Analysis of interview data

Inductive or deductive approaches are used in analysing qualitative data in research (Spencer, Ritchie and O’Connor; 2004; Lathlean, 2006). The inductive approach involves analysing qualitative data without any predetermined theory, framework or structure by using the actual data generated during the study to attain the structure for the analysis (Lathlean, 2006; Burnard et al., 2008), most commonly used approach to analyse qualitative data. Conversely, the deductive approach involves the use of predetermined structure,

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theories or framework in analysing research data (Burnard et al., 2008). Fundamentally, researchers using deductive approach impose their own structure or framework or theories on the data and then use these in analysing the interview transcripts (Williams, Bower and Newton, 2004). Although the deductive approach is relatively quicker and easier in analysing qualitative data, the inflexibility of the approach could potentially bias the entire data analysis process since the coding framework has been predetermined which limits the chances of theme and theory development (Burnard et al., 2008).

The framework analysis (FA) approach was applied in the analysis of the data generated in this study. The choice of this approach was guided by the research aims and the research questions, which the study sought to answer. The analysis aimed to organise, explore and explain the data collected with a view to understanding: the meaning of living with ESKD to the participants and how this influenced their treatment, describing the experiences of the dialysis patients over time, and how societal, financial, cultural, geographical, familial and capture other wider contextual issues influencing their ability to access and sustain dialysis treatment. FA was selected because it is a flexible approach that can be adapted for use in many qualitative approaches, not aligned with any theoretical, philosophical or epistemological approach (Gale et al., 2013). It provides a clear track of how decisions were made in arriving at the themes, enhances transparency in the data analysis process and encourages teamwork (Ritchie and Lewis, 2003; Dixon-Woods, 2011; Swallow, Lambert, Santacrose and Macfadyen, 2011; Ward et al., 2013). The FA is a method of data analysis rather than a research paradigm and its ontological position adheres closely to subtle relativism (Snape and Spencer, 2003), which argues that the social world researchers seek to study exists independently of individual subjective understanding, however is only accessible in qualitative research through participants` interpretations which are further interpreted by the researcher (Hammersley and Atkinson, 2007). Using this approach in the analysis of the research data enabled me to remain true to the data and remain open-minded in ensuring that my preconceptions did not influence the interpretations given to the interview extracts. The analysis process of the data occurred throughout the entire period of data collection and beyond until the final research report was produced.

The FA approach involved five distinct stages which were interlinked, methodical and rigorous (Ritchie and Spencer, 1994) (Box 3). The process of the analysis started during the phase of data collection and it involved actively looking for issues that relate to the research questions in the data, thinking about the patterns of meaning and reflecting on the entire

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experience of the data collection (interviews, documents and field notes/research diary). The final stage was the reporting of the final themes from the data.

Box 3: Stages of framework analysis (Ritchie and Spencer, 1994; p.173)

 Familiarisation with the data

 Developing and testing an analytical or theoretical framework  Indexing

 Charting

 Synthesising data by mapping and interpreting

4.9.2.1 Familiarisation with the data

Having conducted all the interviews at every stage of the data collection myself, it was easier and less time-consuming for me to familiarise myself with the depth of the data from the beginning of the data collection process (Spencer, Ritchie, Ormston, O`Connor and Barnard, 2013). The interviews were transcribed verbatim shortly after the completion of each stage of the data collection before the commencement of the next wave of interviews. Familiarisation involved listening to each interview recordings, reading the transcripts, and making any analytical notes of preliminary impressions and thoughts in the margins of each transcript. Despite the large volume of the data gathered in each stage, it was necessary to review each of the transcripts in detail at each phase. All the key phrases were highlighted using pen highlighters and labelled as the initial codes as a means of staying true to the data instead of using the Nvivo (Spencer et al., 2013). Reviewing the entire transcripts ordinarily is not required when using FA in data analysis as this normally occurs in the later stages of the data analysis process (Srivastava and Thomson, 2009). However, to familiarise myself with all the key issues, ideas, and themes during each wave of data collection, to inform the subsequent interview questions continuous immersion in the data was necessary. All the field notes and reflexive journal excerpts documented through each data collection wave were read alongside the transcripts and referred to during analysis.

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4.9.2.2 Developing and testing an analytical or thematic framework

The development of the framework started with the comparison of all the codes that were applied to each transcript to ensure uniformity. Ritchie and Spencer (1994) highlight that the process of developing framework categories is informed by both a priori concerns and the emergent issues arising from the earlier familiarisation stage. However, the degree to which this is used involved a process of trial and error to identify the categories that provide the best fit for the research data and the research questions. Accommodating both a priori and emergent issues in the development of framework categories ensured that the framework not only focused on the research questions but also incorporated the research interests of the researcher and the issues most pertinent to the participants (Parkinson et al., 2016). Initially, the framework was used as a data management system rather than interpreting the data. This led to the formation of framework categories and the generation of initial codes, reflecting the concepts uncovered by the guiding conceptual framework (discussed in the previous Chapter), and incorporating key issues followed up through the data collection waves. The framework categories had to be flexible and remain open to participant-oriented issues arising from the research data, so the framework was tested through a pilot analysis of the first wave interviews. There were codes which did not fit into the framework, which resulted in the inclusion of “other” code under each category to capture relevant data that did not fit but could not be ignored.

Box 4: Analytical framework

 Meaning of living with ESKD  Impact of ESKD on quality of life  What influences attendance to dialysis  Challenges to accessing dialysis treatment  Experiences of symptoms and its impact  Experiences of care by doctors and nurses

 Interaction between the interviewee and the interviewer  Others

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There was also overlap across categories which led to the adjustment and refining of categories and clarity of key emerging sub-categories. This refinement helped ensure data fit and reduced repetition (Ward et al., 2013), as shown in Box 4.

4.9.2.3 Indexing and Charting

The refined analytical framework (Box 4) was applied to each of the subsequent interview transcripts. Working through each of the transcript texts using the Microsoft Word document, chunks of the text were highlighted, indexed and categorised (Spencer et al., 2013). Data which did not fit into any of the categories were placed under the “Other” category.

After the completion of the indexing processes as described, data was organised into a more manageable format, to enhance data analysis for the next stage of the FA approach (Parkinson et al., 2016). Summaries of data were extracted from each transcript and charted using a spreadsheet to generate a framework matrix. Efforts to maintain a balance between the reductions of the data and retain original meaning, views, and opinions of the participants were made. To ensure this, quotes of the participants` responses to the interview questions and references to illustrative or interesting quotes were copied word-for-word and included in the charts (Gale et al., 2013). Alongside a summary of thoughts regarding the quotes for each participant was noted on the margin of the Microsoft Word spreadsheet (Furber, 2010) as shown in Appendix 11. Considering the frailty of many of the participants in this study, there was no need for me to paraphrase their words as they were taciturn in the interviews, so the words of the participants were used, as verbalised by them without altering the meanings underpinning and upholding the philosophical positions of the study. Appendix 11 shows extracts from the framework matrix used in the study. The rows show the categories from the framework while the participants are shown in the columns in a case-based chart. This enabled the summaries to be read across for either within-case analyses, or downwards for between between-case analyses or for the analysis of any specific theme (Ward et al., 2013).

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4.9.2.4 Synthesising data by mapping and interpreting

The aim of this stage was to move beyond data management towards understanding what the data means, to pull together the data maps/charts and interpret the entire data set (Ritchie and Spencer, 1994). Patterns in the data were explored and sets of themes developed to capture the participants’ experiences of living with ESKD, dialysing within systems with unequal access to healthcare. Charts were reviewed, checking all the extracts or quotes in the charts against the original interview transcripts, the field notes and the audio recordings and compared the various themes and sub-themes with each other to identify their relationships and identify if any changes or merging was required (Ward et al., 2013; Parahoo, 2014). This led to the merging of some themes where there was insufficient data to support. This was necessary to avoid what Bryman (2016) refers to as “anecdotalism”- a term which describes situations where few instances of a phenomenon are erroneously considered as a theme or a pattern when they are truly idiosyncratic and could therefore not represent a theme. Other themes were expanded, separated or even renamed to appropriately reflect the data and the relationships existing between them (Parahoo, 2014). The entire process of the data analysis and theme development was reflective and iterative, independently checked by the supervisory team for accuracy, truth and credibility. Each theme (discussed in the next two findings chapter) were constructed to reflect the contents and the hierarchy of meaning within the data, while the labels given to each theme and sub- theme were deliberately concise to express the core meaning of the themes and the sub- themes.