A key intention of data analysis in qualitative research is mapping the meanings found in the data, rather than, for example, calculating statistics, which is typical in quantitative analysis. The essence of meaning or experience is captured in the themes distilled from the empirical data. As Coffey and Atkinson (1996) argued, the exploratory, developmental nature of qualitative research relies upon an ongoing interaction between research design, data collection and data analysis. Indeed, Merriam (2002) contended that its effectiveness is wholly dependent upon simultaneous data collection and analysis. Further, inductive techniques are emphasised, such as searching for patterns, categories and themes within the data, rather than pre-determining and imposing them prior to data collection (Patton 2002). Some (Hatch 2002; LeCompte & Schensul 2010), however,
103
argued that all data analysis includes a degree of both inductive and deductive thinking. Similarly, Hennink, Hutter and Bailey (2011) identified that inductive and deductive techniques play a role in the complex cycle of data analysis activities. Glaser (1978), for example, recommended using an inductive approach to generate codes from data, followed by a deductive phase where developing theory guides the direction of on-going data collection, as do conceptual frameworks and reflective insights of the researcher.
The research approach and design in this study were predicated on the value of inductive knowledge development (Thorne 2008), where theory building was intricately connected with the empirical evidence (Eisenhardt & Graebner 2007). The analysis strategy relied heavily, but not exclusively, on inductive techniques. Analysis stages were informed and systemised by adopting elements of Creswell’s (2013) Data Analysis Spiral, Hutter and Hennink’s Qualitative Research Cycle (Hennink, Hutter & Bailey 2011), and Hatch’s (2002) Inductive Analysis Model, and Interpretive Analysis Model. These models were compatible with the constructivist perspective. Table 3.2 collates the similar elements in these models that contributed to the approach to data analysis and interpretations.
104
Although the information is displayed in the table in a linear sequence for simplicity and clarity of presentation, as Marshall and Rossman (2006) noted, qualitative data analysis is not a linear, neat or stepwise process in practice, but rather, data collection and data analysis activities are undertaken, simultaneously, and revisited, cyclically and episodically, to refine existing and new themes.
Guided by the informing models, the data analysis occurred in four broad, but overlapping, phases. They were: transcribing the interview recordings; categorising data sets; identifying and making sense of themes; and synthesising, classifying, and representing final overarching themes. Sandelowski and Leeman (2012) defined a theme as a coherent integration of the disparate pieces of data that constitutes the findings, and Braun and Clarke (2006) viewed a theme as capturing something significant about data in regard to the research question, and representing a response pattern or meaning within the data set. The central focus on themes is closely aligned with the theory building intentions of this study, therefore, thematic analysis was chosen to bring out the richness of the data, and to provide a logical structure for the presentation of findings and conclusions.
Boyatzis (1998) described thematic analysis as ‘a way of seeing’ that moves through the three inquiry phases of recognising or seeing important moments, then encoding and interpreting them. Furthermore, as qualitative researchers are an integral part of the data, effective thematic analysis relies on specific researcher characteristics. These are: conceptual flexibility, or sustained openness and flexibility to perceive patterns; tacit knowledge relevant to the research topics; and cognitive complexity, which involves the ability to perceive multiple causes and variables, and to conceptualise the relationships between them, within a contextual or conceptual framework. In this study, these desirable researcher features were practiced through conscious application of a reflexive stance, sustained engagement with the data in conjunction with relevant discourses, and adherence to systematic analysis and reporting. The following paragraphs outline the key steps in the analysis process, and Chapters Four and Five report the detailed findings of the thematic analysis.
105
Hatch (2002) contended that interpretations are better grounded in the data if researchers spend time engaging with the data in descriptive and analytical ways. The interview guide acted as the initial descriptive-analytical framework for data analysis (Patton 2002). Merriam (2002), Patton (2002), Ravitch and Carl (2016) and Silverman (2011) proposed early ‘immersive engagement’ with the data. In this study, verbatim transcriptions of recordings, notes taken during interviews, and reflections after interviews were held in individual files, which were read several times to ensure familiarity with the raw data, and to note down initial ideas.
The next iteration grouped each participant’s raw data under the four sub-research questions, and a second set of files were created. While not themes, in themselves, these questions provided a broad organising framework for aggregation of data across individual interviews. As recommended by Braun and Clarke (2006), initial codes were systematically generated for ‘interesting features’ collated across data sets. This open coding approach to categorising data (Neuman 1997; Silverman 2011; Strauss & Corbin 1998), included highlighting similar key words and phrases, as well as contrasts and gaps.
In conjunction with relevant concepts raised in the literature review, further ‘playing’ with the data (Yin 2014), including axial coding to reveal interconnectivities, culminated in the construction of numerous tables of tentative data categories, and provisional lists and maps of possible themes. In order to further refine and prioritise these themes, interview responses were revisited, as were observational notations taken during interviews, such as the intensity and frequency of comments, specific jargon, variations in tone of voice, and changes in body language. The combination of these activities assisted in surfacing and labelling themes from deep within the data (Yin 2014), and generating a thematic map which demonstrated interconnectivity amongst themes (Braun & Clarke (2006).
To ensure the integrity of the analysis process, and to select vivid and compelling extracts for supporting findings and conclusions, interview transcriptions were frequently cross- referenced for illustrative examples. This familiarity with the data ensured participants’ voices were prominently and authentically represented in the thematic interpretive
106
(re)constructions in this study (Hammersley & Atkinson 1983). This thematic analysis, which utilised a dynamic, cyclical process to integrate both specific concrete details and abstract concepts (Silverman 2011; Tuckett 2005), meant that identified themes and their components were finely honed and firmly grounded in the data. The unifying intention was to encapsulate participants’ descriptions of the phenomenon of offshore quality academic work, through the construction of a coherent narrative that described concepts and themes, and the linkages between them (Hennink, Hutter & Bailey 2011). For Strauss and Corbin (1990), storylines that utilised descriptive narratives about a study’s central phenomenon, were an articulation of theory built from the data analysis. The data analysis process described above, demonstrated the evolution of this study’s narrative, from the raw data in interview transcripts, towards the identification and interpretation of patterns and overarching or core themes, which related directly to the research questions.