Grounded theory is a qualitative approach that focuses on developing theories that emerge directly from systematically collected and analyzed data. Its goal is to construct new theoretical frameworks that explain phenomena based on the lived experiences, behaviors, and processes of participants. For example, grounded theory can be used to develop a framework for understanding how individuals cope with major life transitions, such as career changes or moving. This approach is widely used in fields such as sociology, psychology, health care, and education.
Methods and methodologies
Iterative data collection – Grounded theory uses an iterative process for data collection, where insights from ongoing analysis inform subsequent data collection. This dynamic approach allows researchers to refine the questions and explore emerging concepts as they develop. For example, interviews may start broadly and evolve to focus on specific themes or gaps identified in previous cycles.
Data is collected using methods qualitative methods such as interviews, focus groups, or direct observations. Researchers document their ideas and thoughts through memos, which serve as an essential tool for tracking theoretical developments. Data collection continues until theoretical saturation is reached, meaning no new themes or concepts emerge. This process ensures that the resulting theory is well grounded in the data.
Open, axial and selective coding – Coding is the cornerstone of data analysis in grounded theory, involving three key phases to organize and interpret data:
Open coding: Data are broken down into smaller units, such as sentences or phrases, to identify initial concepts. For example, in a study of work adjustment, codes might include “team support,” “stress management,” and “training programs.”
Axial coding: Connections between initial codes are identified, organizing them into categories and subcategories. For example, “team support” can be linked to broader categories such as “collaborative culture” and “job satisfaction.”
Selective coding: A central category is identified to integrate and unify themes into a coherent theory that explains the phenomenon being studied.
Methodology:
Researchers segment data and assign codes during open coding, then organize and connect these codes during axial coding. The final step, selective coding, involves developing a central narrative or framework that ties all categories together. This process is iterative and reflexive, ensuring that theory remains deeply connected to the data.
Good practices
Take part in an iterative analysis:
Let them results of each stage of the analysis guide subsequent data collection and refine the direction of the research.
Maintain theoretical sensitivity:
Stay open to unexpected results and let data guide theory development rather than imposing pre-existing frameworks.
Use memos extensively:
Write detailed memos throughout the research process to document analytic ideas, coding decisions, and reflections on emerging themes.
Reaching theoretical saturation:
Continue data collection until no new themes or concepts are identified, ensuring that the theory is complete and well supported.
Collaborate with peers:
Participate in peer debriefing or collaborative coding to validate results, improve rigor, and minimize researcher bias.
Stay flexible:
Adapt questions, methods, and focus as new themes or directions emerge during the analysis process.
What to avoid
Premature theorizing:
Developing a theory too early in the research process without sufficient data undermines its credibility and validity.
Rigid frames:
Imposing pre-existing theories or frameworks contradicts the exploratory nature of grounded theory and limits innovation.
Overgeneralization:
Avoid drawing conclusions that go beyond the scope of the data, as grounded theory favors depth over breadth.
Ignore negative cases:
Failure to address data that contradicts emerging trends can lead to incomplete or biased theories.
Lack of transparency:
Failure to fully document the coding and analysis process reduces the credibility and reproducibility of the study.
Data overload:
Collecting excessive data without iterative analysis risks overwhelming the researcher and diluting the study's objective.
Conclusion
Grounded theory research design is a dynamic, iterative approach to developing theories that are deeply grounded in real-world data. Using methods such as iterative data collection and open, axial, and selective coding, researchers can create robust frameworks that explain complex phenomena. Adherence to best practices, including achieving theoretical saturation, maintaining flexibility, and transparently documenting the process, ensures the validity and trustworthiness of grounded theory studies. This approach makes valuable contributions to theoretical advances and practical applications across a variety of disciplines.
EN 
FR