Resultados de la entrevista

Thematic Analysis (TA) was used across all parts of the research to analyse verbal and visual data from the children and SPs. TA offered a flexible qualitative approach for organising and describing rich data in detail. TA is a method for identifying, analysing and reporting patterns within data, which is theoretically and methodologically sound (Braun & Clark, 2006). TA also fits with a constructionist qualitative approach and examines how events, meanings and experiences are the effects of discourses operating within society (Braun & Clarke, 2006). In order to manage the large dataset, analysis was conducted in

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the qualitative analysis programme QSR NVivo 11. Transcripts ranged from approximately 25 minutes to one hour 10 minutes in length.

Braun and Clark (2006) wrote that while it is important for the researcher to be explicit about their epistemological and ontological positioning, TA

provides “qualitative analysis guidelines” rather than “rules” for analysis (Braun & Clark, 2006 p.86). The flexibility of TA enabled the researcher to adopt this approach to analyse all aspects of the children’s data, including that gathered through use of vignettes and visual data. When children were constructing their visual accounts of relocation, the researcher sought to understand the story being told by the participant (Riessman, 2007). The researcher asked about the sequence of unfolding events, and this was central to the methodology.

However, by adopting a TA rather than a narrative TA frame (Riessman, 2007, 2008), the researcher was not wedded to keeping the child’s sequential story intact or to taking a case-centred approach (Riessman, 2007). Greenhalgh (2005) commented that in narrative analysis the story is taken as a whole rather than being segmented into themes (Greenhalgh, 2005). The flexibility of TA allowed the researcher to use this approach while applying the tenets of narrative TA and remaining reflective of the sequential journey being

constructed by the participant. Epistemologically, TA and narrative TA are well suited to the social constructionist position. The researcher was aware that children may not wish to divulge information in the order it occurred, and this may be related to sensitive topics arising. This led to TA becoming the ideal approach to adopt.

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Data analysis was conducted following Braun and Clark’s suggested framework, taking into account open and focused coding (Esterberg, 2002; Braun & Clark, 2006). Open coding was used to identify the dominant

experiences in the children’s lives, identified through persistent words, phrases and concepts in the data. Emphasis was placed on identifying the most

important data in relation to the research question, rather than the frequency of coding to theme and subtheme (Braun & Clark, 2006). The role of the

researcher was active. Patterns and themes were selected intentionally with interest for reporting partially led by the theoretical positioning of the researcher. The researcher was aware that it would not be possible to purely voice

participant views, due to the selection, editing and decisions she made in relation to the data (Braun & Clark, 2006). Data analysis took place across the data sets, incorporating the data used from the data corpus: the data items, individual pieces of data and data extracts (Braun & Clark, 2006).

The researcher followed a series of six steps outlined by Braun and Clark’s framework.

1. Familiarisation with the data was achieved through reading, re-reading and generating initial connections in the data (verbal and visual). This was

supplemented by consideration of the research aims and the theoretical models contributing to the development of the research.

2. Code generation (termed “nodes”) occurred across the datasets using NVivo11.

3. NVivo11 was used to gather “nodes” into overarching themes. Themes contained subthemes, which were also generated using NVivo11. Further exploration of subthemes took place using diagrams to check the density of

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coding to notes. This enabled prominent themes to be identified. A selection of thematic maps can be found in the data analysis chapters. The number of references coded to each sub theme was shown to indicate the degree to which a topic was discussed and the number of SPs who contributed to that

subtheme. Using NVivo11 also enabled identification of times when data had been coded to more than one node; this enabled greater clarity of themes and subthemes.

4. The themes were reviewed in relation to a thematic map of the analysis. Coded nodes were refined to ensure that all participants had contributed across parts 1 and 2 of the research.

5. Themes and subthemes were defined and named.

6. The researcher produced the analysis chapters using a selection of supportive quotes to illuminate elements of subthemes and the difference between participants across themes and subthemes. An example coded

transcript from parts 1 and 2 of the research can be found in Appendices N and O.

4.7.1 Visual analysis. TA was applied as a means of eliciting common themes from the images produced by the children (Braun & Clark, 2006). During the TA, each child participant’s verbal and visual data was analysed as a single unit. A similar approach was used by Campbell, Skovdal, Mupambireyi, and Gregson (2010), when they used drawings and stories to analyse children’s stigmatisation of AIDS-affected children (Campbell et al., 2010). The research also drew on guidelines provided by Saldaña (2013), Kuhn (2003) and Freeman and Mathison (2009) to guide analysis of the visual images created by the children.

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Saldaña (2013) suggested a holistic, interpretive lens guided by strategic questions. He wrote that careful scrutiny of and reflection on images

documented through field notes and memos will generate language-based data to accompany visual data (Saldaña, 2013). Saldaña added: “I have yet to find a single satisfactory approach that rivals the tactic capabilities of human

interpretation and reflection. Trust your intuitive impression when analysing and writing about visual materials” (Saldaña, 2013 p.57). Similarly to Saldaña

(2013), Kuhn (2003) did not advise creating too narrow and restrictive a lens when interpreting visual images. Instead he suggested analysing qualitative categories such as: elements (objects, persons), structure (people’s actions), people (e.g. child, teacher), environment (e.g. grass, sun, animals), objects (e.g. table, bridge) or text (title, label, words). Kuhn also proposed an evaluation of space, location, social relationships and activities while paying attention to the thematic evaluation of drawings (Kuhn, 2003). The researcher was guided by these suggestions as well as strategic questioning suggested in the literature for interpreting children’s drawings (Freeman & Mathison, 2009). A selection of Freeman & Mathison’s questions given to guide visual analysis, are presented in Table 4 on the next page.

Table 4

Questions Used to Guide Visual Analysis Focus Analysis questions

Subject Matter

 What are the physical features of the image?

 What is the relationship of the image to the current practices? To identities? How is the image socially situated?

 What common experiences are invoked?

 How does the image relate to bigger ideas, values, cultural constructions?

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Creation

 What are the design features? Are images scaled or mapped?  What values and knowledge are communicated by the

creator?

 How does the image relate to values of the time and place? Audience

Viewers

 What impact does the image have on viewers?

A breakdown of the stages of data analysis are illustrated in Table 5 below:

Table 5

Stages of Data Analysis

Dataset 1 Dataset 2 Data Corpus

Step 1 Step 2 Step 3

Analysis of Visual and Verbal Data using TA and supplementary approaches (Kuhn, 2003; Freeman & Mathison, 2009; Saldaña, 2013). Analysis of SPs Data using TA including presentation of thematic maps. Discussion pertaining to Datasets 1 and 2.

Chapter 5 Chapter 6 Chapter 7