In light of the feedback from my transfer viva, I am now aiming to revisit my data analysis and specifically aim to apply a thematic analysis to identify a number of key themes, ideas and patterns that have emerged and to apply a recognised academic framework for analysis to my data.
As Clarke and Braun (2013) have recognised, Thematic Analysis (TA) is essentially an analytic method (rather than a methodology) used for identifying and analysing patterns in qualitative data, which is flexible in its’ approach so that it can be applied within a range of theoretical frameworks. In other words, it ‘does not require adherence to any particular theory of language, or explanatory meaning framework for human beings, experiences or practices’ (2013, p. 121). In an earlier work, Clarke and Braun (2006) proposed a six phase approach to thematic analysis, which is a model that I shall adopt going forward in my own research. The first step is familiarisation with the data which means that I now need to go back over all the data that I have collected so far, ranging from my own field notes made in and after meetings, the blog posts that students had completed for my pilot study, the scale charts that they had also completed for me and more recently the interviews that I recorded with participants.
Indeed, it seems that it is a good point in my studies to go back over the data that I have already collated, as it would not only help to provide verification and validity, but it would also help to fulfil the cyclical nature of my action research standpoint, particularly in terms of clearly identifying some of the key theoretical themes and ideas that I am hoping to analyse. Going back over the data will help me move into the second stage of the model – coding – which involves generating pithy labels for important features of the data, to capture both a semantic and conceptual reading. After this the researcher is encourage to collate all of their codes and relevant data extracts. (Braun & Clarke, 2006)
Having established codes and relevant data extracts I will then start the process of searching for themes, whereby a theme is identified as ‘a coherent and meaningful pattern in the data relevant to the research question’. Braun and Clarke (2006)see this as an active searching process, in that themes are not hidden in the data waiting to be discovered, but rather an active process whereby the researcher constructs the themes. Theoretically this is in line with my constructionist research paradigm, whereby the process of research is collaborative, co-constructed, qualitative and shared. Braun and Clarke complete their proposed approach by: reviewing themes, defining and naming themes and lastly writing up. After submitting this post viva document, this is exactly the approach that I will be following.
Ending this particular document, it is probably worth noting some of the possible pitfalls of applying such a framework to my data. For instance, it is recognised that a common feature of weak TA is using the data collection questions as themes, in other words the themes should go beyond the questions (Braun & Clarke, 2013) Also, the lack of validity, from perhaps a vague coding system could mean research results from the data appear so subjective that they hold little reliability, meaning or validity and therefore lack transferability to other frames of reference or study. Similarly, the thematic analysis may reflect a single and particular standpoint and again may lack any degree of universality or standardisation. However, this does not deny that such an approach to analysing qualitative data should not ignore the multiple and competing stories that may emerge, which is a process ‘consistent with the principles of social constructionism’ (Braun & Clarke, 2013, p. 126)
In summary, I hope I have answered the three prompt questions from the transfer viva and I hope that I am now ready to embark on a more detailed enquiry into my own data to identify key theoretical ideas and themes that can used as part of the conclusions of this research study.
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