Qualitative Data Analysis

This study used semi-structured interviews to collect data from patients with T2DM. Interviews were recorded and transcribed verbatim. Transcripts from interviews are the raw data which are descriptive record of the research, but they cannot provide explanations without analysing the data (Pope et al. 2000).

The researcher has to make sense of the data by closely examining and interpreting them. The process for data analysis used thematic analysis approach. The following table (see Table 3) summarises the steps involved in thematic analysis which I adapted for the data analysis in this study (Ziebland

& McPherson 2006; Burnard et al. 2008; Brikci & Green 2007; Braun &

Clarke 2006)

The steps involved for data analysis are described in stages (refer Table 3).

However, in practice they are not independent processes but cyclic in nature. It is important to execute the principle of constant comparison in this study.

Therefore, both data collection and analysis processes were cyclic in nature, iterative process and not a linear process (Miles & Huberman 1984). The data analysis is discussed as a linear step for the purpose of this report.

Table 3 Steps of thematic analysis

Phase Description of the process Familiarising with

collected data

Transcribing data, reading and re-reading the data, immersing oneself in the data, start thinking of the themes.

Generating initial codes (open codes)

Noting down ideas (codes) to summarise what is being said in each of the transcripts.

Searching for themes Collecting together all of the initial codes from all of the interviews and collates codes into potential themes, gathering all data relevant to each

Producing the report The final opportunity for analysis. Selection of vivid, compelling extract examples, final analysis of selected extracts, relating back of the analysis to the research question and literature, producing a scholarly report of the analysis

2.4.9.1 Computer Software for Data Analysis

During data analysis process, coding technique was used. Coding can be done manually which involve the use of coloured pens or highlighters, scissors and glue to literally cut and paste sections of text onto cards or piece of poster that could later be examined together in a bigger picture (Ziebland & McPherson 2006). In this study, both manual cut and paste as well as a computer-assisted qualitative data analysis (CAQDA) software Nvivo¨ software were used.

Nvivo¨ software is a sophisticated software tool to store and manage the data, however it does not provide analysing function. It facilitates accurate and transparent data analysis process whilst also providing a quick and simple way of counting who said what and when (Welsh 2002). It can display and assist in categorising themes and making links between sections of the data for easy retrieval later. Thus software package such as Nvivo¨ software enables the researcher to look across all the data easily and effectively to label codes and identify themes. Especially during the thematic analysis process, the transcript is read and re-read several times and sections of the text are highlighted under different headings, which might be merged or subdivided or changed through the analysis process.

2.4.9.2 Familiarisation with Data

The first step of analysing using thematic analysis is to get familiar with the collected data and start thinking about the data collected (Braun & Clarke 2006; Ziebland & McPherson 2006). After transcribing the interview data, I checked through the transcripts together with the audio files. At this stage, the field notes were used to further conceptualise the conversation. It is crucial to get familiar and immerse oneself into all aspects of the collected data so that the researcher can get familiar with the depth and breadth of the content. The process of familiarisation with the data is to build a strong foundation of structure for data analysis later (Ritchie & Lewis 2003). If this foundation is weak or incomplete, it could jeopardise the integrity of the research.

The method to get familiar and immerse oneself into the collected data involved Ôrepeated readingÕ of the data and searching for meanings and patterns. Due to the intense repeated reading process, qualitative research tends to use smaller samples compared to quantitative research as it can potentially generate huge amount of data (Braun & Clarke 2006). After reading the transcripts a few times, I made a preliminary observation of my data and started to develop a few general ideas which were noted down to be used in the following analysis process: coding. It is an important step to get familiarised with the first few transcripts first to generate an overall feel of the data.

2.4.9.3 Initial Coding

Before any theme is identified, a coding technique is used to mark and identify themes and subsequently applied or linked to the collected interview data, as a way of organising data into manageable units and as summary markers for later analysis (Guest et al. 1971). After re-reading a transcript a few times, I noted down short phrases (initial codes) that sum up what was being said in that transcript. The aim of initial coding is to offer a summary statement or word for each element that is discussed in the transcript (Burnard et al. 2008).

They refer to the most basic segment, or element, of the raw data or information that can be assessed in a meaningful way regarding the phenomenon (Braun & Clarke 2006).

After re-reading the first few transcripts and reviewing the range and depth of the data, I generated a list of initial codes to categorise or index the data. Some researchers note down such concepts as they emerge during reading or listening to a piece of paper (Ziebland & McPherson 2006; Ritchie & Lewis 2003). I used similar method but creating nodes (codes) in Nvivo¨ software during the reading or listening of the transcripts. Table 4 is an example of the initial coding framework used in the data generated from one of the interview:

Table 4 An example of an initial coding framework

Interview transcript Initial coding framework

I created several codes to organising the data into meaningful groups such as diagnosis, symptoms, diabetes complication, initial oral treatment, healthcare services and other descriptions of the transcripts data. These are referred as initial coding or open coding which describes the raw data and provides a summary statement for each element that is discussed in the transcripts but they are not the units of analysis or the themes yet (Braun & Clarke 2006;

Burnard et al. 2008). Themes are often broader and are only developed in the following steps (see next section 2.4.9.4) where the interpretative analysis of the data occurred, and in relation to which theories about the phenomenon being examined are made (Braun & Clarke 2006).

2.4.9.4 Searching for Themes

Next step in data analysing started after all the raw data were initially coded and collated. I have now created a long list of the different nodes or codes that were identified across all the transcripts. This is followed by the next step, that is to sort the different nodes into potential themes, and collating all the relevant coded data extracts within the identified themes (Braun & Clarke 2006).

Essentially, it is a process to re-focus the created codes at the broader level and consider how different codes may combine to form an overarching theme.

Some researchers use tables, or mind-maps, or write a name for each code with a brief description to create a thematic map to search for the overarching theme (Braun & Clarke 2006). I adapted the Ôone sheet of paperÕ (OSOP) method as described by Ziebland and McPherson to search for potential themes. Nvivo¨

software allows exporting the list of initial codes together with the coded section of the transcripts. Using the printout, I switched to manual method: cut and paste on a sheet of paper. OSOP involves reading through each section of data in turn and noting on a single sheet of paper all the different issues raised by the coded extracts (Ziebland & McPherson 2006). From Ôone sheet of paperÕ (OSOP) it was easy to observe that some potential themes were not really themes as there were not enough data from transcripts to support them.

Using the OSOP method, I have developed a set of overarching themes, and then I created them as new nodes in Nvivo¨ software to categorise the initial codes. Example of the categories are shown below:

¥ Positive effect of insulin

¥ Side effect of insulin

¥ Peer influence on diet

¥ Availability of healthy food

¥ Restriction on travelling

¥ Effect on social activities

¥ Restriction on food choices

2.4.9.5 Reviewing Themes

After I started to develop a set of overarching themes, at this point it is important to review and refine these themes. During this step, I also merged two apparently separate themes to form one overall theme and some themes were split into separate themes. Data within themes should cohere together meaningfully, while there should be clear and identifiable distinctions between themes (Braun & Clarke 2006). There were constant reflections throughout this process as well as discussion with supervisors to enrich the analysis.

Discussing the findings with colleagues from another disciplinary background can provide new insights and interpretations (Ziebland & McPherson 2006).

During this analysis process, themes were reported and discussions with the endocrinologist at the data collection site.

These potential themes were then created in Nvivo¨ software and linked to all previously created nodes (codes). A screenshot of an overarching theme created for this study is displayed as follow:

Figure 5 Screenshot of Nvivo¨ software showing a theme and the nodes associated to it.

Nvivo¨ software not only stores qualitative data for easy referencing but also efficient in retrieving the transcripts data. The newly created themes linked across the transcripts and encoded the part of the transcripts that converse to that particular theme. It is an essential function in Nvivo¨ software, which facilitated constant comparison between themes and also between transcripts.

The next step was to define and name the themes and this process was easily managed in Nvivo¨ software.

2.4.9.6 Defining and Naming Themes

At this step, researcher is required to define and further refine the themes and analyse the data within them (Braun & Clarke 2006). The identified themes should capture the ÔessenceÕ of what each theme is about, it needs to fit overall ÔstoryÕ. I have identified the overarching themes and linked them to the individual ÔstoriesÕ told by the participants. In Nvivo¨ software, I can easily retrieve the identified themes and the individual stories that linked to that particular theme. Then I refined and renamed these themes to create a stronger connection to the stories being told and capture the essence from the stories.

During the process of refining and renaming of themes, reviewing literature was again being carried out. Ziebland and McPherson suggested going back to the literature as the most common method to seek depth in qualitative data analysis (Ziebland & McPherson 2006). Apart from the constant reflection, this analysis process is also enriched by going back to the literature to explore how other theoretical literature fitted and how it could further support the analysis.

2.4.9.7 Producing the Report

Quantitative studies are often designed to answer one or more main research questions; in contrast qualitative interview studies explore the experiences and meanings (Ziebland & McPherson 2006). The resulting data are rich and have the potential for many different analyses. It is necessary to identify the story that can be told with the qualitative data to produce new and useful theories.

It is important that the analysis, and the resulting report, provides a concise, coherent, logical, non-repetitive and interesting account of the story the data describe (Braun & Clarke 2006). During the study, findings were constantly discussed with supervisors. Interesting and potentially useful theories were presented and discussed in this report.

The findings are presented in the order of patientsÕ lived-experience of using insulin treatment to manage their T2DM. This report begins with exploring participantsÕ reactions to T2DM diagnosis, and then followed by their reactions to initiation of insulin treatment, and lastly looking at how the participants coping insulin injection in their day-to-day life.