Main Study Data Analysis

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Analysing data in the main study became a much more daunting task when these voluminous piles of interview transcripts were analysed verbatim (word-by-word), using a reflective approach with the help of QSR NVivo packages (to be specific: NVivo version 8.0 for pilot study and version 9.0/9.2 for main study respectively). These software packages were specifically designed to facilitate the management, organisation as well as the analysis of qualitative data. Initially, this involved the development of preliminary codes in NVivo to capture the emerging themes. This involved identifying thematically similar sections within and across the transcripts and placing them in their designated codes. “Coding is a technical name for sorting or grading data to be aggregated or filed, it is a procedure that pulls the story together” (Stake, 2004, p.130). As the main study analysis began, I adopted a more open coding style in order to identify and capture new themes and concepts. Initially, I had largely adopted Charmaz’s (2006) approach of line-by-line coding, slightly modified by my sentence-by-sentence approach. This allowed me to reduce the data by retrieving only those sections of the text that related to each of the themes (Welsh, 2002). The data analysis at this stage was primarily inductive and comparative (Merriam, 2009). The QSR NVivo software package also allowed the researcher to place memos (or ‘databites’ as NVivo refers to them) alongside the data to record analytic ideas as they arise – a similar process to the conventional note taking that can be done in parallel with data analysis. Although the larger part of analysis been done by myself through a reflective process, I found that this NVivo software had in some ways helped me in managing as well as analysing part of the data, particularly at the initial stage when I had to code and encode all themes found in the data (because at this stage everything can be coded) before selecting only the important themes/categories to proceed with more detailed analysis.

These important themes were later identified as focused themes (Charmaz, 2006).

The data analysis then proceeded to another level in which the analysis becomes more focused on the important or significant themes that were identified. The process of grouping these open codes known as axial coding (Corbin and Strauss, 2008), or analytical coding as defined by Richards (2005, p.94) as “coding that comes from the interpretation and

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reflection on meaning.” But my axial coding was mostly descriptive coding rather than the more extensive analytical coding. After that, the interview scripts were analysed using constant comparison methods focusing on the selected focused themes, where data were coded to generate frequencies and correlations (Stake, 2004). This method was originally developed for use in grounded theory. However it has now been widely used as a method of analysis in qualitative research (Janesick, 1994; Strauss and Corbin, 1998), without building a grounded theory (Merriam, 2009). It requires the researcher to take one piece of data (one interview or one theme) and compare it to all other pieces of data that are either similar or different. This method of analysis is inductive as the researcher begins to examine data critically and draw new meaning from the data (Dye et al., 2000). Although my role in the research process now became more prominent, I still relied on the QSR NVivo software package to organise and manage data, and data presented illustrate only significant themes drawn from the perspectives of Malaysian event management practitioners – I have thus applied a mix of manual, mental, and computer management. As the work progressed, a coding framework (thematic network) was developed to capture the core themes that came out in the interview transcripts. These tentative core themes have been further tested in the later stage of data analysis to see if they hold significance across all the interviews. The objective here is to identify those themes that hold true across the entire set of interviews, rather than being specific to just a sparse few (Marshall & Rossman, 2006).

The end product of this process delivered more robust and substantiated themes while separating out and setting aside the weaker ones. The aim of this process is to capture the global themes that would become the core findings of the research. The most difficult part at this stage was to construct important themes or categories that captured some recurring patterns that cut across my data because the process was still highly inductive at this stage (Merriam, 2009).

This data-driven analysis was continued until the researcher finally identified all (or probably most) significant themes from the two main aspects focused by this research. The first scope was related to the risk and safety issues emerged from the participants’ point of view, while the other aspect was focusing on major important risk factors that constituted on the

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proposed risk typology for event management industry in Malaysia. In the final analysis stage, the constant comparison method adopted was been improvised/supported by the use of Kasperson’s et al. (1988) SAR (social amplification risk) framework to help in enhancing the researcher’s understanding of participants’ perception and to help in writing up the discussion part (refer findings and discussions in chapter 4). On the other hand, “the iceberg model of threats to an organisation” drawn from Rose (2006, p.27), which originally introduced by Smithson (1990), and has been used to describe the emergent of important risk categories on the proposed event safety risk typology based on a Malaysian perspectives (refer the development of the thematic typology in chapter 5 and 6).