CHAPTER 4: METHODOLOGY AND RESEARCH DESIGN
4.4 Research methodology
4.4.3 Data analysis
The complex and timely process of analysing data is ambitious, as it involves working through the large amount of data collected from participants and processing the data to identify themes and categories (Castleberry & Nolen, 2018; Creswell & Poth, 2016:41). Analysing qualitative data involves coding processes to identify particular themes from the data (Simoni et al., 2019:2185).
Data retrieved from participants cannot be analysed like recordings or transcriptions. Lune and Berg (2017:40) state that methods to process and organise raw data are needed before the data analysis process can begin. Interviews with participants that were recorded were transcribed, edited and corrected (cf. Lune & Berg, 2017:40). These interviews were recorded with the participants’ consent and transcribed afterwards so as to be organised for thematic analysis (Forman & Damschroder, 2008; Vaismoradi et al., 2016). Thematic analysis is a popular method of data analysis due to its ability to address various topics or research questions (Castleberry &
Nolen, 2018:808). In this study, thematic analysis was used because it allowed systematic analysis and coding, which could then be linked to the theoretical discourse of this research
(Braun & Clarke, 2012:58).
According to Vaismoradi et al. (2016:100), thematic analysis is a technique associated with descriptive qualitative designs. It is aimed at analysing textual data to explicate themes (Evans &
Lewis, 2018). According to Clarke and Braun (2014:58), thematic analysis is used in qualitative research to systematically generate themes. Themes are the main product of thematic analysis with practical results (Braun & Clarke, 2012; Evans & Lewis, 2018; Vaismoradi et al., 2016:101).
While this approach has a large focus on descriptions and interpretations, researchers who use thematic analysis tend to favour thick, rich descriptions (Vaismoradi & Snelgrove, 2019). While analysing the data, I engaged with the phenomenon through a variety of subjective realities and perceptions from participant perspectives by breaking down the textual data to identify themes and categories through critical insights to create a story line informed by the data (Connelly &
Peltzer, 2016; Joung Ji & Eun-Hee, 2014). This required the construction of data clusters and repeated to-and-fro comparisons of these clusters as they related to the textual data as a whole, my knowledge and experiences, and previous studies related to the phenomenon (Erlingsson &
Brysiewicz, 2013; Vaismoradi & Snelgrove, 2019; Vaismoradie et al., 2016). Braun and Clarke (2012:57) argue that researchers who use thematic analysis to look for and explore the subjective meanings throughout data can make sense of and navigate shared or collective experiences and
meanings. Thematic analysis does not seek to simply recapitulate the collected data but to detect and interpret key features of the data as they relate to the identified research objectives and questions (Clarke & Braun, 2014:1950).
Evans and Lewis (2018:5) state that a theme is dependent on the research questions and theoretical considerations of the study. Themes are used to describe, attribute and conceptualise the data. Thematic analysis allows the researcher to answer the “how” and “why” questions through themes constructed from underlying meanings, tied together by data sharing similarities (Clarke & Braun, 2014; Vaismoradie & Snelgrove, 2019; Vaismoradie et al., 2016). By analysing the transcribed participant responses through thematic analysis, I could explore the phenomenon in-depth while maintaining a flexible and interpretative approach throughout data analysis process (Evans & Lewis, 2018:5). The following steps, as suggested by Braun and Clarke (2012) and Castleberry and Nolen (2018), were employed in this study: compiling and familiarising oneself with the data; disassembling and coding the data; reassembling and constructing themes;
reviewing and interpreting; defining and naming themes; concluding and reporting the findings.
These broad steps are elaborated on below.
4.4.3.1 Compiling and familiarising
Castleberry and Nolen (2018:808), supported by Lune and Berg (2017), argue that data need to be compiled in a useable format as the first step to address the research goals and questions.
This can include transcribing interview or focus group recordings (Castleberry & Nolen, 2018;
Forman & Damschroder, 2008; Lune & Berg, 2017). Castleberry and Nolen (2018:808) suggest that researchers transcribe recordings themselves to kickstart the process of becoming familiar with the data and the context from which the data stems. Qualitative researchers can become familiarised with the data by immersing themselves in the data (Braun & Clarke, 2012:60) by continuously re-reading the textual data and re-listening the recorded interviews (Terry et al., 2017). This requires the researcher to critically and analytically “comb” through the data while taking notes to explore the subjective meanings embedded in the data (Braun & Clarke, 2012;
Castleberry & Nolen, 2018). In the present study, the interview schedule was read through section by section, as each section had a specific focus. While reading, I took notes on the codes that arose for each question and the themes that emerged to answer each question.
4.4.3.2 Disassembling and coding
After becoming familiarised with the data, I disassembled or categorised the data into codes (Braun & Clarke, 2012; Castleberry & Nolen, 2018; Clarke & Braun, 2014). Austin and Sutton (2014:438) define coding as the processing of raw data into data that can be used to identify
ideas, concepts or themes which are connected through similarities. This is where the research is driven via an inductive process by allowing codes or meaning to emerge and develop from the data rather than a hypothesis-centred deductive approach (Braun & Clarke, 2012; Castleberry &
Nolen, 2018:808). Terry et al. (2017:32) define good coding as “inclusive, identifying and labelling all segments of interest and relevance within the dataset”. The textual data were coded through the use of the Atlas.ti 9 programme. Castleberry and Nolen (2018:809) argue that such a program’s semantic and linguistic algorithms assist researchers to systematically identify and organise re-occurring phrases or ideas but do not assist in the actual analysis of the codes – this I engaged in thoroughly.
4.4.3.3 Reassembling and constructing themes
After combining similar ideas or concept into codes, these codes were placed in context with the aim of constructing themes (Castleberry & Nolen, 2018:809). As discussed above, themes are compiled of significant data related to the research objectives and questions, which represent patterns or similarities of meanings across the data set (Castleberry & Nolen, 2018; Clarke &
Braun, 2014; Vaismoradie & Snelgrove, 2019). Braun and Clarke (2012:61) use the analogy of a house to describe codes and themes: “your themes are the walls and roof and your codes are the individual bricks and tiles”. Themes are indicative of patterns within codes combined to create a bigger picture (Castleberry & Nolen, 2018:809; Vaismoradi et al., 2016). Terry et al. (2017:36) argue that thematic analysis emphasises flexible, systematic and back-and-forth analysis and consideration of data; therefore, the first analysis as an attempt to generate themes should result in initial themes instead of absolute or definitive themes. Furthermore, Castleberry and Nolen (2018:812) suggest that researchers wait two days after analysis to re-analyse the exact portion of data to increase the internal consistency of the coding process. It is important in this phase to start considering how themes interact, overlap or fit into each other (Braun & Clarke, 2012:65).
4.4.3.4 Reviewing and interpreting
Interpreting and reviewing themes is seen as a critical stage in the analysis process (Castleberry
& Nolen, 2018:812), as up until this point, the analysis has only candidate or produced possible themes (Terry et al., 2017:36). Reviewing the possible themes has been compared to taking stock, quality control, verification and a process of reappraisal (Braun & Clarke, 2012:65; Terry et al, 2017:38; Vaismoradi et al., 2016:106). Braun and Clarke (2012:65) highlight the importance of the reviewing phase for novice researchers, especially when large quantities data are analysed – remembering the complete collection of data at all times can be difficult (Terry et al., 2017).
Reviewing possible themes together with a literature review and the collected data assists the
researcher in ensuring that definitive themes are related to the context while addressing the research question (Castleberry & Nolen, 2018; Terry et al.,2017; Vaismoradi et al., 2016).
4.4.3.5 Naming and defining themes
When naming and defining themes, the description of each theme should include a statement of what makes that theme specific and unique (Braun & Clarke, 2012:66). By this phase, the researcher should move away from viewing themes as summaries and more towards an interpretive approach (Terry et al., 2017:38). Braun and Clarke (2012:66) argue that a theme produced by a good thematic analysis will have singular focus, represents the data but does not overlap, and relates to the research question. This phase involves writing up the analysis through the use of extracts and narratives from the data (Braun & Clarke, 2012; Terry et al., 2017). These extracts and narratives should clearly display the point the researcher is making to assist with the structure of the analysis (Braun & Clarke, 2012). Extracts and narratives from participants should support the description and scope of the theme with which they are connected (Terry et al., 2017).
In this phase, extracts are critically analysed through interpretations, while simultaneously forming a summary to speak to the broader context (Braun & Clarke, 2012). Through this method, the data are not only organised and summarised but interpreted in relation to the study’s theoretical framework (Braun & Clarke, 2012; Terry et al., 2017).
The following section delineates the ethical considerations and steps taken while working with participants.