Qualitative data analysis comprises the processes used to make meaning from the voluminous material accumulated during data collection (LeCompte &
Schensul, 1999; K. Willis, 2006). Despite an array of techniques and guidelines on how to conduct data analysis (Patton, 2002), there are no shared standards (Miles & Huberman, 1994) or recipe for how to transform data into findings:
“because each qualitative study is unique, the analytical approach used will be unique” (Patton, 2002, p. 433). The researcher must therefore familiarise herself with the technical and intuitive-based processes available (Miles &
Huberman, 1994) and then construct an approach that meets the specific needs of the project at hand.
In keeping with the constructionist epistemological orientation of the study outlined in Chapter 3, I adopted Braun’s and Clarke’s (2006) and Charmaz’s (2006) argument that social knowledge is constructed. Findings would not
‘emerge’ from the data because, as the constructionist perspective asserts, reality is not ‘out there’ waiting to be uncovered (Heywood & Stronach, 2005) but is produced through active engagement with the data:
An account of themes ‘emerging’ or being ‘discovered’ is a passive account of the process of analysis, and it denies the active role the researcher always plays in identifying patterns/themes, selecting which are of interest, and reporting them to the readers (Taylor & Ussher, 2001). (Braun & Clarke, 2006, p. 80)
The approach I used to support a constructionist analytic approach was that of recursive analysis. Recursive analysis is often used in ethnography
(LeCompte & Preissle, 1994), and is congruent with the bottom-up, top-down
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interface between data and higher level concepts described in Chapter 3.83 It requires the researcher to engage in a continual process of examination, comparison, condensing and revisiting of data, thus resulting in a
sophisticated understanding of participants’ social worlds (LeCompte &
Schensul, 2013).
Recursive analysis was incorporated into the study to achieve two main objectives: 1) maintaining the rich description and narrative required by the ethnographic tradition; and 2) addressing the research questions and objectives. I adopted four key techniques that ran parallel to each other throughout data analysis and interpretation in order to achieve these goals, each of which is briefly described in the following sections.
Thematic analysis
LeCompte and Schensul (2013) argue that the inductive element of recursive analysis is accomplished through thematic analysis. Thematic analysis is a highly flexible approach to qualitative data analysis and “a method for identifying, analysing and reporting patterns (themes) within data” (Braun &
Clark, 2006, p. 79). It was one of the key strategies I used to organise and code data for easy retrieval, and explore specific content related to the research questions and objectives. The approach to thematic analysis adopted in the study paralleled the widely used, six-stage process explicated by Braun and Clark (2006). I condensed their process into four stages: 1) becoming familiar with the data; 2) generating codes; 3) identifying themes; and 4) refining themes.84 In order to maintain the richness of meaning and sense of
83 Recursive analysis parallels grounded theory in that it works inductively and deductively across data and theory. However, recursive analysis does not require a theory-free starting point, or rely on multi-layered coding and categorisation for the derivation of constructs (see, for example, Corbin & Holt, 2005, for a discussion on the core tenets of grounded theory).
84 Despite the usefulness of coding with regard to addressing the research questions, I was constantly aware of its limitations. Bergheim (2011) states that there are two main arguments against coding within data analysis: 1) coding results in removing the data from context; and 2) tends to fragment the data and interrupt the narrative flow (see also Braun & Clarke, 2006;
Bryman, 2008; Richards & Richards, 1994). I experienced both of these issues as problematic.
Additionally I found that separating the data into codes had the effect of linking topically-related data into an apparently single moment in time, as if such considerations as data
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wholeness of the data (see Weitzman, 2000; K. Willis, 2006) and to avoid distancing myself from the data (O’Reilly, 2005), I decided not to use data analysis software, instead relying on a multi-coloured system of hard-copy coding and indexing, and the incorporation of a range of other data analysis techniques which are outlined below.
Visual displays
The use of visual mechanisms in data analysis is identified as a useful strategy by a number of researchers and writers (e.g. Braun & Clarke, 2006; DeVault &
McCoy, 2006; Liamputtong, 2009; Patton, 2002; Strauss, 1987). Mapping enables information to be presented visually, systematically and holistically (Miles & Huberman, 1994), and, in this study, comprised an important
strategy for data refinement and tracking the evolution of my thinking during the construction of findings. The types of data displays were based on Miles and Huberman (1994), and included: time-ordered displays (narratives and chronological events); conceptually-ordered displays (researcher impressions;
inductive analysis using codes, categories and themes; deductive analysis using research questions and objectives); and causal displays (theoretical maps and models).
Conversation
Conversation not only generated a significant component of field note data85 but became one of the primary data analysis strategies for the project.
Discussions with participants, contributors, academic supervisors, and academic mentors were employed continuously to gather, clarify, interpret and refine the data. Most importantly, on-going, robust discussions about the findings with participants became an important technique in the construction
potency and researcher analysis, interpretation and maturity of thinking were steady and static entities. Without context, all content on a particular topic was equal even if there were good reasons for not considering it so. Thus coding was used more for the indexing of data than it was for the construction of the final themes.
85 See the section entitled The Ethnographic Conversation in this chapter.
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of negotiated meaning,86 and enabled me to analyse and interpret data in line with Whitehead’s (2005) call to incorporate the greatest degree of emic validity possible in the ethnographic account.
Writing and reflecting
Richardson (1994), and Richardson and St. Pierre (2005), argue that writing is also a method for inquiry and analysis. In this instance, I use the word ‘writing’
to summarise the cognitive and literary processes involved in constructing the research account. Every aspect of writing became part of data analysis,
including: writing up field notes and memos; working with theory and
literature; constructing reflections, narratives and vignettes; and the continual drafting and redrafting of chapters. Each of these tasks required on-going analysis and decision-making.
It was through writing that the findings were eventually situated within a structure and given coherence and meaning. I note that Madden (2010) views the process of writing findings not as LeCompte’s and Schensul’s (2013) crunching data, but as value adding (p. 148) or fattening up (p. 151). While I argue that data is distilled (it is not possible to include every instance of data in a thesis), I agree that it is also enriched through the construction process:
Ethnographic writing should aspire to meet the challenge of conveying an interesting, accessible and believable portrait of a culture or society, and to do so it has to find a balance between the duty to facts and validity and a literary voice that conveys rich, evocative and persuasive description. (Madden, 2010, p. 166)
In summary, Figure 4.3 provides a visual overview of the four-part, interactive nature of the components of recursive analysis.
86 See the section entitled Member Checking and the Construction of Findings in this chapter.
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Figure 4.3. The recursive analytical process.
Constructing the whole
While the individual processes displayed in Figure 4.3 can be explained with relative clarity, the experience of synthesising material into the final account was considerably more nebulous. The on-going accumulation of data
throughout the study resulted in a continuous and fresh flow of writing, reflection, summarisation, indexing, mapping, discussion and refinement, with the cycle operating according to its own demands and, more importantly, the demands of the data and the research process. As such, the process was a reflection of the continual evolution in my thinking. As Basit (2003) suggests, qualitative data analysis “is not fundamentally a mechanical or technical exercise. It is a dynamic, intuitive and creative process of inductive reasoning, thinking and theorizing” (p. 143). Data analysis was, therefore, a complex, continual and challenging component of the research venture and crucial to constructing a coherent ethnographic narrative.