Once the interview transcriptions were carefully checked against the recording, thematic analysis was utilised. Thematic analysis is described by Braun and Clarke (2006, p. 79) as “a method for identifying, analysing and reporting patterns (themes) within data”. This analysis method is widely used in qualitative research, and seen as a foundational method for analysis. It is used as a contributory tool within other qualitative analysis methods, including grounded theory (Glaser & Strauss, 1967; Strauss & Corbin, 1990), and is proposed by Braun and Clarke (2006) to be an analysis method in its own right. It is flexible, and is not constrained in its application to certain epistemological and theoretical perspectives. It can provide a “rich and detailed, yet complex, account of data” (Braun & Clarke, 2006, p. 78). In this type of analysis, themes are identified, which are recognised as a pattern of meaning identified from the data set. An inductive approach to analysis was followed, allowing themes to be identified that were different to the initial research and interview questions and analytic preconceptions – a ‘bottom-up’, data driven approach. This has similarities with grounded theory analysis, but without the theoretically-driven constraints.
Themes were developed through coding of the data. Coding is the process of grouping together ‘chunks’ of text which are thought to have a similar meaning (Miles & Huberman, 1994). The ‘chunks’ are labelled, and referred to as codes. A list of codes was initially created (Appendix 6), with a mix of data-driven and theory-driven codes (Braun & Clarke, 2006). These
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codes were based on the initial reading of the transcripts and using my notes, and were
structured according to initial thoughts on their relationships (adding clarity to the meanings of the codes). The transcripts were then systematically coded, with a small number of additional codes developed during this procedure (for example ‘scientists hiding information from the end-users’). Context was retained as much as possible during the coding process through consideration of text surrounding each coded extract. NVivo (v9) software was used to aid the qualitative analysis of the transcriptions. This software allows the written transcript, audio files, and notes to be viewed together to retain context. Additional notes were added to the transcript file from those taken during and after the interview.
Codes were collated into common concepts, and structured into themes (Appendix 7). In order to contain the topic and focus on the VAL system, information provided by the participants on wider subjects (such as general volcanic crisis management information) was not investigated further. These codes were not included in any thematic maps or analysed further from this point. At this stage, the concepts (and related sub-concepts), themes, and relationships relating directly to the VAL system were investigated in more detail by reading and
interpreting coded extracts, and creating memos (a record of the researchers thoughts and hypotheses). Differences in participants’ opinions were noted and possible reasons for these differences hypothesised. Field notes from observations contributed to this processes. As patterns and relationships were identified, some concepts were re-coded. Concepts and themes were defined based on coded text. Some definitions were developed throughout the interview process. For example, the concept ‘what is the purpose of the VAL system?’ was identified early on in the interview process based on comments made by initial interview participants. After the realisation that participants had different meanings of what the purpose is, this question was included in subsequent interviews. The concept was developed and defined based on a number of related codes (some resulting from responses to other questions).
The final list of 82 codes used is presented in Appendix 8. Throughout this process, thematic maps (e.g., Appendix 9) were drawn and re-drawn based on these concepts and themes (Miles & Huberman, 1994; Braun & Clarke, 2006), and all relationships were described (or
hypothesised) based on the data. Main themes grew and shrank, merged together, and were renamed, and concepts within them were reorganised. For example, the theme which started out being named as ‘current phenomena-based VAL system’ in Appendix 7 changed to ‘content and structure of VAL systems’ in Appendix 9 and was finalised as ‘a review of the
current VAL system’ with the main concepts of ‘structure’ and ‘content’ (discussed further in section 4.4, with the final version of the thematic map in Figure 4.3). By the end of the analysis process, five major themes had been identified from the data. These were:
1) Establishing the context of the VAL system
2) The relationship between end-users and the current VAL system 3) A review of the current VAL system
4) Influences on scientists’ determination of the VAL 5) Future VAL systems
These themes (which are described in more detail in Chapter 4) were investigated in further detail by the analysis of related coded text. The concepts and sub-concepts in each theme were described, and their relationships to the theme defined. The themes are descriptive (semantic), and identified by their ‘containment’ of related concepts. Themes 4 and 5 in particular also contain interpretive (latent) concepts. The variation in the number of coded extracts for each theme (i.e. the sum of the codes for all concepts within each theme) is between approximately 280 (end-user’s relationship to the VALs theme) and 990 (VAL table review theme). The proportion of codes per theme reflects the focus of the questions asked during the interviews. Relationships between the themes and research questions are also described (as suggested by Braun & Clarke, 2006) in Chapter 7 of this thesis.