new theory or explanatory framework directly from systematically collected and analyzed qualitative data, rather than testing an existing theory.

How is grounded theory different from thematic analysis?

Thematic analysis identifies and describes patterns or themes in data, while grounded theory goes further by using iterative coding, constant comparison, and theoretical sampling to build an explanatory theory about a social process or phenomenon.

Is a literature review allowed in grounded theory research?

Yes. However, many grounded theory traditions recommend approaching the literature in a way that informs the research without allowing existing theories to dictate coding or interpretation prematurely.

What software can be used for grounded theory analysis?

Researchers often use qualitative data analysis software such as NVivo, ATLAS.ti, MAXQDA, or Dedoose to organize data and support coding, although grounded theory can also be conducted effectively using manual coding techniques and spreadsheets.