RESEARCH METHOD
6.3 Data Analysis
6.3.2 Substantive Coding, Comparative Analysis and Theoretical Coding
As discussed previously, Glaserian Grounded Theory utilised the constant comparative method with two phases to the data analysis (Glaser, 1992), the first phase was substantive coding of the data consisting of open (and ‘in vivo') and selective coding producing categories and describing their properties, with the second phase being coding that occurred ‘at the conceptual level, weaving the substantive codes together into a hypothesis and theory’ (Walker and Myrick, 2006: 550).
As a novice Grounded Theorist seeking credibility, the two phase process of coding outlined above was adhered to strictly (Glaser, 1992). This gave me the confidence that the analysis was robust and allowed me to be open to emerging theoretical concepts. NVivo software was used an aid to management of large volumes of data and did not replace the hard work of analysis. It facilitated storage of data, linking of memos to transcripts for analysis, allowed easy access to the data within ‘free nodes’ that related to the open coding grounded in the students’ discourse, and the subsequent ‘tree nodes’ that related to the selective coding that in turn could generate the new theoretical concepts in understanding the student experience. Using this software did not take away from me the responsibility for thinking, identifying and creating codes, or theorising; it enhanced management of the mass of data and made the audit trail required in Grounded Theory more easily accessible (Artinian et al., 2009).
Before data coding commenced the anonymity of the participants was protected by assigning letter and numerical identification to each individual. The first four students were named EP1 to EP4 (reflecting their ‘exploratory participant’ contributions to the focus of my
study) and the other student participants were named P1 to P15, providing a unique identifier for each of the 19 students that could enable their experiences to be expressed in written form while upholding confidentiality. Nurse teacher participants were similarly named using TP1 to TP5. In later reporting of findings for publication and conference presentations, the participant numerical identifiers were simplified to P1-P19. All records of these identifiers assigned to participant identities were kept securely to maintain their anonymity (RCN, 2009a).
Open or ‘in vivo’ codes were assigned to the content of the student interview transcriptions through a line by line analysis, or sentence by sentence analysis where a line of type only partially provided a coding opportunity. The analysis of the transcriptions was therefore started through open coding, with some open codes being ‘in vivo’ coding that reflected actual words and phrases used by the students. The data were defined on the actions on which they rested, using the activity the students reported (Glaser, 1992). The data were scrutinised for tacit assumptions, meanings and the significance of the points made in order to follow new leads, code these leads to help develop selective codes, and build up theory from the ground without ‘taking off on theoretical flights of fancy’ (Charmaz, 2006:51). Using constant comparisons between previous and new interview content, relationships appeared within the open and in vivo codes which were captured in memos.
Analysis of the similarities and differences in student experiences was achieved through asking questions of the data such as ‘what process is happening here?’, ‘how does this process develop?’, ‘how does the participant react while involved in this process?’, ‘what does the participant think and feel while involved in this process?’, ‘when why and how does the process change?’ and ‘what are the consequences of the process?’ (Charmaz, 2006). Using these questions to identify new or unexpected lines of enquiry within the data already collected and using theoretical sampling to explore these new concepts in further data collection, enabled selective codes to emerge from the data. The selective codes were more conceptual and helped to explain larger segments of data, and the researcher made
decisions about which of the open and ‘in vivo’ codes made most analytic sense to create selective codes that could explain the data, recording this decision-making within theoretical memos. Moving to selective coding was not a linear process as the data collected earlier in the research, including that from the exploratory participants, were reviewed again. This returning to previous data also helped to refine the selective codes (Artinian et al., 2009).
The selective codes were recorded as ‘tree nodes’ and some early theory in the form of potential core categories and concepts started to emerge from these. The analysis moved from line by line coding to experience by experience coding during the later stages of the analysis, as students’ socialisation experiences that had similarities and differences were explored using the same set of questions as above. This ensured the findings were grounded in the data and were meaningful in terms of being relevant to whole student experiences compared to concepts within students’ experiences. In this way the properties and ‘fit’ within the emerging theoretical categories were confirmed.
Examples of free nodes representing open codes were: ‘being compassionate’, ‘feeling vulnerable’, ‘seeking support’, ‘experiencing challenges’ and expressions such as ‘being a good nurse’ and ‘having a bad experience’. A few in vivo codes were identified that captured a variety of students’ described experiences, the word ‘McDonaldisation’ was used by one student to describe the fast track and standardised approaches to admissions and discharges experienced within a day surgery unit. This open code was then assigned other students’ described experiences relating to fast throughput of patients and lack of individualisation in care, even though they did not use the word McDonaldisation. These open codes were analysed within each individual student’s transcript and a smaller number of selective codes emerged from the similarities and differences between the open codes and were stored within the software as ‘tree nodes’.
The substantive coding phase generated over 110 open (and in vivo) codes which were further analysed to generate 44 substantive codes that related to student nurse socialisation
in compassionate practice. These 44 substantive codes were further analysed and 14 selective codes arose, set within three main conceptual areas (Appendix 8). Within each code the NVivo software allowed links to memos, easy access to each open code transcript contribution and provided opportunity to scrutinise the detail of each reference such as ‘having opportunities to be with patients’ (Appendix 8a). The open coding was subjected to repeated scrutiny, moving through the data, identifying similarities and differences between students’ experiences. The open codes became more defined as further data were collected and analysed, eventually allowing the researcher to raise some selective codes and early theoretical concepts (Figure 4), as required within Glaserian Grounded Theory (Artinian et al., 2009). The constant comparison, recoding, and analysis developed in time into fourteen selective codes that made up emerging theoretical concepts. These arose through repeated theoretical memoing of codes such as ‘understanding the RN role’ (appendix 8b).
As already discussed, the constant comparative analysis allowed for more focussed interviews to be undertaken with later participants and enabled theoretical sampling to recruit participants who could enlighten theoretical constructs. Using Glaserian Grounded Theory required patience and persistence, and coding was complete when analysis identified a core category that could embrace all the data, a process by which I identified the emerging theory.
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