*2.4. Data Analysis*

Thematic analysis was the method chosen to support the analysis of the study's data. The Braun and Clarke [47] six-step proposition, which consists of eight discreet cycles, in conjunction with the QDA Training [48] material, formed the basis of the analysis technique. A practical iterative approach was adopted throughout the analysis where the data were formally arranged into discrete phases. The eight individual stages of analysis distributed over the six phases were designed to support a robust and rigorous analysis of the data. Table 4 below illustrates the stages and processes outlined and performed in NVivo and links this to the practical guidelines set out in Braun and Clarke [47]. Their six-step approach that supports the application of thematic analysis is shown in column one and the corresponding application in NVivo is shown in column two. The third column features the strategic elements of coding as the researcher moved from the initial participant-led descriptive coding, to the secondary coding which was more interpretative in nature indicating this phase of coding was both researcher- and participant-led. The final abstraction to themes was researcher informed only. This phase was designed to allow the researchers to engage the participant in direct dialogue with a wider arena such as literature and policy or strategy for example. The fourth and final column illustrates the more iterative nature of the coding, analysis and reporting of proceedings that terminate in a conclusion.

Phase 1 activity involves familiarizing oneself with the transcribed data. In this first phase, the data were loaded into NVivo. It was checked and re-read several times to ensure accuracy of the uploaded transcripts. At the end of the phase activity, initial codes were noted down and retained.

Generating initial codes (open coding: phase 2)—According to Lincoln and Guba [49] (p. 345), a data unit can be defined as the "smallest piece of information about something that can stand by itself, that is, it must be interpretable in the absence of any additional information other than a broad understanding of the context in which the inquiry is carried out". The open coding is intended to systemically organize the data and uncover the essential ideas found in the data [50]. Each discrete unit of data is labelled in line with the phenomenon it represents. The second phase required broad participant-driven open coding of the interview transcripts recorded during the data gathering step of the research study. Features of interest were coded in a systematic way across the complete dataset where data relevant to each code were collected. Clear labels were allocated to these codes and definitions to serve as rules for inclusion [26].


**Table 4.** Stages and Process Involved in Qualitative Analysis. Adapted from Braun and Clarke [47] and QDATRAINING Training [48] material.

A set of provisional categories was generated for the segmented data to be coded to. These categories were descriptions of concepts and themes in broad terms. They took two forms: researcher-driven and participant-driven. The former was derived from a theoretical framework underpinning the study and the latter from the knowledge gained of the participants' language and customs. Hammersley and Atkinson [51] (p.153) consider the importance of participant-driven categories: "the actual words people use can be of considerable analytic importance as the 'situated vocabularies' employed provide valuable information about the way in which members of a particular culture organize their perceptions of the world, and so engage in the social construction of reality".

Searching for themes—In phase 3, codes from phase 2 were collated into categories of codes by structuring all the data relevant to each potential category into a framework that could be used in support of further analysis. This phase also included distilling, re-labelling and merging common codes that were generated in phase 3 to ensure the labels and definitions for inclusion were an accurate reflection of the coded content. These first-round categories are best described as broad descriptions of concepts and themes. During the analytical process they underwent content and definition change and the existence of the two forms of category provides an important means of traversing between "natural" and "theoretical" discourses. Araujo [52] (p.68) suggests that "codes should be viewed in two ways: as part of the analyst's wider theoretical framework and as grounded in the data.; the process of coding data should be regarded as an important intermediary step in translating social actors' frames of meaning into the frame of theoretical discourse; coding frames therefore, mediate between the 'natural' everyday discourse and the theoretical discourses in social science".

Reviewing themes (coding on) in phase 4 required further decomposition of the study units of data identified in phase 1. This activity was intended to support a greater understanding of the highly qualitative elements and gain a deeper appreciation of the meanings contained within. It should be noted that not every task could be further broken down and this meant that the activity was performed only as required. Restructured codes were broken down into further sub-codes in order to augmen<sup>t</sup> a greater understanding of the meanings embedded within them. These distinctive aspects included communication with management, discovering latent issues, just culture, learning lessons, reporting, root causes and story of an incident.

Defining and naming themes in phase 5 of the data analysis was concerned with analyzing the tentative categories identified in phase 2 for their properties and characteristics. This is a pre-cursor to drafting a propositional statement for each category. Developing analytical memos moves the process beyond identification and description of broad categories to a position of analyzing and fusing meanings in the data under each category. This progressed to drafting a statement that aspires to illustrate the concerted meaning of the segments of data coded to each category. Maykut and Morehouse [26] (p.140) defines a propositional statement as, "*a statement of fact the researcher tentatively proposes, based on data*". This phase in addition to further data analysis to refine the specifics for each theme, generated clear definitions and a name for each theme. It also involved data reduction by consolidating categories from all three cycles into a more abstract, philosophical and literature-based thematic framework and conceptually mapping and exploring their relationships with one another for reporting purposes.

Producing the report in phase 6 required analytical memos to be written against the higher-level themes to present an accurate summary of the content of each category and its codes and to also propose findings. The tasks associated with phase 6 included (i) generating analytical memos, (ii) testing and validating and (iii) synthesizing the memos coherently and cohesively, and were performed simultaneously. Writing the analytical memos against the higher-level codes (i.e., learning from incidents, learning process and learning product) required an accurate summary of each category and its codes and findings against categories. These memos considered a few key areas:


Testing, validating and revising analytical memos was performed in phase 7. The purpose of this was to provide a self-audit of the proposed findings by soliciting evidence in the data beyond just textual quotes in support of the recorded findings and to also expand on deeper meanings within the data. This required the data to be interrogated, not only relying on relationships across and between categories, but also a degree of cross tabulation with demographics, observations and the literature. The outcome of this phase was evidence-based findings as each proposed finding was validated by being rooted in the data themselves and was reliant on the creation of reports in support of substantiated findings.

The discipline of writing analytical memos was used during the data analysis process. Birks et al. [53] believe "memoing serves to assist the researcher in making conceptual leaps from raw data to those abstractions that explain research phenomena in the context of which it is examined". In general, memos were employed at the "ideation" stage when the researcher was developing thought processes and early in the data capture phase. As decisions were made, the early processes and rationale for final analysis iterations were recorded using this medium. Memos were further employed to preserve an objective closeness to the harvested data and to maintain the context of each semi-structured interview at the participating individuals' level. Developing ideas, reasons for considering possible category relationships and connections was also possible through the application of the analytical memo process. The rigorous support memoing o ffered served to guide the analysis of the data through di fferent levels of abstraction [54]. The rule of this activity served to ensure a high degree of continuity between the outputs of ideation and the evolving interpretation that were honed through the researchers' articulation, exploration and their iterations of the data. Overall, this drew out the meanings in the data through the increased sensitivity the researchers were o ffered by applying the memoing process [53].

In phase 8, the analytical memos were synthesized into a coherent and cohesive report with the findings well supported. The final phase involved the assembly of the narrative with the data extracts while appreciating the product of this amalgam in the context of the related literature. The example features the finding, clear links to the interview data and literature and an explanatory narrative in the form of a memo. This finally resulted in the compilation of the report which contained the results and discussion elements of the body of work.

In summary, this study adopted an interpretative approach pivoting on the fact that it was of an exploratory nature. The study performed thirty-four interviews in eight aircraft maintenance and managemen<sup>t</sup> organizations based in Ireland. An analysis of various potential research methods and means of data collection resulted in the following research design being implemented. A thematic analysis approach was employed as a research methodology:


• Qualitative analysis based on the guidelines from Braun and Clarke [47] (thematic analysis) employing a six-phase approach was used in the study.
