example of grounded theory research proposal

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• Open access
• Published: 09 September 2011

How to do a grounded theory study: a worked example of a study of dental practices

• Alexandra Sbaraini 1 , 2 ,
• Stacy M Carter 1 ,
• R Wendell Evans 2 &
• Anthony Blinkhorn 1 , 2  

BMC Medical Research Methodology volume  11 , Article number:  128 ( 2011 ) Cite this article

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Qualitative methodologies are increasingly popular in medical research. Grounded theory is the methodology most-often cited by authors of qualitative studies in medicine, but it has been suggested that many 'grounded theory' studies are not concordant with the methodology. In this paper we provide a worked example of a grounded theory project. Our aim is to provide a model for practice, to connect medical researchers with a useful methodology, and to increase the quality of 'grounded theory' research published in the medical literature.

We documented a worked example of using grounded theory methodology in practice.

We describe our sampling, data collection, data analysis and interpretation. We explain how these steps were consistent with grounded theory methodology, and show how they related to one another. Grounded theory methodology assisted us to develop a detailed model of the process of adapting preventive protocols into dental practice, and to analyse variation in this process in different dental practices.

Conclusions

By employing grounded theory methodology rigorously, medical researchers can better design and justify their methods, and produce high-quality findings that will be more useful to patients, professionals and the research community.

Peer Review reports

Qualitative research is increasingly popular in health and medicine. In recent decades, qualitative researchers in health and medicine have founded specialist journals, such as Qualitative Health Research , established 1991, and specialist conferences such as the Qualitative Health Research conference of the International Institute for Qualitative Methodology, established 1994, and the Global Congress for Qualitative Health Research, established 2011 [ 1 – 3 ]. Journals such as the British Medical Journal have published series about qualitative methodology (1995 and 2008) [ 4 , 5 ]. Bodies overseeing human research ethics, such as the Canadian Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans, and the Australian National Statement on Ethical Conduct in Human Research [ 6 , 7 ], have included chapters or sections on the ethics of qualitative research. The increasing popularity of qualitative methodologies for medical research has led to an increasing awareness of formal qualitative methodologies. This is particularly so for grounded theory, one of the most-cited qualitative methodologies in medical research [[ 8 ], p47].

Grounded theory has a chequered history [ 9 ]. Many authors label their work 'grounded theory' but do not follow the basics of the methodology [ 10 , 11 ]. This may be in part because there are few practical examples of grounded theory in use in the literature. To address this problem, we will provide a brief outline of the history and diversity of grounded theory methodology, and a worked example of the methodology in practice. Our aim is to provide a model for practice, to connect medical researchers with a useful methodology, and to increase the quality of 'grounded theory' research published in the medical literature.

The history, diversity and basic components of 'grounded theory' methodology and method

Founded on the seminal 1967 book 'The Discovery of Grounded Theory' [ 12 ], the grounded theory tradition is now diverse and somewhat fractured, existing in four main types, with a fifth emerging. Types one and two are the work of the original authors: Barney Glaser's 'Classic Grounded Theory' [ 13 ] and Anselm Strauss and Juliet Corbin's 'Basics of Qualitative Research' [ 14 ]. Types three and four are Kathy Charmaz's 'Constructivist Grounded Theory' [ 15 ] and Adele Clarke's postmodern Situational Analysis [ 16 ]: Charmaz and Clarke were both students of Anselm Strauss. The fifth, emerging variant is 'Dimensional Analysis' [ 17 ] which is being developed from the work of Leonard Schaztman, who was a colleague of Strauss and Glaser in the 1960s and 1970s.

There has been some discussion in the literature about what characteristics a grounded theory study must have to be legitimately referred to as 'grounded theory' [ 18 ]. The fundamental components of a grounded theory study are set out in Table 1 . These components may appear in different combinations in other qualitative studies; a grounded theory study should have all of these. As noted, there are few examples of 'how to do' grounded theory in the literature [ 18 , 19 ]. Those that do exist have focused on Strauss and Corbin's methods [ 20 – 25 ]. An exception is Charmaz's own description of her study of chronic illness [ 26 ]; we applied this same variant in our study. In the remainder of this paper, we will show how each of the characteristics of grounded theory methodology worked in our study of dental practices.

Study background

We used grounded theory methodology to investigate social processes in private dental practices in New South Wales (NSW), Australia. This grounded theory study builds on a previous Australian Randomized Controlled Trial (RCT) called the Monitor Dental Practice Program (MPP) [ 27 ]. We know that preventive techniques can arrest early tooth decay and thus reduce the need for fillings [ 28 – 32 ]. Unfortunately, most dentists worldwide who encounter early tooth decay continue to drill it out and fill the tooth [ 33 – 37 ]. The MPP tested whether dentists could increase their use of preventive techniques. In the intervention arm, dentists were provided with a set of evidence-based preventive protocols to apply [ 38 ]; control practices provided usual care. The MPP protocols used in the RCT guided dentists to systematically apply preventive techniques to prevent new tooth decay and to arrest early stages of tooth decay in their patients, therefore reducing the need for drilling and filling. The protocols focused on (1) primary prevention of new tooth decay (tooth brushing with high concentration fluoride toothpaste and dietary advice) and (2) intensive secondary prevention through professional treatment to arrest tooth decay progress (application of fluoride varnish, supervised monitoring of dental plaque control and clinical outcomes)[ 38 ].

As the RCT unfolded, it was discovered that practices in the intervention arm were not implementing the preventive protocols uniformly. Why had the outcomes of these systematically implemented protocols been so different? This question was the starting point for our grounded theory study. We aimed to understand how the protocols had been implemented, including the conditions and consequences of variation in the process. We hoped that such understanding would help us to see how the norms of Australian private dental practice as regards to tooth decay could be moved away from drilling and filling and towards evidence-based preventive care.

Designing this grounded theory study

Figure 1 illustrates the steps taken during the project that will be described below from points A to F.

Study design . file containing a figure illustrating the study design.

A. An open beginning and research questions

Grounded theory studies are generally focused on social processes or actions: they ask about what happens and how people interact . This shows the influence of symbolic interactionism, a social psychological approach focused on the meaning of human actions [ 39 ]. Grounded theory studies begin with open questions, and researchers presume that they may know little about the meanings that drive the actions of their participants. Accordingly, we sought to learn from participants how the MPP process worked and how they made sense of it. We wanted to answer a practical social problem: how do dentists persist in drilling and filling early stages of tooth decay, when they could be applying preventive care?

We asked research questions that were open, and focused on social processes. Our initial research questions were:

What was the process of implementing (or not-implementing) the protocols (from the perspective of dentists, practice staff, and patients)?

How did this process vary?

B. Ethics approval and ethical issues

In our experience, medical researchers are often concerned about the ethics oversight process for such a flexible, unpredictable study design. We managed this process as follows. Initial ethics approval was obtained from the Human Research Ethics Committee at the University of Sydney. In our application, we explained grounded theory procedures, in particular the fact that they evolve. In our initial application we provided a long list of possible recruitment strategies and interview questions, as suggested by Charmaz [ 15 ]. We indicated that we would make future applications to modify our protocols. We did this as the study progressed - detailed below. Each time we reminded the committee that our study design was intended to evolve with ongoing modifications. Each modification was approved without difficulty. As in any ethical study, we ensured that participation was voluntary, that participants could withdraw at any time, and that confidentiality was protected. All responses were anonymised before analysis, and we took particular care not to reveal potentially identifying details of places, practices or clinicians.

C. Initial, Purposive Sampling (before theoretical sampling was possible)

Grounded theory studies are characterised by theoretical sampling, but this requires some data to be collected and analysed. Sampling must thus begin purposively, as in any qualitative study. Participants in the previous MPP study provided our population [ 27 ]. The MPP included 22 private dental practices in NSW, randomly allocated to either the intervention or control group. With permission of the ethics committee; we sent letters to the participants in the MPP, inviting them to participate in a further qualitative study. From those who agreed, we used the quantitative data from the MPP to select an initial sample.

Then, we selected the practice in which the most dramatic results had been achieved in the MPP study (Dental Practice 1). This was a purposive sampling strategy, to give us the best possible access to the process of successfully implementing the protocols. We interviewed all consenting staff who had been involved in the MPP (one dentist, five dental assistants). We then recruited 12 patients who had been enrolled in the MPP, based on their clinically measured risk of developing tooth decay: we selected some patients whose risk status had gotten better, some whose risk had worsened and some whose risk had stayed the same. This purposive sample was designed to provide maximum variation in patients' adoption of preventive dental care.

Initial Interviews

One hour in-depth interviews were conducted. The researcher/interviewer (AS) travelled to a rural town in NSW where interviews took place. The initial 18 participants (one dentist, five dental assistants and 12 patients) from Dental Practice 1 were interviewed in places convenient to them such as the dental practice, community centres or the participant's home.

Two initial interview schedules were designed for each group of participants: 1) dentists and dental practice staff and 2) dental patients. Interviews were semi-structured and based loosely on the research questions. The initial questions for dentists and practice staff are in Additional file 1 . Interviews were digitally recorded and professionally transcribed. The research location was remote from the researcher's office, thus data collection was divided into two episodes to allow for intermittent data analysis. Dentist and practice staff interviews were done in one week. The researcher wrote memos throughout this week. The researcher then took a month for data analysis in which coding and memo-writing occurred. Then during a return visit, patient interviews were completed, again with memo-writing during the data-collection period.

D. Data Analysis

Coding and the constant comparative method.

Coding is essential to the development of a grounded theory [ 15 ]. According to Charmaz [[ 15 ], p46], 'coding is the pivotal link between collecting data and developing an emergent theory to explain these data. Through coding, you define what is happening in the data and begin to grapple with what it means'. Coding occurs in stages. In initial coding, the researcher generates as many ideas as possible inductively from early data. In focused coding, the researcher pursues a selected set of central codes throughout the entire dataset and the study. This requires decisions about which initial codes are most prevalent or important, and which contribute most to the analysis. In theoretical coding, the researcher refines the final categories in their theory and relates them to one another. Charmaz's method, like Glaser's method [ 13 ], captures actions or processes by using gerunds as codes (verbs ending in 'ing'); Charmaz also emphasises coding quickly, and keeping the codes as similar to the data as possible.

We developed our coding systems individually and through team meetings and discussions.

We have provided a worked example of coding in Table 2 . Gerunds emphasise actions and processes. Initial coding identifies many different processes. After the first few interviews, we had a large amount of data and many initial codes. This included a group of codes that captured how dentists sought out evidence when they were exposed to a complex clinical case, a new product or technique. Because this process seemed central to their practice, and because it was talked about often, we decided that seeking out evidence should become a focused code. By comparing codes against codes and data against data, we distinguished the category of "seeking out evidence" from other focused codes, such as "gathering and comparing peers' evidence to reach a conclusion", and we understood the relationships between them. Using this constant comparative method (see Table 1 ), we produced a theoretical code: "making sense of evidence and constructing knowledge". This code captured the social process that dentists went through when faced with new information or a practice challenge. This theoretical code will be the focus of a future paper.

Memo-writing

Throughout the study, we wrote extensive case-based memos and conceptual memos. After each interview, the interviewer/researcher (AS) wrote a case-based memo reflecting on what she learned from that interview. They contained the interviewer's impressions about the participants' experiences, and the interviewer's reactions; they were also used to systematically question some of our pre-existing ideas in relation to what had been said in the interview. Table 3 illustrates one of those memos. After a few interviews, the interviewer/researcher also began making and recording comparisons among these memos.

We also wrote conceptual memos about the initial codes and focused codes being developed, as described by Charmaz [ 15 ]. We used these memos to record our thinking about the meaning of codes and to record our thinking about how and when processes occurred, how they changed, and what their consequences were. In these memos, we made comparisons between data, cases and codes in order to find similarities and differences, and raised questions to be answered in continuing interviews. Table 4 illustrates a conceptual memo.

At the end of our data collection and analysis from Dental Practice 1, we had developed a tentative model of the process of implementing the protocols, from the perspective of dentists, dental practice staff and patients. This was expressed in both diagrams and memos, was built around a core set of focused codes, and illustrated relationships between them.

E. Theoretical sampling, ongoing data analysis and alteration of interview route

We have already described our initial purposive sampling. After our initial data collection and analysis, we used theoretical sampling (see Table 1 ) to determine who to sample next and what questions to ask during interviews. We submitted Ethics Modification applications for changes in our question routes, and had no difficulty with approval. We will describe how the interview questions for dentists and dental practice staff evolved, and how we selected new participants to allow development of our substantive theory. The patients' interview schedule and theoretical sampling followed similar procedures.

Evolution of theoretical sampling and interview questions

We now had a detailed provisional model of the successful process implemented in Dental Practice 1. Important core focused codes were identified, including practical/financial, historical and philosophical dimensions of the process. However, we did not yet understand how the process might vary or go wrong, as implementation in the first practice we studied had been described as seamless and beneficial for everyone. Because our aim was to understand the process of implementing the protocols, including the conditions and consequences of variation in the process, we needed to understand how implementation might fail. For this reason, we theoretically sampled participants from Dental Practice 2, where uptake of the MPP protocols had been very limited according to data from the RCT trial.

We also changed our interview questions based on the analysis we had already done (see Additional file 2 ). In our analysis of data from Dental Practice 1, we had learned that "effectiveness" of treatments and "evidence" both had a range of meanings. We also learned that new technologies - in particular digital x-rays and intra-oral cameras - had been unexpectedly important to the process of implementing the protocols. For this reason, we added new questions for the interviews in Dental Practice 2 to directly investigate "effectiveness", "evidence" and how dentists took up new technologies in their practice.

Then, in Dental Practice 2 we learned more about the barriers dentists and practice staff encountered during the process of implementing the MPP protocols. We confirmed and enriched our understanding of dentists' processes for adopting technology and producing knowledge, dealing with complex cases and we further clarified the concept of evidence. However there was a new, important, unexpected finding in Dental Practice 2. Dentists talked about "unreliable" patients - that is, patients who were too unreliable to have preventive dental care offered to them. This seemed to be a potentially important explanation for non-implementation of the protocols. We modified our interview schedule again to include questions about this concept (see Additional file 3 ) leading to another round of ethics approvals. We also returned to Practice 1 to ask participants about the idea of an "unreliable" patient.

Dentists' construction of the "unreliable" patient during interviews also prompted us to theoretically sample for "unreliable" and "reliable" patients in the following round of patients' interviews. The patient question route was also modified by the analysis of the dentists' and practice staff data. We wanted to compare dentists' perspectives with the perspectives of the patients themselves. Dentists were asked to select "reliable" and "unreliable" patients to be interviewed. Patients were asked questions about what kind of services dentists should provide and what patients valued when coming to the dentist. We found that these patients (10 reliable and 7 unreliable) talked in very similar ways about dental care. This finding suggested to us that some deeply-held assumptions within the dental profession may not be shared by dental patients.

At this point, we decided to theoretically sample dental practices from the non-intervention arm of the MPP study. This is an example of the 'openness' of a grounded theory study potentially subtly shifting the focus of the study. Our analysis had shifted our focus: rather than simply studying the process of implementing the evidence-based preventive protocols, we were studying the process of doing prevention in private dental practice. All participants seemed to be revealing deeply held perspectives shared in the dental profession, whether or not they were providing dental care as outlined in the MPP protocols. So, by sampling dentists from both intervention and control group from the previous MPP study, we aimed to confirm or disconfirm the broader reach of our emerging theory and to complete inductive development of key concepts. Theoretical sampling added 12 face to face interviews and 10 telephone interviews to the data. A total of 40 participants between the ages of 18 and 65 were recruited. Telephone interviews were of comparable length, content and quality to face to face interviews, as reported elsewhere in the literature [ 40 ].

F. Mapping concepts, theoretical memo writing and further refining of concepts

After theoretical sampling, we could begin coding theoretically. We fleshed out each major focused code, examining the situations in which they appeared, when they changed and the relationship among them. At time of writing, we have reached theoretical saturation (see Table 1 ). We have been able to determine this in several ways. As we have become increasingly certain about our central focused codes, we have re-examined the data to find all available insights regarding those codes. We have drawn diagrams and written memos. We have looked rigorously for events or accounts not explained by the emerging theory so as to develop it further to explain all of the data. Our theory, which is expressed as a set of concepts that are related to one another in a cohesive way, now accounts adequately for all the data we have collected. We have presented the developing theory to specialist dental audiences and to the participants, and have found that it was accepted by and resonated with these audiences.

We have used these procedures to construct a detailed, multi-faceted model of the process of incorporating prevention into private general dental practice. This model includes relationships among concepts, consequences of the process, and variations in the process. A concrete example of one of our final key concepts is the process of "adapting to" prevention. More commonly in the literature writers speak of adopting, implementing or translating evidence-based preventive protocols into practice. Through our analysis, we concluded that what was required was 'adapting to' those protocols in practice. Some dental practices underwent a slow process of adapting evidence-based guidance to their existing practice logistics. Successful adaptation was contingent upon whether (1) the dentist-in-charge brought the whole dental team together - including other dentists - and got everyone interested and actively participating during preventive activities; (2) whether the physical environment of the practice was re-organised around preventive activities, (3) whether the dental team was able to devise new and efficient routines to accommodate preventive activities, and (4) whether the fee schedule was amended to cover the delivery of preventive services, which hitherto was considered as "unproductive time".

Adaptation occurred over time and involved practical, historical and philosophical aspects of dental care. Participants transitioned from their initial state - selling restorative care - through an intermediary stage - learning by doing and educating patients about the importance of preventive care - and finally to a stage where they were offering patients more than just restorative care. These are examples of ways in which participants did not simply adopt protocols in a simple way, but needed to adapt the protocols and their own routines as they moved toward more preventive practice.

The quality of this grounded theory study

There are a number of important assurances of quality in keeping with grounded theory procedures and general principles of qualitative research. The following points describe what was crucial for this study to achieve quality.

During data collection

1. All interviews were digitally recorded, professionally transcribed in detail and the transcripts checked against the recordings.

2. We analysed the interview transcripts as soon as possible after each round of interviews in each dental practice sampled as shown on Figure 1 . This allowed the process of theoretical sampling to occur.

3. Writing case-based memos right after each interview while being in the field allowed the researcher/interviewer to capture initial ideas and make comparisons between participants' accounts. These memos assisted the researcher to make comparison among her reflections, which enriched data analysis and guided further data collection.

4. Having the opportunity to contact participants after interviews to clarify concepts and to interview some participants more than once contributed to the refinement of theoretical concepts, thus forming part of theoretical sampling.

5. The decision to include phone interviews due to participants' preference worked very well in this study. Phone interviews had similar length and depth compared to the face to face interviews, but allowed for a greater range of participation.

During data analysis

1. Detailed analysis records were kept; which made it possible to write this explanatory paper.

2. The use of the constant comparative method enabled the analysis to produce not just a description but a model, in which more abstract concepts were related and a social process was explained.

3. All researchers supported analysis activities; a regular meeting of the research team was convened to discuss and contextualize emerging interpretations, introducing a wide range of disciplinary perspectives.

Answering our research questions

We developed a detailed model of the process of adapting preventive protocols into dental practice, and analysed the variation in this process in different dental practices. Transferring evidence-based preventive protocols into these dental practices entailed a slow process of adapting the evidence to the existing practices logistics. Important practical, philosophical and historical elements as well as barriers and facilitators were present during a complex adaptation process. Time was needed to allow dentists and practice staff to go through this process of slowly adapting their practices to this new way of working. Patients also needed time to incorporate home care activities and more frequent visits to dentists into their daily routines. Despite being able to adapt or not, all dentists trusted the concrete clinical evidence that they have produced, that is, seeing results in their patients mouths made them believe in a specific treatment approach.

Concluding remarks

This paper provides a detailed explanation of how a study evolved using grounded theory methodology (GTM), one of the most commonly used methodologies in qualitative health and medical research [[ 8 ], p47]. In 2007, Bryant and Charmaz argued:

'Use of GTM, at least as much as any other research method, only develops with experience. Hence the failure of all those attempts to provide clear, mechanistic rules for GTM: there is no 'GTM for dummies'. GTM is based around heuristics and guidelines rather than rules and prescriptions. Moreover, researchers need to be familiar with GTM, in all its major forms, in order to be able to understand how they might adapt it in use or revise it into new forms and variations.' [[ 8 ], p17].

Our detailed explanation of our experience in this grounded theory study is intended to provide, vicariously, the kind of 'experience' that might help other qualitative researchers in medicine and health to apply and benefit from grounded theory methodology in their studies. We hope that our explanation will assist others to avoid using grounded theory as an 'approving bumper sticker' [ 10 ], and instead use it as a resource that can greatly improve the quality and outcome of a qualitative study.

Abbreviations

grounded theory methods

Monitor Dental Practice Program

New South Wales

Randomized Controlled Trial.

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Acknowledgements

We thank dentists, dental practice staff and patients for their invaluable contributions to the study. We thank Emeritus Professor Miles Little for his time and wise comments during the project.

The authors received financial support for the research from the following funding agencies: University of Sydney Postgraduate Award 2009; The Oral Health Foundation, University of Sydney; Dental Board New South Wales; Australian Dental Research Foundation; National Health and Medical Research Council Project Grant 632715.

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Alexandra Sbaraini, Stacy M Carter & Anthony Blinkhorn

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Alexandra Sbaraini, R Wendell Evans & Anthony Blinkhorn

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Authors' contributions

All authors have made substantial contributions to conception and design of this study. AS carried out data collection, analysis, and interpretation of data. SMC made substantial contribution during data collection, analysis and data interpretation. AS, SMC, RWE, and AB have been involved in drafting the manuscript and revising it critically for important intellectual content. All authors read and approved the final manuscript.

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Additional file 1: Initial interview schedule for dentists and dental practice staff. file containing initial interview schedule for dentists and dental practice staff. (DOC 30 KB)

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Additional file 2: Questions added to the initial interview schedule for dentists and dental practice staff. file containing questions added to the initial interview schedule (DOC 26 KB)

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Additional file 3: Questions added to the modified interview schedule for dentists and dental practice staff. file containing questions added to the modified interview schedule (DOC 26 KB)

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Sbaraini, A., Carter, S.M., Evans, R.W. et al. How to do a grounded theory study: a worked example of a study of dental practices. BMC Med Res Methodol 11 , 128 (2011). https://doi.org/10.1186/1471-2288-11-128

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10 Grounded Theory Examples (Qualitative Research Method)

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

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Grounded theory is a qualitative research method that involves the construction of theory from data rather than testing theories through data (Birks & Mills, 2015).

In other words, a grounded theory analysis doesn’t start with a hypothesis or theoretical framework, but instead generates a theory during the data analysis process .

This method has garnered a notable amount of attention since its inception in the 1960s by Barney Glaser and Anselm Strauss (Corbin & Strauss, 2015). 

Grounded Theory Definition and Overview

A central feature of grounded theory is the continuous interplay between data collection and analysis (Bringer, Johnston, & Brackenridge, 2016).

Grounded theorists start with the data, coding and considering each piece of collected information (for instance, behaviors collected during a psychological study).

As more information is collected, the researcher can reflect upon the data in an ongoing cycle where data informs an ever-growing and evolving theory (Mills, Bonner & Francis, 2017).

As such, the researcher isn’t tied to testing a hypothesis, but instead, can allow surprising and intriguing insights to emerge from the data itself.

Applications of grounded theory are widespread within the field of social sciences . The method has been utilized to provide insight into complex social phenomena such as nursing, education, and business management (Atkinson, 2015).

Grounded theory offers a sound methodology to unearth the complexities of social phenomena that aren’t well-understood in existing theories (McGhee, Marland & Atkinson, 2017).

While the methods of grounded theory can be labor-intensive and time-consuming, the rich, robust theories this approach produces make it a valuable tool in many researchers’ repertoires.

Real-Life Grounded Theory Examples

Title: A grounded theory analysis of older adults and information technology

Citation: Weatherall, J. W. A. (2000). A grounded theory analysis of older adults and information technology. Educational Gerontology , 26 (4), 371-386.

Description: This study employed a grounded theory approach to investigate older adults’ use of information technology (IT). Six participants from a senior senior were interviewed about their experiences and opinions regarding computer technology. Consistent with a grounded theory angle, there was no hypothesis to be tested. Rather, themes emerged out of the analysis process. From this, the findings revealed that the participants recognized the importance of IT in modern life, which motivated them to explore its potential. Positive attitudes towards IT were developed and reinforced through direct experience and personal ownership of technology.

Title: A taxonomy of dignity: a grounded theory study

Citation: Jacobson, N. (2009). A taxonomy of dignity: a grounded theory study. BMC International health and human rights , 9 (1), 1-9.

Description: This study aims to develop a taxonomy of dignity by letting the data create the taxonomic categories, rather than imposing the categories upon the analysis. The theory emerged from the textual and thematic analysis of 64 interviews conducted with individuals marginalized by health or social status , as well as those providing services to such populations and professionals working in health and human rights. This approach identified two main forms of dignity that emerged out of the data: “ human dignity ” and “social dignity”.

Title: A grounded theory of the development of noble youth purpose

Citation: Bronk, K. C. (2012). A grounded theory of the development of noble youth purpose. Journal of Adolescent Research , 27 (1), 78-109.

Description: This study explores the development of noble youth purpose over time using a grounded theory approach. Something notable about this study was that it returned to collect additional data two additional times, demonstrating how grounded theory can be an interactive process. The researchers conducted three waves of interviews with nine adolescents who demonstrated strong commitments to various noble purposes. The findings revealed that commitments grew slowly but steadily in response to positive feedback, with mentors and like-minded peers playing a crucial role in supporting noble purposes.

Title: A grounded theory of the flow experiences of Web users

Citation: Pace, S. (2004). A grounded theory of the flow experiences of Web users. International journal of human-computer studies , 60 (3), 327-363.

Description: This study attempted to understand the flow experiences of web users engaged in information-seeking activities, systematically gathering and analyzing data from semi-structured in-depth interviews with web users. By avoiding preconceptions and reviewing the literature only after the theory had emerged, the study aimed to develop a theory based on the data rather than testing preconceived ideas. The study identified key elements of flow experiences, such as the balance between challenges and skills, clear goals and feedback, concentration, a sense of control, a distorted sense of time, and the autotelic experience.

Title: Victimising of school bullying: a grounded theory

Citation: Thornberg, R., Halldin, K., Bolmsjö, N., & Petersson, A. (2013). Victimising of school bullying: A grounded theory. Research Papers in Education , 28 (3), 309-329.

Description: This study aimed to investigate the experiences of individuals who had been victims of school bullying and understand the effects of these experiences, using a grounded theory approach. Through iterative coding of interviews, the researchers identify themes from the data without a pre-conceived idea or hypothesis that they aim to test. The open-minded coding of the data led to the identification of a four-phase process in victimizing: initial attacks, double victimizing, bullying exit, and after-effects of bullying. The study highlighted the social processes involved in victimizing, including external victimizing through stigmatization and social exclusion, as well as internal victimizing through self-isolation, self-doubt, and lingering psychosocial issues.

Hypothetical Grounded Theory Examples

Suggested Title: “Understanding Interprofessional Collaboration in Emergency Medical Services”

Suggested Data Analysis Method: Coding and constant comparative analysis

How to Do It: This hypothetical study might begin with conducting in-depth interviews and field observations within several emergency medical teams to collect detailed narratives and behaviors. Multiple rounds of coding and categorizing would be carried out on this raw data, consistently comparing new information with existing categories. As the categories saturate, relationships among them would be identified, with these relationships forming the basis of a new theory bettering our understanding of collaboration in emergency settings. This iterative process of data collection, analysis, and theory development, continually refined based on fresh insights, upholds the essence of a grounded theory approach.

Suggested Title: “The Role of Social Media in Political Engagement Among Young Adults”

Suggested Data Analysis Method: Open, axial, and selective coding

Explanation: The study would start by collecting interaction data on various social media platforms, focusing on political discussions engaged in by young adults. Through open, axial, and selective coding, the data would be broken down, compared, and conceptualized. New insights and patterns would gradually form the basis of a theory explaining the role of social media in shaping political engagement, with continuous refinement informed by the gathered data. This process embodies the recursive essence of the grounded theory approach.

Suggested Title: “Transforming Workplace Cultures: An Exploration of Remote Work Trends”

Suggested Data Analysis Method: Constant comparative analysis

Explanation: The theoretical study could leverage survey data and in-depth interviews of employees and bosses engaging in remote work to understand the shifts in workplace culture. Coding and constant comparative analysis would enable the identification of core categories and relationships among them. Sustainability and resilience through remote ways of working would be emergent themes. This constant back-and-forth interplay between data collection, analysis, and theory formation aligns strongly with a grounded theory approach.

Suggested Title: “Persistence Amidst Challenges: A Grounded Theory Approach to Understanding Resilience in Urban Educators”

Suggested Data Analysis Method: Iterative Coding

How to Do It: This study would involve collecting data via interviews from educators in urban school systems. Through iterative coding, data would be constantly analyzed, compared, and categorized to derive meaningful theories about resilience. The researcher would constantly return to the data, refining the developing theory with every successive interaction. This procedure organically incorporates the grounded theory approach’s characteristic iterative nature.

Suggested Title: “Coping Strategies of Patients with Chronic Pain: A Grounded Theory Study”

Suggested Data Analysis Method: Line-by-line inductive coding

How to Do It: The study might initiate with in-depth interviews of patients who’ve experienced chronic pain. Line-by-line coding, followed by memoing, helps to immerse oneself in the data, utilizing a grounded theory approach to map out the relationships between categories and their properties. New rounds of interviews would supplement and refine the emergent theory further. The subsequent theory would then be a detailed, data-grounded exploration of how patients cope with chronic pain.

Grounded theory is an innovative way to gather qualitative data that can help introduce new thoughts, theories, and ideas into academic literature. While it has its strength in allowing the “data to do the talking”, it also has some key limitations – namely, often, it leads to results that have already been found in the academic literature. Studies that try to build upon current knowledge by testing new hypotheses are, in general, more laser-focused on ensuring we push current knowledge forward. Nevertheless, a grounded theory approach is very useful in many circumstances, revealing important new information that may not be generated through other approaches. So, overall, this methodology has great value for qualitative researchers, and can be extremely useful, especially when exploring specific case study projects . I also find it to synthesize well with action research projects .

Atkinson, P. (2015). Grounded theory and the constant comparative method: Valid qualitative research strategies for educators. Journal of Emerging Trends in Educational Research and Policy Studies, 6 (1), 83-86.

Birks, M., & Mills, J. (2015). Grounded theory: A practical guide . London: Sage.

Bringer, J. D., Johnston, L. H., & Brackenridge, C. H. (2016). Using computer-assisted qualitative data analysis software to develop a grounded theory project. Field Methods, 18 (3), 245-266.

Corbin, J., & Strauss, A. (2015). Basics of qualitative research: Techniques and procedures for developing grounded theory . Sage publications.

McGhee, G., Marland, G. R., & Atkinson, J. (2017). Grounded theory research: Literature reviewing and reflexivity. Journal of Advanced Nursing, 29 (3), 654-663.

Mills, J., Bonner, A., & Francis, K. (2017). Adopting a Constructivist Approach to Grounded Theory: Implications for Research Design. International Journal of Nursing Practice, 13 (2), 81-89.

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Grounded Theory In Qualitative Research: A Practical Guide

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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Grounded theory is a useful approach when you want to develop a new theory based on real-world data Instead of starting with a pre-existing theory, grounded theory lets the data guide the development of your theory.

What Is Grounded Theory?

Grounded theory is a qualitative method specifically designed to inductively generate theory from data. It was developed by Glaser and Strauss in 1967.

• Data shapes the theory:  Instead of trying to prove an existing theory, you let the data guide your findings.
• No guessing games:  You don’t start with assumptions or try to confirm your own biases.
• Data collection and analysis happen together:  You analyze information as you gather it, which helps you decide what data to collect next.

It is important to note that grounded theory is an inductive approach where a theory is developed from collected real-world data rather than trying to prove or disprove a hypothesis like in a deductive scientific approach

You gather information, look for patterns, and use those patterns to develop an explanation.

It is a way to understand why people do things and how those actions create patterns. Imagine you’re trying to figure out why your friends love a certain video game.

Instead of asking an adult, you observe your friends while they’re playing, listen to them talk about it, and maybe even play a little yourself. By studying their actions and words, you’re using grounded theory to build an understanding of their behavior.

This qualitative method of research focuses on real-life experiences and observations, letting theories emerge naturally from the data collected, like piecing together a puzzle without knowing the final image.

When should you use grounded theory? 

Grounded theory research is useful for beginning researchers, particularly graduate students, because it offers a clear and flexible framework for conducting a study on a new topic.

Grounded theory works best when existing theories are either insufficient or nonexistent for the topic at hand.

Since grounded theory is a continuously evolving process, researchers collect and analyze data until theoretical saturation is reached or no new insights can be gained.

What is the final product of a GT study?

The final product of a grounded theory (GT) study is an integrated and comprehensive grounded theory that explains a process or scheme associated with a phenomenon.

The quality of a GT study is judged on whether it produces this middle-range theory

Middle-range theories are sort of like explanations that focus on a specific part of society or a particular event. They don’t try to explain everything in the world. Instead, they zero in on things happening in certain groups, cultures, or situations.

Think of it like this: a grand theory is like trying to understand all of weather at once, but a middle-range theory is like focusing on how hurricanes form.

Here are a few examples of what middle-range theories might try to explain:

• How people deal with feeling anxious in social situations.
• How people act and interact at work.
• How teachers handle students who are misbehaving in class.

Core Components of Grounded Theory

This terminology reflects the iterative, inductive, and comparative nature of grounded theory, which distinguishes it from other research approaches.

• Theoretical Sampling: The researcher uses theoretical sampling to choose new participants or data sources based on the emerging findings of their study. The goal is to gather data that will help to further develop and refine the emerging categories and theoretical concepts.
• Theoretical Sensitivity:  Researchers need to be aware of their preconceptions going into a study and understand how those preconceptions could influence the research. However, it is not possible to completely separate a researcher’s history and experience from the construction of a theory.
• Coding: Coding is the process of analyzing  qualitative data  (usually text) by assigning labels (codes) to chunks of data that capture their essence or meaning. It allows you to condense, organize and interpret your data.
• Core Category:  The core category encapsulates and explains the grounded theory as a whole. Researchers identify a core category to focus on during the later stages of their research.
• Memos: Researchers use memos to record their thoughts and ideas about the data, explore relationships between codes and categories, and document the development of the emerging grounded theory. Memos support the development of theory by tracking emerging themes and patterns.
• Theoretical Saturation:  This term refers to the point in a grounded theory study when collecting additional data does not yield any new theoretical insights. The researcher continues the process of collecting and analyzing data until theoretical saturation is reached.
• Constant Comparative Analysis:  This method involves the systematic comparison of data points, codes, and categories as they emerge from the research process. Researchers use constant comparison to identify patterns and connections in their data.

Barney Glaser and Anselm Strauss first introduced grounded theory in 1967 in their book, The Discovery of Grounded Theory .

Their aim was to create a research method that prioritized real-world data to understand social behavior.

However, their approaches diverged over time, leading to two distinct versions: Glaserian and Straussian grounded theory.

The different versions of grounded theory diverge in their approaches to  coding , theory construction, and the use of literature.

All versions of grounded theory share the goal of generating a  middle-range theory  that explains a social process or phenomenon.

They also emphasize the importance of  theoretical sampling ,  constant comparative analysis , and  theoretical saturation  in developing a robust theory

Glaserian Grounded Theory

Glaserian grounded theory emphasizes the  emergence of theory from data  and discourages the use of pre-existing literature.

Glaser believed that adopting a specific philosophical or disciplinary perspective reduces the broader potential of grounded theory.

For Glaser, prior understandings should be based on the general problem area and reading very wide to alert or sensitize one to a wide range of possibilities.

It prioritizes  parsimony ,  scope , and  modifiability  in the resulting theory

Straussian Grounded Theory

Strauss and Corbin (1990) focused on developing the analytic techniques and providing guidance to novice researchers.

Straussian grounded theory utilizes a more structured approach to coding and analysis and acknowledges the role of the literature in shaping research.

It acknowledges the role of  deduction  and  validation  in addition to induction.

Strauss and Corbin also emphasize the use of  unstructured interview questions  to encourage participants to speak freely

Critics of this approach believe it produced a rigidity never intended for grounded theory.

Constructivist Grounded Theory

This version, primarily associated with Charmaz, recognizes that knowledge is situated, partial, provisional, and socially constructed. It emphasizes abstract and conceptual understandings rather than explanations.

Kathy Charmaz expanded on original versions of GT, emphasizing the researcher’s role in interpreting findings

Constructivist grounded theory acknowledges the researcher’s influence on the research process and the co-creation of knowledge with participants

Situational Analysis

Developed by Clarke, this version builds upon Straussian and Constructivist grounded theory and incorporates  postmodern ,  poststructuralist , and  posthumanist  perspectives.

Situational analysis incorporates postmodern perspectives and considers the role of nonhuman actors

It introduces the method of  mapping  to analyze complex situations and emphasizes both  human and nonhuman elements .

• Discover New Insights:  Grounded theory lets you uncover new theories based on what your data reveals, not just on pre-existing ideas.
• Data-Driven Results:  Your conclusions are firmly rooted in the data you’ve gathered, ensuring they reflect reality. This close relationship between data and findings is a key factor in establishing trustworthiness.
• Avoids Bias:  Because gathering data and analyzing it are closely intertwined, researchers are truly observing what emerges from data, and are less likely to let their preconceptions color the findings.
• Streamlined data gathering and analysis:  Analyzing and collecting data go hand in hand. Data is collected, analyzed, and as you gain insight from analysis, you continue gathering more data.
• Synthesize Findings : By applying grounded theory to a qualitative metasynthesis , researchers can move beyond a simple aggregation of findings and generate a higher-level understanding of the phenomena being studied.

Limitations

• Time-Consuming:  Analyzing qualitative data can be like searching for a needle in a haystack; it requires careful examination and can be quite time-consuming, especially without software assistance6.
• Potential for Bias:  Despite safeguards, researchers may unintentionally influence their analysis due to personal experiences.
• Data Quality:  The success of grounded theory hinges on complete and accurate data; poor quality can lead to faulty conclusions.

Practical Steps

Grounded theory can be conducted by individual researchers or research teams. If working in a team, it’s important to communicate regularly and ensure everyone is using the same coding system.

Grounded theory research is typically an iterative process. This means that researchers may move back and forth between these steps as they collect and analyze data.

Instead of doing everything in order, you repeat the steps over and over.

This cycle keeps going, which is why grounded theory is called a circular process.

Continue to gather and analyze data until no new insights or properties related to your categories emerge. This saturation point signals that the theory is comprehensive and well-substantiated by the data.

Theoretical sampling, collecting sufficient and rich data, and theoretical saturation help the grounded theorist to avoid a lack of “groundedness,” incomplete findings, and “premature closure.

1. Planning and Philosophical Considerations

Begin by considering the phenomenon you want to study and assess the current knowledge surrounding it.

However, refrain from detailing the specific aspects you seek to uncover about the phenomenon to prevent pre-existing assumptions from skewing the research.

• Discern a personal philosophical position.  Before beginning a research study, it is important to consider your philosophical stance and how you view the world, including the nature of reality and the relationship between the researcher and the participant. This will inform the methodological choices made throughout the study.
• Investigate methodological possibilities.  Explore different research methods that align with both the philosophical stance and research goals of the study.
• Plan the study.  Determine the research question, how to collect data, and from whom to collect data.
• Conduct a literature review.  The literature review is an ongoing process throughout the study. It is important to avoid duplicating existing research and to consider previous studies, concepts, and interpretations that relate to the emerging codes and categories in the developing grounded theory.

2. Recruit participants using theoretical sampling

Initially, select participants who are readily available ( convenience sampling ) or those recommended by existing participants ( snowball sampling ).

As the analysis progresses, transition to  theoretical sampling , involving the deliberate selection of participants and data sources to refine your emerging theory.

This method is used to refine and develop a grounded theory. The researcher uses theoretical sampling to choose new participants or data sources based on the emerging findings of their study.

This could mean recruiting participants who can shed light on gaps in your understanding uncovered during the initial data analysis.

Theoretical sampling guides further data collection by identifying participants or data sources that can provide insights into gaps in the emerging theory

The goal is to gather data that will help to further develop and refine the emerging categories and theoretical concepts.

Theoretical sampling starts early in a GT study and generally requires the researcher to make amendments to their ethics approvals to accommodate new participant groups.

3. Collect Data

The researcher might use interviews, focus groups, observations, or a combination of methods to collect qualitative data.

• Observations : Watching and recording phenomena as they occur. Can be participant (researcher actively involved) or non-participant (researcher tries not to influence behaviors), and covert (participants unaware) or overt (participants aware).
• Interviews : One-on-one conversations to understand participants’ experiences. Can be structured (predetermined questions), informal (casual conversations), or semi-structured (flexible structure to explore emerging issues).
• Focus groups : Dynamic discussions with 4-10 participants sharing characteristics, moderated by the researcher using a topic guide.
• Ethnography : Studying a group’s behaviors and social interactions in their environment through observations, field notes, and interviews. Researchers immerse themselves in the community or organization for an in-depth understanding.

4. Begin open coding as soon as data collection starts

Open coding   is the first stage of coding in grounded theory, where you carefully examine and label segments of your data to identify initial concepts and ideas.

This process involves scrutinizing the data and creating codes grounded in the data itself.

The initial codes stay close to the data, aiming to capture and summarize critically and analytically what is happening in the data

To begin open coding, read through your data, such as interview transcripts, to gain a comprehensive understanding of what is being conveyed.

As you encounter segments of data that represent a distinct idea, concept, or action, you assign a code to that segment. These codes act as descriptive labels summarizing the meaning of the data segment.

For instance, if you were analyzing interview data about experiences with a new medication, a segment of data might describe a participant’s difficulty sleeping after taking the medication. This segment could be labeled with the code “trouble sleeping”

Open coding is a crucial step in grounded theory because it allows you to break down the data into manageable units and begin to see patterns and themes emerge.

As you continue coding, you constantly compare different segments of data to refine your understanding of existing codes and identify new ones.

For instance, excerpts describing difficulties with sleep might be grouped under the code “trouble sleeping”.

This iterative process of comparing data and refining codes helps ensure the codes accurately reflect the data.

Open coding is about staying close to the data, using in vivo terms or gerunds to maintain a sense of action and process

5. Reflect on thoughts and contradictions by writing grounded theory memos during analysis

During open coding, it’s crucial to engage in memo writing. Memos serve as your “notes to self”, allowing you to reflect on the coding process, note emerging patterns, and ask analytical questions about the data.

Document your thoughts, questions, and insights in memos throughout the research process.

These memos serve multiple purposes: tracing your thought process, promoting reflexivity (self-reflection), facilitating collaboration if working in a team, and supporting theory development.

Early memos tend to be shorter and less conceptual, often serving as “preparatory” notes. Later memos become more analytical and conceptual as the research progresses.

Memo Writing

• Reflexivity and Recognizing Assumptions:  Researchers should acknowledge the influence of their own experiences and assumptions on the research process. Articulating these assumptions, perhaps through memos, can enhance the transparency and trustworthiness of the study.
• Write memos throughout the research process.  Memo writing should occur throughout the entire research process, beginning with initial coding.67 Memos help make sense of the data and transition between coding phases.8
• Ask analytic questions in early memos.  Memos should include questions, reflections, and notes to explore in subsequent data collection and analysis.8
• Refine memos throughout the process.  Early memos will be shorter and less conceptual, but will become longer and more developed in later stages of the research process.7 Later memos should begin to develop provisional categories.

6. Group codes into categories using axial coding

Axial coding is the process of identifying connections between codes, grouping them together into categories to reveal relationships within the data.

Axial coding seeks to find the axes that connect various codes together.

For example, in research on school bullying, focused codes such as “Doubting oneself, getting low self-confidence, starting to agree with bullies” and “Getting lower self-confidence; blaming oneself” could be grouped together into a broader category representing the impact of bullying on self-perception.

Similarly, codes such as “Being left by friends” and “Avoiding school; feeling lonely and isolated” could be grouped into a category related to the social consequences of bullying.

These categories then become part of the emerging grounded theory, explaining the multifaceted aspects of the phenomenon.

Qualitative data analysis software often represents these categories as nested codes, visually demonstrating the hierarchy and interconnectedness of the concepts.

This hierarchical structure helps researchers organize their data, identify patterns, and develop a more nuanced understanding of the relationships between different aspects of the phenomenon being studied.

This process of axial coding is crucial for moving beyond descriptive accounts of the data towards a more theoretically rich and explanatory grounded theory.

7. Define the core category using selective coding

During  selective coding , the final development stage of grounded theory analysis, a researcher focuses on developing a detailed and integrated theory by selecting a  core category  and connecting it to other categories developed during earlier coding stages.

The core category is the central concept that links together the various categories and subcategories identified in the data and forms the foundation of the emergent grounded theory.

This core category will encapsulate the main theme of your grounded theory, that encompasses and elucidates the overarching process or phenomenon under investigation.

This phase involves a concentrated effort to refine and integrate categories, ensuring they align with the core category and contribute to the overall explanatory power of the theory.

The theory should comprehensively describe the process or scheme related to the phenomenon being studied.

For example, in a study on school bullying, if the core category is “victimization journey,” the researcher would selectively code data related to different stages of this journey, the factors contributing to each stage, and the consequences of experiencing these stages.

This might involve analyzing how victims initially attribute blame, their coping mechanisms, and the long-term impact of bullying on their self-perception.

Continue collecting data and analyzing until you reach theoretical saturation

Selective coding focuses on developing and saturating this core category, leading to a cohesive and integrated theory.

Through selective coding, researchers aim to achieve theoretical saturation, meaning no new properties or insights emerge from further data analysis.

This signifies that the core category and its related categories are well-defined, and the connections between them are thoroughly explored.

This rigorous process strengthens the trustworthiness of the findings by ensuring the theory is comprehensive and grounded in a rich dataset.

It’s important to note that while a grounded theory seeks to provide a comprehensive explanation, it remains grounded in the data.

The theory’s scope is limited to the specific phenomenon and context studied, and the researcher acknowledges that new data or perspectives might lead to modifications or refinements of the theory

• Constant Comparative Analysis:  This method involves the systematic comparison of data points, codes, and categories as they emerge from the research process. Researchers use constant comparison to identify patterns and connections in their data. There are different methods for comparing excerpts from interviews, for example, a researcher can compare excerpts from the same person, or excerpts from different people. This process is ongoing and iterative, and it continues until the researcher has developed a comprehensive and well-supported grounded theory.
• Continue until reaching theoretical saturation : Continue to gather and analyze data until no new insights or properties related to your categories. This saturation point signals that the theory is comprehensive and well-substantiated by the data.

8. Theoretical coding and model development

Theoretical coding is a process in grounded theory where researchers use advanced abstractions, often from existing theories, to explain the relationships found in their data. 

Theoretical coding often occurs later in the research process and involves using existing theories to explain the connections between codes and categories.

This process helps to strengthen the explanatory power of the grounded theory. Theoretical coding should not be confused with simply describing the data; instead, it aims to explain the phenomenon being studied, distinguishing grounded theory from purely descriptive research.

Using the developed codes, categories, and core category, create a model illustrating the process or phenomenon.

Here is some advice for novice researchers on how to apply theoretical coding:

• Begin with data analysis:  Don’t start with a pre-determined theory. Instead, allow the theory to emerge from your data through careful analysis and coding.
• Use existing theories as a guide:  While the theory should primarily emerge from your data, you can use existing theories from any discipline to help explain the connections you are seeing between your categories. This demonstrates how your research builds on established knowledge.
• Use Glaser’s coding families:  Consider applying Glaser’s (1978) coding families in the later stages of analysis as a simple way to begin theoretical coding. Remember that your analysis should guide which theoretical codes are most appropriate.
• Keep it simple:  Theoretical coding doesn’t need to be overly complex.   Focus on finding an existing theory that effectively explains the relationships you have identified in your data.
• Be transparent:  Clearly articulate the existing theory you are using and how it explains the connections between your categories.
• Theoretical coding is an iterative process : Remain open to revising your chosen theoretical codes as your analysis deepens and your grounded theory evolves.

9. Write your grounded theory

Present your findings in a clear and accessible manner, ensuring the theory is rooted in the data and explains the relationships between the identified concepts and categories.

The end product of this process is a well-defined, integrated grounded theory that explains a process or scheme related to the phenomenon studied.

• Develop a dissemination plan : Determine how to share the research findings with others.
• Evaluate and implement : Reflect on the research process and quality of findings, then share findings with relevant audiences in service of making a difference in the world

Reading List

Grounded Theory Review : This is an international journal that publishes articles on grounded theory.

• Birks, M., & Mills, J. (2015).  Grounded theory: A practical guide . Sage.
• Corbin, J., & Strauss, A. (1990). Grounded theory research: Procedures, canons, and evaluative criteria. Qualitative Sociology, 13, 3-21.
• Charmaz, K. (2006). Constructing Grounded Theory: A practical guide through Qualitative Analysis. Thousand Oaks, California: Sage.
• Clarke, A. E. (2003). Situational analyses: Grounded theory mapping after the postmodern turn .  Symbolic interaction ,  26 (4), 553-576.
• Glaser, B. G. (1978).  Theoretical sensitivity . University of California.
• Glaser, B. G. (2005).  The grounded theory perspective III: Theoretical coding . Sociology Press.
• Glaser, B. G., & Holton, J. (2004, May). Remodeling grounded theory. In  Forum qualitative sozialforschung/forum: qualitative social research  (Vol. 5, No. 2).
• Charmaz, K. (2012). The power and potential of grounded theory.  Medical sociology online ,  6 (3), 2-15.
• Glaser, B., & Strauss, A. (1965). Awareness of dying. New Brunswick. NJ: Aldine. This was the first published grounded theory study
• Glaser, B., & Strauss, A. (2017).  Discovery of grounded theory: Strategies for qualitative research . Routledge.
• Pidgeon, N., & Henwood, K. (1997). Using grounded theory in psychological research. In N. Hayes (Ed.), Doing qualitative analysis in psychology Press/Erlbaum (UK) Taylor & Francis.

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Home » Grounded Theory – Methods, Examples and Guide

Grounded Theory – Methods, Examples and Guide

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Grounded Theory

Definition:

Grounded Theory is a qualitative research methodology that aims to generate theories based on data that are grounded in the empirical reality of the research context. The method involves a systematic process of data collection, coding, categorization, and analysis to identify patterns and relationships in the data.

The ultimate goal is to develop a theory that explains the phenomenon being studied, which is based on the data collected and analyzed rather than on preconceived notions or hypotheses. The resulting theory should be able to explain the phenomenon in a way that is consistent with the data and also accounts for variations and discrepancies in the data. Grounded Theory is widely used in sociology, psychology, management, and other social sciences to study a wide range of phenomena, such as organizational behavior, social interaction, and health care.

History of Grounded Theory

Grounded Theory was first introduced by sociologists Barney Glaser and Anselm Strauss in the 1960s as a response to the limitations of traditional positivist approaches to social research. The approach was initially developed to study dying patients and their families in hospitals, but it was soon applied to other areas of sociology and beyond.

Glaser and Strauss published their seminal book “The Discovery of Grounded Theory” in 1967, in which they presented their approach to developing theory from empirical data. They argued that existing social theories often did not account for the complexity and diversity of social phenomena, and that the development of theory should be grounded in empirical data.

Since then, Grounded Theory has become a widely used methodology in the social sciences, and has been applied to a wide range of topics, including healthcare, education, business, and psychology. The approach has also evolved over time, with variations such as constructivist grounded theory and feminist grounded theory being developed to address specific criticisms and limitations of the original approach.

Types of Grounded Theory

There are two main types of Grounded Theory: Classic Grounded Theory and Constructivist Grounded Theory.

Classic Grounded Theory

This approach is based on the work of Glaser and Strauss, and emphasizes the discovery of a theory that is grounded in data. The focus is on generating a theory that explains the phenomenon being studied, without being influenced by preconceived notions or existing theories. The process involves a continuous cycle of data collection, coding, and analysis, with the aim of developing categories and subcategories that are grounded in the data. The categories and subcategories are then compared and synthesized to generate a theory that explains the phenomenon.

Constructivist Grounded Theory

This approach is based on the work of Charmaz, and emphasizes the role of the researcher in the process of theory development. The focus is on understanding how individuals construct meaning and interpret their experiences, rather than on discovering an objective truth. The process involves a reflexive and iterative approach to data collection, coding, and analysis, with the aim of developing categories that are grounded in the data and the researcher’s interpretations of the data. The categories are then compared and synthesized to generate a theory that accounts for the multiple perspectives and interpretations of the phenomenon being studied.

Grounded Theory Conducting Guide

Here are some general guidelines for conducting a Grounded Theory study:

• Choose a research question: Start by selecting a research question that is open-ended and focuses on a specific social phenomenon or problem.
• Select participants and collect data: Identify a diverse group of participants who have experienced the phenomenon being studied. Use a variety of data collection methods such as interviews, observations, and document analysis to collect rich and diverse data.
• Analyze the data: Begin the process of analyzing the data using constant comparison. This involves comparing the data to each other and to existing categories and codes, in order to identify patterns and relationships. Use open coding to identify concepts and categories, and then use axial coding to organize them into a theoretical framework.
• Generate categories and codes: Generate categories and codes that describe the phenomenon being studied. Make sure that they are grounded in the data and that they accurately reflect the experiences of the participants.
• Refine and develop the theory: Use theoretical sampling to identify new data sources that are relevant to the developing theory. Use memoing to reflect on insights and ideas that emerge during the analysis process. Continue to refine and develop the theory until it provides a comprehensive explanation of the phenomenon.
• Validate the theory: Finally, seek to validate the theory by testing it against new data and seeking feedback from peers and other researchers. This process helps to refine and improve the theory, and to ensure that it is grounded in the data.
• Write up and disseminate the findings: Once the theory is fully developed and validated, write up the findings and disseminate them through academic publications and presentations. Make sure to acknowledge the contributions of the participants and to provide a detailed account of the research methods used.

Data Collection Methods

Grounded Theory Data Collection Methods are as follows:

• Interviews : One of the most common data collection methods in Grounded Theory is the use of in-depth interviews. Interviews allow researchers to gather rich and detailed data about the experiences, perspectives, and attitudes of participants. Interviews can be conducted one-on-one or in a group setting.
• Observation : Observation is another data collection method used in Grounded Theory. Researchers may observe participants in their natural settings, such as in a workplace or community setting. This method can provide insights into the social interactions and behaviors of participants.
• Document analysis: Grounded Theory researchers also use document analysis as a data collection method. This involves analyzing existing documents such as reports, policies, or historical records that are relevant to the phenomenon being studied.
• Focus groups : Focus groups involve bringing together a group of participants to discuss a specific topic or issue. This method can provide insights into group dynamics and social interactions.
• Fieldwork : Fieldwork involves immersing oneself in the research setting and participating in the activities of the participants. This method can provide an in-depth understanding of the culture and social dynamics of the research setting.
• Multimedia data: Grounded Theory researchers may also use multimedia data such as photographs, videos, or audio recordings to capture the experiences and perspectives of participants.

Data Analysis Methods

Grounded Theory Data Analysis Methods are as follows:

• Open coding: Open coding is the process of identifying concepts and categories in the data. Researchers use open coding to assign codes to different pieces of data, and to identify similarities and differences between them.
• Axial coding: Axial coding is the process of organizing the codes into broader categories and subcategories. Researchers use axial coding to develop a theoretical framework that explains the phenomenon being studied.
• Constant comparison: Grounded Theory involves a process of constant comparison, in which data is compared to each other and to existing categories and codes in order to identify patterns and relationships.
• Theoretical sampling: Theoretical sampling involves selecting new data sources based on the emerging theory. Researchers use theoretical sampling to collect data that will help refine and validate the theory.
• Memoing : Memoing involves writing down reflections, insights, and ideas as the analysis progresses. This helps researchers to organize their thoughts and develop a deeper understanding of the data.
• Peer debriefing: Peer debriefing involves seeking feedback from peers and other researchers on the developing theory. This process helps to validate the theory and ensure that it is grounded in the data.
• Member checking: Member checking involves sharing the emerging theory with the participants in the study and seeking their feedback. This process helps to ensure that the theory accurately reflects the experiences and perspectives of the participants.
• Triangulation: Triangulation involves using multiple sources of data to validate the emerging theory. Researchers may use different data collection methods, different data sources, or different analysts to ensure that the theory is grounded in the data.

Applications of Grounded Theory

Here are some of the key applications of Grounded Theory:

• Social sciences : Grounded Theory is widely used in social science research, particularly in fields such as sociology, psychology, and anthropology. It can be used to explore a wide range of social phenomena, such as social interactions, power dynamics, and cultural practices.
• Healthcare : Grounded Theory can be used in healthcare research to explore patient experiences, healthcare practices, and healthcare systems. It can provide insights into the factors that influence healthcare outcomes, and can inform the development of interventions and policies.
• Education : Grounded Theory can be used in education research to explore teaching and learning processes, student experiences, and educational policies. It can provide insights into the factors that influence educational outcomes, and can inform the development of educational interventions and policies.
• Business : Grounded Theory can be used in business research to explore organizational processes, management practices, and consumer behavior. It can provide insights into the factors that influence business outcomes, and can inform the development of business strategies and policies.
• Technology : Grounded Theory can be used in technology research to explore user experiences, technology adoption, and technology design. It can provide insights into the factors that influence technology outcomes, and can inform the development of technology interventions and policies.

Examples of Grounded Theory

Examples of Grounded Theory in different case studies are as follows:

• Glaser and Strauss (1965): This study, which is considered one of the foundational works of Grounded Theory, explored the experiences of dying patients in a hospital. The researchers used Grounded Theory to develop a theoretical framework that explained the social processes of dying, and that was grounded in the data.
• Charmaz (1983): This study explored the experiences of chronic illness among young adults. The researcher used Grounded Theory to develop a theoretical framework that explained how individuals with chronic illness managed their illness, and how their illness impacted their sense of self.
• Strauss and Corbin (1990): This study explored the experiences of individuals with chronic pain. The researchers used Grounded Theory to develop a theoretical framework that explained the different strategies that individuals used to manage their pain, and that was grounded in the data.
• Glaser and Strauss (1967): This study explored the experiences of individuals who were undergoing a process of becoming disabled. The researchers used Grounded Theory to develop a theoretical framework that explained the social processes of becoming disabled, and that was grounded in the data.
• Clarke (2005): This study explored the experiences of patients with cancer who were receiving chemotherapy. The researcher used Grounded Theory to develop a theoretical framework that explained the factors that influenced patient adherence to chemotherapy, and that was grounded in the data.

Grounded Theory Research Example

A Grounded Theory Research Example Would be:

Research question : What is the experience of first-generation college students in navigating the college admission process?

Data collection : The researcher conducted interviews with first-generation college students who had recently gone through the college admission process. The interviews were audio-recorded and transcribed verbatim.

Data analysis: The researcher used a constant comparative method to analyze the data. This involved coding the data, comparing codes, and constantly revising the codes to identify common themes and patterns. The researcher also used memoing, which involved writing notes and reflections on the data and analysis.

Findings : Through the analysis of the data, the researcher identified several themes related to the experience of first-generation college students in navigating the college admission process, such as feeling overwhelmed by the complexity of the process, lacking knowledge about the process, and facing financial barriers.

Theory development: Based on the findings, the researcher developed a theory about the experience of first-generation college students in navigating the college admission process. The theory suggested that first-generation college students faced unique challenges in the college admission process due to their lack of knowledge and resources, and that these challenges could be addressed through targeted support programs and resources.

In summary, grounded theory research involves collecting data, analyzing it through constant comparison and memoing, and developing a theory grounded in the data. The resulting theory can help to explain the phenomenon being studied and guide future research and interventions.

Purpose of Grounded Theory

The purpose of Grounded Theory is to develop a theoretical framework that explains a social phenomenon, process, or interaction. This theoretical framework is developed through a rigorous process of data collection, coding, and analysis, and is grounded in the data.

Grounded Theory aims to uncover the social processes and patterns that underlie social phenomena, and to develop a theoretical framework that explains these processes and patterns. It is a flexible method that can be used to explore a wide range of research questions and settings, and is particularly well-suited to exploring complex social phenomena that have not been well-studied.

The ultimate goal of Grounded Theory is to generate a theoretical framework that is grounded in the data, and that can be used to explain and predict social phenomena. This theoretical framework can then be used to inform policy and practice, and to guide future research in the field.

When to use Grounded Theory

Following are some situations in which Grounded Theory may be particularly useful:

• Exploring new areas of research: Grounded Theory is particularly useful when exploring new areas of research that have not been well-studied. By collecting and analyzing data, researchers can develop a theoretical framework that explains the social processes and patterns underlying the phenomenon of interest.
• Studying complex social phenomena: Grounded Theory is well-suited to exploring complex social phenomena that involve multiple social processes and interactions. By using an iterative process of data collection and analysis, researchers can develop a theoretical framework that explains the complexity of the social phenomenon.
• Generating hypotheses: Grounded Theory can be used to generate hypotheses about social processes and interactions that can be tested in future research. By developing a theoretical framework that explains a social phenomenon, researchers can identify areas for further research and hypothesis testing.
• Informing policy and practice : Grounded Theory can provide insights into the factors that influence social phenomena, and can inform policy and practice in a variety of fields. By developing a theoretical framework that explains a social phenomenon, researchers can identify areas for intervention and policy development.

Characteristics of Grounded Theory

Grounded Theory is a qualitative research method that is characterized by several key features, including:

• Emergence : Grounded Theory emphasizes the emergence of theoretical categories and concepts from the data, rather than preconceived theoretical ideas. This means that the researcher does not start with a preconceived theory or hypothesis, but instead allows the theory to emerge from the data.
• Iteration : Grounded Theory is an iterative process that involves constant comparison of data and analysis, with each round of data collection and analysis refining the theoretical framework.
• Inductive : Grounded Theory is an inductive method of analysis, which means that it derives meaning from the data. The researcher starts with the raw data and systematically codes and categorizes it to identify patterns and themes, and to develop a theoretical framework that explains these patterns.
• Reflexive : Grounded Theory requires the researcher to be reflexive and self-aware throughout the research process. The researcher’s personal biases and assumptions must be acknowledged and addressed in the analysis process.
• Holistic : Grounded Theory takes a holistic approach to data analysis, looking at the entire data set rather than focusing on individual data points. This allows the researcher to identify patterns and themes that may not be apparent when looking at individual data points.
• Contextual : Grounded Theory emphasizes the importance of understanding the context in which social phenomena occur. This means that the researcher must consider the social, cultural, and historical factors that may influence the phenomenon of interest.

Advantages of Grounded Theory

Advantages of Grounded Theory are as follows:

• Flexibility : Grounded Theory is a flexible method that can be used to explore a wide range of research questions and settings. It is particularly well-suited to exploring complex social phenomena that have not been well-studied.
• Validity : Grounded Theory aims to develop a theoretical framework that is grounded in the data, which enhances the validity and reliability of the research findings. The iterative process of data collection and analysis also helps to ensure that the research findings are reliable and robust.
• Originality : Grounded Theory can generate new and original insights into social phenomena, as it is not constrained by preconceived theoretical ideas or hypotheses. This allows researchers to explore new areas of research and generate new theoretical frameworks.
• Real-world relevance: Grounded Theory can inform policy and practice, as it provides insights into the factors that influence social phenomena. The theoretical frameworks developed through Grounded Theory can be used to inform policy development and intervention strategies.
• Ethical : Grounded Theory is an ethical research method, as it allows participants to have a voice in the research process. Participants’ perspectives are central to the data collection and analysis process, which ensures that their views are taken into account.
• Replication : Grounded Theory is a replicable method of research, as the theoretical frameworks developed through Grounded Theory can be tested and validated in future research.

Limitations of Grounded Theory

Limitations of Grounded Theory are as follows:

• Time-consuming: Grounded Theory can be a time-consuming method, as the iterative process of data collection and analysis requires significant time and effort. This can make it difficult to conduct research in a timely and cost-effective manner.
• Subjectivity : Grounded Theory is a subjective method, as the researcher’s personal biases and assumptions can influence the data analysis process. This can lead to potential issues with reliability and validity of the research findings.
• Generalizability : Grounded Theory is a context-specific method, which means that the theoretical frameworks developed through Grounded Theory may not be generalizable to other contexts or populations. This can limit the applicability of the research findings.
• Lack of structure : Grounded Theory is an exploratory method, which means that it lacks the structure of other research methods, such as surveys or experiments. This can make it difficult to compare findings across different studies.
• Data overload: Grounded Theory can generate a large amount of data, which can be overwhelming for researchers. This can make it difficult to manage and analyze the data effectively.
• Difficulty in publication: Grounded Theory can be challenging to publish in some academic journals, as some reviewers and editors may view it as less rigorous than other research methods.

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An Example of a Grounded Theory Research Proposal

Additional Citation Information

Additional notes, collections.

• Business Studies Staff Publications [47]

Grounded theory and the PhD – notes for novice researchers

Journal of Humanities and Applied Social Sciences

ISSN : 2632-279X

Article publication date: 26 October 2020

Issue publication date: 19 November 2020

This paper aims to consider the realities and problematics of applying a grounded theory (GT) approach to research, as a novice, within a mixed methods study during post graduate research. Its intention is to provide the novice user with a framework of considerations and greater awareness of the issues that GT can expose during research activity.

Design/methodology/approach

Using empirical evidence and a comparative approach, the paper compares the efficacy of both the classic Glaserian and Straussian models. It observes the effects of a positivist academic environment upon the choice of approach and its application. This study was specific to design education; however, its reliance upon a social science epistemology results in findings beneficial to research novices across broader disciplines.

GT presents the novice researcher with several potential pitfalls. Most problematic were the immutable, positivist institutional requirements, researcher a priori knowledge, the reliance upon literature for the research proposal and structure of the proposal itself. These include suspension of the notion that the purist use of either model can be applied in the current academic environment, the need for a close relationship with the data and toleration of a non-linear process with unexpected results.

Originality/value

The practicalities of GT research are often reflected upon by the academy, but use by novice researchers is little considered. The findings from this study provide a novel set of guidelines for use by those embarking on GT research and particularly where the requirements of formal education may cause a conflict.

• Grounded theory
• Social science research
• Novice researcher
• Glaserian grounded theory
• Post graduate research
• Straussian grounded theory
• Mixed methods research

Thurlow, L. (2020), "Grounded theory and the PhD – notes for novice researchers", Journal of Humanities and Applied Social Sciences , Vol. 2 No. 4, pp. 257-270. https://doi.org/10.1108/JHASS-05-2020-0079

Emerald Publishing Limited

Copyright © 2020, Lisa Thurlow.

Published in Journal of Humanities and Applied Social Sciences . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence maybe seen at http://creativecommons.org/licences/by/4.0/legalcode

Introduction

This paper considers the practicalities and problematics of applying grounded theory (GT) as a novice researcher during a mixed methods research project. Presented as a critical review of GT via a case study, it observes postgraduate (PhD) investigation into the nature of sketch inhibition among undergraduates within design higher education. The aim of the study was to build an effective theory of sketch inhibition as along with a set of pedagogic tools for its management in higher education – sketch inhibition is defined as a phenomenon whereby the suffer feels or demonstrates a reluctance or inability to engage with the mark-making aspect of design ideation and development ( Author, 2019 ). This type of problem-solving and conceptual activity is also evident in broader environments including the social sciences, sciences and business. Although often criticised for its lack of formal epistemology ( Doherty, 2015 ; Downs, 2017 ), the design disciplines have historically borrowed heavily from the social sciences, and an unintended consequence of this study offers methodological insight, not only to design research but also to other disciplines.

The following considers GT as a research approach and methodological framework (deleted phrase) alongside the personality of post graduate research. Its characteristics (both Glaserian and Straussian) are considered by the literature, together with a critical evaluation of their relationship. The benefits and problematics of GT applied within the mainly positivist environment of independent post graduate study are considered – from initial proposal, data gathering and analysis; thesis-writing; and identifying points along the research process where method particular attention or method slurring ( Baker et al. , 1992 ) were required. By way of conclusion, the findings from this have been developed into a set of considerations for prospective users of the method, intended as a decision-making tool for novices to GT research.

The personality of grounded theory research – comparison of the schools during project proposal development

Suddaby (2006) and Muratovski (2016) believe GT is the best used to observe a phenomenon where little extant theory is available: it “relies on the absence of an existing theory and its purpose is to set up a new theory,” ( Muratovski, 2016 , p. 99). Such lack of theory relating to the phenomenon of sketch inhibition drew the research towards an inductive process, and paradigmatically, GT. A constructivist approach was identified as the most appropriate – sketch inhibition being phenomenological and perceived at both the macro-level across discipline and by individual sufferers. GT, ( Glaser and Strauss, 1967 ; Strauss and Corbin, 1990 ), offering both method and result ( Bohm, 2004 ) in providing understanding ( Furniss, 2011 ) of sketch inhibition was the most appropriate approach for observing the phenomenon.

Based on this initial understanding of GT research, and its apparent suitability for the study, further review of evaluation method was conducted. Being data-driven, GT study demands identification of an area of interest or research question to be investigated but no explicit methodology at the outset – this was perplexing; the antithesis of the requirement for post graduate study. GT and phenomenology paradigms occupying a close relationship within the social sciences, method slurring is often an unavoidable consequence of its use ( Baker et al. , 1992 ). The close relationship between research activity and data and the recursive nature of their method were noted by Glaser and Strauss (1967 , p. 6): “most hypotheses and concepts not only come from the data, but are systematically worked out in relation to the data during the course of the research.” A notable trait of GT research is the requirement for analysis of data as it is collected, rather than as afterwards – this in contrast to previous activity, the body of data sought in their entirety before analysis begins. Identification and saturation of categories is data-driven ( Muratovski, 2016 ) using an emergent approach to classification: pre-defined categories unnecessary and potentially harmful to the process. Both Glaserian and Straussian approaches to GT use constant comparison – the evaluation of new data against existing categories and development of new categories should these emerge during the process. Locke (1996) observing this recursive process necessary for subsequent growth of the research: “the materializing theory drives ongoing data collection” ( Locke, 1996 , p. 240).

Both approaches use theoretical sampling – the identification of further sources of data to be evaluated, these reliant upon the development of theory emerging from existing data ( Suddaby, 2006 ). Via the process of constant comparison and purposive sampling, identification of clear categories and the relationships between them emerge from the data:

Categories or codes […] are the basic building blocks of a grounded theory. As they are developed, the same recursive, theory driven, comparative processes are used to surface the links and relationships among the categories to construct a complete theoretical framework ( Locke, 1996 , p. 241)

This would allow the data to drive the research and obviate the need for a preformulated methodology.

GT’s separation into two individual schools, to include Straussian in 1990 offered the research a choice: that of emergence versus forcing of data. The Glaserian model of emergence relied upon allowing the data to simply appear during analysis characterized by the separateness between researcher and the external world that incorporates their subject matter ( Howell, 2013 ). Locke (1996 , p. 241) considered the benefit of this, the Glaserian model favouring a passive, neutral approach, thereby avoiding contamination of pre-conception, providing a “one-way mirror” on the data: “categories emerge upon comparison and properties emerge upon more comparison. And that is all there is to it” ( Glaser, 1992 , p. 43). Locke (1996 , p. 239) suggested the Glaserian model enabled researchers “to use their intellectual imagination and creativity to develop theories related to the areas of enquiry” through the gathering of naturalistic data. Borgatti (2020) suggested theory developed from such activity (deleted phrase) aims to “focus on making implicit belief systems explicit”.

The Straussian model’s ( Strauss and Corbin, 1990 ) requirement of questioning the data to develop theory provides the researcher with a world view through a Constructivist lens, ( Guba and Lincoln, 2005 ). Strauss and Corbin (1990) suggested both induction, whereby data is used to build a picture of a reality, and deduction based upon hypothesis testing, (deleted phrase) were intrinsic to such research and the act of conceptualization by the researcher would by default, involve deduction, suggesting interaction between the two was necessary for theory-building. With a dearth of knowledge around sketch inhibition, the methodological purity of the Glaserian model was attractive, and further considered.

Coding, according to Walker and Myrick (2006 , p. 549), “transports researchers and their data from transcript to theory,” observing that both models use the same basic functions: “gather data, code, compare, categorize, theoretically sample, develop a core category, and generate a theory” (p. 550). Glaser’s (1978) approach involves two separate processes – substantive coding (fracturing the gathered data into categories based upon its properties), followed by selective, or theoretical coding; grouping into codes at the conceptual level and allowing the theory to develop as a result ( Walker and Myrick, 2006 ). Strauss and Corbin (1990) , coding is more complex, involving open, axial and selective coding, although individual stages are subject to blurring and can be used both sequentially and concurrently ( Walker and Myrick, 2006 ).

Initial, open coding allows reduction of data into concise manageable themes that accurately reflect the phenomenon. Axial coding allows for interpretation of categories to be identified. Muratovski (2016) refers to Leedy and Ormrod’s (2010) questioning (provoking) of data to facilitate this. They ask, “What are the conditions that have given rise to this process? What is the context in which this process is embedded? […] What are the consequences of these strategies?” ( Leedy and Ormrod, 2010 , p. 143).

The final, selective coding stage involves the bringing together of categories and connections, their development into a storyline to describe the mechanics of the issue. According to Muratovski (2016) , this is the point where theory can be developed. This more constructivist approach to coding was criticised by Glaser for being too aggressive, negatively affecting the research outcome. “Strauss’ sampling is controlled by the evolving relevant concepts, and relevance comes from testing out what is looked for, not what is emerging” ( Glaser, 1992 , p. 103). He believed this caused contamination of the analysis and favoured anticipation over emergence – that it could “force conceptual descriptions” as opposed to enabling the natural emergence of “grand theories,” ( Glaser, 1992 , p. 8). Borgatti (2020) , however, endorsed the Straussian model for consisting of “a set of steps whose careful execution is thought to guarantee a good theory as the outcome”. At this point, the purity of the Galserian model was still considered the most appropriate for the study, enabling a natural emergence of knowledge about sketch inhibition, and an almost effortless development of theory.

The contentious nature of the researcher’s a priori experience illustrated how the Glaserian approach could be problematic to the study. Glaser’s (1992 , p. 50) belief that previous knowledge was detrimental to effective theorizing – the researcher should, “just not know as he approaches the data,” was problematic, Suddaby (2006 , p. 634) suggesting the negation of researcher experience, agenda and the literature impossible: “the researcher is a blank sheet devoid of experience or knowledge” being unattainable within any research scenario.

The Straussian paradigm allowed for, and even endorsed for their insight, the benefit of the researcher’s a priori experience and exposure to the issues under scrutiny – this including engagement with relevant literature. Potter (2006) believed unintentional researcher influence unavoidable, subsequent knowledge considered to be of a Constructivist epistemology. Baker et al. (1992) believed that fully understanding the realities of social or psycho-social situations within a GT study could only happen through observation, listening, inferring from the literature and reflecting upon one’s own experiences – effectively, everything could be considered data within a GT study – this, via the lens of the researcher. This more structured approach was potentially more manageable than a classic Glaserian style and appeared to provide a robust and justifiable route towards growth of theory ( Wacker, 2008 ). Despite the unease between the two schools, Suddaby (2006 , p. 635) maintains that GT offers, “a practical middle ground between a theory-laden view of the world and an unfettered empiricism”.

Using grounded theory to research sketch inhibition

The essentialist concept of measurability being vital to success in education – the award of academic qualifications impossible without it – creates an immutable environment for within which post graduate researchers must function. Such need for measurability requires, by default, a set of criteria to measure against, the research proposal being central to this. With most influence on the study was the requirement for a formal, developed proposal: this was completely at odds with GT and effectively precluded its use purest form – methodological concessions were already being made.

A review of the literature was necessary to frame the scope of the research and with so little literature referring directly to sketch inhibition, a wider search allowed context to be established. This continued for over a year, almost exclusively, to build a research framework robust enough to carry the primary research through to completion of the study. If the Glaserian model were to be observed, the use of the literature review together with empirical data would deem the data already contaminated. The Straussian model, by default, had become the approach to the study.

According to the institutional requirement, the research proposal was submitted. The initial aim was:

An investigation into the reasons for design students and early career designers avoiding manual drawing tools during design development and the proposal of a pedagogical framework to address this.

The stages of design development where drawing is used and an investigation into its purpose within creative development.

Current practice of designers across a range of disciplines regarding their use of drawing techniques during design development.

Reasons for students choosing not to use drawing as a tool for development and presentation – explicit reasoning.

An investigation into the use of drawing as a tool for design development within HE – tactic reasoning.

The position and value of drawing within current frameworks for design education – tacit reasoning ( Author, 2015 ).

This proposal was problematic on several levels. As a statement of intent, it was far too complex. In contradiction to GT it made assumptions about the nature and extent of sketch inhibition and presupposed that it was indeed an issue. In addition to this, and in further contradiction to GT, the proposal required submission of a literature review and proposed methodology for data collection and analysis. The methodology was also submitted for ethical approval: pre-empting methods and samples was required despite there being little data upon which to base their need. Regardless of this, without such approval, the research could not have been conducted.

The formal review

At this point, a full literature review together with the developed methodology for data collection and analysis was required. The standard PhD model demanded the literature review prior to primary research. This again was in conflict with a GT approach. Based upon institutional requirements, the methodology presented for review was as follows:

Semi-structured interviews: Divided into two groups, with those who observed sketch inhibition, i.e. industry and education specialists; and with those who suffered sketch inhibition, i.e. undergraduates of design. The semi-structured approach was considered the most appropriate mode and accordingly, a standard operating procedure had to be developed and a set of questions designed.

Protocol analysis experiment and observation: to identify the symptoms of sketch inhibition among sufferers, a sample of inhibited students would complete an ideation task to be observed and coded. This was based upon similar methodologies of Suwa et al. (1998) , Bilda and Gero (2005) and Kim et al. (2010) identified from the literature used to investigate designers’ processes. It was intended that data would be analysed using a coding system based on precedents set by Suwa et al. (1998) and Tang et al. (2011) .

NASA TLX questionnaire: to be applied post-protocol analysis experiment to establish participants’ emotional response to the activity to provide data about the soft issues of sufferers.

Questionnaire and Delphi study: once a proposal for sketch inhibition management had been developed, this would be submitted for feedback to interview subjects from Group 1. In addition to this, the Delphi study ( Hsu and Sandford, 2007 ) was intended to produce a normalised set of moderated pedagogic tools for use by educators ( Author, 2016 ).

Getting it wrong

Based upon this proposal, the formal review was passed and progression to a PhD was approved. However, as a piece of GT research, the project was already failing: the methodology up to this point had been driven entirely by institutional requirements and not by the data. The remit of the study was the development of a theory of sketch inhibition and pedagogic framework; however, the proposed tools would not facilitate the constant comparative and purposive sampling essential to achieve this. In fact, the research process had developed into a series of box-ticking exercises to fulfil the requirements of the institution and understanding sketch inhibition had become subordinate to the research proposal. This was completely at odds with the aim and approach of the study and a watershed moment – the GT literature was revisited, the protocol experiment, NASA TLX questionnaire and questionnaire and Delphi study duly scrapped and restructuring of the project undertaken.

Getting it right

Based solely upon the emergence of issues from data, the interview method alone was kept, albeit in a form more reflective of true GT. The semi-structured approach was scrapped, instead, identifying issues to be discussed with subjects based, simply, upon the question, “what do I need to know about sketch inhibition?” This would be applied to the same two groups, i.e.; observers and sufferers of sketch inhibition. From this, further interviews were conducted, data coded immediately after each one, and emergent themes used to inform the next interview, i.e.; adding to the body of issues to be discussed.

The interviews provided both data for the study, and insight into the problematics of conducting GT research. Digression was a common issue, particularly among industry subjects and often difficult to manage: if everything was considered as data within a GT study, to what extent could digression be allowed in case it offered up some new and unexpected insight? This was difficult to resolve – it also resulted in lengthy transcriptions and data extraction that were the most time-consuming part of the study.

Lack of structure during the interviews with students was particularly problematic. It was assumed the unstructured approach favoured in GT studies would elicit breadth and depth of data, but this was not the case: students were simply unaware of what they didn’t know. Lack of maturity and experience may have affected the way subjects responded, and it was evident their understanding of sketching and the design process was somewhat poor. The frustration of trying to tease out responses from some subjects created a tendency to ask leading questions – this had to be carefully monitored to avoid corrupted the data. Data from educators was very high and proved most valuable to the study. Constant comparison and theoretical sampling led to an interview with one subject whose data approved pivotal to the whole study: without using GT, this subject not have been identified.

Always a conundrum for qualitative research, interview sample size was surprisingly simple to establish. Where the literature offered a plethora of notions about this, GT made it simpler: the interviews continued until no further new issues emerged from the data. Instead of an arbitrarily-set sample, constant comparison enabled identification of the point of saturation.

Data management

Depth and breadth of data during GT research is difficult to predict, Fassinger (2005) noting the complexity of data handling as potentially problematic. NVivo software was used throughout the study for storage, management, coding and analysis, thereby mitigating some of the complexity observed by Charmaz (2000) . NVivo’s graphic tools enabled visual macro-analysis of the data – this, essential for interrogating the quantity of data generated by the study. Charmaz (2000 , p. 520) suggested that such software had a tendency to “unintentionally foster an illusion that interpretive work can be reduced to a set of procedures”. This did not, however, appear problematic: emergent themes rather than software parameters were the driver of data handling.

Data analysis

The coding process, “identifying patterns and discovering theoretical properties in the data,” ( Bowen, 2008 , p. 144), adhered to the Straussian method, initially developing open coding. Individual nodes were created as they emerged from the data, observing Borgatti's (2020) “nouns and verbs of a conceptual world.” Boyatzis’ (1998 , p. 161) definition of a theme was observed as closely as possible; “a pattern in the information that at minimum describes and organises the possible observations and at maximum interprets aspects of the phenomenon” – (process illustrated in Figure 1 ).

A hierarchy of themes emerged as coding progressed. Meta-themes became structured into parent nodes, for example, “cognitive issues” and “definitions of sketching.” As new interview data were analysed, additional themes emerged and iterative (constant comparison) process of revisiting already coded data to code for new themes was conducted. And so, the number of parent nodes increased, as did child nodes within these. Braun and Clarke’s (2006) method was also observed: to reduce loss of context during coding some of the surrounding data was kept: whole sentences and sometimes paragraphs relevant to the theme were coded to maintain clarity of meaning. Multiple coding also formed part of the constant comparison process – coding data as many times as necessary to ensure it was coded into all nodes it related to. Throughout the coding process, axial coding, using mind mapping techniques, identified further issues within and between themes, according to Walker and Myrick (2006 , p. 553), to “understand categories in relationship to other categories and their subcategories” ( Figure 2 ).

Selective coding, “the process of selecting the central or core category, systematically relating it to other categories, validating those relationships and filling in categories that need further refinement and development,” ( Strauss and Corbin, 1990 , p. 116), began towards the end of the data gathering process. This underpinned the structure of findings and their presentation as a narrative of sketch inhibition ( Figure 3 ).

Theoretical sampling

Where theoretical sampling offered efficiency to the study, the lack of time to research new methodologies was problematic. During coding, the potential benefit of a learning style survey emerged. Responses from the interviews with sufferers of sketch inhibition suggested that there may be a link between inhibition and learning preference or learning difference. As such, a new data gathering methodology was applied. Similarly, the interview data suggested a possible issue among sufferers of inhibition and their employability – the benefit of a longitudinal study emerged.

The findings from the learning preference study were valuable to the study; however, the longitudinal study failed to gather any purposeful data: the GT approach of developing methodology according to emerging need was proving problematic. The fixed timeframe of the study prevented the development of an effective methodology and its application in an effective way. Instead, a rushed study with limited sample, based upon revisiting interview subjects via email was applied, very unsuccessfully.

Thesis structure

The thesis, in traditional PhD study, requires a linear set of content to be presented for examination. A product of the positivist tradition, such structure tends to favour the sciences. This is endorsed by the institution’s Code of Practice for Research Degree Students ( De Montfort University, 2018 , p. 52), which describes the structure of “a conventional dissertation”. Additionally, the mandatory training modules provided by the doctoral training programme, specifically, Structuring and Completing Your Thesis ( De Montfort University, 2020 ), further validate this, describing the required format for thesis presentation ( Figure 4 ).

Despite pouring through many theses during the course of the study in search of non-traditional formats, these requirements appear to have never been challenged. It was tempting to present the study in a non-linear format truly reflective of Grounded Theory, but too much was at stake and thus a version of the traditional structure was submitted.

Positivist issues for grounded theory research

A typical PhD taking between three and seven years to complete, timeframe is an immutable factor and certainly impinged upon this study. Without the limits of time, a truer reflection of the possibilities of GT research would have been achieved. The study would have continued as long as was necessary, the data and findings growing far beyond those presented in the thesis. Time restrictions were a constant issue – the joy of observing the emergence of a new issue to research, coupled with the lack of available time to investigate a potential methodology was problematic. This was particularly apparent during the learning style questionnaire and longitudinal study. Despite this, it was also an essential mechanism for the study – an ensuing deadline guaranteed to sharpen the mind. Although positivism could be criticised for placing restrictions upon the study it would have looked very different without it – and not necessarily for the better. PhD requirements actually lent a beneficial framework to the research, structure providing helpful boundaries to work within.

Henwood and Pidgeon (2003) believe, “The excitement and challenge of GT is finding a way out of its maze, but there is no one legitimate way out of the maze,” and GT research is certainly nothing if not complex. Fernández and Lehmann (2005) considered creativity an important part of GT research coupled with the need to conceptualise to develop theory from the data. They also believed the researcher should be able to tolerate confusion, and sporadic regression of the research process. These factors were certainly reflective of the study, challenging traditional linear approaches to previous projects.

GT appears to relate closely to Complexity Theory, ( Kuhn, 2008 ; Wang, 2010 ). Although in its infancy, this could provide a paradigm for the future of design education, and potentially benefit research into creative issues – both approaches able to accommodate complex, creative, non-linear systems and emergence of unexpected data. Despite the methodological and epistemological benefits that could accompany this, such a reality is probably distant, and the shoe-horning of non-positivist endeavours into positivist structure would have to continue.

Novice use of GT can be fraught with complexity and initially perceived as in-compatible with traditional post graduate research. However, a version of such an approach within a finite research structure is possible and very rewarding. The Glaserian model suffers most as a result of institutional requirements. Its purity is compromised from the outset by researcher prior knowledge and the use of literature during the proposal development stage (deleted phrase). The Straussian version is more accepting of the realities of constructivism – and more forgiving of research structure. Despite this, the research proposal and ethical requirements of contemporary research projects have a huge impact upon such a study.

The dual approach of constant comparison and theoretical sampling of both models are invaluable. They enable close observation of the study, both in terms of the data analysis and as a tool for the management of processes. They also support the researcher in dealing with novel and unexpected findings – the greatest joy of conducting research.

Based upon the observations of the case study, a set of considerations is offered for the novice researcher:

Understand what GT is about before you start. Make sure you fully understand its purpose and nature before you embark on research of this type: its remit is theory-building within an area of lack. It may be easier and less stressful to embrace other paradigms with more structured methodologies. The question is how hard do you want to make it for yourself?

It is almost impossible to apply GT in its purest form during post graduate research; identification of an issue to investigate is not enough for many institutions, (or funding bodies). They require certainty, a developed proposal with clear objectives and a methodology early on in the process, this being essential for ethical approval. This will require considerable reference to literature, understanding of GT and possible negotiation over the proposal before starting. At this point, theoretically, your research is no longer GT, but becomes a hybridized, institutionally acceptable form of the approach. Live with this, as there is little you can do.

Know the research will grow – it is not linear. GT research, being data driven, relies on the last piece of data to inform the next activity, (constant comparison and theoretical sampling). This implies a degree of flying by the seat of one’s pants, and allowing yourself to be taken wherever the data dictates. Where other types of research can be planned, GT is different and may lead to heated discussion with supervisors over matters of project management.

Allow the data to drive you at all times. This is almost a mantra when conducting such a project. The urge to lean towards a highly structured proposal is huge, especially during times of isolation and hopelessness that characterize PG research. Keep in mind that theory while covering unchartered territory is never going to be easy.

Being data driven, rather than relying on prescribed samples, data saturation can be easily identified using GT. Theoretical sampling is also efficient for focussing effort where it is required. The close relationship between research data and research activity allows this – and why coding data at the point of collection is so important.

Time will be problematic. Researching entire new methods of data gathering and applying these effectively may be problematic. It is somewhat of a Catch 22 for the researcher: such methods cannot be fully investigated and piloted prior to a GT study, as the data has not guided you there. However, during a GT study, getting to grips with unfamiliar and unexpected methodologies takes time. This can result potentially, in poor application and results of little benefit to the research.

It is not tidy. If you prefer a clear, highly managed approach to research, GT may not be your bag. The snowball effect of research and data growing in different and unexpected directions at the same time can be overwhelming. A pragmatic disposition is required in this situation – the ability to detach necessary to maintain control of the process.

Positivist factors should be embraced – timeframes, deadlines and structure are the antithesis of GT, but without them the novice handler will struggle to maintain focus and momentum.

GT research offers a steep learning curve and the balancing between immersion in the data and maintaining objectivity. If all these factors do not deter the novice researcher, such projects can be creative, exciting and hugely rewarding.

Emergence of initial themes from the data according to Strauss’ method of open coding

Model of axial coding using mind mapping techniques

Selective coding to build the narrative of sketch inhibition

Required thesis structure

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Corresponding author

About the author.

Lisa Thurlow is a Lecturer within the School of Design, researcher and writer. Her teaching crosses multiple disciplines including interior design, fashion and textiles, footwear, design management, research methodologies and design cultures. Her PhD (2019) used Grounded Theory to consider the cause, symptoms and management of sketch inhibition among under-graduate designers across multiple disciplines. Her interests include design cognition and visual learning pedagogies, developing tools for students with learning differences and international students for whom such approaches are beneficial. She runs workshops in design process sketching and inhibition management and is currently working on various related publications.

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An Introduction to Basics of Grounded Theory Methodology

Ravindran, Vinitha

Dean and Professor, College of Nursing, CMC, Vellore, Tamil Nadu, India

Address for correspondence: Dr. Vinitha Ravindran, College of Nursing, CMC, Vellore, Tamil Nadu, India. E-mail: [email protected]

This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-Share Alike 4.0 Unported, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Grounded theory is a qualitative research methodology often used in nursing research to develop theories about social processes or human behavior directly from the data collected. It was originally developed by sociologists Barney Glaser and Anselm Strauss in the 1960s. Grounded theory is used to explore and understand patient experiences and behaviors. By understanding the underlying social processes, grounded theory can help in designing effective nursing interventions. This articles outlines grounded theory methods in detail.

I NTRODUCTION

Using qualitative research design is increasingly becoming popular in health research in India. Although fieldwork, focus group and individual interviews are efficiently used for qualitative research, there is a lack of understanding about the different methodologies and the principles and procedures that are unique to each methodology within the qualitative research paradigm. To become an efficient qualitative researcher and to enjoy the maximum benefits of the qualitative research paradigm in solving health-related puzzles, it is imperative that nurses know about different methodologies and their steps. Grounded theory (GT) methodology is increasingly used to develop theories in nursing. It is a challenging yet attractive methodology, especially for nurse researchers as it enables the researcher to identify the social and psychological processes that individuals and families use in their everyday lives. Understanding these processes enables theory development followed by theoretically based clinical interventions.

G ROUNDED T HEORY

GT methodology explores the social processes that are present within human interactions and generates a theory about human behaviour that is grounded in the data (Speziale et al. , 2011). [ 1 , 2 ] GT methods are flexible guidelines that enable the researchers to conduct data collection and analysis. [ 3 ] GT is the possible outcome of using GT methods based on the methodology. [ 4 ] The stress given to theory development significantly differentiates GT from other qualitative methodologies. [ 1 , 2 ] A few distinguishing characteristics of GT are: (a) GT focuses on a process and trajectory that identifies phases and stages; [ 5 ] (b) it uses gerunds [ 3 , 6 ] to capture the actions rather than focusing on the person and (c) it has a core variable or category and basic social process/es that portrays the evolving theory and aims to develop the theory that is abstract. [ 6 ]

T HEORETICAL B ACKGROUND: S YMBOLIC I NTERACTIONISM

The classical GT evolved as a method based on symbolic interactionism theory. Instead of focusing on the individuals or their personal characteristics, symbolic interactionism focuses on the nature of interactions and the dynamic social activities that take place between persons. [ 7 ] Interaction implies that people are relating to each other, acting, perceiving, interpreting and acting again. Meanings mediate these interpretations. [ 8 ] Based on symbolic interactionism, GT focuses on the meanings given to the events, the actions and interactions that are attached to these events within the individual/groups’ social, cultural, historical or religious contexts. [ 1 ] This interpretive theoretical perspective assumes that people construct selves, society and reality through everyday interactions. [ 5 ] Other frameworks or theoretical backgrounds may be used as per the focus of the study.

D ATA C OLLECTION

The design of GT is based on exploration and not predetermined strategies from one step to another. [ 9 ] No specific procedures for data collection are elaborated in GT as have been done for data analysis. Since data are from where theories are constructed, grounded theorists need solid, rich data. [ 5 ] It is difficult to separate data collection and data analysis in GT as they are done simultaneously. The analysis of initial data directs further data collection. Grounded theorists shape their data collection based on their analysis so that their observations and enquiries become more focused and directed. [ 5 ] This strategy strengthens both the data and the concepts emerging from the data.

S AMPLE AND S AMPLING

In GT, there are no hard and fast rules regarding sample inclusion and exclusion. The number of participants interviewed is based on strengthening the evolving theory or core variable and not on the representation of the population being studied (Charmaz 2021). However, a sample of 25–30 interviews is typical in contrast to 8–10 participants in a phenomenology study. [ 10 ] GT follows purposive sampling as in most of the qualitative methodologies, in that participants are selected on the basis of their ability to help with solving the puzzle related to the research question. This means informants who can give rich and complete descriptions of the experiences that are related to the research problem being studied are approached for data collection. [ 9 ]

D ATA C OLLECTION M ETHODS

Although interviews are generally used as a method to collect rich data, field notes, case histories, material collected from diaries by participants, journals, interactional maps, historical documents or any type of documents and videotapes may also be used as methods of data collection/sources of data. [ 1 , 3 , 5 ] According to everything is data. Intense interviewing that attends to participants’ stories within their context is emphasised by Charmaz. [ 5 ] Pre- and post-interview field notes complement the interview data and are useful in analysis. Generally, a semi-structured or unstructured interview is suggested for GT studies. The initial interview questions should be broad, open-ended and non-judgemental. The interviews are unstructured, with an overview question and some follow-up probes. [ 11 ] An overview question such as ‘Can you tell me about your experiences related to your child’s burn injury?’ will assist in opening up the conversation. Probing questions can then follow as individuals share their narratives. The why and how questions help to bring out the implicit assumptions and meanings that the participants attach to their actions which is a vital part of developing GT.

In the GT method, data collection and data analysis are closely related and occur simultaneously and hence questions may be added or deleted. Probing questions may be used in different sequences as the theory emerges, thus allowing flexibility. Emerging concepts informs subsequent data collection. [ 5 ] With the GT approach, the researcher constantly compares the similarities and contrasts in the data and theoretically further extends the data collection as needed. After initial interviews, the subsequent interviews may be more structured to clarify emerging concepts and categories. This helps to saturate the categories, an essential step in GT. As data analysis progresses, the questions become more focused to elicit data relevant to a concept or category that is emerging from the data.

In GT, progressive sampling is also done based on the emerging categories. This step, identified as theoretical sampling, enables saturation of categories, a high level of conceptualisation of data which accounts for variations, and also fills gaps in emerging theory. [ 5 , 6 ] Theoretical sampling is a unique feature of GT. It illustrates the inductive-abductive logic of GT. [ 5 ] The inductive process involves moving back and forth between data and conceptualisation, and the abductive process involves attempting to check and confirm initial concepts that have evolved from data by further sampling and data collection. For example, in a study on parenting children with burns, blame and stigma emerged as categories in the initial analysis. To saturate these categories, second interviews with three families were conducted after 8 months–1 year based on their post-burn stage as well as the site of burn event to compare data from different points of time and place. All interviews have to be audiotaped as audio taping is essential to preserve all stories that are shared by the participants. The recordings need to be converted into text data by transcribing and if necessary translated. Transcription and translation are essential parts of any qualitative study.

D ATA A NALYSIS

Classic GT methodology purports rigorous analysis methods. The successive levels of data analysis include open coding, selective coding, identifying core categories and basic social processes and theory development. Writing memos is an essential process in GT that is ongoing as analysis continues.

Getting immersed by reading and re-reading the interview transcripts helps in understanding what the participants are actually saying/meaning. Coding involves a process of categorisation and sorting of data. Codes are short labels that are given to parts of all types of data such as interview transcripts or field notes. Codes are used to develop categories from the participants’ accounts or observational notes. Coding is a two-phase process of open and selective coding and is explained in depth. [ 12 , 13 ]

Open coding involves line-by-line coding of data for whatever theoretical possibilities can be discerned from the data. As Glazer suggests, questions such as ‘What are these data a study of?’ will allow the data to declare themselves. Line-by-line coding refers to naming each line in the transcribed text data. [ 12 ] Questions such as what is this person saying or doing or what is happening here [ 6 , 13 ] are part of the initial coding of data. Initial coding may even begin at the time of interview. Some phrases that the participants repeat may be included as codes. These are called in vivo codes. Codes can be in the form of gerunds. Labels such as ‘travelling to the hospital’, ‘doing wound dressing’ or ‘worry about the child’ are initial codes. Initial coding reveals gaps in the data and raises questions. These gaps and questions help in identifying what questions need to be asked in subsequent interviews.

Once line-by-line coding is done selective (focused) coding is done in consequent data as further data are analysed. Focused codes are more directed, selective and conceptual. Focused coding involves using the most frequent or most significant earlier codes to sift through large amounts of data. Focused codes are employed to raise the sorting of data to an analytical level. [ 11 ] Categories such as ‘managing the wound’, ‘protecting the child’ and ‘being blamed’ are examples that emerged for focused coding and categorising.

Explicates a further level of coding, which is theoretical coding. Theoretical codes are conceptual and ‘weave the fractured data together’ (p. 72). Theoretical codes relate concepts and categories and assist in developing core categories/variables. Theoretical codes help the researchers to rise above the details of the data to a synthesising and conceptual level. The process of coding is non-linear. What is implicit in earlier data may become explicit in subsequent data or field notes. New meanings and insights will lead back to earlier data. Therefore, constant comparison of data is an essential part of data analysis in GT. Comparisons of incidents and categories within and between the data texts of different participants identify uniformity or variations in participants’ accounts.

W RITING M EMOS

Memos are elaborations of thoughts on data, codes and categories that are written down. [ 12 ] As data are coded, the researcher writes down his/her thoughts on the codes and their relationships as they occur. [ 11 ] Memo-writing is an ongoing process and memos lead to abstraction and theorising for the write-up of ideas. [ 6 ] Initial or early memos help in exploring and filling out the initial qualitative codes. Advanced memos help in the emergence of categories and identify the beliefs and assumptions that support the categories. Memos also help in looking at the categories and the data within those categories from different vantage points. [ 5 ] Memos are a link between the codes and the consolidation of findings. [ 11 ]

I DENTIFYING B ASIC S OCIAL P ROCESSES

Identifying the core category/variable from the coding activity, memos and constant comparisons is the first step in moving towards theory development. The core category is the main theme that the researcher is looking for as he/she is analysing the data. The core category is the theme that best fits the data, is central and relates to as many other categories as possible, recurs frequently and relates meaningfully to other categories. Once the core issue is identified, the analysis is focused on the stages and phases of this core issue, the patterns of behaviour observed or narrated, the strategies used for managing the core issue in each of the phases and the connectedness of the strategies. This part of the analysis helps to delineate the basic social process.

T HEORY D EVELOPMENT

Theory development hinges on the vital concept of theoretical sensitivity that is stressed in GT. Theoretical sensitivity is ‘the ability of the researcher to recognise what is important in the data and give it meaning’. [ 12 ] Theoretical sensitivity is developed by being immersed in the data through data collection and analysis and also being well-grounded in the substantive literature. Theory consists of plausible relationships proposed amongst concepts and sets of concepts. [ 13 ] GT aims for substantive theory development. The mid-range theory that is developed is focused on the substantive area that is being studied. [ 5 ] The emergence of hypothetical relationships represents the emergence of theory. [ 12 ]

M ANAGING D ATA A NALYSIS

Qualitative data management can be done using software such as NVivo and Atlas-it. However, these sources are helpful only to organise data. The thinking and conceptualisation need to be done by the researcher.

R IGOR IN G ROUNDED T HEORY

Glaser’s (1967) ‘fit’, ‘work’, ‘relevance’ and ‘modifiability’ and Charmaz et al .’s (2006, 2014) ‘credibility’, ‘originality’, ‘resonance’ and ‘usefulness’ can be used for assessing rigor in GT. [ 3 , 5 , 6 , 12 ] Fit means that the categories of the theory fit the data and emerge from the data. As categories emerge from data, researchers seek to refit them to the data that they purport to indicate. The theory that emerges from the research should work. That is, it should provide predictions, explanations and interpretations of what was going on in the area under study. The third criterion, relevance denotes the core issue and the problem and processes that are accepted by participants as truly theirs. Modifiability is being open to new or differing data that may change the research question or take the study in a new direction and a changing theory. Charmaz’s credibility denotes the need for ensuring a relevant and sufficient amount of data for comparison and abstraction. Originality defines the new concepts and outlooks that emerge from taken-for-granted narratives or behaviours. Resonance is the ability to project what the participants truly experience and the insight that can be used by others. The researcher should avoid pitfalls such as under-analysis or superficial analysis of data that can lead to premature theory development. [ 12 ]

C ONCLUSION

Doing GT research is interesting. However, it can also be overwhelming for a novice researcher who may not have adequate guidance. Although following the steps of data collection and analysis is important, flexibility in the process is always welcomed.

Financial support and sponsorship

Conflicts of interest.

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Grounded Theory: Approach And Examples

Grounded theory is a qualitative research approach that attempts to uncover the meanings of people’s social actions, interactions and experiences….

Grounded theory is a qualitative research approach that attempts to uncover the meanings of people’s social actions, interactions and experiences. These explanations are called ‘grounded’ because they are grounded in the participants’ own explanations or interpretations.

Barney Glaser and Anselm Strauss originated this method in their 1967 book, The Discovery Of Grounded Theory . The grounded theory approach has been used by researchers in various disciplines, including sociology, anthropology, psychology, economics and public health.

Grounded theory qualitative research was considered path-breaking in many respects upon its arrival. The inductive method allowed the analysis of data during the collection process. It also shifted focus away from the existing practice of verification, which researchers felt didn’t always produce rigorous results.

  Let’s take a closer look at grounded theory research.

What Is Grounded Theory?

How to conduct grounded theory research, features of grounded theory, grounded theory example, advantages of grounded theory.

  Grounded theory is a qualitative method designed to help arrive at new theories and deductions. Researchers collect data through any means they prefer and then analyze the facts to arrive at concepts. Through a comparison of these concepts, they plan theories. They continue until they reach sample saturation, in which no new information upsets the theory they have formulated. Then they put forth their final theory.

  In grounded theory research, the framework description guides the researcher’s own interpretation of data. A data description is the researcher’s algorithm for collecting and organizing data while also constructing a conceptual model that can be tested against new observations.

  Grounded theory doesn’t assume that there’s a single meaning of an event, object or concept. In grounded theory, you interpret all data as information or materials that fit into categories your research team creates.

  Now that we’ve examined what is grounded theory, let’s inspect how it’s conducted. There are four steps involved in grounded theory research:

• STAGE 1: Concepts are derived from interviews, observation and reflection
• STAGE 2: The data is organized into categories that represent themes or subplots
• STAGE 3: As the categories develop, they are compared with one another and two or more competing theories are identified
• STAGE 4: The final step involves the construction of the research hypothesis statement or concept map

Grounded theory is a relatively recent addition to the tools at a researcher’s disposal. There are several methods of conducting grounded theory research. The following processes are common features:

  Theoretical Memoing

  compile findings.

Data collection in the grounded theory method can include both quantitative and qualitative methods.

By now, it’s clear that grounded theory is unlike other research techniques. Here are some of its salient features:

It Is Personal

It is flexible, it starts with data, data is continually assessed.

Grounded theory qualitative research is a dynamic and flexible approach to research that answers questions other formats can’t.

Grounded theory can be used in organizations to create a competitive advantage for a company. Here are some grounded theory examples:

• Grounded theory is used by marketing departments by letting marketing executives express their views on how to improve their product or service in a structured way
• Grounded theory is often used by the HR department. For instance, they might study why employees are frustrated by their work. Employees can explain what they feel is lacking. HR then gathers this data, examines the results to discover the root cause of their problems and presents solutions
• Grounded theory can help with design decisions, such as how to create a more appealing logo. To do this, the marketing department might interview consumers about their thoughts on their logo and what they like or dislike about it. They will then gather coded data that relates back to the interviews and use this for a second iteration

These are just some of the possible applications of grounded theory in a business setting.

Its flexibility allows its uses to be virtually endless. But there are still advantages and disadvantages that make the grounded theory more or less appropriate for a subject of study. Here are the advantages:

• Grounded theory isn’t concerned with whether or not something has been done before. Instead, grounded theory researchers are interested in what participants say about their experiences. These researchers are looking for meaning
• The grounded theory method allows researchers to use inductive reasoning, ensuring that the researcher views the participant’s perspectives rather than imposing their own ideas. This encourages objectivity and helps prevent preconceived notions from interfering with the process of data collection and analysis
• It allows for constant comparison of data to concepts, which refines the theory as the research proceeds. This is in contrast with methods that look to verify an existing hypothesis only
• Researchers may also choose to conduct experiments to provide support for their research hypotheses. Through an experiment, researchers can test ideas rigorously and provide evidence to support hypotheses and theory development
• It produces a clearer theoretical model that is not overly abstract. It also allows the researcher to see the connections between cases and have a better understanding of how each case fits in with others
• Researchers often produce more refined and detailed analyses of data than with other methods
• Because grounded theory emphasizes the interpretation of the data, it makes it easier for researchers to examine their own preconceived ideas about a topic and critically analyze them.

As with any method, there are some drawbacks too that researchers should consider. Here are a few:

• It doesn’t promote consensus because there are always competing views about the same phenomenon
• It may seem like an overly theoretical approach that produces results that are too open-ended. Grounded theory isn’t concerned with whether something is true/false or right/wrong
• Grounded theory requires a high level of skill and critical thinking from the researcher. They must have a level of objectivity in their approach, ask unbiased, open-minded questions and conduct interviews without being influenced by personal views or agenda.

While professionals may never have to conduct research like this themselves, an understanding of the kinds of analytical tools available can help when there are decisions to be made in the workplace. Harappa’s Thinking Critically course can help with just this. Analytical skills are some of the most sought-after soft skills in the professional world. The earlier managers can master these, the more value they’ll bring to the organization. With our transformative course and inspiring faculty, empower your teams with the ability to think through any problem, no matter how large.

Explore Harappa Diaries to learn more about topics such as Meaning Of Halo Effect , Different Brainstorming Methods , Operant Conditioning Theory of learning and How To Improve Analytical Skills to upgrade your knowledge and skills.

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How to do a grounded theory study: a worked example of a study of dental practices

Alexandra sbaraini.

1 Centre for Values, Ethics and the Law in Medicine, University of Sydney, Sydney, New South Wales, Australia

2 Population Oral Health, Faculty of Dentistry, University of Sydney, Sydney, New South Wales, Australia

Stacy M Carter

R wendell evans, anthony blinkhorn, associated data.

Qualitative methodologies are increasingly popular in medical research. Grounded theory is the methodology most-often cited by authors of qualitative studies in medicine, but it has been suggested that many 'grounded theory' studies are not concordant with the methodology. In this paper we provide a worked example of a grounded theory project. Our aim is to provide a model for practice, to connect medical researchers with a useful methodology, and to increase the quality of 'grounded theory' research published in the medical literature.

We documented a worked example of using grounded theory methodology in practice.

We describe our sampling, data collection, data analysis and interpretation. We explain how these steps were consistent with grounded theory methodology, and show how they related to one another. Grounded theory methodology assisted us to develop a detailed model of the process of adapting preventive protocols into dental practice, and to analyse variation in this process in different dental practices.

Conclusions

By employing grounded theory methodology rigorously, medical researchers can better design and justify their methods, and produce high-quality findings that will be more useful to patients, professionals and the research community.

Qualitative research is increasingly popular in health and medicine. In recent decades, qualitative researchers in health and medicine have founded specialist journals, such as Qualitative Health Research , established 1991, and specialist conferences such as the Qualitative Health Research conference of the International Institute for Qualitative Methodology, established 1994, and the Global Congress for Qualitative Health Research, established 2011 [ 1 - 3 ]. Journals such as the British Medical Journal have published series about qualitative methodology (1995 and 2008) [ 4 , 5 ]. Bodies overseeing human research ethics, such as the Canadian Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans, and the Australian National Statement on Ethical Conduct in Human Research [ 6 , 7 ], have included chapters or sections on the ethics of qualitative research. The increasing popularity of qualitative methodologies for medical research has led to an increasing awareness of formal qualitative methodologies. This is particularly so for grounded theory, one of the most-cited qualitative methodologies in medical research [[ 8 ], p47].

Grounded theory has a chequered history [ 9 ]. Many authors label their work 'grounded theory' but do not follow the basics of the methodology [ 10 , 11 ]. This may be in part because there are few practical examples of grounded theory in use in the literature. To address this problem, we will provide a brief outline of the history and diversity of grounded theory methodology, and a worked example of the methodology in practice. Our aim is to provide a model for practice, to connect medical researchers with a useful methodology, and to increase the quality of 'grounded theory' research published in the medical literature.

The history, diversity and basic components of 'grounded theory' methodology and method

Founded on the seminal 1967 book 'The Discovery of Grounded Theory' [ 12 ], the grounded theory tradition is now diverse and somewhat fractured, existing in four main types, with a fifth emerging. Types one and two are the work of the original authors: Barney Glaser's 'Classic Grounded Theory' [ 13 ] and Anselm Strauss and Juliet Corbin's 'Basics of Qualitative Research' [ 14 ]. Types three and four are Kathy Charmaz's 'Constructivist Grounded Theory' [ 15 ] and Adele Clarke's postmodern Situational Analysis [ 16 ]: Charmaz and Clarke were both students of Anselm Strauss. The fifth, emerging variant is 'Dimensional Analysis' [ 17 ] which is being developed from the work of Leonard Schaztman, who was a colleague of Strauss and Glaser in the 1960s and 1970s.

There has been some discussion in the literature about what characteristics a grounded theory study must have to be legitimately referred to as 'grounded theory' [ 18 ]. The fundamental components of a grounded theory study are set out in Table ​ Table1. 1 . These components may appear in different combinations in other qualitative studies; a grounded theory study should have all of these. As noted, there are few examples of 'how to do' grounded theory in the literature [ 18 , 19 ]. Those that do exist have focused on Strauss and Corbin's methods [ 20 - 25 ]. An exception is Charmaz's own description of her study of chronic illness [ 26 ]; we applied this same variant in our study. In the remainder of this paper, we will show how each of the characteristics of grounded theory methodology worked in our study of dental practices.

Fundamental components of a grounded theory study

Study background

We used grounded theory methodology to investigate social processes in private dental practices in New South Wales (NSW), Australia. This grounded theory study builds on a previous Australian Randomized Controlled Trial (RCT) called the Monitor Dental Practice Program (MPP) [ 27 ]. We know that preventive techniques can arrest early tooth decay and thus reduce the need for fillings [ 28 - 32 ]. Unfortunately, most dentists worldwide who encounter early tooth decay continue to drill it out and fill the tooth [ 33 - 37 ]. The MPP tested whether dentists could increase their use of preventive techniques. In the intervention arm, dentists were provided with a set of evidence-based preventive protocols to apply [ 38 ]; control practices provided usual care. The MPP protocols used in the RCT guided dentists to systematically apply preventive techniques to prevent new tooth decay and to arrest early stages of tooth decay in their patients, therefore reducing the need for drilling and filling. The protocols focused on (1) primary prevention of new tooth decay (tooth brushing with high concentration fluoride toothpaste and dietary advice) and (2) intensive secondary prevention through professional treatment to arrest tooth decay progress (application of fluoride varnish, supervised monitoring of dental plaque control and clinical outcomes)[ 38 ].

As the RCT unfolded, it was discovered that practices in the intervention arm were not implementing the preventive protocols uniformly. Why had the outcomes of these systematically implemented protocols been so different? This question was the starting point for our grounded theory study. We aimed to understand how the protocols had been implemented, including the conditions and consequences of variation in the process. We hoped that such understanding would help us to see how the norms of Australian private dental practice as regards to tooth decay could be moved away from drilling and filling and towards evidence-based preventive care.

Designing this grounded theory study

Figure ​ Figure1 1 illustrates the steps taken during the project that will be described below from points A to F.

Study design . file containing a figure illustrating the study design.

A. An open beginning and research questions

Grounded theory studies are generally focused on social processes or actions: they ask about what happens and how people interact . This shows the influence of symbolic interactionism, a social psychological approach focused on the meaning of human actions [ 39 ]. Grounded theory studies begin with open questions, and researchers presume that they may know little about the meanings that drive the actions of their participants. Accordingly, we sought to learn from participants how the MPP process worked and how they made sense of it. We wanted to answer a practical social problem: how do dentists persist in drilling and filling early stages of tooth decay, when they could be applying preventive care?

We asked research questions that were open, and focused on social processes. Our initial research questions were:

• What was the process of implementing (or not-implementing) the protocols (from the perspective of dentists, practice staff, and patients)?

• How did this process vary?

B. Ethics approval and ethical issues

In our experience, medical researchers are often concerned about the ethics oversight process for such a flexible, unpredictable study design. We managed this process as follows. Initial ethics approval was obtained from the Human Research Ethics Committee at the University of Sydney. In our application, we explained grounded theory procedures, in particular the fact that they evolve. In our initial application we provided a long list of possible recruitment strategies and interview questions, as suggested by Charmaz [ 15 ]. We indicated that we would make future applications to modify our protocols. We did this as the study progressed - detailed below. Each time we reminded the committee that our study design was intended to evolve with ongoing modifications. Each modification was approved without difficulty. As in any ethical study, we ensured that participation was voluntary, that participants could withdraw at any time, and that confidentiality was protected. All responses were anonymised before analysis, and we took particular care not to reveal potentially identifying details of places, practices or clinicians.

C. Initial, Purposive Sampling (before theoretical sampling was possible)

Grounded theory studies are characterised by theoretical sampling, but this requires some data to be collected and analysed. Sampling must thus begin purposively, as in any qualitative study. Participants in the previous MPP study provided our population [ 27 ]. The MPP included 22 private dental practices in NSW, randomly allocated to either the intervention or control group. With permission of the ethics committee; we sent letters to the participants in the MPP, inviting them to participate in a further qualitative study. From those who agreed, we used the quantitative data from the MPP to select an initial sample.

Then, we selected the practice in which the most dramatic results had been achieved in the MPP study (Dental Practice 1). This was a purposive sampling strategy, to give us the best possible access to the process of successfully implementing the protocols. We interviewed all consenting staff who had been involved in the MPP (one dentist, five dental assistants). We then recruited 12 patients who had been enrolled in the MPP, based on their clinically measured risk of developing tooth decay: we selected some patients whose risk status had gotten better, some whose risk had worsened and some whose risk had stayed the same. This purposive sample was designed to provide maximum variation in patients' adoption of preventive dental care.

Initial Interviews

One hour in-depth interviews were conducted. The researcher/interviewer (AS) travelled to a rural town in NSW where interviews took place. The initial 18 participants (one dentist, five dental assistants and 12 patients) from Dental Practice 1 were interviewed in places convenient to them such as the dental practice, community centres or the participant's home.

Two initial interview schedules were designed for each group of participants: 1) dentists and dental practice staff and 2) dental patients. Interviews were semi-structured and based loosely on the research questions. The initial questions for dentists and practice staff are in Additional file 1 . Interviews were digitally recorded and professionally transcribed. The research location was remote from the researcher's office, thus data collection was divided into two episodes to allow for intermittent data analysis. Dentist and practice staff interviews were done in one week. The researcher wrote memos throughout this week. The researcher then took a month for data analysis in which coding and memo-writing occurred. Then during a return visit, patient interviews were completed, again with memo-writing during the data-collection period.

D. Data Analysis

Coding and the constant comparative method.

Coding is essential to the development of a grounded theory [ 15 ]. According to Charmaz [[ 15 ], p46], 'coding is the pivotal link between collecting data and developing an emergent theory to explain these data. Through coding, you define what is happening in the data and begin to grapple with what it means'. Coding occurs in stages. In initial coding, the researcher generates as many ideas as possible inductively from early data. In focused coding, the researcher pursues a selected set of central codes throughout the entire dataset and the study. This requires decisions about which initial codes are most prevalent or important, and which contribute most to the analysis. In theoretical coding, the researcher refines the final categories in their theory and relates them to one another. Charmaz's method, like Glaser's method [ 13 ], captures actions or processes by using gerunds as codes (verbs ending in 'ing'); Charmaz also emphasises coding quickly, and keeping the codes as similar to the data as possible.

We developed our coding systems individually and through team meetings and discussions.

We have provided a worked example of coding in Table ​ Table2. 2 . Gerunds emphasise actions and processes. Initial coding identifies many different processes. After the first few interviews, we had a large amount of data and many initial codes. This included a group of codes that captured how dentists sought out evidence when they were exposed to a complex clinical case, a new product or technique. Because this process seemed central to their practice, and because it was talked about often, we decided that seeking out evidence should become a focused code. By comparing codes against codes and data against data, we distinguished the category of "seeking out evidence" from other focused codes, such as "gathering and comparing peers' evidence to reach a conclusion", and we understood the relationships between them. Using this constant comparative method (see Table ​ Table1), 1 ), we produced a theoretical code: "making sense of evidence and constructing knowledge". This code captured the social process that dentists went through when faced with new information or a practice challenge. This theoretical code will be the focus of a future paper.

Coding process

Memo-writing

Throughout the study, we wrote extensive case-based memos and conceptual memos. After each interview, the interviewer/researcher (AS) wrote a case-based memo reflecting on what she learned from that interview. They contained the interviewer's impressions about the participants' experiences, and the interviewer's reactions; they were also used to systematically question some of our pre-existing ideas in relation to what had been said in the interview. Table ​ Table3 3 illustrates one of those memos. After a few interviews, the interviewer/researcher also began making and recording comparisons among these memos.

Case-based memo

We also wrote conceptual memos about the initial codes and focused codes being developed, as described by Charmaz [ 15 ]. We used these memos to record our thinking about the meaning of codes and to record our thinking about how and when processes occurred, how they changed, and what their consequences were. In these memos, we made comparisons between data, cases and codes in order to find similarities and differences, and raised questions to be answered in continuing interviews. Table ​ Table4 4 illustrates a conceptual memo.

Conceptual memo

At the end of our data collection and analysis from Dental Practice 1, we had developed a tentative model of the process of implementing the protocols, from the perspective of dentists, dental practice staff and patients. This was expressed in both diagrams and memos, was built around a core set of focused codes, and illustrated relationships between them.

E. Theoretical sampling, ongoing data analysis and alteration of interview route

We have already described our initial purposive sampling. After our initial data collection and analysis, we used theoretical sampling (see Table ​ Table1) 1 ) to determine who to sample next and what questions to ask during interviews. We submitted Ethics Modification applications for changes in our question routes, and had no difficulty with approval. We will describe how the interview questions for dentists and dental practice staff evolved, and how we selected new participants to allow development of our substantive theory. The patients' interview schedule and theoretical sampling followed similar procedures.

Evolution of theoretical sampling and interview questions

We now had a detailed provisional model of the successful process implemented in Dental Practice 1. Important core focused codes were identified, including practical/financial, historical and philosophical dimensions of the process. However, we did not yet understand how the process might vary or go wrong, as implementation in the first practice we studied had been described as seamless and beneficial for everyone. Because our aim was to understand the process of implementing the protocols, including the conditions and consequences of variation in the process, we needed to understand how implementation might fail. For this reason, we theoretically sampled participants from Dental Practice 2, where uptake of the MPP protocols had been very limited according to data from the RCT trial.

We also changed our interview questions based on the analysis we had already done (see Additional file 2 ). In our analysis of data from Dental Practice 1, we had learned that "effectiveness" of treatments and "evidence" both had a range of meanings. We also learned that new technologies - in particular digital x-rays and intra-oral cameras - had been unexpectedly important to the process of implementing the protocols. For this reason, we added new questions for the interviews in Dental Practice 2 to directly investigate "effectiveness", "evidence" and how dentists took up new technologies in their practice.

Then, in Dental Practice 2 we learned more about the barriers dentists and practice staff encountered during the process of implementing the MPP protocols. We confirmed and enriched our understanding of dentists' processes for adopting technology and producing knowledge, dealing with complex cases and we further clarified the concept of evidence. However there was a new, important, unexpected finding in Dental Practice 2. Dentists talked about "unreliable" patients - that is, patients who were too unreliable to have preventive dental care offered to them. This seemed to be a potentially important explanation for non-implementation of the protocols. We modified our interview schedule again to include questions about this concept (see Additional file 3 ) leading to another round of ethics approvals. We also returned to Practice 1 to ask participants about the idea of an "unreliable" patient.

Dentists' construction of the "unreliable" patient during interviews also prompted us to theoretically sample for "unreliable" and "reliable" patients in the following round of patients' interviews. The patient question route was also modified by the analysis of the dentists' and practice staff data. We wanted to compare dentists' perspectives with the perspectives of the patients themselves. Dentists were asked to select "reliable" and "unreliable" patients to be interviewed. Patients were asked questions about what kind of services dentists should provide and what patients valued when coming to the dentist. We found that these patients (10 reliable and 7 unreliable) talked in very similar ways about dental care. This finding suggested to us that some deeply-held assumptions within the dental profession may not be shared by dental patients.

At this point, we decided to theoretically sample dental practices from the non-intervention arm of the MPP study. This is an example of the 'openness' of a grounded theory study potentially subtly shifting the focus of the study. Our analysis had shifted our focus: rather than simply studying the process of implementing the evidence-based preventive protocols, we were studying the process of doing prevention in private dental practice. All participants seemed to be revealing deeply held perspectives shared in the dental profession, whether or not they were providing dental care as outlined in the MPP protocols. So, by sampling dentists from both intervention and control group from the previous MPP study, we aimed to confirm or disconfirm the broader reach of our emerging theory and to complete inductive development of key concepts. Theoretical sampling added 12 face to face interviews and 10 telephone interviews to the data. A total of 40 participants between the ages of 18 and 65 were recruited. Telephone interviews were of comparable length, content and quality to face to face interviews, as reported elsewhere in the literature [ 40 ].

F. Mapping concepts, theoretical memo writing and further refining of concepts

After theoretical sampling, we could begin coding theoretically. We fleshed out each major focused code, examining the situations in which they appeared, when they changed and the relationship among them. At time of writing, we have reached theoretical saturation (see Table ​ Table1). 1 ). We have been able to determine this in several ways. As we have become increasingly certain about our central focused codes, we have re-examined the data to find all available insights regarding those codes. We have drawn diagrams and written memos. We have looked rigorously for events or accounts not explained by the emerging theory so as to develop it further to explain all of the data. Our theory, which is expressed as a set of concepts that are related to one another in a cohesive way, now accounts adequately for all the data we have collected. We have presented the developing theory to specialist dental audiences and to the participants, and have found that it was accepted by and resonated with these audiences.

We have used these procedures to construct a detailed, multi-faceted model of the process of incorporating prevention into private general dental practice. This model includes relationships among concepts, consequences of the process, and variations in the process. A concrete example of one of our final key concepts is the process of "adapting to" prevention. More commonly in the literature writers speak of adopting, implementing or translating evidence-based preventive protocols into practice. Through our analysis, we concluded that what was required was 'adapting to' those protocols in practice. Some dental practices underwent a slow process of adapting evidence-based guidance to their existing practice logistics. Successful adaptation was contingent upon whether (1) the dentist-in-charge brought the whole dental team together - including other dentists - and got everyone interested and actively participating during preventive activities; (2) whether the physical environment of the practice was re-organised around preventive activities, (3) whether the dental team was able to devise new and efficient routines to accommodate preventive activities, and (4) whether the fee schedule was amended to cover the delivery of preventive services, which hitherto was considered as "unproductive time".

Adaptation occurred over time and involved practical, historical and philosophical aspects of dental care. Participants transitioned from their initial state - selling restorative care - through an intermediary stage - learning by doing and educating patients about the importance of preventive care - and finally to a stage where they were offering patients more than just restorative care. These are examples of ways in which participants did not simply adopt protocols in a simple way, but needed to adapt the protocols and their own routines as they moved toward more preventive practice.

The quality of this grounded theory study

There are a number of important assurances of quality in keeping with grounded theory procedures and general principles of qualitative research. The following points describe what was crucial for this study to achieve quality.

During data collection

1. All interviews were digitally recorded, professionally transcribed in detail and the transcripts checked against the recordings.

2. We analysed the interview transcripts as soon as possible after each round of interviews in each dental practice sampled as shown on Figure ​ Figure1. 1 . This allowed the process of theoretical sampling to occur.

3. Writing case-based memos right after each interview while being in the field allowed the researcher/interviewer to capture initial ideas and make comparisons between participants' accounts. These memos assisted the researcher to make comparison among her reflections, which enriched data analysis and guided further data collection.

4. Having the opportunity to contact participants after interviews to clarify concepts and to interview some participants more than once contributed to the refinement of theoretical concepts, thus forming part of theoretical sampling.

5. The decision to include phone interviews due to participants' preference worked very well in this study. Phone interviews had similar length and depth compared to the face to face interviews, but allowed for a greater range of participation.

During data analysis

1. Detailed analysis records were kept; which made it possible to write this explanatory paper.

2. The use of the constant comparative method enabled the analysis to produce not just a description but a model, in which more abstract concepts were related and a social process was explained.

3. All researchers supported analysis activities; a regular meeting of the research team was convened to discuss and contextualize emerging interpretations, introducing a wide range of disciplinary perspectives.

Answering our research questions

We developed a detailed model of the process of adapting preventive protocols into dental practice, and analysed the variation in this process in different dental practices. Transferring evidence-based preventive protocols into these dental practices entailed a slow process of adapting the evidence to the existing practices logistics. Important practical, philosophical and historical elements as well as barriers and facilitators were present during a complex adaptation process. Time was needed to allow dentists and practice staff to go through this process of slowly adapting their practices to this new way of working. Patients also needed time to incorporate home care activities and more frequent visits to dentists into their daily routines. Despite being able to adapt or not, all dentists trusted the concrete clinical evidence that they have produced, that is, seeing results in their patients mouths made them believe in a specific treatment approach.

Concluding remarks

This paper provides a detailed explanation of how a study evolved using grounded theory methodology (GTM), one of the most commonly used methodologies in qualitative health and medical research [[ 8 ], p47]. In 2007, Bryant and Charmaz argued:

'Use of GTM, at least as much as any other research method, only develops with experience. Hence the failure of all those attempts to provide clear, mechanistic rules for GTM: there is no 'GTM for dummies'. GTM is based around heuristics and guidelines rather than rules and prescriptions. Moreover, researchers need to be familiar with GTM, in all its major forms, in order to be able to understand how they might adapt it in use or revise it into new forms and variations.' [[ 8 ], p17].

Our detailed explanation of our experience in this grounded theory study is intended to provide, vicariously, the kind of 'experience' that might help other qualitative researchers in medicine and health to apply and benefit from grounded theory methodology in their studies. We hope that our explanation will assist others to avoid using grounded theory as an 'approving bumper sticker' [ 10 ], and instead use it as a resource that can greatly improve the quality and outcome of a qualitative study.

Abbreviations

GTM: grounded theory methods; MPP: Monitor Dental Practice Program; NSW: New South Wales; RCT: Randomized Controlled Trial.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

All authors have made substantial contributions to conception and design of this study. AS carried out data collection, analysis, and interpretation of data. SMC made substantial contribution during data collection, analysis and data interpretation. AS, SMC, RWE, and AB have been involved in drafting the manuscript and revising it critically for important intellectual content. All authors read and approved the final manuscript.

Pre-publication history

The pre-publication history for this paper can be accessed here:

http://www.biomedcentral.com/1471-2288/11/128/prepub

Supplementary Material

Initial interview schedule for dentists and dental practice staff . file containing initial interview schedule for dentists and dental practice staff.

Questions added to the initial interview schedule for dentists and dental practice staff . file containing questions added to the initial interview schedule

Questions added to the modified interview schedule for dentists and dental practice staff . file containing questions added to the modified interview schedule

Acknowledgements

We thank dentists, dental practice staff and patients for their invaluable contributions to the study. We thank Emeritus Professor Miles Little for his time and wise comments during the project.

The authors received financial support for the research from the following funding agencies: University of Sydney Postgraduate Award 2009; The Oral Health Foundation, University of Sydney; Dental Board New South Wales; Australian Dental Research Foundation; National Health and Medical Research Council Project Grant 632715.

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Examining influences of academic resilience among minority adolescent students.

Sylvia Alice Okpon , Liberty University Follow

School of Behavioral Sciences

Doctor of Education in Community Care and Counseling (EdD)

Tracy N. Baker

Academic resilience, racial minority, minority middle school students, adolescents, academic success

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Okpon, Sylvia Alice, "Examining Influences of Academic Resilience Among Minority Adolescent Students" (2024). Doctoral Dissertations and Projects . 5973. https://digitalcommons.liberty.edu/doctoral/5973

This quantitative and correlational study aimed to investigate which variables of minority adolescent students promote academic resilience and focus on African American and Hispanic students within a public charter school located in Southeast Texas. Grounded on resilience theory, the research investigated the cognitive and emotional regulation of students and student–teacher connections in the process of academic resilience. A purposive sample composed of more than 100 students was used to collect data using the Cognitive Emotional Regulation Questionnaire, the Inventory of the Student–Teacher Relationship, and the Academic Resilience Scale for an online survey. Data analysis indicated that cognitive and emotional resilience mitigating coping skills demonstrate significant or greater adaptation to academic hardships. Furthermore, the correlations of the strong teacher–student ties on academic resilience emphasized the mediation effect on the repercussions of emotional regulation. This research highlights the importance of identifying and cultivating factors that give academic success among vulnerable groups of students. In this case longitudinal aspects of the influence of resilience strategies and broader demographic factors emerged as an important direction of further investigation. This research study sought to deliver valuable recommendations to educators who are not only learning specialists but also psychologists and policymakers striving to improve the academic achievements of minority students who are studying in disadvantaged environments.

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An Example of a Grounded Theory Research Proposal

• Alex Benjamin Madzivire
• Published 2011

11 References

Inspirational leadership: destiny, calling and cause, evolution and revolution as organizations grow, economic development in the third world., cracking the code of change., changing roles: leadership in the 21st century., managing change: cases and concepts, decision-making: going forward in reverse: harvard business review, 87 (1), 66–70 (january–february 1987), africa: the time has come : selected speeches, related papers.

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Key things to know about U.S. election polling in 2024

Confidence in U.S. public opinion polling was shaken by errors in 2016 and 2020. In both years’ general elections, many polls underestimated the strength of Republican candidates, including Donald Trump. These errors laid bare some real limitations of polling.

In the midterms that followed those elections, polling performed better . But many Americans remain skeptical that it can paint an accurate portrait of the public’s political preferences.

Restoring people’s confidence in polling is an important goal, because robust and independent public polling has a critical role to play in a democratic society. It gathers and publishes information about the well-being of the public and about citizens’ views on major issues. And it provides an important counterweight to people in power, or those seeking power, when they make claims about “what the people want.”

The challenges facing polling are undeniable. In addition to the longstanding issues of rising nonresponse and cost, summer 2024 brought extraordinary events that transformed the presidential race . The good news is that people with deep knowledge of polling are working hard to fix the problems exposed in 2016 and 2020, experimenting with more data sources and interview approaches than ever before. Still, polls are more useful to the public if people have realistic expectations about what surveys can do well – and what they cannot.

With that in mind, here are some key points to know about polling heading into this year’s presidential election.

Probability sampling (or “random sampling”). This refers to a polling method in which survey participants are recruited using random sampling from a database or list that includes nearly everyone in the population. The pollster selects the sample. The survey is not open for anyone who wants to sign up.

Online opt-in polling (or “nonprobability sampling”). These polls are recruited using a variety of methods that are sometimes referred to as “convenience sampling.” Respondents come from a variety of online sources such as ads on social media or search engines, websites offering rewards in exchange for survey participation, or self-enrollment. Unlike surveys with probability samples, people can volunteer to participate in opt-in surveys.

Nonresponse and nonresponse bias. Nonresponse is when someone sampled for a survey does not participate. Nonresponse bias occurs when the pattern of nonresponse leads to error in a poll estimate. For example, college graduates are more likely than those without a degree to participate in surveys, leading to the potential that the share of college graduates in the resulting sample will be too high.

Mode of interview. This refers to the format in which respondents are presented with and respond to survey questions. The most common modes are online, live telephone, text message and paper. Some polls use more than one mode.

Weighting. This is a statistical procedure pollsters perform to make their survey align with the broader population on key characteristics like age, race, etc. For example, if a survey has too many college graduates compared with their share in the population, people without a college degree are “weighted up” to match the proper share.

How are election polls being conducted?

Pollsters are making changes in response to the problems in previous elections. As a result, polling is different today than in 2016. Most U.S. polling organizations that conducted and publicly released national surveys in both 2016 and 2022 (61%) used methods in 2022 that differed from what they used in 2016 . And change has continued since 2022.

One change is that the number of active polling organizations has grown significantly, indicating that there are fewer barriers to entry into the polling field. The number of organizations that conduct national election polls more than doubled between 2000 and 2022.

This growth has been driven largely by pollsters using inexpensive opt-in sampling methods. But previous Pew Research Center analyses have demonstrated how surveys that use nonprobability sampling may have errors twice as large , on average, as those that use probability sampling.

The second change is that many of the more prominent polling organizations that use probability sampling – including Pew Research Center – have shifted from conducting polls primarily by telephone to using online methods, or some combination of online, mail and telephone. The result is that polling methodologies are far more diverse now than in the past.

(For more about how public opinion polling works, including a chapter on election polls, read our short online course on public opinion polling basics .)

All good polling relies on statistical adjustment called “weighting,” which makes sure that the survey sample aligns with the broader population on key characteristics. Historically, public opinion researchers have adjusted their data using a core set of demographic variables to correct imbalances between the survey sample and the population.

But there is a growing realization among survey researchers that weighting a poll on just a few variables like age, race and gender is insufficient for getting accurate results. Some groups of people – such as older adults and college graduates – are more likely to take surveys, which can lead to errors that are too sizable for a simple three- or four-variable adjustment to work well. Adjusting on more variables produces more accurate results, according to Center studies in 2016 and 2018 .

A number of pollsters have taken this lesson to heart. For example, recent high-quality polls by Gallup and The New York Times/Siena College adjusted on eight and 12 variables, respectively. Our own polls typically adjust on 12 variables . In a perfect world, it wouldn’t be necessary to have that much intervention by the pollster. But the real world of survey research is not perfect.

Predicting who will vote is critical – and difficult. Preelection polls face one crucial challenge that routine opinion polls do not: determining who of the people surveyed will actually cast a ballot.

Roughly a third of eligible Americans do not vote in presidential elections , despite the enormous attention paid to these contests. Determining who will abstain is difficult because people can’t perfectly predict their future behavior – and because many people feel social pressure to say they’ll vote even if it’s unlikely.

No one knows the profile of voters ahead of Election Day. We can’t know for sure whether young people will turn out in greater numbers than usual, or whether key racial or ethnic groups will do so. This means pollsters are left to make educated guesses about turnout, often using a mix of historical data and current measures of voting enthusiasm. This is very different from routine opinion polls, which mostly do not ask about people’s future intentions.

When major news breaks, a poll’s timing can matter. Public opinion on most issues is remarkably stable, so you don’t necessarily need a recent poll about an issue to get a sense of what people think about it. But dramatic events can and do change public opinion , especially when people are first learning about a new topic. For example, polls this summer saw notable changes in voter attitudes following Joe Biden’s withdrawal from the presidential race. Polls taken immediately after a major event may pick up a shift in public opinion, but those shifts are sometimes short-lived. Polls fielded weeks or months later are what allow us to see whether an event has had a long-term impact on the public’s psyche.

How accurate are polls?

The answer to this question depends on what you want polls to do. Polls are used for all kinds of purposes in addition to showing who’s ahead and who’s behind in a campaign. Fair or not, however, the accuracy of election polling is usually judged by how closely the polls matched the outcome of the election.

By this standard, polling in 2016 and 2020 performed poorly. In both years, state polling was characterized by serious errors. National polling did reasonably well in 2016 but faltered in 2020.

In 2020, a post-election review of polling by the American Association for Public Opinion Research (AAPOR) found that “the 2020 polls featured polling error of an unusual magnitude: It was the highest in 40 years for the national popular vote and the highest in at least 20 years for state-level estimates of the vote in presidential, senatorial, and gubernatorial contests.”

How big were the errors? Polls conducted in the last two weeks before the election suggested that Biden’s margin over Trump was nearly twice as large as it ended up being in the final national vote tally.

Errors of this size make it difficult to be confident about who is leading if the election is closely contested, as many U.S. elections are .

Pollsters are rightly working to improve the accuracy of their polls. But even an error of 4 or 5 percentage points isn’t too concerning if the purpose of the poll is to describe whether the public has favorable or unfavorable opinions about candidates , or to show which issues matter to which voters. And on questions that gauge where people stand on issues, we usually want to know broadly where the public stands. We don’t necessarily need to know the precise share of Americans who say, for example, that climate change is mostly caused by human activity. Even judged by its performance in recent elections, polling can still provide a faithful picture of public sentiment on the important issues of the day.

The 2022 midterms saw generally accurate polling, despite a wave of partisan polls predicting a broad Republican victory. In fact, FiveThirtyEight found that “polls were more accurate in 2022 than in any cycle since at least 1998, with almost no bias toward either party.” Moreover, a handful of contrarian polls that predicted a 2022 “red wave” largely washed out when the votes were tallied. In sum, if we focus on polling in the most recent national election, there’s plenty of reason to be encouraged.

Compared with other elections in the past 20 years, polls have been less accurate when Donald Trump is on the ballot. Preelection surveys suffered from large errors – especially at the state level – in 2016 and 2020, when Trump was standing for election. But they performed reasonably well in the 2018 and 2022 midterms, when he was not.

During the 2016 campaign, observers speculated about the possibility that Trump supporters might be less willing to express their support to a pollster – a phenomenon sometimes described as the “shy Trump effect.” But a committee of polling experts evaluated five different tests of the “shy Trump” theory and turned up little to no evidence for each one . Later, Pew Research Center and, in a separate test, a researcher from Yale also found little to no evidence in support of the claim.

Instead, two other explanations are more likely. One is about the difficulty of estimating who will turn out to vote. Research has found that Trump is popular among people who tend to sit out midterms but turn out for him in presidential election years. Since pollsters often use past turnout to predict who will vote, it can be difficult to anticipate when irregular voters will actually show up.

The other explanation is that Republicans in the Trump era have become a little less likely than Democrats to participate in polls . Pollsters call this “partisan nonresponse bias.” Surprisingly, polls historically have not shown any particular pattern of favoring one side or the other. The errors that favored Democratic candidates in the past eight years may be a result of the growth of political polarization, along with declining trust among conservatives in news organizations and other institutions that conduct polls.

Whatever the cause, the fact that Trump is again the nominee of the Republican Party means that pollsters must be especially careful to make sure all segments of the population are properly represented in surveys.

The real margin of error is often about double the one reported. A typical election poll sample of about 1,000 people has a margin of sampling error that’s about plus or minus 3 percentage points. That number expresses the uncertainty that results from taking a sample of the population rather than interviewing everyone . Random samples are likely to differ a little from the population just by chance, in the same way that the quality of your hand in a card game varies from one deal to the next.

The problem is that sampling error is not the only kind of error that affects a poll. Those other kinds of error, in fact, can be as large or larger than sampling error. Consequently, the reported margin of error can lead people to think that polls are more accurate than they really are.

There are three other, equally important sources of error in polling: noncoverage error , where not all the target population has a chance of being sampled; nonresponse error, where certain groups of people may be less likely to participate; and measurement error, where people may not properly understand the questions or misreport their opinions. Not only does the margin of error fail to account for those other sources of potential error, putting a number only on sampling error implies to the public that other kinds of error do not exist.

Several recent studies show that the average total error in a poll estimate may be closer to twice as large as that implied by a typical margin of sampling error. This hidden error underscores the fact that polls may not be precise enough to call the winner in a close election.

Other important things to remember

Transparency in how a poll was conducted is associated with better accuracy . The polling industry has several platforms and initiatives aimed at promoting transparency in survey methodology. These include AAPOR’s transparency initiative and the Roper Center archive . Polling organizations that participate in these organizations have less error, on average, than those that don’t participate, an analysis by FiveThirtyEight found .

Participation in these transparency efforts does not guarantee that a poll is rigorous, but it is undoubtedly a positive signal. Transparency in polling means disclosing essential information, including the poll’s sponsor, the data collection firm, where and how participants were selected, modes of interview, field dates, sample size, question wording, and weighting procedures.

There is evidence that when the public is told that a candidate is extremely likely to win, some people may be less likely to vote . Following the 2016 election, many people wondered whether the pervasive forecasts that seemed to all but guarantee a Hillary Clinton victory – two modelers put her chances at 99% – led some would-be voters to conclude that the race was effectively over and that their vote would not make a difference. There is scientific research to back up that claim: A team of researchers found experimental evidence that when people have high confidence that one candidate will win, they are less likely to vote. This helps explain why some polling analysts say elections should be covered using traditional polling estimates and margins of error rather than speculative win probabilities (also known as “probabilistic forecasts”).

National polls tell us what the entire public thinks about the presidential candidates, but the outcome of the election is determined state by state in the Electoral College . The 2000 and 2016 presidential elections demonstrated a difficult truth: The candidate with the largest share of support among all voters in the United States sometimes loses the election. In those two elections, the national popular vote winners (Al Gore and Hillary Clinton) lost the election in the Electoral College (to George W. Bush and Donald Trump). In recent years, analysts have shown that Republican candidates do somewhat better in the Electoral College than in the popular vote because every state gets three electoral votes regardless of population – and many less-populated states are rural and more Republican.

For some, this raises the question: What is the use of national polls if they don’t tell us who is likely to win the presidency? In fact, national polls try to gauge the opinions of all Americans, regardless of whether they live in a battleground state like Pennsylvania, a reliably red state like Idaho or a reliably blue state like Rhode Island. In short, national polls tell us what the entire citizenry is thinking. Polls that focus only on the competitive states run the risk of giving too little attention to the needs and views of the vast majority of Americans who live in uncompetitive states – about 80%.

Fortunately, this is not how most pollsters view the world . As the noted political scientist Sidney Verba explained, “Surveys produce just what democracy is supposed to produce – equal representation of all citizens.”

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Scott Keeter is a senior survey advisor at Pew Research Center .

Courtney Kennedy is Vice President of Methods and Innovation at Pew Research Center .

How do people in the U.S. take Pew Research Center surveys, anyway?

How public polling has changed in the 21st century, what 2020’s election poll errors tell us about the accuracy of issue polling, a field guide to polling: election 2020 edition, methods 101: how is polling done around the world, most popular.

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