Attride-Stirling (2001) indicated that as qualitative research becomes increasingly recognised and valued, it is imperative that it be conducted in a rigorous and methodical manner to yield meaningful results. Nowell et al. (2017) further indicate that this is vital to facilitate trustworthy qualitative research which can be transparently communicated to others.
Thematic analysis is one of several methods for identifying, analysing, organizing, describing, and reporting themes found within a data set (Braun & Clarke, 2013). It was chosen in preference to other approaches, such as Interpretative Phenomenological Analysis (IPA) (Smith, Flowers, & Larkin, 2009) because the study required a range of data sources which went beyond providing insights into how the participants made sense of the programme. Thematic Analysis also offers a theoretically flexible approach compatible with both essentialist and constructionist paradigms within psychology, and is therefore, appropriate for the critical realist position taken in this study. It also provides clear guidelines to conduct analysis in a systematic and rigorous manner, providing core skills that are useful for conducting many other forms of qualitative analysis in the future (Braun
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& Clarke, 2006; 2013). Joffe (2012) further indicated that Thematic Analysis is best suited to elucidating the specific nature of a given group’s conceptualization of the phenomenon under study, which, in this case is the Sensory Intelligence programme. Braun & Clarke (2006) also commented specifically on the advantages of using Thematic Analysis for working within collaborative research paradigms with participants as collaborators and for producing qualitative analyses suited to informing policy development, both of which are pertinent in this case.
Thematic analysis can be inductive or deductive. An inductive approach is data- driven and can provide a ‘rich description’ of the data set, whilst a deductive approach is much narrower and is guided by the researcher’s theoretical interests (Joffe, 2012). The use of a mainly deductive thematic analysis was considered appropriate in this study to answer the very specific research questions. A deductive approach was also deemed appropriate to provide more detailed accounts of causal mechanisms with relation to individual profiles.
A semantic approach was selected to identify patterns in semantic content, which are then interpreted to theorise the significance of these patterns and their broader implications in relation to previous literature. Given the sociocultural context of the research, some latent themes were also generated.
Braun & Clarke (2013) suggest six key stages which were adhered to in this study: 1. Reading and familiarisation with the data.
2. Generating Codes.
3. Identifying patterns across data (from Codes to Candidate Themes). 4. Reviewing and revising Candidate Themes.
5. Defining themes-theme definitions. 6. Producing the report.
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Braun & Clarke’s (2013) Key Stages Adhered to in the Study.
Stage Action
1. Reading and familiarisation with the data.
All of the interviews were conducted by the researcher facilitating familiarity with the data throughout collection. The writing of field-notes on the day of school visits and the personal transcription of interview data was also conducted by the researcher. Transcription was orthographic (‘verbatim’) using the transcription notation suggested by Braun & Clarke (2013) to produce a through record of the words spoken to ‘give voice’ to the group of people involved. Sections of text were preserved in their entirety in the transcriptions as removing codes from their contexts can strip data of its meaning (Joffe, 2012).
Braun and Clarke (2013) suggested repeated listening and reading of the data, making note of anything of interest, which should be recorded as ‘noticings’. ‘Noticings’ with respect to individual children were collated in their case study ‘pen pictures’ (see Appendix 18) as well as highlighted on the transcripts (see Appendix 20) prior to coding and development of themes.
2. Generating Codes. Braun & Clarke (2013) indicate that a code is a word or brief phrase that captures the essence of its relevance to answering the research questions and they recommend coding as much data as possible as this may prove useful later. All data relevant to the research study, other than that excluded on the ethical grounds of ‘nonmaleficence’ was coded. Boyatzis (1998) suggested that a good code is one that captures the qualitative richness of the phenomenon. Attride-Stirling (2001) added that codes should have quite explicit boundaries, ensuring that they do not overlap with others. Closer reading of individual utterances led to the production of initial codes, produced manually, which were written into the margins of the transcribed material. This involved working systematically through the entire data set, giving due regard to each data item without bias, and identifying interesting aspects that appeared to be repeated across individuals. Different coloured highlighters were used, noting codes within the margins of the transcripts.
Initial codes were checked with an EP colleague, not involved in the research, who independently generated her own codes for randomly selected samples of interview transcripts, and discussion contributed to general areas of agreement. A working
123 CONTINUATION: Table 27
Braun & Clarke’s (2013) Key Stages Adhered to in the Study.
Stage Action
3. Identifying patterns across data (from Codes to Candidate Themes).
example of initial coding is included in Appendix 21.
This involved sorting and collating the different codes into potential ‘Candidate’ (provisional) themes by considering how different codes may combine to form sub-themes and main themes.
All codes were categorised as a result of general similarities and differences between themes. Nothing was discarded during this phase as Braun & Clarke (2006; 2013) stated that following refinement certain themes may need amalgamation, re-categorisation or deletion. Subthemes were also identified as important aspects of main themes in different ways.
4. Reviewing and revising Candidate Themes.
This involves allocating each extract to the Candidate theme selected to consider whether the collated extracts form a coherent pattern. It is a process of double-checking the entire dataset as an important form of quality checking.
This exercise, in conjunction with discussion with the research supervisor, led to slight revision of one central organising theme and some shifting of coded data in or out of different themes. A more coherent set of main themes and subthemes, distinctive from one another, but working together and relating to the research questions in different ways, was achieved as a result. This is provided in Appendix 22.
5. Defining themes-theme definitions. This involves clearly defining themes to state what is unique and specific about each, by distilling to an essence what each theme represents. (Braun & Clarke, 2013). By the end of this phase, researchers should be able to clearly define what the themes are and what they are not, otherwise further refinement may be required. This phase therefore requires the investment of considerable time to develop the themes, which will increase the probability of developing credible findings (Lincoln & Guba, 1985). Together, the themes are designed to provide a rich, coherent and meaningful representation of dominant patterns in the data which address the research questions. This can be represented visually in a Thematic Map (provided in Table 31).
124 CONTINUATION: Table 27
Braun & Clarke’s (2013) Key Stages Adhered to in the Study.
Stage Action
6. Producing the report. A narrative is written which tells the reader the ‘story’ of the results via a rich, interconnected, logical analysis, starting with a key theme, if apparent, then building on the previously discussed theme (s) to provide a rich and detailed narrative. Each theme is supported by extracts from the data. Braun & Clarke (2013) suggested that ‘vivid and compelling’ extracts should be used to illustrate analytic points being made about the data, with use of extracts across the data showing the breath as well as depth of a theme.
Analysis involves telling the reader what is interesting and why, with extracts provided to enable the reader to make their own sense of the data. At the end of the analyses, the conclusions reached are drawn from across all the themes.