Data analysis

Creswell (2002) acknowledged specific variations in the application of analytical procedures, and presented a series of generic steps involved in the analysis of qualitative data. Data analysis includes the thorough review, deconstruction and reconstruction of data. This process aims to transform understanding of what the data are revealing through carefully reviewing and ‘breaking down’ elements of the data. Accurate description of all data is the first stage of this essential process and lays the basis for careful analysis. There should be detailed review of all contributions from the research participants, although comparable views need not

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all be necessarily quoted to avoid unnecessary repetition. It is essential to consider exactly what the data reveal and the context in which they emerged. Thick description pays attention to contextual detail. Within interpretive qualitative research, data analysis must extend beyond description of data to consider context, concepts and the interconnections between them. Silverman (2013) advocated extending beyond the identification of individual units or themes to envisioning the connections between the units and therefore to considering how they fit together. Data analysis can commence as soon as data start to be collected and continues throughout the data collection phases. The depth and detail inherent within qualitative data requires an analytical process that carefully extracts the most pertinent and informative elements from within the data. This can done by filtering through the data, allowing the most pertinent elements or patterns of the data to surface. This can be termed ‘fracturing out themes’ which exemplify those core elements.

The data collected within this research emerged from the three methods and was illustrative of participant views, both individually and collectively. As an essential part of this research process I needed to be integrally involved at every stage of collecting and considering the data. The observation method was recorded through my notes based on the observations. This commentary created a foundation on which to reflect on both the process of using the new trigger and whether the prompt questions would be appropriate. The guided group

discussions included hand recorded data that I transcribed as statements and conceptual maps using Word software. The interviews were audio recorded in full and transcribed. Transcription in itself can be viewed as a potentially subjective process, one of the key early stages within analysis and worthy of tracking

through a careful audit (Markle, et al. 2011). All data were reviewed in full several times, as well as being annotated as the data were analysed thematically. All interview transcriptions were annotated using track change comments, again using Word software. Each step in the analytic process helped to explore details, surface patterns, reinforce emergent themes and achieve data saturation. Data saturation was deemed to have occurred when data revealed recurrent words, phrases and other elements, such as non-verbal responses.

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These structured steps were incorporated into the specific approach to the data analysis within this research. I completed all transcription of data personally. Interview recordings were reviewed in full several times before, during and after transcription. Once I had transcribed the data I thoroughly read through each transcript on several occasions to take on board the entirety of participant

responses. Manual data analysis was a clear choice for this qualitative research. This process extended beyond full transcription of the data, noting any additional non-verbal or verbal cues within the margins of the data transcripts. It was

particularly helpful to have the whole digital audio recordings kept available to enable listening to the interview conversations more than once, even after the data had been fully transcribed. Digitally recorded audio data enables the absorption of the entire conversation, inclusive of some of the complexity of interpersonal communication, for example, tone of voice, some verbal cues and pauses. All of these constituents of the process facilitate a sense of the ‘whole data’.

The context in which the data were collected was regarded as highly important, so this background was reflected on as the data were reviewed. The data were analysed with awareness maintained of the organisation involved, the time frame in which the study had progressed, the social context and the network of

relationships involved. Through each step of the data analysis, additional meaning was emerged. Meanings were not always immediately explicit within data and therefore recurrent engagement with the data was required to identify the themes across all of the data emerging from within the three methods. In order to achieve a satisfactory outcome in the data analysis, the three facets of description, context and intention have been regarded as integral and co-

dependent. It was essential therefore to concurrently and recurrently engage with the detail of the data, as well as background in which it was collected as well as relating this back to the specific aims of the research.

3.11.1 Thematic data analysis

In line with the research approach and type of data collected, I used a thematic approach to the data analysis. The influence of Creswell’s (2003) six steps of

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data analysis, as considered in the previous section, is recognised. The data analysis progressed to achieve a detailed appreciation of the content and context of the data, whilst delineating specific elements of meaning relevant to the

research aim and questions. After extracting initial key examples of data in full, the analytic process was refined to progress toward the establishment and verification of units of meaning, moving through key words, key phrases, looking for patterns in the data. Redundant or extraneous data were discounted if they distracted from the research aim and questions. The notes made during the guided group discussions and observations were also subject to careful review, highlighting key words and phrases. This process had been strengthened by the participant engagement in producing data, so the participants’ notes on the flip chart paper following the film viewings could be referred back to. Therefore it could be argued that this enabled a further layer of cognitive engagement with what they had thought and what they wanted to say.

Clustering of relevant meanings involved exploring links within the data, determining themes from clusters of meaning, which was visualised and

represented in thematic maps. This was a particularly helpful part of the approach to analysing the group discussion data. Thereafter key words can be identified. The most pertinent sections from the data were highlighted in colours, coded for each theme. These sections were then developed through coding, through to themes, again highlighting what was most meaningful. The research identified and entitled themes based on the language used by the participants. In effecting these steps I was able to identify general and unique themes for all data. Each method had built on the last, with increasing levels of detail. The interviews contributed particularly rich data and one of the interviews, revealing the style of colour coding, is included in Appendix II. The transcripts were all also annotated by hand.