Grounded theory (GT) is a well-known method for qualitative research. It is different because it focuses on building theories directly from carefully analyzed data (Glaser & Strauss, 1967). This method focuses on inductive reasoning, which lets researchers build conceptual frameworks from real-world evidence instead of testing hypotheses they already have. The core of GT lies in its rigorous coding process, a systematic approach to organizing data into meaningful categories and discerning relationships between these categories to construct robust theoretical insights.
The Coding Process: A Foundation for Theory Building
The coding process in GT unfolds through three iterative yet interconnected stages: open coding, axial coding, and selective coding (Corbin & Strauss, 2015). Open coding, the initial phase, involves dissecting raw data, which might include interview transcripts, field notes, or other textual materials, into discrete units of meaning. Researchers assign descriptive labels, or codes, to these units, capturing the essence of key concepts within the data. For example, in a study exploring patient resilience in the face of chronic illness, a researcher might encounter phrases such as “actively sought support from family” or “developed coping mechanisms for pain management.” These phrases might be coded as “social support utilization” and “pain management strategies,” respectively.
Axial coding assumes a central role after open coding. This stage focuses on reorganizing the initial codes by linking categories to subcategories, exploring the relationships between them, and identifying causal conditions, contexts, and consequences. Returning to the patient resilience example, the code “social support utilization” might be connected to broader themes like “emotional well-being” or “access to healthcare resources.” Axial coding helps to build a more nuanced understanding of how different concepts interact and influence one another.
The final coding stage, selective coding, aims to integrate the various categories and subcategories into a coherent theoretical narrative. This involves identifying a core category, a central theme that explains the phenomenon under study, and relating all other categories to this core category (Charmaz, 2006). In the resilience study, the core category might be “self-management strategies,” encompassing various aspects of how patients actively manage their condition and its impact on their lives.
Theoretical Sampling and Saturation: Guiding Data Collection
The process of coding in GT is closely connected to theoretical sampling, a way of gathering data that is based on new ideas rather than set criteria. As researchers analyze data and develop codes, they identify areas where further information is needed to refine their understanding. They then strategically collect additional data, seeking participants or situations that can shed light on these underdeveloped categories. This iterative process continues until theoretical saturation is achieved. The point at which new data no longer add new insights or significantly change the emerging theory is called theoretical saturation (Glaser & Strauss, 1967). In a study investigating teacher burnout, for example, initial coding might reveal variations in institutional support across different school types. Theoretical sampling would then guide the researcher to seek out teachers from a wider range of school settings to explore this variation further until saturation is reached.
Memo-Writing and Constant Comparison: Tools for Rigorous Analysis
Memo writing forms an integral part of the GT process. Throughout the coding and analysis phases, researchers document their analytical decisions, emerging hypotheses, reflections, and interpretations in the form of memos. These memos serve as a valuable audit trail, enhancing the transparency and rigor of the research process. They provide a record of the researcher’s thinking and the evolution of the theory.
Another hallmark of GT is the constant comparative method. This involves continuously comparing and contrasting data segments, codes, and categories to refine theoretical constructs. Researchers engage in a systematic process of comparing new data with existing codes and categories, identifying similarities, differences, and patterns. This process of constant comparison allows researchers to progressively refine their understanding of the phenomenon under study and develop more nuanced theoretical explanations (Birks & Mills, 2015). For instance, a researcher analyzing workplace conflict might compare different conflict incidents across various departments within an organization to identify recurring patterns in conflict resolution strategies.
Challenges and Criticisms: Navigating the Complexities of GT
While GT offers a powerful framework for generating theory from data, its inherent flexibility can also present challenges. Critics have pointed out that the reliance on researcher interpretation in the coding process can introduce subjective bias, particularly if the coding process lacks systematic validation (Bryant & Charmaz, 2007). The potential for subjective bias underscores the importance of rigorous memo writing, constant comparison, and peer review to enhance the credibility of GT findings. Also, reaching theoretical saturation, which is a necessary condition for robust GT, usually takes a lot of time and data, which might not be possible in studies with limited resources.
Applications and Adaptations: Extending the Reach of GT
GT has found application across a diverse range of disciplines, demonstrating its versatility as a research methodology. Over time, GT has also been adapted and refined to address specific research needs and philosophical perspectives. As an example, constructivist grounded theory recognizes that the researcher and the participants co-create meaning. It stresses how important context and subjectivity are in the research process (Charmaz, 2006). In healthcare research, GT has been instrumental in elucidating patient experiences of chronic illness, leading to the development of more patient-centered care models. Educational researchers have employed GT to explore barriers to student engagement, informing the design of targeted interventions to improve student outcomes. The adaptability of GT makes it a valuable tool for researchers seeking to understand complex social phenomena and develop theories grounded in real-world experiences.
References
Birks, M., & Mills, J. (2015). Grounded theory: A practical guide (2nd ed.). Sage.
Bryant, A., & Charmaz, K. (2007). The Sage handbook of grounded theory. Sage.
Charmaz, K. (2006). Constructing grounded theory: A practical guide through qualitative analysis. Sage.1
Corbin, J., & Strauss, A. (2015). Basics of qualitative research (4th ed.). Sage.
Glaser, B. G., & Strauss, A. L. (1967). The discovery of grounded theory: Strategies for qualitative research. Aldine.
