Data analysis

NVIVO software was used to collate data and organise themes. NVIVO is a software package designed to assist in the management and analysis of qualitative data (Udo, 2014). Use of software packages has increased in popularity with over 400 qualitative papers outlining its use in qualitative data analysis (Flick, 2013). Its benefits are twofold: it clearly outlines to external reviewers how data has been coded and organised into themes and it allows for verification of themes by a second reviewer thereby enhancing the credibility of findings. For large multicentre qualitative studies, it allows for merging of data thus strengthening the impact

136 Chapter 3: Methods

of qualitative research (Pope, Ziebland, & Mays, 2000). It results in a more flexible, systematic and transparent approach to analysis of qualitative data than manual coding.

However, unlike SPSS it does not make any analytic decisions: this is the responsibility of the research team. NVIVO and all other qualitative software packages do not teach researchers how to analyse data nor do they provide a standardized method of data analysis.

Furthermore, it is a generic data analysis tool designed for any qualitative research approach rather than focussing on specific qualitative approach, for example, ethnography, grounded theory or phenomenology. Therefore, rigour in data analysis and presentation of findings is demonstrated by clearly articulating the method by which conclusions were drawn and through verification of themes identified and, although the view that a second analyst should be involved has been contested (Pope et al, 2000), it is considered good practice to have a second person verify themes (UK, 2017).

In this study data analysis was carried out using the thematic analysis method involving the supervision team to sense-check data, discuss themes and verify against findings. The next section of this thesis will outline the step by step method by which qualitative data was analysed.

Chapter 3: Methods 137

Exploration of data according to factors associated with adherence and additional factors associated with diabetes management

All data were analysed thematically and in the context of the stage two research question and associated aims. Thematic analysis involves the systematic analysis of qualitative data to explore and understand in depth the research question posed. It is a method of identifying and analysing patterns in qualitative data whilst maintaining structure focussing on the research questions. Braun and Clarke (2006) argue that thematic analysis is not a methodology rather it is a method of analysing data within qualitative research. They describe six phases of thematic analysis:

1. Familiarisation with the data: This is an integral part of all qualitative analysis.

Data is read and re-read the data and audio-recordings are listened to. Initial analytic observations are noted;

2. Coding: Data items are categorised and collated in relevant data extracts. This allows for deep analysis and understanding of the data. In this study stage one and two data sets and service user and carer data sets were matched and analysed together to create an in-depth explanation of the research questions and verification of stage one results. Results of stage one participants were presented in table format and analysed in the context of stage one findings;

3. Searching for themes: A theme is a coherent and meaningful pattern in the data relevant to the research question. This ‘searching’ is an active process and culminates in collating all the coded data relevant to each theme. Themes were arranged according to stage one dependent and independent factors with an additional theme addressing other factors related to adherence;

4. Reviewing the themes: This involves checking that the themes ‘work’ in relation to both the coded extracts and the full data set. At this stage, a story about the

138 Chapter 3: Methods

data is emerging and themes split, combined or deleted. It synthesises the evidence and helps to select relevant information and discard less relevant. At this stage, themes are checked against each other and back to the original data set until themes emerging from data are internally coherent, consistent and distinctive. In the context of this study, themes were reviewed by an expert in the field of qualitative research. This verification allowed for themes to be confirmed and for additional emergent themes to be identified that may have been missed by the primary researcher;

5. Defining and naming themes: The themes are defined and quotes selected according to relevance to identified theme. A detailed analysis of each theme is outlined. It synthesises the evidence and helps to select relevant information and discard less relevant. Data was defined in the context of the topic guide and the research question.

6. Writing up: Writing-up involves weaving together the analytic narrative and data extracts to tell the reader a coherent and persuasive account of the qualitative data. To improve cohesiveness in this study, the findings will be considered in the context of stage one data, the researcher’s relationship to the study and the existing research literature on the subject. This transparency, particularly in relation to the researcher’s position in the research process is intended to help those reading the research to apply findings to other settings and contexts.