Bloque V. Compañeros de trabajo

The method of thematic analysis was used to analyse the data collected in this study.

This involves the researcher searching across datasets to find repeated patterns of meaning. There is some debate about the nature and implications of ‘thematic analysis’. Some argue that it is an ‘unsophisticated’ method which merely describes patterns in data or is merely part of a process within larger analytic traditions, such as grounded theory (as highlighted by Braun and Clarke, 2006; Braun et al., 2015).

Others however, emphasise the value of thematic analysis and argue that it is a method of analysis in its own right (Boyatzis, 1998; Joffe and Yardley, 2004; Braun and Clarke, 2006).

Unlike other data analysis methods, which are theoretically committed methodologies, thematic analysis is typically described as a “flexible” method (Boyatzis, 1998; Joffe and Yardley, 2004). Indeed, Braun et al. (2015: 96) refer to “flexibility” as being the

“hallmark” of their approach to thematic analysis. As a method, thematic analysis can be used with quantitative, qualitative, small and large datasets, it can be used

inductively or deductively and it does not tie researchers to pre-existing theoretical commitments (Guest et al., 2011; Braun et al., 2015), although it is essential that researchers are explicit and transparent about their epistemological assumptions (Braun and Clarke, 2006). Thematic analysis has also been praised for being a useful

’contextualist’ method (Braun and Clarke, 2006: 9) which enables insight into the meaning that individuals attach to their experiences and for these to be related back to the wider social context shaping such meanings (Golafshani, 2003; Braun et al. 2015).

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Braun and Clarke (2006) outline 6 key stages in this process of thematic analysis, which are often drawn upon by scholars using this method. These comprise: (1) Become familiar with the data, (2) Generate initial codes, (3) Search for themes, (4) Review themes, (5) Define themes and refine and (6) Write-up. As acknowledged by Braun et al., (2015), despite the analytical distinction between these phases in reality the relationship between them is better described as ‘fluid’ and ‘recursive’ rather than linear. This is true of the application of this process to the data in this study.

In order to organise and manage the large amount of data collected I used NVivo qualitative data management software) to organise and code data. However, I

undertook more in-depth analysis by hand. Familiarisation with the data began whilst I was still in the field, where I began to organise and review collected data and undertook some preliminary analysis. This included noting down emergent themes, points of uncertainty and information that I felt was missing, which enabled me to clarify issues and explore particular themes in-depth with participants and check the accuracy of my own collated information about matches against the clinics’ own recorded information (where this information was recorded in different places). I also became more familiar with the data as I wrote up extended field notes and transcribed interviews. After leaving the field I read the data I had collected several times to familiarise myself with its nature, contents and feel (e.g. allowing myself to be immersed in thick description), noting down my reflections about recurrent, differentiating, emergent and interesting themes.

I made the decision to analyse and write-up the perceptions and practices of clinicians and recipients separately and discuss similarities and differences between these two groups in the conclusion chapter (other decisions made throughout the analytical process will be outlined further below). Data were sorted according to ‘codes’ to begin organising it in a meaningful way (Braun and Clarke, 2006; Lofland et al.,

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2006). A “code” refers to “a succinct label (a word or short phrase) that captures a key analytical idea in the data and conveys this to the researcher” (Braun et al., 2015:

100). To begin with, ‘open coding’ was used so that codes ‘emerged’ from the data.

These codes were developed and modified throughout the analytical process. The analysis of ‘negative’ cases which departed from dominant accounts were especially helpful in providing contrasting interpretations of the data and illuminating and refining understanding of dominant cases (Lofland et al., 2006).

I undertook ‘semantic coding’, where descriptive information from or about participants was coded, and ‘latent coding’, which was more interpretative and

focused on the “underlying ideas, assumptions and conceptualisations –and ideologies – that are theorised as shaping or informing the semantic content of the data” (Braun and Clarke, 2006: 13). This allowed me to capture descriptive information- such as the categories used by the clinics and more interpretative data – such as the meanings that clinicians’ and recipients’ attached to these categories. Some codes were

developed from the language used by participants themselves whilst others were

‘observer-identified’ codes which reflected the meaning of the data and were sometimes borrowed or adapted from existing literature (Lofland et al., 2006;

Hammersley and Atkinson, 2007). I coded paragraphs, rather than merely sentences of interest, within individual data sources to enable contextualisation of the data encompassed under codes. Thus, single data excerpts (e.g. a paragraph) and single data sources (e.g. an interview transcript) were coded re-coded and reviewed multiple times as new relationships in the data were identified.

The process of deciding which individual codes might combine to form an

overarching theme took place throughout the process of analysis. The large amount of codes generated during coding meant that the process of reviewing and re-organising codes and themes could not wait until the end of the coding process (as ideally

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prescribed by Braun and Clarke (2006)). As I generated a longer list of codes and built up the data excerpts under individual codes some codes and themes were re-conceptualised and re-named, some were merged and some were deleted. I reviewed all of the themes, sub-themes, codes and data within them and progressively re-organised these so that there was ‘internal homogeneity’, i.e. coherence between themes, and ‘external heterogeneity’, i.e. clear distinctions between themes (Braun and Clarke, 2006) (See Appendix 8 for an example of the recipient coding frame).

This analytical work took place manually. The themes and my interpretation of them were checked across the dataset as was the relationship between individual themes and the overall thesis. I paid particular attention to similarities and differences within and across themes, exploring how different people and different levels of analysis addressed the same theme. The use of different methods and sources of data collection were especially helpful in enabling me to contrast and triangulate differing accounts of the same phenomena and to understand the nuanced ways in which different contexts, situations, tools, people and interactions shaped this. Thick descriptions of the settings and phenomena observed were also included as a means to situate findings within the context and different circumstances in which data was gathered (Geertz, 1973). Such descriptions also helped to ground interpretation of participants’

accounts in relation to wider social norm, e.g. situating participants’ perceptions of

‘race’ within wider historical, political and academic understandings of ‘race’.

I analysed qualitative data alongside descriptive statistics of the overall matches made at each clinic (outlined further below) which enabled comparison of participants’

discourses and with their material practices and contextualisation of each of these data sets. The identification, reviewing and refining of emergent themes was

predominantly undertaken by hand. Whilst I found NVivo useful for organising and retrieving data I found it less useful for undertaking in-depth analysis and this much

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of this manually. I used a range of paper materials (from sticky labels to large flip charts) and coloured pens to explore links, patterns and variability within and between the coded data, themes and sub-themes, to develop key emergent concepts.

As noted by Braun and Clarke (2006), data cannot be analysed in an epistemological vacuum and researchers should make their epistemological assumptions explicit.

Although themes were derived from the data they were also informed by the

theoretical frameworks chosen before entering the field. The main theory utilised was (bio)medicalisation theory, which informed the use of concepts such as

‘medicalisation’, ‘consumerism’, ‘stratification’ and (to a lesser extent) ‘racialisation’.

‘Sensitising concepts’ provide analysts with a general sense of reference and direction in which to look, rather than prescriptive guidance on what to see (Patton, 2015).

These concepts were related to the research questions and included: ‘matching’,

‘classification’, ‘allocation’, ‘recipient preferences’, ‘recipient selection practices’,

‘recipient agency’, ‘welfare of the child’ ‘race’ and ‘ethnicity’. The research questions framing this thesis too were refined during the research process in order to provide a detailed, nuanced and coherent examination of a particular phenomenon, i.e. the matching process.

The writing up of this thesis was an integral part of the analytical process, as

acknowledged by Braun and Clarke (2006). This process was shaped by constraints of space and time, as well as the processes outlined above. Several decisions were made in the writing up of this thesis, including: defining the scope of matching-related themes, the presentation and organisation of findings and which themes to include or exclude. It was tempting to explore the organisation of egg donation more generally in the clinics, particularly as I had accumulated a lot of data on this, and to locate

‘matching’ within this wider framework. However, I felt that this would have diluted the specific insights gained by putting matching at the central focus of study.

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Furthermore, I felt that this is what previous researchers had done and I wanted to contribute new understandings to the existing literature. A large array of coding and analysis were not used for the purposes of this thesis, including themes and codes which did not specifically enhance or relate to understanding of matching, e.g.

clinicians’ differing constructions of kinship with donors and recipients and recruitment and screening of donors and recipients.

I made the decision to organise the empirical findings chapter according to four overarching categories: clinicians’ classification of donors and recipients, clinicians’

allocation of donors, recipients’ preferences for donor characteristics and recipients’

practices of accepting/declining donors. There were overlapping themes across these categories, for example in recipients’ preferences of exclusion and their practices of declining donors (see Appendix 8). These decisions were informed by the existing literature, the research questions and the endeavour to represent the complex nuances in the different stages of matching as a process.

When writing up the themes I found the need to draw on wider theoretical concepts.

Sometimes these concepts added to analysis by helping to understand and contextualise findings and sometimes my findings also illuminated or developed aspects of the pre-existing concepts in different ways. Sometimes, when no specific concepts to explain the meaning that participants attached to their actions and interactions or the consequences of this were available, I developed some ‘new’

concepts based on existing literature and participants’ accounts (e.g. ‘kinship risk’ and

‘marked whiteness’).

122 3.7.2 Descriptive Statistics

At both clinics I collated information about the matches that I had observed, although this process was undertaken differently at each clinic. This included the ethnicity and physical characteristics of matched donors and recipients, whether the matches had been accepted or declined by recipients and their reason for declining donors (when given). At The Fertility Centre, only the ‘ethnicity’, height and weight of matched donors and recipients was obtained (for the reasons outlined earlier in this chapter).

This information was deductively coded from observational field notes of donor allocations (Boyatzis, 1998) and put into a separate excel spreadsheet for each clinic.

Before undertaking analysis I cleaned the data to make it more manageable and consistent. Each donor and recipient was assigned a code, making it possible to

distinguish individual donors and recipients with similar characteristics, and data were checked for accuracy with the clinics’ own records. Each clinic used different

categories for recording donors and recipients characteristics and so it was necessary to re-code data to higher order categories for consistency.

For example, one clinic used the categories ‘light brown’ and ‘dark brown’ to classify hair colour whilst the other clinic merely used ‘brown’, and so these categories were all coded under ‘brown’. Another example is the classification of ‘ethnicity’, where ethnic categories were haphazardly and inconsistently used between and within clinics; thus ‘Indian’, ‘Pakistani’, ‘Bangladeshi’ and ‘Sri Lankan’ were recoded to

‘Asian’. It is recognised that the racialised category of ‘Asian’ is contentious and that it loses the nuances of differences between individuals within this categories.

However, this category reflected the use of ‘ethnicity’ by clinicians and recipients, enabled comparative analysis and where relevant differences within these categories are made clear.

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Microsoft Excel was used to analyse the extent to which the ‘ethnicity’ and physical characteristics of donors and recipients matched. Each category (e.g. blue, green, brown) within each characteristic (e.g. eye colour) was compared and coded for sameness and difference, where the number 1 indicated same and 0 indicated difference. To compare height/weight differences, recipients’ height/weight was subtracted from their matched donors’ height/weight (with + indicating that recipients were bigger than donors and – indicating that recipients were smaller than donors).

Descriptive statistics, i.e. Average, Median, Mean, Mode and Maximum, were then applied to this coded data. These statistics were reported and contextualised within the wider data. At Creative Fertility, I additionally documented whether each match had been accepted or declined by donors. Using the numerical code assigned to individual donors I counted the number of times that donors had been accepted or declined by the first recipient they were matched with and the number of times that individual donors were declined, enabling insight into donors’ that were most excluded by recipients.

Analysis of this data enabled longitudinal insight into clinicians’ and recipients’

material practices and patterns of matching over time and comparison and

contextualisation with their discourses. That is, this information showed the outcome of the material practices of clinicians and recipients (separate from their negotiations and discourses). However, some caveats are necessary to note about their use. Firstly, this study is primarily a qualitative study, framed by an interpretive paradigm, and so the meaning of numbers not as important as the meaning behind them (gained through qualitative methods). Secondly, having observed the subjective, contingent and

socially constructed nature of how clinicians’ classified patients’ characteristics these categories are not taken to be objective or valid in and of themselves. However, they were included because of their importance in enabling insight into clinicians’

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practices (via clinicians’ own recording mechanisms) and contextualising qualitative findings from a different level of analysis.