The initial task undertaken was to transcribe fully all of the video-recorded
interviews. This was an arduous task which I undertook myself. While it was very costly in terms of the time invested, I felt it greatly added to my understanding of the data, as it provided an opportunity to become fully “immersed” in it (Drever, 1995; Wellington, 2000). Full transcripts (Drever, 1995 and Cohen et al. 2000) were made including the informal conversation before and after the interview, which provided a picture of the personality of the interviewee, as well as indicators of their attitudes to both science and the intervention programme.
Having complete transcripts of each interview in electronic format allowed me to store the data safely on two external hard-drives which were kept in two metal filing cabinets in different offices at all times. This provided me with peace of mind that the data were very secure. Having the data in electronic format facilitated the use of the qualitative analysis package WinMax in order to process it. In addition, having
110 the data so easily accessible in electronic format facilitated the easy retrieval of exact quotations.
Each transcript was checked for accuracy on two separate occasions by reading it while watching the interviews. Any errors observed were corrected. All hand- gestures, body-language and facial expressions were observed in order to assess the accuracy of interpretation of the language used (Cohen et al 2000, p. 282). In order to analyse the large amount of interview data and to address the difficulty of “decontextualised data”, the advice from the literature was followed (Miles and Huberman, 1994; Cohen et al, 2000; Wellington, 2000) and the following stages were used in the data analysis.
Data immersion: To begin the process of analysis I transcribed all the interviews onto an Excel spreadsheet, placing all the answers to individual questions in the same column. This process of transcribing and allocating answers to columns served to immerse me in the data. I then carried out a preliminary frequency count of occurrence of each theme that emerged for each answer. This method of
transcription involved watching and listening to the interviews on multiple occasions while making notes and attempting, even at this early stage, to identify recurring themes. In this manner I became very au fait with the data.
Reflecting on Data: Having spent considerable time reading the transcripts, I put them aside and reflected on what appeared to be emerging from the data. I spent considerable time discussing the interviews with my supervisor and with colleagues. This was an effort to distance myself from the data in order to gain an objective viewpoint on the emerging themes. This desire for objectivity was driven by advice from the literature which warns how easy it is for a qualitative researcher “to jump to hasty, partial, unfounded conclusions” (Miles and Huberman 1984, p. 21).
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Data Analysis:
1. Prior to using the software package WinMax, considerable time was spent uploading the written transcripts onto an Excel spreadsheet, counting the frequency of occurrence of themes and allocating them to various categories identified in the theoretical framework as described in Table 3.3. This was done in an effort to “reduce the data” (Miles and Huberman, 1984). This contributed to my immersion in the data and was used to cross-check the codes that eventually emerged from the work with WinMax. Time was then spent identifying and noting relationships between the themes identified.
2. A formal effort to code the responses was then undertaken. This was done using the software package WinMax. This package has a facility to indicate how many lines of data have been assigned to each code, thus indicating whether or not the theme elicited a strong or weak response from the interviewees. As the coding proceeded, it emerged that some responses could be categorised into more than one category. This necessitated the use of clusters i.e. more general coding categories which reduced the number of codes. In order to improve the reliability of the coding process, the coding was repeated after an interval of six weeks. A
colleague, who was asked to check the coded data, noted some errors which were duly changed. The work of transcribing and coding the interview data was
extremely demanding in terms of time and concentration. However, the fact that relevant quotations from the transcripts could be easily moved from WinMax to a word document meant that the information from the interviews was “in an immediately accessible, compact form” which allowed for great flexibility in the use of quotations to support conclusions drawn.
112 3. Once the first coding had been completed using WinMax, themes began to emerge
which corresponded with those arising from the literature and used in the
theoretical framework. This increased my confidence that the themes arising were not merely subjective choices on my part, but in fact, reflected international research on the topic. This adds to the reliability of conclusions drawn from the interview data. The second coding carried out after an interval of six weeks confirmed the initial themes. Some responses, which had been somewhat
ambiguous during the initial coding and could have been assigned to a number of codes, were gathered together under broader clusters. This meant that valuable data were not lost. In order to validate this reassignment a colleague kindly agreed to carry out a third independent coding. This resulted in the correction of some minor errors which increased the validity and reliability of the data.
Having followed religiously the advice from the literature about the appropriate steps to follow in data analysis, and by subjecting the interview data to three separate assignment procedures, it is believed that the data analysis was reliable.
The conclusions drawn from the analysis are presented in Chapters 5, 6, 7, 8, 9, 10 and 11.