In this section I explain the process of data analysis that was undertaken, with a step by step description of thought processes and activity. The data set
formed 10 transcripts ranging from 2,550 to 5,859 words, over 43,000 words in total. The interviews were spaced out over the autumn and spring of the
academic year 2015-2016 and this meant that I could note down reflections on the interviews soon after, usually the same evening (an example of a piece of reflection is presented in Appendix 2.5). I transcribed the tapes verbatim the same week, adding in the associated non-verbal behaviours recorded in the field notes, so that these could be drawn upon in the later analysis.
When I set about the first reading of the transcripts I recalled Guest et al.’s comment that:
“Transcribed conversations are full of oblique references, incomplete statements, hemming and hawing, incoherent mumblings, and cognitive leaps from one idea to a seemingly unrelated other.” (2012, p.65)
My first impression of the nurse-participants was that they were enthusiastic about expressing their views on the learning process, but they had not come
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prepared to make a statement, in the sense that their thoughts tumbled out in chaotic bursts of instant question responses, followed by a slightly more detailed account and then snippets of deeper reflection completed the story.
Subjects that appeared to worry them were frequently returned to and this pattern is identified in the findings chapters where I refer to the frequency of a word or phrase used and the amount of time that was spent on a subject in the interview as a whole.
I read each of the transcripts first for general understanding and to grasp key concerns. Although I had my own agenda in terms of guiding the interviews through a series of questions I think it is important to recognise that as volunteers the nurse-participants too have reasons for agreeing to be interviewed. Silverman (2010) points out that it is worth looking at the discussion as a whole, to learn what interviewees are ‘doing’ in the way they present activity, and this was done on the second reading. For my
nurse-participants it was a mixture of sharing enthusiasm for the learning process and their successes, then raising issues they believed needed addressing and
debriefing over unanswered questions they had.
My task was to set about “locating meaning in the data” (Guest et al, 2012, p.
49) and to do this I followed Guest et al.’s guidance on applied thematic analysis. I already had a structured coding frame (ibid, p.56) set out in the interview schedule that linked to the stages of WBL project work and related to the three research questions: beginning with background motivations, then moving on to influences on the learning journey, and finally the learning that was gained from the experience. However, these were large categories that needed themes and codes identifying under these headings. Table 8 below sets out the stages of analysis that for me commenced with the third read of the transcripts. The process outlined here ensured that a precise and rigorous approach was adopted and was transparent to the outsider.
It does not indicate how many times the text needed to be read for the themes and codes to be collated, and for me this was a laborious process of repeated reading, as it involved a constant checking back to original material to ensure that all possible issues were covered and there were no gaps. As a researcher I
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did feel as though I had immersed myself in the data. I found drawing up separate tables of transcript analyses for each nurse-participant with
reorganised text material grouped under themed headings helped me to pull together references that recurred through the text.
Table 8- Stages of Applied Thematic Analysis
Read the text and propose themes.
Refine themes into codes with well-developed definitions.
Have two or more analysts read a sample to discuss coding.
Compare the way each analyst coded the sample.
If the results are the same, continue coding with periodic rechecks or if not identify why there are differences and adjust code definitions and recheck and repeat if necessary.
(Adapted from Guest et al 2012, p.70)
To cross reference material across transcripts I did experiment with different methods of selecting sections of text. Miles and Huberman (1994) suggest playing with the data in terms of looking at frequencies of terms used, making comparisons across interviewees and considering patterns. However, Guest et al. warn that the segmenting of text into themes and codes leads to further and further abstraction (2012, p.52).The nurse-participants seemed to me to be very individual in their accounts and in my findings I have tried to keep large parts of individual accounts together to contextualise remarks and to help
understanding of the project activity discussed. This is a thematic analysis, but I have tried to gather sections where nurse participants have gone back to a topic, and this I think has the advantage of elucidating comments and
strengthening the data by showing the consistency of the individual’s stance.
This is illustrated, very briefly, in the example of coding discussion in Appendix 2.5.
In Table 8 I have combined the last three of Guest et al.’s (2012) stages into one, because the process could be short or prolonged discussion depending on the transcripts. For instance, when asking: ‘what does a particular sentence reveal about someone’s beliefs,’ there are going to be differences of opinion and debating these different perspectives was important to deciding the most
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accurate interpretation. I found referring back to the audio recordings,
checking the emotional tenor of particular phrasings and the context in which statements occurred helped to clarify stances. The accounts the
nurse-participants told were their interpretations of what had happened and needed to be deconstructed, identifying the thoughts, feelings and actions reported.
Habermas refers to a ‘double hermeneutic’ (1984, p.109-110) meaning that there is a striving to interpret a world that is already an interpretation and not objective fact and therefore great care needed to be taken with this level of abstraction. Once common socio-political or educational themes were
identified they were compared to the Habermasian concepts identified earlier (Life-World, Systems and Colonisation and Communicative Reason).
Alvesson argues: “Without a theoretical understanding, any use of interview material risks being naive” (2003, p.14). By questioning the terminology used by the nurse-participants and querying how these linked to health service mantras, acceptance or rejection of these influences could be explored.
Scambler (2001) suggests it is about identifying which language has become the ‘norm’ in ‘life-world’ discourse.
The question for me was what constituted and differentiated evidence of the different concepts. Table 9 highlights the kinds of evidence I sought in the transcripts. However this was not a precise list, but took the form of an initial consideration of what could be elements of these different phenomena and each was refined more carefully into a codebook of definitions (Guest et al.
2012).
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Table 9 - Elements Sought for Interpretation
Concept Possible Examples
Close working relationships with patients Sense of professional identity
Professional networking Hierarchical structures Openness to gradual change
Colonisation by Systems
Unquestioning acceptance of the following:
Emphasis on the competitive market
Emphasis on performance measurement and efficiency Cost cutting
Customer orientation
Blurring of professional boundaries Increased skill mix in nursing
Economic cuts to educational development Reduced respect for clinical management view Contradictions between innovation and risk aversion Isolated / alienated working
Emphasis on individual responsibility - blame culture Narrow scientific view of evidence-based practice as sole source of knowledge
Communicative
Discourse and processes of argumentation Social networks
In order to demonstrate how the larger category of influences on the learning journey was broken down into different aspects of the learning environment I
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present a short example here in Table 10 of a breakdown of a theme16 into a code17 definition that relates to research question two.
Table 10 - Structure and Thematic Coding
R.Q. 2. How did the learning around the project process and the overall educational experience appear to be affected by the wider socio political aspects of healthcare and could these factors be viewed as ‘colonising’ the nurses’ existing ‘life-world’?
Theme Potential Colonising Influence Coding definition
Economic Driver
Textual reference to the importance of financial considerations without reference to patient clinical benefit.
Transcript Illustration
Beryl:
“Because of the end of the day it is all down to beds isn’t it?”
Habermas (1984, p.114) emphasises that accounts should be seen as rational claims not subjective statements and to me this was important in respecting individual views of a situation. Appendix 2.7 contains an illustration of a piece of transcript analysis. The detailed findings are presented in chapters six, seven and eight. Data analysis and interpretation are integrated under the headings of
‘The WBL Life-World of Nurses’, ‘UKNHS Market Systems and Colonisation’ and ‘Communicative Reason’.
In this section I have explained how the analysis was approached: first individually, then comparatively, and I have gone on to indicate how interpretive themes were drawn out. This process was complex, because it involved getting underneath the language of health service reform, unravelling deeper concerns and dealing with unexpected findings. In the next section I acknowledge how as an insider researcher I had a unique opportunity to illuminate ‘new forms of micro-politics’ (Anderson and Herr, 2010, p.18), but needed to be aware of my own influence upon this process of exposure.
16“Theme: A unit of meaning that is observed (noticed) in the data by a reader of the text.” (Guest et al.
2012, p.50)
17 “Code: A textual description of the semantic boundaries of a theme or a component of a theme.”
(Guest et al. 2012, p.50)
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