3.7.3.1. Manual analysis
Data arising from audio-recorded interviews were transcribed and then transferred to the software package NVivo, as a way of managing and organising the data. The aim of the analysis was to look for common codes and categories throughout the interview texts which could be grouped to form major themes, using a general inductive approach (Patton 2002, Thomas 2006).
In order to gain thorough familiarity and understanding of my data, I read each transcript several times. These continuous readings of the transcripts helped me to make sense of what was said and served as a great help in the identification of initial concepts and ideas for possible themes. This first identification, following Miles and Huberman’s (1994) methods for finding patterns and developing conceptual themes, was undertaken manually. So, key words or words expressing a concept were written in the margins of the transcripts in order to obtain a sense of the overall data. For example, when a participant said that she will apply for headship when her ‘child will be old *...+ in two years time he will be 10, he will be independent’, I wrote in the margins of my transcription ‘old children=not so many needs’.
As a second step, by scanning each data type, looking at differences and similarities, gleaning out underlying and recurrent constructs, I was able to provisionally identify several closely related initial themes, like the reactions of their families when (or if) they decided to apply for a headship position.
Thirdly, keeping the list of initial themes in the forefront, I carefully read the transcripts again, to identify passages of texts which exemplified the themes. These passages were underlined. Themes were allowed to emerge from the data directly and literature was used to identify themes in the data. This list of themes constituted the thematic framework that served as the coding tree for the purpose of coding in the NVivo programme [The coding tree is included in the appendix section as APPENDIX 6].
3.7.3.2. Use of NVivo in data analysis
NVivo software was chosen because I could easily alternate between the data and the coding scheme and let categories emerge and change as the research proceeded.
As the interviews were long and generated a large amount of data they needed to be organised in an effective way. So, in this instance, using NVivo was a useful organisational tool and an efficient way to extract and search the data according to themes and labels that had been attributed to it. The speed and organisational qualities of NVivo were especially useful due to the importance of what Weitzman (2000, p. 813) calls ‘poking around in your data’ for a more inductive approach. It was also useful to store memos and keep organised nodes and ideas as the analysis process was unfolding.
Like other qualitative data analysis packages NVivo is only a tool and cannot compensate for poorly constructed research or lack of interpretive capacity. Although it allows for multiple coding of a particular segment of texts, there may be a danger of chopping that data into different segments and losing the overall accounts (Bazeley 2007). In my research care was taken to select and code significant segments of the transcript that discuss particular constructions or issues and thus retain the overall meaning. Silverman (2010) argues that computer softwares do not display visually the sequential events of how some things are necessary for others to happen, so this had to be done manually in order not to lose any connections between the data.
To begin with the analysis process, once the interviews were inserted into the programme, attributes were created for each of the interviewees (pseudonym, role, school type, region, age group, family, years of teaching experience, years as head teacher). These attributes were used to search the text and analyse responses according to specific interview characteristics, as relationships started to develop between particular categories and attributes.
The coding was done using the distinct analytical categories that had been devised in the handwritten analysis and by adding new ones when necessary. Care was taken to ensure that the coding developed uniformly throughout the analysis of interviews, and that I did not mix the codes. To ensure this it was necessary to make detailed notes providing a rationale for each of the codes within the overall main aim of the research.
The most difficult stage in the analysis was refining and editing the coding categories that had been created as the data was rich and there were multiple codes attached to it. To move to the next stage of analysis, these codes (or free nodes as they are called in NVivo) were coded into broader codes and then transferred into tree nodes, organising them into overall themes. For example, the free node I called ‘children’ and included accounts on how
the heads’ children were reacting to their mothers/fathers wish to apply for headship, was transferred into the tree node that I called ‘family’, which was included in the node ‘barriers’. This brought greater clarity and structure to the analysis. It also facilitated the on going process of writing.