Data Analysis

“Data don’t speak for themselves. We have to goad them into saying things” (Turner in: Richards 2005:67)

A crucial stage in any research is data analysis (Gerrish and Lacey 2010). In qualitative research this involves the researcher immersing themselves in the data in order to make sense of it. The narrative and observational data produced in my study had to be painstakingly ploughed through in order to extrapolate meaning (Grbich 2007; Polit and Beck 2008). A review of the literature revealed no standard way to accomplish this, as it is dependant on the goal of the research.

The non-participant observations provided a snapshot view of the students’ in practicum and as such did not yield a large amount of data – derived from field notes. However, large amounts of data were produced from the focus groups and interviews. Each interview was transcribed verbatim as soon after the interview as possible and field notes compiled while events were fresh in my mind. Verbatim transcription is believed to give the best opportunity for rich data to be unearthed (Holloway and Wheeler 2010).

Transcription was a time consuming and onerous task, mainly because I found the student’s regional accents and dialect difficult to decipher at times, especially in the focus group discussions. However, this became marginally easier with each round of interview data as I ‘tuned in’ to the accents. Secondly, my skills of transcription hindered the process. Dearnley (2005) highlighted that a one-hour interview took five hours to type verbatim, while other sources advised four to six hours for those familiar with audio-typing (Richards 2005; Holloway and Wheeler 20010). However, in reality I found that a 45-minute interview took around eight to 10 hours to transcribe. Consequently, the time implications for this study were considerable due to the number of times the participants were interviewed and the longitudinal nature of the study. This resulted in a delay in transcribing interviews and was a cause of some anxiety.

Coupled with work demands I took the decision to out source the remaining transcriptions to a team of professionals. Although the literature advised that personally undertaking the transcription helps the researcher immerse themselves in the data (Jootun et al., 2009: Gerrish and Lacey 2010; Holloway and Wheeler 2010), I found my lack of transcribing prowess to be a major hurdle.

On completion, I checked each transcription for accuracy by listening to the taped interviews whilst reading the scripts (Holloway and Wheeler 2010), with amendments made at this stage. This process was particularly helpful (and important) with the outsourced transcriptions as owing to the transcribers’ lack of familiarity with healthcare dialogue some errors were made with interpretation of some words.

My knowledge and experience within healthcare and simulation allowed me to pick up anomalies and correct them. It also helped me to re-immerse myself in the data.

Once transcribed, the scripts, with pseudonyms were stored on a password- protected computer and two hard copies were made (Data Protection Act 1998). One was used for reference, whilst another was used in the analytical process. Initially I used Nvivo™ software to help manage the data, but after several technological hitches, which limited or denied access and use of the software, the decision to undertake manual thematic analysis was made. This was a laborious task, however it helped me to connect more effectively with the data.

The physical aspects of highlighting key words and phrases from the protocols and arranging them into clusters suited my style of working. I felt physically connected – immersed - and was able to reflect back to the actual interviews and recall some of the additional elements of the interviews. This was also the experience of Clark (2009) who reflected on how connected she felt with her data when forced to undertake manual analysis.

Qualitative data analysis can be a major cause for concern for researchers (Corben 1999; Smith et al., 2009) being recognised as the most challenging aspect of a research project (Cohen et al., 2000; Whiting 2001; Miller 2002; Gerrish and Lacey 2010). Polit and Beck (2008) asserted that the challenges inherent within qualitative data analysis were due to a lack of analytical benchmark procedures. Certainly, Richards (2005) described the process as ‘messy’, which may be due to the fact that whilst there are common features across qualitative data analysis there are also distinct differences relating to the particular approach used (Holloway and Wheeler 2010). It is a complex and convoluted process, which at the same time is, or should be, methodical, orderly and structured (Holloway and Wheeler 2010) and aligned to the chosen research method.

Holloway and Wheeler (2010: 282) outlined the common features of qualitative analysis:

o Transcribing interviews and sorting field notes; o Organising, ordering and storing the data;

o Repeatedly listening to and reading/viewing the material collected.

All this means immersion in and engagement with the data. Other stages depend on the process taken by the qualitative researcher:

o Coding and categorising (particularly in interpretive methods); o Building themes;

o Describing a cultural group (in ethnography); o Describing a phenomenon (in phenomenology).

Numerous approaches to the analysis of qualitative data exist along with several frameworks, which offer structured guidance to qualitative analysts. During the process of researching and evaluating the topic, approaches advocated by Giorgi (1975: in Whiting 2001); Colaizzi (1978: in Valle and King 1978); Van Manen (1990) and Interpretive Phenomenological Analysis (Smith et al., 2009) were considered. These approaches, all situated within interpretive research had, as illustrated by Holloway and Wheeler (2010) shared processes. They all aimed to interpret the meaning, or essence of a phenomenon of everyday experience via thematic analysis.

Gomm (2008) described thematic analysis as a version of content analysis (CA). However, unlike CA, which is used to analyse written or broadcast materials, thematic analysis is used to extract meaning from interview data. Van Manen (1990) proffered that humans have an inherent desire for meaning or understanding, which drives the pursuit to unearth something significant. In discussing the meaning of ‘theme’ in the context of hermeneutic research, Van Manen (1990) acknowledged it as both a skill and a cognitive process.

As stated earlier, the literature produced a number of sources of theoretical and practical guidance regarding thematic analysis (Giorgi 1975: in Whiting 2001; Colaizzi 1978: in Valle and King 1978; Van Manen 1990; Richards 2005; Smith et al., 2009). I wanted one, which offered clear, systematic guidance.

Each was reviewed. Generally they all involved the researcher repeatedly reading transcripts in order to find themes, identified through the use of common language.

I opted to be guided by Colaizzi (1978), but took note of Van Manen (1990) who asserted that the process of thematic analysis should not be viewed as prescriptive - it was a more open and intuitive way of seeking meaning and understanding.