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Abstract

Classic Grounded Theory (classic GT), developed by Glaser and Strauss (1967), is a powerful qualitative methodology designed to generate theory grounded in data. Despite its enduring relevance across disciplines, classic GT is often perceived as too time-consuming and complex for doctoral candidates, particularly within the constraints of a 12-month research timeline. This manuscript challenges that perception and presents a practical framework demonstrating that classic GT can be both rigorous and feasible within a single year. Through a detailed synthesis of classic GT principles and processes, including constant comparative analysis, theoretical sampling, memoing, and the deliberate deferral of the literature review, this paper outlines a streamlined six-phase timeline designed to guide doctoral candidates efficiently from field entry to final write-up. Emphasis is placed on the early identification of the core category, which acts as a conceptual anchor, accelerating data collection and focused analysis. In this manuscript, we offer practical strategies to manage common challenges such as achieving theoretical saturation, maintaining theoretical sensitivity, and avoiding conceptual drift. It also underscores the critical role of supervision and peer support in sustaining momentum and methodological integrity throughout the research process. A streamlined timeline (Appendix B) illustrates how doctoral researchers can generate high-quality, substantive theory without compromising the emergent and discovery-driven foundations of classic GT. This work contributes to the growing body of literature advocating for classic GT’s wider adoption and demonstrates its compatibility with the demands of contemporary doctoral research. This work offers timely, actionable guidance for candidates, supervisors, and institutions seeking to support grounded theory research within structured academic timeframes.

Keywords

classic grounded theory doctoral research qualitative methodology theory generation research design theoretical sampling

Introduction

Classic grounded theory (classic GT), first introduced by Glaser and Strauss (1967), is a systematic methodology designed to generate theories grounded in real-world data. Unlike other qualitative approaches, classic GT emphasizes the discovery of theory through iterative data collection and analysis rather than testing pre-existing hypotheses (Glaser & Strauss, 1967). By discovering the main concern in the substantive area and conceptualising how it is addressed, classic GT offers a rich conceptual account of human behaviour and social processes (Holton & Walsh, 2017).

The core category, which explains how people in the substantive area address the main concern, acts as a conceptual compass for the study, guiding theoretical sampling and focused analysis (Glaser, 1992a). Early recognition of the core category enables researchers to streamline data collection through targeted theoretical sampling and avoid unnecessary diversions. The conceptual clarity it provides supports deeper theoretical development and accelerates saturation, making it feasible to complete a classic GT study within a 12-month timeframe.

Classic GT has found widespread application across a range of disciplines. In healthcare, it has been instrumental in exploring complex phenomena such as patient safety, clinical decision-making, and organizational change (Connor et al., 2023; Didier et al., 2023; Jørgensen et al., 2017; Leger & Phillips, 2021; Nathaniel, 2004; Vander Linden & Palmieri, 2021). In education, classic GT supports the development of theories related to teaching practices and learner experiences (Chametzky, 2015; Nathaniel, 2019). In business and management, it has been used to investigate leadership styles, consumer behaviour, and organizational dynamics (Goulding, 2002; Parry & Kempster, 2014; Wright et al., 2022). The methodology’s versatility lies in its ability to adapt to diverse contexts, offering deep insights into the behaviours and concerns of individuals and groups (Glaser, 2014a).

Despite its adaptability and rigor, classic GT is often misunderstood as a time-intensive methodology unsuitable for projects with strict timelines, such as doctoral studies. This perception stems from the iterative and non-linear nature of classic GT, which involves data collection, analysis, and refinement. Critics argue that achieving theoretical saturation, identifying the main concern, and developing a core category require extensive time and effort, making classic GT impractical for short-term research (Dahwa, 2024). However, these misconceptions overlook the inherent efficiency of classic GT when implemented with clear focus and structure. The simultaneous collection and analysis of data streamline the research process, and theoretical sampling ensures that data collection is targeted and purposeful. Additionally, deferring the literature review until after the development of a theory allows researchers to concentrate on emergent insights without being constrained by existing frameworks. With proper planning and adherence to methodological principles, classic GT can be successfully completed within a 12-month timeline.

The purpose of this manuscript is to challenge the prevailing misconception that classic GT is too time-consuming for doctoral research. It aims to provide a practical roadmap for conducting a classic GT study within 12 months, emphasizing its feasibility and relevance for PhD candidates. Through a structured timeline and actionable strategies, this manuscript aims to demystify classic GT and support researchers in harnessing its potential to generate meaningful and impactful theory. The significance of this work lies in its contribution to the broader conversation about research methodologies in doctoral programs. In addressing common barriers to adopting classic GT and positioning it as both accessible and efficient, this manuscript advocates for its broader application across disciplines. Furthermore, it provides practical guidance to researchers, offering a step-by-step framework that ensures rigor and depth without sacrificing timeliness. This work highlights the value of classic GT in fostering theoretical innovation and advancing knowledge in diverse fields.

Methodological Foundations of Classic GT

Grounded theory has evolved into several distinct methodological traditions, each reflecting different ontological and epistemological commitments. Longstanding tensions between classic and constructivist grounded theory stem from these differences: classic GT rests on a conceptualist ontology and emphasises theory emergence, whereas constructivist GT adopts an interpretivist epistemology oriented toward co-constructed meaning. Clarifying these distinctions is critical for situating the present manuscript, which is firmly positioned within the classic GT tradition and draws on its discovery-driven, conceptual orientation. The following section outlines the methodological foundations of classic GT and contrasts them with other grounded theory approaches to establish this positioning.

Classic GT was introduced by Glaser and Strauss in their seminal work The Discovery of Grounded Theory in 1967 (Glaser & Strauss, 1967). Developed as a response to the dominance of quantitative research, classic GT emphasized generating theory directly from data, rather than testing pre-existing hypotheses. This methodological innovation allowed researchers to systematically explore social phenomena by analysing patterns of behaviour and their underlying causes. The foundational principles of classic GT include constant comparative analysis, theoretical sampling, and the iterative development of theory. Over time, classic GT has evolved into a rigorous methodology applied across a wide range of disciplines, while retaining its original emphasis on theory generation grounded in empirical data.

Grounded theory has diverged into three primary strands, each distinguished by its philosophical orientation and methodological approach Table 1.

Table 1.

Three Primary Strands of Grounded Theory and Their Key Characteristics

Approach	Key characteristics	Strengths	Critiques
Classic GT	• Adheres to original principles by Glaser and Strauss (1967)• Emphasizes emergent theory without preconceived frameworks• Researchers maintain a neutral stance• Literature review is deferred until after theory emergence to minimize bias	• Allows organic concepts & patterns to arise• Grounded solely in data• Avoids external bias• Conceptual rather than descriptive, increasing generalizability	• Can be challenging for researchers new to unstructured approaches• Relies heavily on the researcher’s sensitivity to data
Straussian GT	• Introduced by Strauss (1987) (Corbin & Strauss, 1990)• Utilizes a structured framework with open, axial, and selective coding• Focuses on systematic rigor in coding	• Provides a detailed, step-by-step coding process• Helps novice researchers navigate data analysis	• Risk of “forcing” data into pre-existing categories• Critics argue it may diverge from classic GT’s emphasis on discovery
Constructivist GT	• Developed by Charmaz (2006). (Charmaz, 2013)• Takes an interpretivist approach• Emphasizes co-construction of meaning between researcher and participants• Reflexive and acknowledges researcher influence• Aligns with constructivist epistemology	• Encourages researcher reflexivity• Accounts for context and subjectivity• Suitable for interpretivist paradigms	• Less emphasis on neutrality• Risks over-reliance on researcher interpretation• May deviate from the traditional emphasis on discovery• Contextually bound

The distinctions among these strands reflect their alignment with varying philosophical paradigms, making classic GT adaptable to diverse research contexts while maintaining its core principles of theory development.

Classic GT is grounded in a post-positivist paradigm (Kenny & Fourie, 2015), emphasizing conceptual objectivity and the discovery of abstract processes and patterns of behaviour that transcend specific contexts. Its methodological flexibility allows researchers to address complex, real-world phenomena without being constrained by rigid frameworks or hypotheses. The integration of data collection, coding, and analysis fosters depth and theoretical robustness, qualities that align seamlessly with the objectives of doctoral research. While this methodological design enhances efficiency and rigor, it is not without challenges. Issues such as avoiding conceptual drift and sustaining theoretical sensitivity throughout the process are potential limitations of this approach. These considerations are addressed explicitly in the discussion to ensure transparency.

For PhD candidates, classic GT’s emphasis on emergent theory is particularly valuable. It enables researchers to uncover previously unarticulated phenomena, contributing novel insights to their fields. Furthermore, classic GT’s systematic approach ensures transparency and rigor, critical factors in meeting the expectations of doctoral committees and academic audiences. Deferring the literature review allows classic GT researchers to minimise bias and enhance originality and theoretical sensitivity (Nathaniel, 2022). These characteristics make classic GT not only an ideal methodology for PhD research but also a powerful tool for addressing interdisciplinary and applied research challenges.

Foundational Concepts of Classic GT

Classic GT is underpinned by several interrelated concepts that guide the research process and support the development of emergent, explanatory theory. Central to classic GT are the main concern and core category, which together provide conceptual structure to the evolving theory. The main concern, which emerges from systematic data analysis, provides the foundation for the core category, the conceptual anchor of the theory (Connor et al., 2024). The core category conceptualises how people in the substantive area address their main concern, integrating all other categories to provide coherence and depth to the resulting theory (Glaser, 1992a).

Theory generation in classic GT is driven by constant comparative analysis (CCA), a recursive process of comparing incidents, codes, and categories across all data sources. This ensures that the theory is continually refined through emerging insights rather than imposed structures (Glaser, 1992b). As researchers progress through data collection and analysis, theoretical sensitivity becomes critical (Glaser, 1978). This capacity enables researchers to identify and interpret relevant patterns within the data while remaining reflexive, perceptive, and attuned to what matters in the data without being constrained by prior assumptions. Theoretical sensitivity ensures that the emerging theory reflects the nuanced realities of people within the substantive area (Glaser, 1978).

Memoing is another core element of classic GT, serving as a reflective and generative process for documenting emergent ideas, theoretical relationships, and conceptual insights (Glaser, 2014b). Maintaining a detailed record of these reflections enables researchers to create a comprehensive audit trail that links raw data to theoretical outcomes. Memos also provide an invaluable resource during later stages of the study, supporting the integration and refinement of categories, as well as facilitating theoretical coding and the final write-up (Glaser, 2013).

Together, these concepts support methodological flexibility while ensuring analytical focus. Although classic GT is often seen as complex, its conceptual structure promotes both clarity and efficiency, especially when grounded in early identification of the main concern and core category, continuous comparative analysis, and rigorous memoing practices. While this section focuses on the central concepts that shape classic GT in practice, a complementary set of guiding principles, discussed later in the manuscript, further support methodological fidelity and ensure that classic GT remains both rigorous and true to its discovery-driven ethos.

A Structured 12-Month Approach to Classic GT

To guide researchers in conducting a classic GT study within a 12-month timeframe, it is essential to adopt a structured and disciplined approach while remaining flexible enough to allow for emergence of the theory. This feasibility was originally demonstrated by Glaser himself when he presented a twelve-month classic GT research timeline to doctoral students at Stanford University (Olson, 2006) [see Appendix A]. This example continues to serve as a foundational illustration of methodological efficiency in classic GT. The timeline outlined in Appendix B serves as a practical guide, offering a structured approach for navigating each phase of the research process - from initial fieldwork to the finalization of a substantive theory. Adhering to the iterative principles of classic GT results in the efficient integration of data collection, coding, and theory development, ensuring rigor without compromising timeliness.

The timeline we have included in Appendix B is divided into six key phases, each with specific activities and anticipated outcomes. In the early open coding phase, the researcher begins analysing participants’ data to discover the main concern operating within the substantive area. Although this phase is often positioned early in the process, the remaining phases unfold across the full twelve-month timeline outlined in Appendix B. The suggested timeframe is indicative rather than prescriptive, intended to demonstrate feasibility for doctoral candidates while maintaining methodological flexibility and fidelity. Importantly, theoretical saturation is never expected to be reached during open coding. The earliest saturation may occur is toward the end of selective coding, although in many studies it is not reached until the theory is being fully written up. Movement from open coding to selective coding is driven by the discovery of the main concern in the substantive area and the core category, whereas theoretical saturation guides the transition from selective to theoretical coding. In this way, analytic transitions are determined by conceptual emergence rather than by calendar deadlines, with the timeline serving only as a pragmatic tool to support disciplined yet flexible progress. The early emergence of the main concern and core category provides a conceptual anchor for the study and directs all subsequent sampling and analysis. It begins with foundational tasks such as entering the field and collecting and open coding initial data. In classic GT, an a priori research question is not required, as the main concern and core category are expected to emerge inductively from early data analysis (Glaser & Strauss, 1967). However, when institutional or supervisory requirements necessitate a research question at the outset, it should be framed broadly enough to allow emergent discovery, focusing on the main concern in the substantive area or on behavioural patterns evident in the data. It is important to emphasise that such framing differs from other qualitative methodologies: research questions in classic GT are provisional and open-ended, serving only as a starting point. The ultimate direction of the study is driven by emergent discovery, with theoretical saturation determining the development and refinement of the core category rather than pre-specified questions.

Importantly, the integration of classic GT’s foundational concepts, constant comparative analysis, memoing, and theoretical sensitivity, throughout the process ensures that the resulting theory remains firmly grounded in the data and reflects the realities of people within the substantive area. Theoretical coding deepens conceptual relationships between categories. Literature integration is intentionally delayed until sorting and partial theory write-up have begun, minimizing conceptual contamination and ensuring that the grounded theory remains genuinely emergent. At this stage, researchers can engage with recent and relevant studies to contextualize and enrich the emergent theory, rather than to predetermine categories. This positioning of the literature ensures that it enhances rather than shapes the grounded theory, aligning with Glaser’s later guidance (Glaser, 2011).

The timeline we provide enables researchers to maintain analytical momentum, stay focused on emergent theory development, and navigate the complexities of classic GT with methodological discipline. It demonstrates that completing a rigorous classic GT study within 12 months is both achievable and well-suited to the structure and expectations of doctoral research. Appendix B provides a detailed breakdown of each phase, outlining specific activities and anticipated outcomes.

Challenges and Strategies for Efficiency

Conducting a classic grounded theory (classic GT) study, especially within a constrained timeline, presents several challenges that researchers must navigate effectively. However, with thoughtful strategies and support, these challenges can be transformed into opportunities to enhance the quality and efficiency of the research process.

One common barrier is achieving theoretical saturation, which requires collecting and analysing sufficient data to fully develop and saturate all relevant categories (Vander Linden, 2017). This process can be time-intensive and lead to data overload if not carefully managed. To address this, researchers should adopt a targeted approach to theoretical sampling, focusing on collecting data that specifically addresses gaps in emerging categories (Vander Linden & Palmieri, 2021). Limiting the scope of the study and setting clear boundaries for data collection can also prevent unnecessary accumulation of extraneous data. Regularly reviewing memos and coding summaries ensures the researcher maintains a clear focus on what is needed to reach saturation (Glaser & Holton, 2005).

Another challenge is maintaining momentum while ensuring that the theory remains emergent and not “forced” (Kwok et al., 2012). The iterative nature of classic GT, where data collection and analysis occur concurrently, can sometimes lead to delays or indecision if the researcher becomes too fixated on fitting data into predefined frameworks. To avoid this, researchers should remain reflexive, allowing patterns and categories to emerge naturally from the data (Holton, 2018). Emphasizing constant comparative analysis keeps the focus on relationships and differences within the data, guiding the development of theory organically. Breaking down the research into manageable phases with clear milestones can also help maintain progress and reduce the risk of stagnation.

Supervisory support and peer feedback play crucial roles in overcoming these challenges. Supervisors with experience in classic GT can provide invaluable guidance on methodological decisions, help troubleshoot analytical roadblocks, and ensure adherence to the principles of classic GT (Elliott & Higgins, 2012). Regular meetings with supervisors allow researchers to share insights, validate their emerging categories, and receive constructive critique (Glaser, 1963). Peer feedback, whether from colleagues or academic networks with experience using classic GT, adds another layer of refinement, offering fresh perspectives on the data and analysis (Wu & Schunn, 2021). Engaging with peers through writing groups or research presentations can also foster accountability and motivation, further sustaining momentum throughout the project (Skarupski & Foucher, 2018).

Through targeted theoretical sampling, reflexive analysis, and strong supervisory and peer support, researchers can navigate the complexities of classic GT with greater efficiency. These approaches not only address the inherent challenges of the methodology but also enhance the overall rigor and depth of the resulting theory.

Key Methods in Classic GT

Conducting a study using classic GT requires a systematic yet flexible approach that integrates data collection, analysis, and reflective practices (Simmons, 2022). Classic GT is designed to generate theories grounded in empirical data, making it particularly effective for exploring complex phenomena where little is known or where existing theories are inadequate (Glaser & Strauss, 1967).

Data Collection

Data collection in Classic GT begins with purposive sampling, a strategy that involves selecting participants or data sources most likely to provide initial insights into the substantive area (Glaser, 2014a). As analysis progresses, theoretical sampling becomes the primary driver of participant selection. This means that subsequent participants are chosen because they can extend, refine, or saturate emerging categories, rather than to meet a predetermined sample size or demographic quota (Breckenridge & Jones, 2009; Glaser & Strauss, 2017). This iterative process continues until theoretical saturation is reached, where no new properties of the core category or its related categories emerge, and the relationships between them are fully developed. In contrast to research designs that rely on a priori sample sizes or experimental controls, Classic GT deliberately avoids such prescriptions, as controls would constrain the flexibility required for emergent discovery. Instead, transparency is ensured through detailed memoing and constant comparative analysis, which provide a rigorous audit trail of how sampling decisions unfold. These processes not only document the rationale for inclusion but also demonstrate how theory generation is grounded in data rather than predetermined variables.

Data Analysis

Constant comparative analysis (CCA) is the method of data analysis used across all forms of grounded theory (Glaser, 1965, 1978; Glaser & Strauss, 1967). CCA in classic GT is an iterative process conducted through two main stages of coding: substantive and theoretical (Holton, 2010) and the constant comparison of data to data, data to concepts, and concepts to concepts (Glaser, 1978, 1998). Substantive coding encompasses open and selective coding. Open coding involves line-by-line examination of the data to identify initial patterns, incidents, and conceptual indicators. Crucially, the purpose of open coding is not merely to generate a list of categories, but to identify the main concern in the substantive area, and the core category that continuously resolves it (Connor et al., 2024; Glaser, 1992a). This core category is the conceptual engine of a classic GT study and should emerge early, driving all subsequent analysis. Glaser emphasized that classic GT is fundamentally a study of the core category, which integrates all other categories and provides coherence to the developing theory (Glaser, 2013). Once the core category has emerged, selective coding follows. At this stage, data collection and analysis are deliberately focused on saturating the core category and its related properties, ensuring that categories are fully developed conceptually rather than remaining descriptive. Selective coding provides the analytic discipline that keeps the study centred on the processes that continuously resolve the main concern. The next stage, theoretical coding, involves integrating the saturated substantive categories through theoretical codes that explicate their conceptual relationships (Hernandez, 2009). This is the point at which the researcher moves beyond description toward conceptualisation and abstraction, weaving categories into a conceptual framework that reflects patterned relationships within the data.

Finally, theory generation occurs as the researcher draws together categories and codes into a coherent substantive theory. This process relies heavily on constant comparative analysis and the sorting of memos, which serve as a bridge across all analytic stages. Theory generation represents the culmination of the study, with the emerging theory refined iteratively until theoretical saturation is reached, often not until late in the write-up stage. Importantly, while the stages are described sequentially, classic GT remains an iterative process in which coding, sampling, and memoing continue to interact throughout the study (Glaser & Holton, 2005). Figure 1 illustrates how these analytic activities continually interact in a non-linear, emergent cycle, consistent with classic grounded theory, and create the conditions through which the main concern and core category become evident.

Figure 1.

The iterative, emergent analytic cycle of classic grounded theory

Memoing

Memoing is a cornerstone of classic GT, providing a mechanism for researchers to document their reflections, emerging ideas, and theoretical insights throughout the study (Elliott, 2014; Glaser, 2013). Memos serve as an intellectual workspace, capturing the researcher’s evolving understanding of the data, the emerging concepts, and their relationships (Glaser, 2014b). These records form a critical audit trail, linking raw data to the final theory and supporting the analytical process. During theoretical coding, memos become invaluable for organizing and synthesizing findings, ensuring that the final theory is coherent and comprehensive. Memoing also enhances theoretical sensitivity by encouraging researchers to think critically about the data and their interpretations.

Guiding Principles

A successful classic GT study relies on adherence to its guiding principles (Glaser, 2014a):

Iterative Process: Data collection, coding, and analysis occur concurrently, allowing the research to adapt to emergent insights (Glaser, 1992a).

Theoretical Sensitivity: Researchers must remain reflexive and open to the nuances of the data, ensuring that theoretical insights are grounded in participants’ realities (Glaser, 1978).

Emergence Over Forcing: Theory should emerge naturally from the data, avoiding the imposition of preconceived frameworks or concepts (Glaser, 1992b).

Focus on the Main Concern: The study’s aim is to identify and explain the main concern in the substantive area and the behaviours used to resolve it (Connor et al., 2024).

Concept and Theory Development: The outcome is a theory that explains the core category and the interrelationship among concepts that are grounded in the data. It is not about describing the data (Glaser, 1992b).

When these principles are consistently applied, researchers are better positioned to remain open to what is truly emerging in the data, build conceptual clarity over time, and generate theory that genuinely accounts for participants’ behaviour, not just describes it.

Discussion

Classic GT provides a robust methodological framework particularly well-suited for PhD students who aim to generate substantive theory from real-world data (Chametzky, 2020). Its flexibility, emphasis on emergent discovery, and systematic approach align seamlessly with the goals and constraints of doctoral research (Nathaniel, 2019).

One of the primary reasons classic GT is ideal for PhD students is its focus on theory development rather than hypothesis testing (Mohajan & Mohajan, 2022). Glaser and Strauss (1967) emphasized that GT allows researchers to explore uncharted phenomena, making it particularly useful for doctoral candidates investigating under-theorized areas or addressing gaps in existing literature. This aligns with the expectation that PhD research should contribute novel insights to the field.

Additionally, conducting data collection and analysis concurrently offers significant efficiency advantages. This process enables researchers to refine their focus as categories emerge, avoiding the time-consuming trap of collecting data that may later prove irrelevant. In particular, the early identification of the core category during open coding accelerates the entire research process. Once the researcher discovers the central behaviour that addresses the main concern, theoretical sampling becomes more focused, memoing becomes more targeted, and data analysis becomes conceptually integrated. As Glaser (2013) noted, the classic GT process is essentially about studying the core category, once that is in place, the rest of the theory can unfold efficiently. Glaser (1978, 1998) emphasised that the flexibility of classic GT enables researchers to follow emerging leads and adapt their analytic focus as categories develop, ensuring a rigorous yet efficient approach to data handling. Classic GT also accommodates the time constraints typical of PhD programs by allowing the literature review to be deferred until later in the research process. Glaser and Holton (2005) argued that starting with an exhaustive literature review risks “contaminating” the research with preconceived notions. Instead, classic GT encourages students to approach their data with an open mind, focusing first on emergent patterns before situating their findings within the broader academic context (Nathaniel, 2022). This approach not only saves time but also fosters originality and theoretical sensitivity. In line with methodological fidelity, the literature was engaged at the point of theoretical integration rather than at the outset of analysis. This approach allows updated and field-relevant studies to be incorporated in a way that strengthens, rather than constrains, the emergent theory. By deferring the review, the manuscript avoids forcing data into pre-existing frameworks while still situating findings within the broader scholarly conversation.

Methodological transparency in classic GT is achieved not through controls or predetermined samples, but through the explicit documentation of sampling and analytic decisions. The transition from purposive to theoretical sampling is guided by the developing theory, with memoing providing a detailed record of how participants or data sources were chosen to refine and saturate categories. Constant comparative analysis further ensures that each coding decision is anchored in the data, demonstrating how interpretation moves from raw incidents toward conceptual abstraction. In this way, transparency and rigor are maintained without reliance on conventional control mechanisms, aligning with the discovery-driven ethos of Classic GT.

For PhD students, who often navigate steep learning curves, classic GT’s structured processes, such as constant comparative analysis, memoing, and theoretical sampling, provide clear guidance while leaving room for creativity. Holton (2010) highlights that classic GT’s reliance on these systematic yet flexible tools ensure methodological rigor without overwhelming novice researchers. The emphasis on memoing, in particular, helps students develop theoretical sensitivity and maintain a detailed audit trail of their conceptual development, which is crucial for defending their methodology during academic assessments.

This manuscript also contributes practical procedural guidance for doctoral candidates by clarifying how to approach selective coding, theoretical coding, and theory generation within classic GT. By outlining how each stage builds on the previous one, selective coding to saturate the core category, theoretical coding to integrate categories conceptually, and theory generation to weave these insights into a coherent substantive theory, the paper provides a roadmap that demystifies later analytic stages. While these phases are presented sequentially for clarity, we emphasise that the process remains iterative, with memoing serving as a bridge across all stages. This transparency strengthens the manuscript’s value as a teaching and supervisory resource, supporting candidates to progress methodically while remaining true to the discovery-driven ethos of classic GT.

Classic GT equips PhD students with transferable research skills. Coding and analysing participants’ data to discover the main concern in the substantive area, conceptualize core categories, and integrate findings into a cohesive theory are competencies that extend beyond the dissertation, preparing students for future independent research. Simmons (2022) underscores that classic GT’s emphasis on emergent discovery nurtures critical thinking and analytical depth, essential attributes for early-career researchers. Classic GT is renowned for its flexibility and robustness, attributes that make it uniquely suited to generating substantive theory grounded in real-world data (Nathaniel, 2019). Unlike methodologies constrained by predefined hypotheses or rigid frameworks, classic GT enables researchers to adapt their approach dynamically as new insights emerge. This flexibility is embedded in its foundational principles, such as theoretical sampling and constant comparative analysis, which guide the researcher to follow the data rather than a predetermined path. Focusing on the main concern in the substantive area and the behaviours participants’ use to address their concern, classic GT fosters the development of theories that are not only conceptually robust but also deeply rooted in the studied context (Connor et al., 2024). This adaptability ensures that the resulting theories are both meaningful and actionable, addressing complex phenomena in diverse disciplines.

Despite its methodological efficiencies, classic GT researchers, particularly doctoral students, may encounter institutional or disciplinary requirements more aligned with other qualitative traditions, such as phenomenology or thematic analysis. These expectations often include demands for pre-specified research questions, fixed sample sizes, or early comprehensive literature reviews, which conflict with the emergent, iterative, and discovery-oriented nature of classic GT. Imposing such requirements can compromise methodological integrity and dilute the strengths of the approach, including its ability to produce conceptually rich, data-driven theory. As Holton and Walsh (2017) caution, conflating classic GT with generic qualitative methods risks eroding the rigour and originality that the methodology is designed to support. Supervisors, reviewers, and institutions must recognise these distinctions to avoid inadvertently hindering the effectiveness and efficiency of classic GT research, particularly in the doctoral context.

A common misconception is that classic GT is incompatible with short-term research goals, such as those often encountered in doctoral studies (Elliott & Higgins, 2012). Critics argue that its iterative nature, which requires ongoing cycles of data collection and analysis, makes it overly time intensive. However, this view fails to consider the efficiency of classic GT’s processes. The simultaneous collection and analysis of data, combined with targeted theoretical sampling, eliminate the need for prolonged, linear research phases. Furthermore, deferring the literature review until after the emergence of a core category streamlines the early stages of research, allowing PhD candidates to focus on the data without being constrained by existing theoretical frameworks.

Far from being a barrier, this design can actually accelerate the research process (Holton & Walsh, 2017). The methodology ensures that every phase of the study directly contributes to theory development, minimizing redundancy and maximizing the relevance of collected data (Glaser, 2014a). When approached with a clear plan and a disciplined adherence to its principles, classic GT aligns well with the timeframes and deliverables expected in doctoral research. Through its inherent flexibility, classic GT enables researchers to efficiently generate high-quality theories, effectively debunking the myth that it is unsuitable for projects with limited timelines.

Several limitations of this approach warrant explicit acknowledgment. First, while classic GT avoids predetermined sample boundaries, doctoral candidates may face institutional or supervisory pressures to specify them, creating potential tension between methodological integrity and academic expectations. Second, the iterative nature of coding carries the risk of conceptual drift, particularly if emerging categories are not consistently anchored through memoing and constant comparative analysis. Finally, the method places intensive demands on maintaining theoretical sensitivity throughout the research process, requiring sustained reflexivity and supervisory support. Recognizing these constraints strengthens the transparency of the framework presented here and underscores the importance of methodological discipline in ensuring rigor.

Implications for Future Researchers

Classic GT offers significant potential for researchers across all career stages, particularly those working in complex, underexplored, or rapidly evolving fields. Its emphasis on emergent discovery, conceptual abstraction, and methodological flexibility makes it especially well-suited to generating grounded, actionable theory that informs both academic knowledge and real-world practice.

For doctoral candidates, classic GT provides a structured yet adaptable framework that fosters originality and critical thinking. The methodology’s emphasis on emergent discovery allows candidates to develop theories that are uniquely responsive to the realities of their research contexts. To maximize its potential, candidates should maintain a disciplined approach to iterative data collection and analysis, engage deeply with memoing to enhance theoretical sensitivity, and adopt theoretical sampling to ensure focused and efficient data collection. A clear timeline, supported by regular reflection and prioritization of core principles, can help candidates balance methodological rigor with the practical constraints of a doctoral program.

Supervisors play a crucial role in guiding students through the intricacies of classic GT. Their support is vital in helping candidates navigate challenges such as achieving theoretical saturation, maintaining momentum, and avoiding the imposition of preconceived ideas on the data. Supervisors should encourage reflexivity, provide constructive feedback during coding and memoing stages, and ensure that candidates remain aligned with the principles of classic GT. Establishing a collaborative relationship enables supervisors to support candidates in developing analytical confidence and producing high-quality theoretical contributions.

Beyond individual projects, classic GT offers unique opportunities to bridge research gaps in healthcare and other disciplines. In healthcare, where the complexity of human behaviours and systems often defies linear explanations, classic GT excels in uncovering the nuanced processes that underpin clinical decision-making, patient experiences, and organizational change. Its ability to identify the main concern of people within the research areas and the behaviours they employ to address these concerns enables researchers to develop theories that are both practically relevant and conceptually robust.

Moreover, the adaptability of classic GT makes it applicable across diverse fields, from education to business and beyond. Its focus on emergent discovery provides a powerful means of addressing interdisciplinary challenges, fostering innovation, and generating insights that transcend traditional academic boundaries. Embracing classic GT enables future researchers to make meaningful contributions that advance theoretical understanding and inform real-world practice.

Classic GT offers a methodologically sound and impactful approach for researchers at all levels. For doctoral candidates and their supervisors, it provides a pathway to originality and rigor, while for the broader research community, it represents a powerful tool for addressing complex, context-dependent phenomena in healthcare and beyond.

Conclusion

This manuscript has demonstrated the feasibility of completing a classic GT study within a 12-month timeframe, debunking the myth that its iterative and emergent nature renders it impractical for doctoral research. A structured yet flexible approach, one that integrates data collection and analysis, emphasizes theoretical sampling, and uses memoing as a reflective and integrative tool, allows researchers to navigate the complexities of classic GT efficiently while maintaining rigor. The deferment of the literature review until the core category emerges further streamlines the research process, allowing doctoral candidates to focus on generating theory that is deeply grounded in the data.

Classic GT stands out as an invaluable methodology for doctoral research. Its flexibility and systematic processes empower PhD candidates to explore complex, under-theorized phenomena, making significant contributions to their fields. The methodology’s emphasis on emergent theory ensures originality, while its practical tools, such as constant comparative analysis and memoing, guide candidates through a rigorous yet manageable research journey. Through this approach, classic GT cultivates critical thinking and theoretical sensitivity, equipping doctoral researchers to develop robust, actionable theories that address complex, real-world problems. Classic GT offers a powerful and efficient approach to theory development, making it not only feasible but also ideal for doctoral candidates seeking to produce impactful, high-quality research within constrained timelines. When anchored early around a core category that resolves the main concern, classic GT provides a clear, disciplined, and efficient pathway for doctoral candidates to generate high-impact theory.

Footnotes

Acknowledgements

The authors thank the broader classic grounded theory research community for continuing to promote rigorous theory development and supporting doctoral researchers. We also acknowledge the foundational contributions of Dr. Barney Glaser,whose original dissertation timeline,reproduced in Appendix A,continues to serve as a powerful example of methodological efficiency and inspiration for doctoral candidates pursuing classic grounded theory.

ORCID iD

Tracy Flenady

Ethical Considerations

As this manuscript is a methodological guide and does not report new empirical data,ethical approval.

Consent to Participate

Informed consent were not applicable.

Author Contributions

Tracy Flenady,Kara Vander Linden,Justine Connor : Conceptualization;Writing – Original Draft;Project Administration. Tracy Flenady,Kara Vander Linden,Justine Connor : Writing – Review & Editing. All authors read and approved the final manuscript.

Funding

The authors received no financial support for the research,authorship,and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research,authorship,and/or publication of this article.

Data Availability Statement

No data were generated or analysed during the preparation of this manuscript. *

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