There were several steps involved in preparing and analysing the data. These stages are outlined in detail in the following steps:
3.8.1
Step 1: Organise and prepare the data for analysis
All interviews were de-identified and then transcribed. Then I imported the transcripts into NVIVO 8 which is a software program for qualitative analysis. The data was arranged into folders and sets with attributes (i.e., demographic information) being created in cases.
3.8.2
Step 2: Code words, phrases, sentences and paragraphs
I wanted to conduct my analysis in accordance with Zuboff’s (1988) ethical stance which meant listening to data without having any
preconceived judgement, just as I had done in the interviews I conducted. I then analysed the data according to Tesch’s (1990) system of de-
contextualisation and re-contextualisation. In effect, this means
segmenting the data, sorting the data, organising systems and exploring connections. In accordance with qualitative research, the codes and categories were developed from the participants’ responses and literature reviewed (Miles & Huberman 1994; Tesch 1990; Patton 2002; Creswell 2009).
Chapter 3: Research design 99 Initially using five transcripts (hard copies), I coded the interviews by reading the transcripts and listening to the audio at the same time. I was looking to gain a general sense of what the participants were saying and their tone of voice. What is central when analysing the data is to try and uncover key aspects in the participants’ stories that are important to them. I also paid particular attention to key words indicating emotion (e.g., feel, sense, think). In this process, I was developing themes or as van Manen (1990) refer to ‘structures of the experience’ (p. 79).
I wrote my comments and reflections in the margin of those five transcripts. This initially took some time because not all the same
questions were asked to the respondents as some interviewees spoke freely without initiating the conversation by questions, and others had answered the next question with the previous question. Some questions were reworded to allow understanding.
3.8.3
Step 3: Begin a detailed analysis
To begin the detailed analysis, I looked at my initial coding for the five transcripts to see where there were common codes across the transcripts. I looked to see if one category overlapped into two or more concepts and if the descriptors or codes I had used had similar meanings. For example, on some transcripts I had used ‘enjoyment’ and other transcripts I had used ‘elation’. I reviewed the theoretical literature to reduce these categories. Once I had narrowed down the codes, I put the material from the transcripts with the same codes together. Here I began to look at which ideas went together to form clusters. This began an organising system so that each cluster became a major coding category and the individual
Chapter 3: Research design 100 themes became sub-categories. There are six major coding categories: demographics, individual lived experiences, team activity, work activity, organisation and culture. Then, within those six major coding categories, there is a set of sub-categories. Then there were more categories within these sub-categories. Table 3.6 provides an example of the coding. Table 3.6 Example of coding
Major coding categories Sub-categories Demographics • role/position • experience in IMT • experience in industry
Individual lived experiences • pleasant experiences
joy surprised not surprised/anticipation • unpleasant experiences anger fear
surprised (not in a good way)
When the categories were completed with the five working transcripts on paper, it was time to develop nodes, which are grouped together in the researcher’s classification system in a tree structure within NVIVO8 (see QRS 2006-08). The working transcripts were then coded within NVIVO8. The rest of the interview transcripts were coded manually (as previously described) then entered in NVIVO8. These steps organise the data and develop the initial level of analysis. Full details of the coding organising system is in Appendix 3 and an example of coded data is in Appendix 4.
Chapter 3: Research design 101
3.8.4
Step 4: Analyse the data within NVIVO8
While I was organising the data and developing my initial level of analysis, I noticed collective terms (e.g., we, our, them, they, their) were frequently used so I began exploring the data by using ‘text search queries’. This led me to develop another set of categories based on group identification.
This initial level of data analysis was descriptive, working inductively with the transcripts to build up a picture of participants’ individual and
collective worlds. With these steps completed, I was able to explore the data further. In exploring the data further, I looked at the relationships between one node and another and wrote memos to assist me in developing a deeper analysis. Memo writing is a valuable means of recording the relationships amongst themes (Ryan & Bernard 2000). This is because memo writing allows the researcher to write down their
thoughts as they come to mind and develop a conceptual framework (Strauss & Corbin 1990).
In developing emerging themes I also participated in industry conferences where I was able to test out my ideas and discuss with industry
Chapter 3: Research design 102 Table 3.7 Industry publications
Conference papers
Douglas, J. & Salter, P. 2009, ‘The roles of work-related emotions and leadership in creating high performing teams', AFAC conference, Gold Coast, QLD. Australia.
Douglas, J. & Short, A. 2008, ‘How well are your Incident Management Teams Coping’? AFAC Fire, Environment & Society Conference, Adelaide, Australia.
Douglas, J. 2007 ‘The role of collective efficacy/confidence and its importance in Incident Management Teams’ (IMTs) work activity: Preliminary findings of a PhD', AFAC conference, September, Hobart, Australia.
Posters
Owen, C. Douglas, J. & Hickey, G. 2009 ‘Information flow and Incident Management Team effectiveness’, AFAC conference, September, Gold Coast, QLD. Australia.
Douglas, J. 2007 ‘The role of collective efficacy/confidence and its importance in Incident Management Teams’ (IMTs) work activity: Preliminary findings of a PhD', AFAC conference, September, Hobart, Australia.
Fire Note
Douglas, J. ‘Investigating perceived teamwork effectiveness in Incident Management Teams’, Issue 39, September 2009.
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