Grounded theory is a qualitative research methodology used to develop theory directly from systematically collected and analyzed data. The approach emphasizes iterative cycles of data collection and analysis, allowing emerging concepts to guide further inquiry. Core practices include coding, constant comparison, memo writing, and theoretical sampling, all of which support theory development grounded in empirical evidence. Grounded theory is best suited to research questions focused on explaining processes, actions, or interactions over time, particularly in under-researched or complex contexts.
Grounded theory is a method of conducting qualitative research that builds an evolving theory directly from data. Instead of starting with a hypothesis, researchers using grounded theory collect and analyze data in cycles, letting patterns and themes emerge naturally. This approach is useful when studying a process, behavior, or social phenomenon that is not yet well understood.
This methodology emphasizes a close connection between the researcher and the data, often involving a hands-on, iterative coding process. It is widely used across fields such as health, education, sociology, and business, especially when the goal is to generate new insights rather than test existing ideas.
In this article, we break down what grounded theory is, how it developed, when to use it, and how it works in practice. We also describe key tools and techniques, provide real-world examples, and show how software like Lumivero’s NVivo and ATLAS.ti can support grounded theory analysis.
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Grounded theory is a qualitative research methodology focused on developing theory from data rather than testing existing theories. It is especially suited for research questions that explore how people experience, interpret, or respond to specific situations or social processes. The method is flexible but structured, requiring systematic data collection, coding, memo writing, and comparison to develop insights that are “grounded” in empirical evidence.
Grounded theory is an inductive approach where theories emerge from the data itself. Rather than beginning with predefined concepts or frameworks, grounded theory emphasizes building understanding directly from the data. This makes it a strong fit for research in unfamiliar or underexplored areas, where theoretical direction is lacking or premature.
Grounded theory methods were first introduced in the 1960s by sociologists Barney Glaser and Anselm Strauss in their book, “The Discovery of Grounded Theory” (1967). Their work in developing grounded theory was a reaction against the dominant quantitative and theory-driven research paradigms of the time. They aimed to legitimize qualitative research by offering a systematic, rigorous method for developing theory. Over time, grounded theory evolved into several distinct approaches as Glaser and Strauss diverged in their views and others introduced further refinements.
The goal of grounded theory is to generate a theoretical explanation of a process, action, or interaction as it occurs in real-life settings. This explanation is developed through an iterative process of collecting, coding, comparing, and refining data. The resulting theory should not just summarize the data but offer an explanatory framework that can be transferred to similar contexts.
Grounded theory is defined by a set of methodological features that shape how data are collected, analyzed, and interpreted. These characteristics distinguish it from other qualitative approaches that prioritize description or interpretation over theory development.
Grounded theory relies on inductive reasoning, where concepts and categories emerge from the data rather than being imposed in advance. Researchers begin analysis early in the research process and remain open to unexpected patterns. Coding is used to break data into smaller units, compare incidents, and identify relationships that support theory building. Existing literature may be consulted later to situate the emerging theory rather than guide initial analysis.
The researcher plays an active role in interpreting data and shaping the analytic direction of the study. Decisions about coding, sampling, and category development require ongoing judgment. Reflexivity is necessary to account for how prior experience, assumptions, and interactions with participants influence analysis. Memo writing is commonly used to document these decisions and make the analytic process transparent.
Grounded theory follows structured analytic procedures while allowing flexibility in how the study unfolds. Data collection, coding, and analysis occur in overlapping cycles rather than fixed stages. As categories develop, researchers adjust sampling strategies and refine analytic focus. This balance allows studies to remain methodologically consistent while responding to insights that arise from the data.
A grounded theory is often used in cases where there is no existing theory that explains the phenomenon being studied. It is also possible to use it if there is an existing theory, but it is potentially incomplete because the information wasn’t gathered from the group you intend to research.
Grounded theory is best suited for studying processes, interactions, and social experiences that are not yet well defined. It is most useful when existing theories do not adequately explain what is happening in the field or when researchers want to understand how participants make sense of their experiences over time. The approach is often used in exploratory research and is especially valuable in fields such as health, education, psychology, and user research where the focus is on lived experience or change over time.
Grounded theory works well when:
Although grounded theory is most often used in interview-based studies, it can also be applied to observational data, documents, and mixed methods designs. The key is that the theory must come from the data—not be imposed before the data are examined.
Grounded theory is not well suited to studies that aim to test existing theories or hypotheses. When a research question is driven by predefined variables or seeks to confirm relationships established in prior work, other qualitative or quantitative approaches are more appropriate.
This methodology is also a poor fit for projects with fixed data sets and no opportunity for additional data collection. Grounded theory relies on theoretical sampling, which requires researchers to follow emerging concepts by gathering new data. Studies limited to secondary data or strict timelines may not allow for this iterative process.
Grounded theory may be inappropriate when the primary goal is detailed description rather than explanation. Approaches such as qualitative description, case study, or phenomenology may be better options when the focus is on summarizing experiences, documenting perspectives, or examining bounded cases without developing a formal theory.
Since the original publication of “The Discovery of Grounded Theory,” grounded theory has developed into several distinct versions. Each of the three main types retains core elements of the original method, such as iterative coding and theory building. However, they differ in how theory is constructed, the role of the researcher, and how data are handled.
The three types of grounded theory are:
Each version offers a slightly different interpretation of what grounded theory should look like in practice.
Classic grounded theory, also known as Glaserian grounded theory, was developed and maintained by Barney Glaser. It emphasizes letting theory emerge from the data without forcing it through pre-existing categories or frameworks. In classic grounded theory:
Glaser discouraged the use of predefined interview questions or structured coding systems. Instead, he advocated for flexibility and openness, believing that researchers should trust the data to lead them to theory. He also emphasized the importance of conceptual abstraction, arguing that grounded theory should go beyond description to offer generalizable theoretical explanations.
Straussian grounded theory was developed by Anselm Strauss in collaboration with Juliet Corbin. This version takes a more structured and analytical approach than Glaser’s. It emphasizes detailed procedures for coding and concept development.
Key features of Straussian grounded theory include:
Strauss and Corbin provided coding manuals and step-by-step guidance on grounded theory procedures to help researchers stay organized during qualitative analysis. They saw this structure as helpful, especially for beginners. Critics, including Glaser, argued that it risked forcing the data into pre-determined categories rather than letting theory emerge organically.
Constructivist grounded theory was introduced by Kathy Charmaz. This version acknowledges that both data and analysis are constructed through the research process. It emphasizes the co-construction of meaning between researcher and participant, aligning with interpretivist paradigms.
Constructivist grounded theory features:
Charmaz’s approach rejects the idea of theory emerging entirely independently of the researcher. Instead, it positions grounded theory as an interpretive process that is shaped by both the data and the analytical lens the researcher brings to it. This version of grounded theory is widely used in fields like nursing, psychology, education, and sociology, where reflexivity and context are central to the research.
Grounded theory involves a series of interrelated steps that guide the researcher from data collection to theory development. These steps are not always linear; researchers often move back and forth between them, adjusting their approach based on new findings. The following components are central to most grounded theory projects, regardless of which version is used.
Data collection in grounded theory is typically qualitative and open-ended. Interviews, observations, documents, and other sources can all be used, but the emphasis is on capturing detailed, contextual information about participants’ experiences and actions. Interviews are the most common method, often conducted in a semi-structured or unstructured format to allow participants to speak freely and introduce new ideas.
Early interviews may start broadly, allowing participants to talk about their experiences without much direction. As coding and analysis progress, the researcher may refine questions to gather more information about emerging themes. This ongoing process, where data collection is adjusted based on earlier analysis, is known as theoretical sampling and plays a major role in grounded theory.
The researcher typically avoids bringing in an existing theoretical framework at the data collection stage. The goal is to stay open to what the data reveal, even if it means moving in an unexpected direction. This openness is balanced with the need to stay focused on a particular phenomenon or process of interest.
Coding is the core analytic activity in constructing grounded theory. It involves breaking data into segments, labeling those segments, and grouping related codes into higher-level categories. Coding is typically done in stages:
Throughout coding, the goal is to look for patterns and relationships. Researchers may ask questions such as “What is happening here?” “What are the conditions that give rise to this behavior?” and “How do participants respond to this situation?” Codes and categories are constantly revised and compared to new data to refine emerging ideas.
Researchers may use qualitative data analysis software like NVivo or ATLAS.ti to support the coding process. These tools help manage large volumes of text, link codes to excerpts, and visualize how categories relate to one another.
The constant comparative method is a foundational technique in grounded theory. It refers to the process of continually comparing new data to existing codes and categories. Each time a new interview or observation is added, the researcher revisits earlier codes to see whether the new data support, extend, or contradict them.
This method serves several functions:
Comparisons can be made in several ways: between different participants, between data collected at different times, or even between different parts of the same interview. The idea is to test developing categories against the data to ensure they are grounded and meaningful. This back-and-forth process helps researchers remain analytic rather than descriptive, even in early stages of theory development.
Constant comparison also supports the idea of theoretical saturation, or the point at which no new information or insights are being generated through additional data collection. Categories are considered saturated when new data confirm rather than expand or challenge them.
Memo writing is a key part of grounded theory that helps bridge the gap between raw data and theoretical concepts. Memos are short, informal documents in which the researcher records insights, questions, hypotheses, and decisions throughout the coding and analysis process. They are written continuously—from the first interview through to the final stages of theory development.
Types of memos include:
Writing memos encourages researchers to pause and think about what the data mean. It helps make abstract ideas visible and forces researchers to clarify the logic of their interpretations. Over time, memos can be sorted, grouped, and connected as part of building the final theory.
In constructivist grounded theory, memo writing is also used as a space to reflect on the researcher’s positionality and influence on the data. In classic and Straussian versions, it plays a more conceptual and analytic role, helping move the researcher toward higher levels of abstraction.
Theoretical sampling is the process of deciding what data to collect next based on the current state of the emerging theory. Instead of following a fixed sampling plan, the researcher allows early findings to guide later decisions. For example, if early interviews reveal that participants frequently mention a certain challenge or turning point, the researcher may seek out others who have had similar or contrasting experiences.
This targeted approach to sampling helps refine categories, fill gaps, and test the relevance of developing ideas. It also means that further data collection continues until the researcher reaches theoretical saturation—the point at which additional data no longer add new insights or prompt significant revisions to categories.
Theoretical sampling is not the same as purposeful sampling, which selects participants based on predefined criteria. Theoretical sampling evolves alongside the analysis and is driven by the needs of the theory. It may lead the researcher to seek out specific groups, contexts, or experiences that challenge or deepen current understandings.
Reaching saturation does not mean that every possible angle has been exhausted, nor is a final grounded theory achieved. It means that the categories being developed are well-supported by the data, internally consistent, and useful for explaining the phenomenon under study. Ultimately, saturation is a practical judgment that determines the extent that critical research findings can be reliably identified from the empirical data.
Grounded theory research has several distinct advantages when used in research that benefits from new, fresh perspectives. Let's look at many of the advantages, as well as some of the disadvantages, of using a grounded theory methodology.
By using grounded theory, one can develop theories that are based on observations and interviews with real subjects in real situations. This results in findings that more closely reflect reality. In contrast, other types of research take place in less natural settings, such as focus groups and lab settings.
The premise of grounded theory is that you discover new theories by inductive means. In other words, you don't assume anything about the outcome and aren't concerned about validating or describing it. Instead, you use the data you collect to inform your analysis and your theoretical construct, resulting in new insights.
Analyzing and collecting data go hand in hand. Data is collected, analyzed, and as you gain insight from analysis, you continue gathering more data. In this way, your data collection will be adequate to explain the results of your analysis.
In grounded theory, the outcome is determined primarily by collected data, so findings are tightly tied to those data. It contrasts with other research methods that are primarily constructed through external frameworks or theories that are so far removed from the data.
Because gathering data and analyzing it are closely intertwined, researchers are truly observing what emerges from data. By having a buffer, you avoid confirming preconceived notions about the topic.
An important aspect of grounded theory is that it provides specific strategies for analysis. Grounded theory may be characterized as an open-ended method, but its analysis strategies keep you organized and analytical throughout the research process.
Despite its strengths, grounded theory presents several challenges that can affect feasibility, rigor, and scope. These limitations should be considered when selecting an appropriate research approach.
Grounded theory requires ongoing data collection, repeated coding cycles, and continuous comparison across data sources. Memo writing and theoretical sampling add further demands. As a result, studies often require extended timelines and sustained researcher involvement.
Effective grounded theory analysis depends on the researcher’s ability to code consistently, make analytic decisions, and recognize emerging patterns. Researchers must balance openness to the data with methodological discipline, which can be difficult without prior experience in qualitative analysis.
Although grounded theory aims to reduce confirmation bias, the researcher remains deeply involved in interpretation. Decisions about which categories to pursue, how to label data, and when saturation is reached are shaped by researcher judgment. Without reflexive practices, personal assumptions may influence theory development.
Because grounded theory evolves in response to emerging findings, the direction of the study may shift over time. New categories can lead to expanded sampling and additional data collection, increasing the risk of an unmanageable scope if analytic boundaries are not carefully maintained.
Grounded theory is commonly used in applied research settings where the goal is to understand how people navigate complex situations, systems, or decisions. Because it prioritizes process and context, it has been adopted across a range of disciplines. The following examples show how grounded theory has been used in healthcare, user experience (UX) research, and education to generate theory from the ground up.
Grounded theory is widely used in healthcare research to explore how patients, caregivers, and providers make decisions, cope with illness, or interact with medical systems. Many health-related behaviors and emotional responses are difficult to capture through surveys or experimental designs. Grounded theory offers a way to understand these experiences in depth, especially when the topic is sensitive or underexplored.
For example, researchers studying the experience of living with chronic pain may conduct interviews with patients to learn how they manage their symptoms over time. Initial interviews might reveal that patients try a combination of medication, exercise, and alternative therapies. As more interviews are conducted, patterns may emerge showing how patients adapt their strategies depending on family responsibilities, work schedules, or access to care. Through constant comparison and memo writing, the researcher may develop a theory of “adaptive pain management” that explains how individuals make treatment choices based on evolving personal and social factors.
Grounded theory has also been applied to topics such as patient-provider communication, end-of-life decision-making, and the emotional labor of healthcare workers. In each case, the goal is to generate a theoretical model that reflects the real-world processes described by participants, rather than apply an existing framework that may not fit the situation.
In UX research, grounded theory is often used to understand how people interact with products, systems, or services. While usability testing can identify technical issues, it does not always capture the full context of use, especially in complex or high-stakes environments. Grounded theory allows UX researchers to identify user goals, strategies, frustrations, and workarounds in ways that structured methods might overlook.
For instance, a grounded theory study on the use of a mobile health app might start with open-ended interviews about how people track their physical activity. Early findings may show that users are motivated by different goals. For example, some want to build long-term habits, while others are focused on short-term weight loss. As coding continues, the researcher may identify categories such as “data-driven motivation,” “social accountability,” and “routine disruption.” These insights could lead to a theoretical model that explains how sustained engagement with the app depends not only on its features but also on life events, social support, and changing priorities.
Grounded theory can also be used in enterprise UX, such as understanding how employees interact with workflow tools, CRM platforms, or onboarding systems. By focusing on users’ reasoning and behavior over time, researchers can develop models that inform design decisions, identify unmet needs, or guide future product iterations.
What sets grounded theory apart in UX research is its ability to explain why users behave in certain ways, not just what they do. These explanations can support strategic product development and human-centered design.
In education research, grounded theory is often used to study how students and teachers experience learning, instruction, and institutional systems. It is particularly useful for examining classroom dynamics, learning processes, and identity formation in educational contexts. Because grounded theory values emergent insights and participant perspectives, it aligns well with studies that seek to understand learning as a lived, contextualized activity.
One example is a study on how first-generation college students navigate academic advising. Interviews might reveal that these students often receive conflicting messages about course selection, major choices, and graduation timelines. Through coding and comparison, the researcher may develop a theory about “navigational capital,” showing how students piece together information from peers, faculty, and official sources to make decisions in an unfamiliar environment.
In teacher education, grounded theory has been used to study how novice teachers develop classroom management strategies, how they interpret student behavior, and how they reconcile institutional expectations with their personal teaching philosophies. These studies often focus on the process of becoming a teacher, rather than evaluating the outcomes of teacher training programs.
Grounded theory has also been applied to research on educational equity, language learning, curriculum development, and online instruction. In each case, the method supports the development of theory that reflects actual student or teacher experiences, often revealing gaps between institutional policies and everyday practices.
Both NVivo and ATLAS.ti support research that adopts a grounded theory perspective by providing tools to manage, code, compare, and reflect on data in a structured yet flexible environment. While grounded theory does not require software, both programs enhance the research process by helping researchers organize and make sense of complex datasets, trace analytic decisions over time, and support the refinement and comparative analysis essential to grounded theory.
Let's outline how grounded theory researchers can use NVivo or ATLAS.ti across several key tasks, from importing data and coding to conducting comparisons and writing memos.
The first step is to create a new project and import your data sources. Both NVivo and ATLAS.ti accept a wide range of file types including:
For grounded theory projects, interview transcripts are typically the primary source. These can be imported as DOCX or TXT files. NVivo allows you to create cases for each participant and assign demographic attributes, while ATLAS.ti provides for classifying documents into document groups to easily filter data into different categories. While demographic data are not central to early-stage grounded theory, categorization can support comparisons later in the analysis.
Once your data are imported, you can organize it into folders or groups by source type, participant group, or data collection phase. Keeping your file structure consistent helps manage iterative cycles of analysis and theoretical sampling.
In the first cycle of grounded theory coding, you work closely with the data, segmenting text into short units and assigning descriptive or action-based labels. NVivo supports this through its coding stripes and code system.
During early analysis, it's common to create dozens or even hundreds of codes. You can organize codes into parent and child codes to reflect developing themes (e.g., Parent - Water quality, child – Contamination). Additionally, you can use NVivo’s autocoding features, including theme detection, sentiment analysis, pattern recognition, and entity recognition, to support and speed up exploratory analysis. NVivo’s AI Assistant further enhances this process by allowing you to summarize content and generate suggested child codes.
In ATLAS.ti, you can turn to Intentional AI Coding to code thematically based on specific questions you might have about the data. Intentional AI Coding creates code categories and subcodes to form a preliminary hierarchy of codes that you can refine later on in your study. While grounded theory researchers typically rely on manual, inductive coding, these tools can help identify patterns or spark early analytical thinking when used alongside more interpretive, hands-on methods.
As coding progresses, grounded theory researchers shift from generating codes to grouping them into more conceptual categories. In NVivo or ATLAS.ti, this can be supported by:
In NVivo, the Coding Comparison Query can be used to assess the degree of coding agreement between two researchers coding the same dataset, although this is less relevant in solo grounded theory projects. More useful is the Matrix Coding Query, which helps identify which cases or sources contribute to each category. This supports the constant comparative method, showing patterns across participants or time points.
In ATLAS.ti, you can use the Code Manager to review the most prominent codes and make changes accordingly. The various visualizations of codes such as code clouds and tree maps make it easier to see how codes are distributed across your data and subsets of data for easy comparison and revision.
Constant comparison involves reviewing how categories change or stabilize across sources. NVivo provides several tools for this:
In ATLAS.ti, you can use the following tools:
These tools support analytic rigor by making coding patterns visible and traceable. However, interpretation remains the researcher’s responsibility.
Memos are central to grounded theory work as they give researchers a space for their thoughts and reflections, which are important to document during the grounded theory process. You can write memos at any point and link them to sources, codes, or entire cases. Types of memos may include:
NVivo and ATLAS.ti both support writing in both plain text and rich text format, allowing you to bold, underline, or structure memos using headings. Memos can also be exported and shared with collaborators.
NVivo helps researchers track which data have been collected and coded, and which areas may need further development. You can use:
Together, these tools help support an audit trail—a documented record of your analytic decisions. This is important for transparency and useful when revisiting earlier stages of analysis or presenting your methodology in a research report.
By keeping memos, coding logs, and classification data updated, NVivo helps demonstrate how your grounded theory evolved over time.
In ATLAS.ti, Code-Document Analysis is a powerful tool to determine which documents and how much of your data have been coded by relevant codes. In conjunction with document groups, you can identify the extent of coding in the data for each group of participants in your project. A lack of coding in certain subsets of data can indicate a need to further develop your coding as you construct a more robust grounded theory.
As you move toward finalizing your grounded theory, NVivo allows you to export coded content, summaries, and visualizations. Common export options include:
These outputs can be customized and formatted for inclusion in presentations, articles, or dissertations. NVivo’s integration with Microsoft Word and Excel also makes it easy to work across platforms.
A variety of reports can be exported from ATLAS.ti for later discussion and dissemination. You can customize reports to display specific segments of your data or analysis and export them as Word or Excel files for sharing with the rest of your research team. Visualizations in Code Co-Occurrence Analysis and Code-Document Analysis such as Sankey diagrams and force-directed graphs provide the means to easily distill qualitative analyses into a more accessible form for your research audience. Finally, the Networks tool helps you to draw theoretical links between your codes to visualize your grounded theory.
Whether you’re building theory from interview data, identifying emerging concepts, analyzing social processes, or developing categories through iterative coding, Lumivero’s research software can help streamline your workflow. Use Lumivero's NVivo or ATLAS.ti to organize your sources, manage coding, write memos, and track emerging insights—all in one place.
Kickstart your qualitative inquiry with the tools trusted by researchers worldwide: purchase NVivo or ATLAS.ti today.
The main purpose of grounded theory research is to develop theory that explains processes, actions, or interactions based on empirical data. Rather than testing existing theories, grounded theory focuses on building explanations that account for how and why social phenomena unfold over time.
Grounded theory studies involve iterative data collection and analysis. Common activities include collecting qualitative data, conducting initial and focused coding, comparing data across cases, writing analytic memos, and using theoretical sampling to refine emerging categories. These activities continue until theoretical saturation is reached.
A researcher should avoid grounded theory when the study aims to test predefined hypotheses or apply an existing theoretical framework. It is also less suitable for projects with fixed data sets, limited time for iterative analysis, or a primary focus on description rather than explanation.
Grounded theory focuses on developing theory that explains processes and relationships, while thematic analysis focuses on identifying and organizing patterns of meaning within data. Grounded theory relies on iterative sampling, constant comparison, and theory development, whereas thematic analysis is often used to summarize key themes without constructing a formal theory.
An example of grounded theory research is a study examining how patients manage long-term treatment decisions for chronic illness. Through interviews and ongoing analysis, the researcher develops categories that explain stages of decision making, influencing factors, and outcomes, resulting in a process-based theoretical model.
Grounded theory research primarily uses qualitative data. Common data sources include interview transcripts, field notes, observations, and documents. Audio and video recordings may also be used when they capture interactions or processes relevant to theory development.
Grounded theory is a qualitative research methodology used to generate theory from systematically collected and analyzed data. It emphasizes iterative analysis, constant comparison, and close alignment between data and emerging theoretical explanations.
Grounded theory is a qualitative research approach. While it may incorporate counts or comparisons to support analysis, its primary focus is on interpreting qualitative data to develop theory rather than measuring variables statistically.
