Grounded theory is a systematic, iterative research methodology used to develop an explanatory theory from data rather than test a theory chosen in advance. Researchers collect and analyse data concurrently, compare incidents and categories, write analytic memos, sample in response to emerging ideas, and continue until categories are sufficiently developed and integrated.
Introduction
Grounded theory is one of the most influential approaches to qualitative research. It is particularly valuable when a researcher wants to explain a process, action, interaction, or experience for which existing theories are incomplete, unsuitable, or insufficiently connected to the setting being studied.
The term can initially be confusing. Grounded theory is not a single theory about society or human behaviour. It is a methodology for constructing a theory that remains closely connected—or “grounded”—in systematically collected and analysed data.
This guide explains:
- What grounded theory means.
- How its major traditions differ.
- How coding, constant comparison, memo-writing, theoretical sampling, and saturation work.
- How to conduct a grounded-theory study step by step.
- How grounded theory differs from other qualitative approaches.
- How software and artificial intelligence can be used responsibly.
- How to describe grounded theory in a thesis or research paper.
Key Takeaways
- Grounded theory aims to produce an explanatory theory, not merely a descriptive list of themes.
- Data collection and analysis normally occur together rather than in separate phases.
- Emerging categories determine what data the researcher should collect next.
- Constant comparison and memo-writing connect raw data to theoretical development.
- Glaserian, Straussian, and constructivist grounded theory should not be mixed without a clear methodological justification.
- Saturation concerns the development of theoretical categories, not simply hearing repeated answers.
What Is Grounded Theory?
Grounded theory is a research methodology in which researchers construct an explanatory theory through the systematic collection, coding, comparison, and interpretation of data.
Instead of beginning with a fixed hypothesis and testing whether it is correct, a grounded-theory researcher normally begins with a broad area of inquiry. Early data are coded and compared. The developing analysis then guides additional data collection, allowing the researcher to refine categories and explain how they relate.
Glaser and Strauss introduced the methodology in The Discovery of Grounded Theory in 1967. Their work challenged the assumption that researchers should only test theories produced by influential scholars. They argued that researchers could systematically generate useful theory from empirical data (Glaser & Strauss, 1967).
What is the purpose of grounded theory?
The purpose of grounded theory is to develop a theory that explains a process, pattern of action, interaction, or problem within a particular area of social life.
For example, a grounded-theory study might explain:
- How international students adapt to a new academic culture.
- How nurses manage competing demands during emergency care.
- How teachers adopt generative AI in their classrooms.
- How remote employees maintain professional boundaries.
- How small businesses recover after a cybersecurity incident.
- How patients make decisions after receiving a chronic illness diagnosis.
The final product should explain more than what participants experienced. It should account for how, why, under what conditions, and with what consequences a process occurs.
Is Grounded Theory a Method, Methodology, or Theory?
The term can refer to both a research methodology and the product generated through that methodology.
| Meaning | Explanation |
|---|---|
| Methodology | A coherent research strategy containing assumptions and procedures for collecting, analysing, and interpreting data. |
| Methods | Specific practices such as interviewing, observation, coding, constant comparison, memo-writing, and theoretical sampling. |
| Grounded theory as an output | The final explanatory account constructed from the relationships among theoretically developed categories. |
Calling a study “grounded theory” therefore involves more than using open coding. The study should apply the methodology’s central procedures and develop an integrated theoretical explanation.
Main Characteristics of Grounded Theory
Although grounded-theory traditions differ, most share several characteristics.
Theory generation
The goal is to construct theory, rather than only summarize experiences or identify recurring subjects.
Concurrent data collection and analysis
Researchers begin analysis after the earliest data are collected. Early interpretations shape subsequent interviews, observations, or document selection.
Constant comparison
Researchers continually compare:
- Incident with incident.
- Incident with code.
- Code with code.
- Code with category.
- Category with category.
- Emerging theory with new and contradictory data.
Theoretical sampling
The emerging analysis determines what data should be collected next. Sampling is used to develop categories and test possible relationships, not simply to achieve demographic representation.
Memo-writing
Researchers write analytic notes throughout the project. Memos record questions, category definitions, comparisons, possible relationships, doubts, alternative explanations, and sampling decisions.
Progressive abstraction
Analysis usually moves from concrete observations toward increasingly abstract concepts and categories.
Theoretical integration
The categories must eventually be connected into a coherent explanation. A collection of disconnected categories is not yet a grounded theory.
Theoretical saturation
Data collection and analysis continue until the important categories are sufficiently developed for the study’s purpose and additional data no longer make a meaningful theoretical contribution.
History and Development of Grounded Theory
Grounded theory was developed by sociologists Barney G. Glaser and Anselm L. Strauss. Their collaboration grew partly from research on awareness and dying in hospitals. Their 1967 book argued for the systematic generation of theory from data.
Glaser and Strauss later disagreed about how structured the method should be. Their disagreement contributed to the development of distinct grounded-theory traditions.
Subsequent scholars, especially Juliet Corbin and Kathy Charmaz, expanded the methodology. As a result, “grounded theory” now refers to a family of related approaches rather than one universally accepted procedure.
The Three Main Types of Grounded Theory
The three most frequently discussed traditions are Glaserian, Straussian, and constructivist grounded theory.
| Feature | Glaserian or classical | Straussian | Constructivist |
|---|---|---|---|
| Key scholars | Barney Glaser | Anselm Strauss and Juliet Corbin | Kathy Charmaz |
| General orientation | Theory is discovered through disciplined emergence | Theory is systematically developed through structured analysis | Theory is constructed through interaction among researcher, participants, data, and context |
| Researcher’s role | Researcher seeks to limit forcing and remain open to emergence | Researcher actively uses analytical procedures to develop relationships | Researcher is an interpretive participant in knowledge construction |
| Common coding language | Open, selective, and theoretical coding | Open, axial, and selective coding | Initial, focused, and theoretical coding |
| Literature use | Intensive topic-specific review may be delayed | Literature may inform analysis without determining findings | Literature can be used reflexively as sensitizing knowledge |
| Reflexivity | Less central in traditional presentations | Acknowledged within analysis | Explicitly central |
| Philosophical tendency | Often objectivist or post-positivist leaning | Pragmatic and post-positivist influences | Constructivist and interpretivist |
| View of theory | Theory emerges from data | Theory is systematically built from data | Theory is an interpretive construction grounded in data |
Glaserian grounded theory
Classical grounded theory emphasizes emergence and warns researchers against forcing data into predetermined categories. Researchers begin with a broad area rather than a tightly specified problem, engage in open coding, identify a core category, and then code and sample more selectively.
Classical grounded theory also evaluates theory using criteria such as fit, work, relevance, and modifiability. Its advocates often describe it as a general methodology that can use many forms of data, not only conventional qualitative interviews.
Straussian grounded theory
Straussian grounded theory is associated with Strauss and Corbin. It offers more explicit analytical procedures and is commonly linked to open, axial, and selective coding.
Axial coding helps researchers examine relationships among:
- Conditions.
- Actions and interactions.
- Context.
- Intervening factors.
- Consequences.
The approach can be helpful for novice researchers because it provides an organised set of analytical questions. However, these procedures should support interpretation rather than become a mechanical form-filling exercise.
Constructivist grounded theory
Constructivist grounded theory, associated with Charmaz, rejects the idea that theory simply exists in data waiting to be discovered. Data are generated and interpreted through social relationships, language, research decisions, and historical contexts.
Researchers are expected to examine their own positions, assumptions, interactions, and influence on the study. Common procedures include initial coding, focused coding, memo-writing, theoretical sampling, theoretical coding, and category integration.
Which type should a researcher choose?
The choice should be based on:
- The researcher’s philosophical position.
- The research question.
- The intended status of the final theory.
- The researcher’s view of subjectivity and reflexivity.
- The preferred coding procedures.
- Disciplinary expectations.
- The guidance of the supervisor, department, or journal.
Researchers should identify and justify their chosen tradition. They should not describe theory as objectively “emerging,” apply Strauss and Corbin’s axial-coding paradigm, and claim Charmaz’s constructivist position without explaining how those elements are philosophically compatible.
Is Grounded Theory Inductive?
Grounded theory is strongly associated with induction, but mature grounded-theory analysis may also involve deduction and abduction.
Induction
Researchers move from particular observations toward more general concepts and explanations.
Deduction
Once a tentative category or relationship develops, researchers may seek additional data to examine whether it holds under different conditions.
Abduction
Researchers consider the most plausible explanation for surprising observations, then return to the data to refine or challenge that explanation.
Grounded-theory studies therefore rarely involve pure induction from a completely neutral mind. Researchers move iteratively among data, concepts, comparisons, existing knowledge, and possible explanations.
Core Grounded-Theory Concepts
Codes
Codes are short analytical labels applied to segments of data. Strong codes capture action, meaning, strategy, condition, or consequence rather than merely restating the topic.
Example:
“I study after midnight because it is the only time nobody needs anything from me.”
Possible codes include:
- Protecting study time.
- Postponing personal goals.
- Studying after family duties.
- Trading sleep for uninterrupted work.
Concepts
Concepts are more abstract analytical ideas developed by comparing multiple coded incidents.
For example, “protecting study time” and “studying after family duties” might contribute to the concept creating protected time.
Categories
A category is a developed analytical concept with defined properties, dimensions, conditions, variations, and relationships.
A category is stronger than a topic. “Time management” is a broad topic. Negotiating protected study time under competing obligations is a more analytical category.
Properties and dimensions
Properties are characteristics of a category. Dimensions describe how those properties vary.
For the category creating protected time, properties might include:
- Predictability.
- Duration.
- Social support.
- Degree of interruption.
- Personal cost.
“Personal cost” might vary from minor inconvenience to chronic sleep deprivation.
Core category
The core category is the central analytical concept that connects the major categories and accounts for the main process identified in the study.
A good core category should have strong explanatory reach without becoming so broad that it loses meaning.
Constant comparison
Constant comparison is the continuous examination of similarities, differences, patterns, exceptions, and relationships within and across data.
It prevents researchers from accepting the first plausible interpretation. It also helps determine:
- Whether two codes represent the same concept.
- Whether a category needs to be divided.
- Whether apparently different experiences share an underlying process.
- Which negative cases challenge the emerging theory.
- What information should be collected next.
Memo-writing
Memos are the engine of grounded-theory development. They move the researcher beyond coding and toward explanation.
A useful memo may include:
- Working category name.
- Definition.
- Supporting evidence.
- Properties and dimensions.
- Conditions under which the category occurs.
- Actions or strategies associated with it.
- Consequences.
- Relationships with other categories.
- Contradictory evidence.
- Questions requiring further data.
- Reflexive observations.
- Next theoretical-sampling decision.
Theoretical sensitivity
Theoretical sensitivity is the researcher’s capacity to recognise analytically significant patterns and relationships without imposing an inflexible explanation.
It develops through:
- Close engagement with data.
- Repeated comparison.
- Disciplinary knowledge.
- Methodological reading.
- Professional and personal experience.
- Memo-writing.
- Reflexivity.
- Deliberate attention to negative cases.
Theoretical sampling
Theoretical sampling is the process of collecting additional data specifically to develop the emerging theory.
Suppose early interviews suggest that supportive supervisors help working students protect study time. The researcher might next:
- Interview students with unsupportive supervisors.
- Compare full-time and part-time employees.
- Observe academic-support meetings.
- Analyse institutional workload policies.
- Interview supervisors about flexibility.
- Seek students who rejected available support.
Each decision is driven by the developing category, not only by a predetermined demographic quota.
Theoretical saturation
Theoretical saturation is reached when further data no longer add important properties, dimensions, variations, or relationships to the categories required by the emerging theory.
Saturation should not be claimed merely because:
- Participants repeat the same topic.
- The researcher has reached a planned sample size.
- No new codes appeared in one interview.
- Time or funding has ended.
- The dataset feels large.
Researchers should document which categories were considered saturated, what evidence supported that judgment, what comparisons were made, and which categories remained less developed.
Substantive and formal theory
A substantive theory explains a process in a particular empirical area, such as how first-generation doctoral students seek academic support.
A formal theory operates at a higher level of abstraction and may explain a process across several substantive areas. Developing formal theory usually requires broader comparison and further research.
When Should Grounded Theory Be Used?
Grounded theory is appropriate when:
- The study aims to explain a process, action, or interaction.
- Existing theories do not adequately explain the setting or population.
- The researcher seeks to generate rather than primarily test theory.
- Data collection can be adjusted as categories develop.
- The researcher can analyse data during collection.
- The project allows repeated comparison, memo-writing, and theoretical sampling.
- The intended result is an explanatory model or theory.
Suitable research-question forms
Grounded-theory questions often ask:
- What process explains…?
- How do people respond to…?
- How do participants manage…?
- What happens when…?
- Under what conditions does…?
- How does a particular strategy develop over time?
- What influences variation in the process?
Examples of grounded-theory research questions
- How do doctoral students respond when their research identity is challenged?
- What process do refugee entrepreneurs use to establish trust with customers?
- How do nurses adapt to the introduction of AI-supported clinical systems?
- How do teachers negotiate institutional and ethical pressures when using generative AI?
- How do remote employees establish boundaries between paid work and family life?
- What process shapes patients’ decisions to continue or discontinue long-term treatment?
- How do cybersecurity teams coordinate during rapidly evolving incidents?
When Should Grounded Theory Not Be Used?
Grounded theory may be unsuitable when:
- The primary objective is to test a predefined causal hypothesis.
- The study only seeks to describe experiences without developing theory.
- The research question asks for the essence of a lived experience, making phenomenology more appropriate.
- The main aim is to understand an entire culture through prolonged field engagement, making ethnography more suitable.
- The project investigates a clearly bounded organisation, programme, event, or case without seeking grounded theory development.
- All data must be collected before analysis begins.
- The sample and questions cannot be modified after ethical approval.
- The researcher lacks time for concurrent analysis and repeated sampling.
- A predetermined coding framework must remain unchanged.
- The intended output is only a frequency table or descriptive thematic summary.
Using grounded-theory terminology does not turn a non-iterative coding project into grounded theory.
What Types of Data Can Grounded Theory Use?
Interviews are common, but they are not the only acceptable source.
Grounded-theory data may include:
- Intensive or semi-structured interviews.
- Informal conversations.
- Focus groups.
- Participant or non-participant observations.
- Field notes.
- Diaries and journals.
- Organisational documents.
- Policy documents.
- Meeting records.
- Online discussions.
- Social-media material collected ethically.
- Images, audio, or video.
- Historical or archival sources.
- Open-ended survey responses.
- Existing datasets.
- Numerical or descriptive information where methodologically justified.
Data sources should be chosen for their ability to develop the emerging categories.
Sampling in Grounded Theory
Grounded-theory sampling often develops in stages.
Initial sampling
The researcher selects an initial set of participants, settings, or documents likely to provide relevant information about the broad area of inquiry.
Initial sampling may be purposive, criterion-based, convenience-based, or based on access. Its purpose is to begin analysis, not to complete the final sample design.
Theoretical sampling
Once preliminary categories develop, the researcher samples to:
- Elaborate a category.
- Compare different conditions.
- Examine variation.
- Investigate an unexpected finding.
- Find a negative case.
- Clarify a category boundary.
- Examine a proposed relationship.
- Strengthen theoretical integration.
Is there a required sample size?
There is no universal sample-size formula for grounded theory.
The required sample depends on:
- The scope of the question.
- The diversity of relevant conditions.
- The complexity of the process.
- The richness of the data.
- The number and development of categories.
- Access to comparison cases.
- The selected grounded-theory tradition.
- The intended level of theory.
A proposal may provide an initial recruitment estimate for planning and ethics purposes, but it should explain that later sampling will be guided by theoretical development where the research context permits it.
How to Conduct Grounded Theory: Step by Step
Grounded theory is iterative rather than strictly linear. The following steps represent recurring activities, not isolated phases completed only once.
Step 1: Select and justify a grounded-theory tradition
State whether the study follows:
- Classical or Glaserian grounded theory.
- Straussian grounded theory.
- Constructivist grounded theory.
- Another clearly identified adaptation.
Explain how the choice aligns with the research philosophy and question.
Step 2: Define a broad area of inquiry
Begin with an open question focused on a process or problem.
Avoid embedding an untested causal explanation in the question.
Too restrictive:
How does transformational leadership increase employee resilience?
More open:
How do employees develop and maintain resilience during organisational restructuring?
Step 3: Review relevant literature appropriately
Researchers must understand the field, establish the significance of the study, avoid unnecessary duplication, prepare an ethics application, and demonstrate methodological competence.
However, existing theories should not become a rigid codebook that forces the data. Use prior literature as context or as sensitizing knowledge, depending on the chosen tradition.
Step 4: Obtain ethics approval and plan data management
The study may evolve, but ethical obligations do not disappear.
Plan for:
- Informed consent.
- Confidentiality.
- Secure recording and storage.
- Transcription.
- Anonymisation or pseudonymisation.
- Changes to recruitment or interview questions.
- Theoretical sampling.
- Sensitive disclosures.
- Withdrawal procedures.
- Software and cloud-service risks.
- Future data reuse.
- AI-assisted transcription or analysis, where applicable.
Where theoretical sampling may introduce new participant groups or data sources, describe the possible range in the original ethics application or seek amendments when required.
Step 5: Collect initial data
Use open-ended methods that encourage participants to explain actions, meanings, conditions, changes, and consequences.
Helpful interview prompts include:
- Tell me what happened.
- What did you do next?
- What influenced that decision?
- When does this process work differently?
- Who was involved?
- What made the situation easier or harder?
- How has your response changed over time?
- Can you give me a specific example?
Step 6: Begin initial or open coding immediately
Analyse the earliest data before collecting the entire dataset.
Possible practices include:
- Line-by-line coding.
- Incident-by-incident coding.
- In-vivo coding using participants’ meaningful terms.
- Process coding with gerunds such as “protecting,” “negotiating,” or “withdrawing.”
Codes should remain provisional. They are tools for thinking rather than final findings.
Step 7: Compare data and write memos
Compare incidents within one interview and across different participants or sources.
Write memos that ask:
- What is happening here?
- What problem is the participant trying to resolve?
- What conditions shape the action?
- What changes the process?
- What are the consequences?
- How does this incident differ from previous incidents?
- Which interpretation best explains the difference?
- What data would challenge this explanation?
Step 8: Develop focused categories
Select the most analytically powerful or frequently significant codes and examine them across the dataset.
Do not promote a code to a category merely because it appears often. A less frequent process may have greater explanatory importance.
Develop each category by identifying:
- Definition.
- Properties.
- Dimensions.
- Conditions.
- Strategies or actions.
- Consequences.
- Variations.
- Connections to other categories.
- Evidence that contradicts it.
Step 9: Conduct theoretical sampling
Use the gaps in the categories and memos to decide what to collect next.
For example:
Emerging category: “protecting professional credibility.”
Possible sampling decisions:
- Interview less-experienced workers.
- Interview senior staff who openly disclose mistakes.
- Examine high-risk and low-risk workplaces.
- Observe meetings where credibility is challenged.
- Analyse organisational incident-reporting policies.
Step 10: Integrate categories
Identify the central process and connect the categories into a coherent explanation.
The developing theory should specify:
- What the central process is.
- What conditions trigger it.
- What strategies people use.
- What modifies those strategies.
- What consequences follow.
- Why the process varies across situations.
Researchers may use diagrams, category maps, storylines, theoretical coding, or integrative memos.
Step 11: Assess theoretical sufficiency or saturation
Review category by category.
Ask:
- Are the properties and dimensions clear?
- Have important variations been examined?
- Have contradictory cases been analysed?
- Are relationships supported by several comparisons?
- Can the theory explain both typical and unusual cases?
- Would another data source probably change the category in a meaningful way?
- Is the category central enough to require further development?
Do not disguise practical limitations as theoretical saturation. State transparently when time, access, or resources ended data collection before every category was fully developed.
Step 12: Compare the theory with existing literature
Position the completed or substantially developed theory in relation to prior research.
Explain whether it:
- Supports existing explanations.
- Extends them.
- Contradicts them.
- Identifies new conditions.
- Reinterprets an established concept.
- Provides a process explanation where previous research was descriptive.
Step 13: Write the theory with supporting evidence
The findings should present an integrated argument, not a transcript catalogue.
Use participant quotations and observational extracts to demonstrate how interpretations are grounded in data, but ensure that the analytical explanation remains central.
Worked Grounded-Theory Coding Example
The following is a hypothetical teaching example, not a report of an actual study.
Research question
How do employed postgraduate students maintain progress when work and family demands compete with study?
Raw interview excerpt
“I wait until everyone is asleep. It is the only quiet time I have, but then I am exhausted at work the next morning. When my assignment is close, I stop answering friends because I cannot afford another interruption.”
Initial codes
- Waiting for household demands to end.
- Creating quiet time at night.
- Trading sleep for study.
- Absorbing the cost at work.
- Withdrawing socially near deadlines.
- Preventing interruptions.
- Protecting limited academic time.
Focused concept
Creating protected study time
Developing category
Carving out academic space under competing obligations
Possible properties:
| Property | Possible dimensions |
|---|---|
| Control over time | Low to high |
| Predictability | Unstable to routine |
| Social support | Resistant to supportive |
| Personal cost | Minor to severe |
| Strategy | Negotiated to concealed |
| Sustainability | Temporary to long-term |
Theoretical-sampling question
Does the process differ for students whose employers formally provide study time?
The researcher might next compare:
- Students with and without employer support.
- Students living alone and students with family-care duties.
- Students using daytime study routines and students studying overnight.
Related categories
- Negotiating legitimacy with family members.
- Converting workplace flexibility into study progress.
- Managing accumulated exhaustion.
- Temporarily withdrawing from social relationships.
- Recovering after disrupted routines.
Possible core process
Negotiating protected time
Emerging theory
Employed postgraduate students maintain academic progress by repeatedly negotiating protected time across work, family, and social domains. When direct negotiation is unavailable or unsuccessful, students create hidden time by studying late, withdrawing socially, or transferring costs to sleep and wellbeing. The sustainability of their progress depends on whether protected time becomes socially supported rather than individually extracted.
This explanation remains provisional until further comparison, theoretical sampling, category development, and integration have been completed.
Grounded-Theory Coding Approaches
Open coding
Open coding breaks data into analytical components and assigns provisional conceptual labels.
It is most closely associated with classical and Straussian terminology.
Axial coding
Axial coding reconnects data around categories by examining conditions, context, actions, interactions, and consequences.
It is strongly associated with Strauss and Corbin. It is not a compulsory stage in every grounded-theory tradition.
Selective coding
Selective coding focuses analysis on categories related to the core category and integrates them into a theory.
Initial coding
Constructivist grounded theory commonly begins with close, provisional coding. Researchers may code line by line, compare actions, and use active language.
Focused coding
Focused coding identifies the most useful initial codes and applies them analytically across larger amounts of data.
Theoretical coding
Theoretical coding examines possible relationships among developed categories. It helps move the study from categorisation toward an integrated explanatory theory.
Codes, Themes, Categories and Theory
| Term | Main function |
|---|---|
| Code | Labels a meaningful segment or incident |
| Topic | Identifies a general subject discussed in the data |
| Theme | Captures a patterned meaning across a dataset |
| Concept | Provides an abstract analytical interpretation |
| Category | A developed concept with properties, dimensions, conditions, and relationships |
| Core category | Integrates the most important categories |
| Theory | Explains how categories relate within a process or phenomenon |
Grounded theory can contain thematic ideas, but identifying themes alone does not fulfil its theory-building purpose.
Grounded Theory vs Other Qualitative Approaches
| Approach | Primary aim | Typical output | Distinguishing feature |
|---|---|---|---|
| Grounded theory | Explain a process or interaction by constructing theory | Integrated substantive or formal theory | Concurrent analysis, theoretical sampling, constant comparison |
| Thematic analysis | Identify and interpret patterned meanings | Themes and an interpretive account | Flexible across many research designs |
| Phenomenology | Understand the nature or meaning of lived experience | Description or interpretation of experiential essence | Detailed attention to lived experience |
| Ethnography | Understand culture, practices, and meaning in a social group | Cultural interpretation or ethnographic account | Prolonged engagement and fieldwork |
| Case study | Investigate a bounded case in context | Detailed case description and analysis | Clearly bounded case using multiple sources |
| Narrative research | Examine how experience is organised through stories | Narrative account or interpretation | Temporality, plot, identity, and storytelling |
| Qualitative content analysis | Systematically classify textual or visual material | Categories, patterns, or content interpretations | Structured analysis of communication content |
Grounded theory vs thematic analysis
Grounded theory is preferable when the aim is to generate an explanatory theory and the project allows theoretical sampling.
Thematic analysis is preferable when the aim is to interpret patterned meanings within a defined dataset without claiming to develop a grounded theory.
Grounded theory vs phenomenology
Grounded theory asks how a process operates and varies. Phenomenology asks what a lived experience is like and how it is meaningfully constituted.
Grounded theory vs case study
A case study is defined primarily by its bounded case. Grounded theory is defined by its theory-generating methodology. A researcher may collect data from cases while using grounded-theory methods, but the methodological relationship must be explained.
The Role of the Literature Review
The claim that grounded-theory researchers must enter the field with no prior knowledge is unrealistic.
Researchers need enough literature to:
- Establish the relevance of the study.
- Identify genuine gaps.
- Avoid unnecessary duplication.
- Prepare ethics and funding applications.
- Understand the substantive field.
- Select a methodology.
- Recognise important methodological debates.
The main risk is not reading literature. It is allowing a favoured theory to dictate every question, code, and interpretation before the data are examined.
A balanced approach
- Review sufficient literature to justify and design the study.
- State how prior concepts will be prevented from becoming a fixed codebook.
- Treat relevant ideas as sensitizing rather than definitive.
- Record when literature influences a coding or sampling decision.
- Revisit the literature more extensively after categories have developed.
- Explain how the grounded theory relates to existing scholarship.
The exact approach should match the selected grounded-theory tradition.
Quality and Rigor in Grounded Theory
Grounded-theory quality cannot be reduced to one universal checklist. Evaluation should be consistent with the chosen tradition.
Useful questions include:
Is the analysis grounded in data?
Readers should be able to see how interpretations connect to data, comparisons, memos, and category development.
Does the theory fit the studied process?
The categories should account for significant patterns without forcing evidence into an unsuitable explanation.
Does the theory work?
The theory should explain how the process operates and why it varies.
Is it relevant?
The core process should address an important concern in the substantive area.
Is it modifiable?
A grounded theory should be open to refinement when new conditions or data are identified.
Is it credible?
The analysis should demonstrate sustained engagement, rich data, systematic comparison, attention to contradictory cases, and transparent reasoning.
Is it original?
The study should provide a new conceptual interpretation, relationship, condition, or process rather than simply renaming familiar topics.
Does it have resonance?
Participants, practitioners, or informed readers should recognise the relevance and depth of the analysis, even if they do not agree with every interpretation.
Is it useful?
The theory should improve understanding and may inform future research, professional practice, policy, or intervention design.
Is the researcher reflexive?
Researchers should examine how their position, relationships, assumptions, questions, access, and analytical choices shaped the data and interpretation.
Practical Strategies for Strengthening Trustworthiness
Researchers can strengthen a grounded-theory study by:
- Maintaining an audit trail.
- Preserving coding versions and memo dates.
- Explaining sampling decisions.
- Actively seeking negative cases.
- Comparing data across participants, settings, and sources.
- Keeping reflexive notes.
- Discussing alternative explanations.
- Demonstrating category development with evidence.
- Reporting limitations honestly.
- Using peer or supervisory discussion without treating consensus as proof of truth.
- Separating participant language from researcher-generated concepts.
- Providing sufficiently detailed methodological reporting.
Is inter-coder reliability required?
Not universally.
Some post-positivist or team-based studies use agreement procedures to promote coding consistency. Constructivist researchers may view coding differences as opportunities for reflexive interpretation rather than errors that must be statistically eliminated.
Researchers should justify their approach instead of automatically calculating a coefficient that conflicts with their epistemology.
Is member checking required?
Member checking can be useful, but it is not a universal test of validity.
Participants may comment on interpretations, but:
- A participant represents one perspective.
- The theory integrates multiple sources and comparisons.
- Participants may disagree with interpretations for legitimate reasons.
- Later theoretical abstraction may extend beyond any one participant’s account.
When member engagement is used, researchers should explain its purpose and how responses affected the analysis.
Advantages of Grounded Theory
It generates theory where existing explanations are weak
Grounded theory is valuable for new, changing, or poorly understood processes.
It connects theory closely to empirical evidence
Constant comparison and memo-writing help researchers demonstrate how conceptual claims developed.
It is responsive
Questions and sampling decisions can change as the analysis becomes more focused.
It explains process and variation
A strong grounded theory identifies not only recurring experiences but also conditions, strategies, transitions, and consequences.
It can produce practical insights
Substantive theories may inform service design, professional training, policy development, organisational change, or future research.
It accommodates different data sources
Interviews, observations, documents, digital materials, and other relevant data can be compared where ethically and methodologically appropriate.
Limitations of Grounded Theory
It is demanding and time-intensive
Concurrent analysis, memo-writing, repeated sampling, transcription, comparison, and integration require substantial effort.
It can be difficult to plan in advance
Funders, ethics committees, and dissertation schedules may expect fixed samples and procedures, while grounded theory requires flexibility.
Its terminology is inconsistent
Different traditions use the same terms differently or use different terms for similar analytical activities.
Researchers may claim it too loosely
A project that conducts one round of interviews, applies open coding, and reports themes has not necessarily produced grounded theory.
The researcher has substantial interpretive influence
Codes and categories do not form themselves. Without reflexivity and transparency, researchers may mistake assumptions for findings.
Saturation is difficult to demonstrate conclusively
It is an analytical judgment rather than a mechanical threshold.
The final theory may have limited scope
A substantive theory is grounded in particular conditions. It should not be treated as universally applicable without further research.
Common Grounded-Theory Mistakes
Treating coding as the final analysis
Codes are the beginning of theory development, not the final product.
Collecting all data before analysing anything
This prevents early findings from guiding theoretical sampling.
Using a fixed interview guide throughout
Questions should develop as the analysis identifies gaps and comparisons.
Confusing purposive and theoretical sampling
Initial participants may be purposively selected. Sampling becomes theoretical when emerging categories direct the next data collection.
Claiming saturation without evidence
Researchers should explain what was saturated, what comparisons were conducted, and how the decision was reached.
Mixing traditions without justification
Using every coding term found in textbooks can produce methodological inconsistency.
Reporting topics instead of categories
“Stress,” “support,” and “technology” are topics. Grounded-theory categories should explain action, conditions, or process.
Forcing data into an existing model
A familiar theory can become a template that prevents genuinely different relationships from being recognised.
Neglecting memo-writing
Without analytic memos, researchers often remain at the descriptive coding level.
Treating software output as theory
Code frequencies, visual maps, and AI summaries are analytical aids. They do not replace theoretical reasoning.
Ignoring negative cases
Contradictory evidence can reveal category boundaries and improve the theory.
How Grounded Theory Is Used in Modern Research
Grounded theory is now applied across:
- Sociology.
- Psychology.
- Education.
- Nursing and healthcare.
- Management.
- Information systems.
- Software engineering.
- Human–computer interaction.
- Public policy.
- Social work.
- Communication.
- Entrepreneurship.
- Organisational research.
Modern studies may investigate rapidly changing phenomena such as:
- Human interaction with artificial intelligence.
- Platform work.
- Remote and hybrid employment.
- Digital healthcare.
- Online learning.
- Cybersecurity practice.
- Algorithmic decision-making.
- Climate adaptation.
- Technology-mediated identity.
- Virtual communities.
Its responsiveness makes grounded theory valuable when practices are changing faster than established theories can explain them.
Grounded-Theory Software
Researchers can conduct grounded theory manually or use computer-assisted qualitative data-analysis software.
Common options include:
- ATLAS.ti.
- MAXQDA.
- NVivo.
- Dedoose.
- Quirkos.
- Taguette.
- Spreadsheets and structured document systems.
Software may assist with:
- Storing and organising data.
- Coding.
- Retrieving coded segments.
- Linking codes and memos.
- Comparing cases.
- Managing attributes.
- Creating diagrams.
- Recording an audit trail.
- Conducting text searches.
- Supporting team collaboration.
Software does not decide:
- Which code is analytically significant.
- Whether a category is conceptually coherent.
- Which case should be theoretically sampled.
- Whether an interpretation fits the data.
- Whether saturation has been reached.
- Which category should become central.
- Whether the final explanation qualifies as theory.
These remain researcher responsibilities.
Can Artificial Intelligence Be Used in Grounded Theory?
Artificial intelligence can support limited research tasks, but it should not replace human interpretation, theoretical sensitivity, reflexivity, or accountability.
Possible uses may include:
- Draft transcription, followed by human verification.
- Data-formatting support.
- Locating passages associated with an established code.
- Generating questions for human consideration.
- Checking consistency in labels.
- Summarising a non-sensitive memo for comparison with the original.
- Producing code to organise approved research data.
- Testing whether an explanation is clearly written.
Major risks
AI-assisted qualitative analysis creates risks involving:
- Participant confidentiality.
- Personal-data protection.
- Inaccurate transcription.
- Invented quotations or interpretations.
- Loss of context.
- Reinforcement of dominant assumptions.
- Hidden model changes.
- Weak reproducibility.
- Intellectual-property restrictions.
- Dilution of the researcher’s reflexive engagement.
- Undisclosed methodological influence.
Responsible-use principles
- Do not upload identifiable or confidential participant data into an unapproved public AI system.
- Follow institutional, ethics-board, funder, data-protection, and journal policies.
- Confirm whether participants consented to the proposed processing.
- Use institutionally approved or locally controlled systems where required.
- Verify every AI-generated transcript, label, quotation, and summary against the original data.
- Preserve human control over coding, comparison, sampling, category construction, and theory development.
- Record the tool, version, date, purpose, inputs, outputs, and human corrections.
- Disclose material AI use in the methodology or acknowledgements.
- Do not list an AI system as an author.
- Avoid presenting machine-generated interpretations as participant meanings.
AI can assist with information management, but a grounded theory requires accountable human theoretical work.
How to Write a Grounded-Theory Methodology Section
A strong methodology section should include the following components.
Research design
Identify grounded theory and the selected tradition.
Example:
This study used constructivist grounded theory to develop an explanatory account of how early-career teachers negotiate institutional expectations surrounding generative AI. Constructivist grounded theory was selected because the study treated data and analysis as context-dependent constructions developed through interaction between participants and the researcher.
Philosophical position
Explain the ontology, epistemology, and researcher’s role where required by the discipline.
Research question
Present an open, process-oriented question.
Setting and initial sampling
Describe the context, eligibility criteria, recruitment, and rationale for the initial sample.
Data collection
Explain interviews, observations, documents, or other sources, including how questions changed over time.
Concurrent analysis
State when coding began and how early analysis informed later collection.
Coding procedures
Use terminology consistent with the chosen tradition.
Constant comparison
Explain what was compared and how comparison changed categories or sampling decisions.
Memo-writing
Describe the frequency, form, and analytical purpose of memos.
Theoretical sampling
Provide examples of decisions made in response to emerging categories.
Saturation or theoretical sufficiency
Define the term used and explain how the judgment was made.
Reflexivity
Discuss the researcher’s position, relationships, assumptions, and influence.
Software and AI
Name the software, explain its function, and disclose material AI assistance.
Ethics
State approval, consent, confidentiality, data security, and amendments.
Quality and reporting
Explain the tradition-appropriate quality criteria and any reporting guideline used.
Reusable Methodology Template
This study adopted [grounded-theory tradition] to develop an explanatory theory of [process or phenomenon]. The methodology was selected because [justification]. The study was informed by [philosophical position], which assumes [brief explanation].
Initial participants or data sources were selected using [initial sampling approach]. Data were generated through [interviews, observations, documents, or other sources]. Analysis began after [first interview/data source] and proceeded concurrently with data collection.
Data were analysed through [coding terminology appropriate to the selected tradition]. Constant comparison was conducted among [incidents, cases, codes, categories, settings, or sources]. Analytic memos documented category development, methodological decisions, reflexive observations, and emerging relationships.
As preliminary categories developed, theoretical sampling was used to [give concrete examples]. Data collection continued until [define saturation or theoretical sufficiency]. Categories were integrated around [core category or integrative process, if known at reporting stage].
[Software] was used to organise, retrieve, and link coded material; interpretive and theoretical decisions remained the responsibility of the research team. [Describe and disclose any AI use.]
Trustworthiness was supported through [audit trail, negative-case analysis, comparison, reflexive practice, peer discussion, rich evidence, and other relevant procedures]. Ethical approval was granted by [committee], reference [number].
Practical Grounded-Theory Templates
Analytic memo template
Memo title:
Date and data source:
Working category:
Current definition:
Key incidents:
Properties:
Dimensions:
Conditions:
Actions or strategies:
Consequences:
Related categories:
Contradictory evidence:
Researcher assumptions:
Questions remaining:
Next theoretical-sampling decision:
Theoretical-sampling log
| Emerging category | Gap in understanding | Data needed | Proposed source | Decision and rationale | Result |
|---|
Category-development table
| Category | Definition | Properties | Dimensions | Conditions | Actions | Consequences | Negative cases | Evidence still needed |
|---|
Saturation-assessment record
| Category | New properties still emerging? | Variation examined? | Negative cases examined? | Relationships supported? | More sampling required? |
|---|
Audit-trail checklist
Record:
- Versions of the research question.
- Recruitment changes.
- Interview-guide revisions.
- Coding framework changes.
- Memos and diagrams.
- Theoretical-sampling decisions.
- Category-definition revisions.
- Negative-case analysis.
- Software and AI use.
- Ethics amendments.
- Saturation decisions.
- Links between evidence and final claims.
Conclusion
Grounded theory is a rigorous and flexible methodology for constructing an explanatory theory from systematically collected and analysed data. Its defining value lies not in open coding alone but in the interaction among constant comparison, memo-writing, theoretical sampling, category development, and theoretical integration.
Researchers should choose a grounded-theory tradition deliberately, maintain consistency between philosophy and method, explain how data collection evolved, and show how their final theory was developed. When these requirements cannot be met, a different qualitative approach—such as thematic analysis, phenomenology, ethnography, or case study research—may provide a more accurate description of the study.
