Semi-Structured Key Informant Interviews

Cohen et al. (2011: 427) conceptualise the process of analysing (typically, coding) qualitative interviews as ‘a reflexive, reactive interaction between the researcher and the decontextualized data that are already interpretations of a social encounter’. Echoing Cohen et al.’s (2011) emphasis on the inseparability of analysis and interpretation, Saldana (2009) comments that ‘the majority of qualitative researchers will code their data both during and after collection as an analytic tactic, for coding is

analysis’. (Saldana, 2009: 7, emphasis original). In parallel with the constructivist stance adopted in the case study, my overall analytical act regarding the interview

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linear. Both interview methods included two sessions with the interviewees (Dr Acar

and four STs) which allowed me the opportunity to transcribe, peruse and digest the

first sessions and only then move on to the second ones. Saldana (2009) uses the term ‘pre-coding’ to frame this tentative, intuitive analytical act of noticing and highlighting powerful, striking participant quotes or passages which allure the researcher to follow

up on and learn more about. It also resonates, in my opinion, with my personal

experience of inevitably generating individual follow up questions for each participant

on account of the reactions I developed while engaging with the first sets of transcripts

(3.6).

Once both interview sessions with Dr Acar were transcribed, I executed ‘holistic coding’ of their content, treating them as a single manuscript in a quest to roam to and fro for noticing possible thematic connections (Saldana, 2009). In this approach the

researcher ‘applies a single code to each large unit of data in the corpus to capture a sense of the overall contents and the possible categories that may develop’ (Saldana, 2009: 118). Then, once I identified meaningful chunks of passages, I started over to apply ‘in vivo’ (or verbatim) coding as it concurred with the case study’s dedication to ‘prioritise and honour participant voices’ by means of using their own expressions to generate codes, categories and themes (Saldana, 2009: 74). Next, I started afresh yet again to apply complementarily ‘descriptive’ codes which are researcher- generated words or short phrases that summarise the prominent topic in a chunk of data because, in Saldana’s (2013: 94) words, ‘sometimes the participant says it the best and sometime the researcher does’. Finally, I studied the in vivo and descriptive code sets carefully and composed marginal ‘analytical memos’ for those emerging participant quotes that struck me as potential categories, linking to the abstract level

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of the conceptual framework as well as other data collected in the study (Appendix

H).

3.7.3 Repertory Grid Interviews

RepGrid interviews produced two types of data source. The first was the immediate

matrix (elements, constructs and ratings) formulated with each participant during the

interview and the second was the set of transcripts of the audio-recorded sessions. For

statistics enthusiasts numerous methods of analysis are at disposal to calculate and

measure the correlations between the elements, between constructs and between

elements and constructs such as cluster analysis and principal component analysis

(Jankowicz, 2004). Several computer programs and websites are also available to

facilitate the numerical analyses of the RepGrid matrix (see Fransella et al. (2004) for

a list of software).

In this study, complex statistical analysis of the STs’ RepGrid matrices was not

necessary as the analytical priority was placed on the content of their constructs – as

indicators of the STs’ perceptions of their lived research education experiences –

rather than any fine-grained calculation of their hierarchical structure and correlation.

Even so, I resorted to the OpenRepGrid on Air online software

(http://www.onair.openrepgrid.org/) to generate basic visual representations of the STs’ grids for presentation purposes (Chapter VI). The figure below shows, as an example, Nil’s colour-coded ‘Bertin-display’ grid. Bright values correspond to Nil’s

low ratings of elements (1 to 3, closer to the emergent construct pole) and dark ones

119 Figure 10: Bertin-Display of Nil’s RepGrid Matrix

In my further analysis and presentation of the STs’ RepGrid matrix data, I only made

use of those ratings given for the STs’ respective RepGrid elements that stood for the research project completed in AWaRS (e.g. Nil’s Deep Research Project above). I

examined these ratings in relation to their proximity to each STs’ favoured pole of

construct to interpret how closely each ST associated the AWaRS experience with a ‘good’ research education experience as they defined it. An assumption I made here was that these particular RepGrid elements, to some extent, represented the AWaRS

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Content Analysis:I analysed the RepGrid interview transcripts in a similar fashion to

that of the key informant interviews (pre-coding, chunking, in vivo/descriptive coding

and analytical memos). Precisely, I engaged in the following analytical act (see

Appendix I):

Step One: I transcribed all eight interview sessions with the STs (two each) and created

hard copies with relatively large left and right margins for coding and note taking.

Step Two: I bound together each ST’s session transcripts (two) to treat them as single manuscripts.

Step Three: I read through all four transcripts to familiarise myself with the data.

Step Four: RepGrid sessions comprised seven stages in total (from opening questions to the identification of favoured constructs in terms of ‘good’ research education experience) so I read through the transcripts once more, marking the beginning and

end of each stage on the left margin.

Step Five: I initiated a close reading of the transcripts, generating rather dense,

numbered descriptive and in vivo codes as I read on the right margin (blue ink).

Step Six: I re-read the transcripts, generating broader categories such as ‘demographic

information’, ‘expectations’, ‘likes and dislikes’, ‘future plans’, ‘conceptions’, ‘values’ etc. on the left margin (blue ink). I also marked where my RepGrid-related specific instructions began and ended (i.e. general introduction, before eliciting

elements, labelling elements, eliciting constructs etc.).

Step Seven: I read through the transcripts once again, trying to notice any striking

similarities between the STs in terms of their backgrounds, constructs and overall

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Step Eight: I re-read the transcripts, adding my reactions and interpretations to those

statements I found striking and interesting (black ink). I highlighted these statements

separately (orange or yellow highlighter) and where needed, noted the reference

number of another code on another page (e.g. code x  interpretative note  “see code y, page #n”) that seemed closely related.

3.7.4 Classroom Observations

My classroom observations of the AWaRS module produced two data resources. The

first was my ‘black notebook’ filled with hand-written session notes (Appendix J).

The second was a compilation of the word processed, tidied up and the re-narrated

versions of my scribbles (post-observation write-ups of about 30.000 words, 14

document files). Before embarking on the content analysis of the latter source, I re-

visited the hand-written notes with an aim to seek and identify any raw data which I

may have overlooked as insignificant or uninteresting at the time of composing my

post-observation records. I in fact located a number of tutor and ST remarks that my

narrations of events excluded rather unjustly and so added these to my post-

observation write-ups. As for the rest, I felt satisfied with the extent of coverage and

representation my re-articulated versions of observed events projected.

I resorted to the QSR-NVivo 10 software package to manage and analyse the 14

documents I generated. Firstly, I adopted an inductive approach to analysing the data,

compiling a largely descriptive, session-by-session coding of my narrations of ‘what happened’ and ‘what was said’. The figure below is an example NVivo screenshot illustrating how a re-constructed interaction between the STs from session four was

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Figure 11: Example NVivo Coding of Post-Observation Write-ups (Session-by- Session)

Next, I grouped the above-presented descriptive ‘steps’ into themes in keeping with the broad, ‘logical’ phases of engaging in a systematic research act (engaging with literature, planning research, conducting research (i.e. fieldwork) and sharing and

dissemination) (Tashakkori and Teddlie 2003, Onwuegbuzie and Leech 2005). These

are, therefore, predictable categories and might as a result look superficial but to have

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misrepresent the students’ perspectives on and experiences of the syllabus content.

The following figure illustrates an example of thematic NVivo coding of AWaRS

activities (see Appendix K for a fully expanded list).

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