QUAGOL: A guide for qualitative data analysis
Introduction
Imagine, a study about nurses’ involvement in euthanasia.1 The data are collected through in-depth interviews with nurses having experience in the care for patients requesting euthanasia. The first respondent is a man, working in a neutral hospital, with a positive attitude toward euthanasia. He has 10 years of experience in oncology care and has been involved in 8 euthanasia cases. The man speaks fluently and with conviction about the subject. ‘Respecting the patient's euthanasia request’ seems to be the main focus of his care. The most important role of the nurse, in his opinion, is to gain absolutely certainty that the euthanasia request is really what the patient wants. Subsequently, the nurse must be sure that all the necessary steps of the procedure are taken. He tells you that the hospital protocol serves as checklist, which is for him the most important instrument in the euthanasia care process.
The second respondent is a woman, working in a neutral hospital. She also has a positive attitude toward euthanasia. She has 5 years of experience on a geriatric care ward and has been involved in 3 euthanasia cases. Here, you are confronted with a quite different story. The nurse tells you how important it is for her to be able to understand the patient's request. Her most important concern is: what is the right attitude for me in guiding and supporting the patient and the patient's family through this process? How should I be? Her primary focus in the care for these patients is to show respect for the patient as person in the broad sense (a person with a specific character, particular life history, own wishes, fears, coping strengths and relationships). She describes in detail how she enters into a close and personal relationship with patients and their family in order to create a communicational atmosphere, within which she helps them spend their final days together in a good way.
A next respondent, again a man, working in a catholic hospital, with a negative attitude toward euthanasia. He has 5 years of experience in a palliative support team and has been involved in 12 euthanasia cases. This time, you hear an emotional story, underlining the emotional intensity of being involved in euthanasia. Caring for a patient requesting euthanasia is intense, difficult and grave, according to this nurse. ‘Truly helping the patient to die serenely’ is the central message in his story. ‘As a nurse I must do everything in my power to contribute to this’, he tells you in the interview. His story makes clear that a euthanasia care process is only successful when everyone involved is able to make one's peace with the situation.
The next participant is a woman, working in a neutral hospital. She has a pro-attitude and has 3 years of experience on an oncology unit; she has been involved in 2 euthanasia cases. You are confronted with a young nurse telling, again, a totally different story about nurses’ involvement in euthanasia. Her story is one about the organization of care. ‘Caring for a patient requesting euthanasia requires, first of all, an efficient, practical organisation of care’, she tells you. According to this nurse, the responsibility of the nurse is to find out what to ‘do’ to make this care process successfully.
And you can go on. You are confronted with pages and pages of interview data. Every respondent has his or her own unique story that can help you understand the nurses’ involvement in euthanasia care processes. How to analyze and interpret all these different data? How to understand their meaning and draw legitimate conclusions? How to grasp the essence of these data while protecting the integrity of each story when responding to the research question? These questions point to the real challenge of qualitative data analysis.
Data analysis is a complex and contested part of the qualitative research process, which has received limited theoretical attention (Savage, 2000). Researchers are often in need of useful instructions or guidelines on how to analyze the mass of qualitative data, but face the lack of clear guidance for using particular analytic methods (Hunter et al., 2002, McCance et al., 2001). Most available guidelines or checklists related to qualitative studies are critical appraisal tools or focus on reporting qualitative research such as the CASP (Public Health Resource Unit, 2006), COREQ (Tong et al., 2007), Malterud's guidelines (2001), and McMaster Critical Review Form (Letts et al., 2007). They do not provide researchers with clear instructions on how to analyze, interpret and summarize qualitative data.
In trying to meet this need and fill this lack, we should not, however, forget to be careful. For on the one hand, there is growing consensus that understanding or using a prescribed method of analysis is not enough to generate new insights. Qualitative data analysis is very complex, and any description of the practical aspects of the analysis process runs the risk of oversimplification. There is no one right way to work with qualitative data. Essentially, qualitative data analysis is a process best ‘learnt by doing’ (Froggatt, 2001).
On the other hand, we need to bear in mind that the ‘Aha-erlebenis’, the moment where one makes meaning beyond the facts, does not just happen out of the blue (Hunter et al., 2002). No themes, categories, concepts or theories will ‘emerge’ without the researcher who must ‘make it so’ (Sandelowski, 1995, p. 371). This requires expertise in reading, thinking, imagining, conceiving, conceptualizing, connecting, condensing, categorizing and thereby creating a new storyline (Jennings, 2007). This implies the development of ‘intellectual craftmanship’ (Mills, 1995/1978, p. 195) without which no valuable qualitative work can be produced (Sandelowski, 1995). Extensive preparation is required to open the researcher's mind to multiple meanings and perspectives and to lay the groundwork for one to be creative (Hunter et al., 2002). In qualitative research it is essential that we ask which techniques or methods can be used to guide and support researchers in this challenging intellectual process (Jennings, 2007, Hunter et al., 2002).
Section snippets
Problem statement
The process of qualitative data analysis is an extensive and challenging activity, confronting the researcher with many problems. Based on the literature and on our own experiences with qualitative data analysis, we can discern six major problems researchers are often struggling with.
Aim
The purpose of this article is to propose and discuss the Qualitative Analysis Guide of Leuven (QUAGOL), a guide that we developed in order to be able to truly capture the rich insights of qualitative interview data. The QUAGOL is based on our own experiences with qualitative research as well as on that of other researchers (as described in the literature) and is inspired by the constant comparative method of the Grounded Theory Approach (Corbin and Strauss, 2008). QUAGOL is proposed as a guide
The Qualitative Analysis Guide of Leuven (QUAGOL)
The proposed method is comprehensive and systematic but not rigid; it offers space that stimulates the researcher's intuition and creativity as maximal and optimal as possible. The method gets the researcher out of his isolated position as the analysis process is predominantly considered as a team activity rather than a purely individual process.
The process of analysis consists of two parts: (1) a thorough preparation of the coding process and (2) the actual coding process using a qualitative
Strengths of the method
The method described in this article is presented as a guiding tool in the analysis of qualitative interview data. According to our experiences, this guide can serve as a valuable aid in the qualitative analysis process. The strengths of the guide lie in the underlying principles on which the guide is built, most of which have been supported by other authors: a case-oriented approach characterized by a continual balancing between within-case and cross-case analysis (e.g. Ayres et al., 2003,
Conclusion
The QUAGOL guide is a theory- and practice-based guide that supports and facilitates the process of analysis of qualitative interview data. Although the method as described above, can facilitate the process of analysis of qualitative data, it cannot guarantee automatic quality of analysis. The method is proposed as a guiding tool rather than as a strict procedure or technique that has to be implemented correctly step by step. The skills of the researcher and the common quality of the research
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