Data analysis process

Step one

Transcribing the interviews allowed me to familiarise myself with the data, language and the nuances of the conversation that were not necessarily apparent on a typed transcript. Once transcripts were returned to me following participant verification, I anonymised the transcripts by allocating each one a pseudonym. I prepared all materials in the same format, e.g. using an A4 sized page in the landscape layout with a blank margin on the right side of each page. I read the transcripts many times, from beginning to end, as suggested by (Merriam, 1998), and jotted down my initial comments in the margin of the transcripts. I also used a highlighter pen to mark off “slices of data” as Ball (1991: 182) terms it. This usefully enabled me to record thoughts that stood out at this initial stage, which Merriam (1998) called, the organising, abstracting and integrating process. Most of my comments focused on labeling the data or recording a brief analytical summary. See APPENDIX 15 for a sample transcript page showing highlights and comments.

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Once I had commented on all transcripts and had a good volume of labels and analytical comments I typed up all the comments that my reading and thinking generated. This demonstrated the common ideas emerging from the data at this early stage, which constituted, what Lincoln and Guba (1985: 344) call “units of information,” see APPENDIX 16 for a list of the units of information.

Step three

Following this, I devised categories which best described the units of information. Each unit was then sorted into a category. If a comment did not fit a category I left it aside and at the end of the sorting process this category was called ‘outliers.’ In devising the categories, I took Guba and Lincoln’s (1981) advice and sorted the units of data according to their suitability within the category, i.e. ensured that the categories were internally homogenous. I also ensured that where the categories were externally heterogenous, the differences between them were “bold and clear” (Guba & Lincoln, 1981: 93), for example ‘pedagogy’ and ‘decision- making.’ Devising the categories was a useful exercise as chunking of several bits of data helped me, as a researcher, to see an “initial plot of the terrain” (Miles & Huberman, 1994: 69). NVivo data analysis software was used at this stage. NVivo is a type of computer-aided qualitative data analysis software (CAQDAS) that is used to sort, organise and manage qualitative data. The transcripts firstly needed to be formatted, in rich text format to then be imported into NVivo. NVivo was only used during this initial part of the data analysis process. Although NVivo was useful for managing the data, coding the data, after hand coding, was a tremendously time-consuming process. See APPENDIX 17 for the NVivo code sheet demonstrating the various categories.

Step four

In the next step, I printed the codes/categories and the data contained within them from NVivo. I worked with the data sheets to further consolidate the categories and units of information. At this stage, I extracted certain ideas that were emerging strongly from the first student interview phase to explore within the second student interview phase, for example, ‘deep thinking versus superficial thinking.’

I then completed and transcribed the second phase interviews. I began the coding process as described above in steps one and two. During this process, I extracted more units of information which were then added to the data already contained within the categories. Some new categories were emerging at this stage, e.g. learning at clinical placement and challenges in developing critical thinking skills. The categories were added to the list of categories and

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suitable units of information were included therein. See APPENDIX 18 for a revised list of categories.

I then extracted certain ideas that were emerging strongly from the second interview data to explore within the third interview data phase, for example, the role of clinical placement learning, linking theory with practice, and the link between reflection and critical thinking. This served as a useful indicator for respondent verification of the emergent ideas that were beginning to consolidate as key findings.

I thereafter completed the tutor participant interviews and the third phase of the student interviews. Units of information from these transcripts were categorised. Following the completion of the tutor and third phase student interviews certain categories were beginning to consolidate, for example, the role of placement learning in critical thinking development, reflection, and challenges experienced. Gaining respondent verification of the strong emergent ideas derived from the transcripts was a useful exercise in establishing the trustworthiness of the findings and helped to cement my interpretation of the data. Also helpful in the interpretation of data was my logging of thoughts at various points during the analysis process. See examples of diary entries presented in Section 3.7, p. 67.

Step five

The categories from each interview phase were thereafter further revised and collapsed into more manageable chunks of data. See APPENDIX 19 which demonstrates the coalescence and evolution of the themes and subthemes from the first to the third student interview phases and tutor interviews, and APPENDIX 20 for the final themes of the study. The data analysis process was useful in developing what Silverman called a good, "working, hands-on empirical, tacit knowledge of the analysis" leading to the development of "a qualitative analytic attitude" (Silverman, 2011: 274). This helped to classify the themes into hierarchical higher and lower order components leading to the final themes of the study. Themes according to Ryan and Bernard (2003) are abstract concepts that are found before, during and after data collection. The two main themes of the study were the meaning of critical thinking and development of critical thinking. These themes represented what Goetz and LeCompte (1984: 36) called “concepts indicated by the data” and although “intuitive” in its nature it is also informed by the purpose of the study, “investigator’s orientation and knowledge and participants of the study” (Goetz & LeCompte, 1984: 191). With the study data securely situated within the aforementioned main themes, this formed the basis for the writing up of the findings.

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