Parámetros eléctricos de los cables

When I had finished the first life history interviews with the teachers, I uploaded the recordings onto my laptop and began transcribing them using the qualitative analysis tool NVivo. Although some authors recommend including aspects such as tone of voice, pauses and emphasis when transcribing (Atkinson and Heritage, 1999; Flick, 2009), I decided not to do so. This was because my study was focusing on the main themes highlighted by the teachers, as opposed to an in-depth “discourse analysis” of their interviews. I have provided a number of screenshots of the transcription process in Section A.4 of the Appendices.

Upon completion of the first drafts of transcription, I went through each interview again, correcting any grammatical mistakes and omitting any repetitions. I decided to do this for two reasons. Firstly, as I have just mentioned, I would not be analysing the data from a discourse analysis perspective, but rather in terms of the key semantic themes that emerged from the interviews. Secondly, given that all of the participants (except Jennifer, who was not included in the final data analysis) were non-native speakers of English, there were naturally quite a few errors in their spoken English. I felt that if I left the mistakes in by transcribing the interviews word-for-word, it might be embarrassing for the teacher participants.

After the interviews were transcribed, I began the process of coding in NVivo. Coding is generally considered a vital component of qualitative data analysis (Kelle, 1995; Gibbs, 2007; Flick, 2009). It consists of allocating certain parts of the data into different categories, which allows the data to be more effectively organised for analysis (Cohen et al., 2011). When coding, Computer-Assisted Qualitative Data Analysis Software (CAQDAS) such as NVivo are extremely useful, given that they assist with the main human problems of data overload and retrieval (Kelle, 1995; Flick, 2009). Of course, these programs do not actually “do” the analysis for the researcher, and this tends to be one of the main reasons they are criticised (Richards, 2002; Gibbs, 2007; García-Horta and Guerra-Ramos, 2009). Nevertheless, I have found using NVivo an extremely useful tool in facilitating my own process of interpreting the data.

There were two main types of coding which I used in my project: chronological and thematic. The first process which I carried out was that of chronological coding. This involved dividing participants’ educational life histories into key time periods, and then coding different segments of the transcription into these periods. The process of deciding upon each participant’s key time periods was quite challenging. Mainly, this was because there was not always clear “start and end points” between different sections of the participants’ lives, and there was often a great deal of overlap between different key events (such as long-term training courses). However, in terms of being able to differentiate between the main tendencies in the participants’ educational life histories, I had to be bold and create somewhat artificial categories. The chronological time periods for each teacher participant are highlighted at the start of each Data Analysis chapter, and each Data Analysis chapter is divided into these key periods (see Section 3.7).

When my first attempt at chronological coding was completed, I began the process of thematic coding. This involved creating categories within each chronological period, and then coding segments of text into these categories. My approach when deciding upon the thematic categories could be described as a combination of deductive and inductive coding (Miles and Huberman, 1994; Cohen et al., 2011). From a deductive perspective, I was always aware that I was going to have to answer my specific research questions. Therefore, it was inevitable that I began the data analysis process with certain (deductive) categories in mind. For example:

 Changes in beliefs about student-centred EFL learning (RQ1);  Changes in teaching practices (RQ2);

 Examples of consistencies between beliefs and practices (RQ2);  Examples of inconsistencies between beliefs and practices (RQ2).

However, from an inductive perspective, I also maintained an open mind regarding the creation of new categories. This was especially relevant when it came to creating sub- categories within the main categories highlighted in the bullet points above. For example, within the category “Changes in beliefs about student-centred EFL learning”, the following sub-categories emerged naturally (inductively) from the data:

 Changes in beliefs about student-centred teaching methods;  Changes in beliefs about teacher-student roles and relationships.

The sub-categories above are just two examples of codes which emerged during the course of the data analysis. Indeed, as tends to be the case with qualitative research approaches such as life history, category generation and coding was a long-term process (more than 12 months from start to finish), which evolved over time as I became increasingly familiar with the data (Goodson and Sikes, 2001; Richards, 2003; Gibbs, 2007). In Section A.5 of the Appendices, I have provided a variety of screenshots which show examples of the coding process.

When the initial chronological and thematic coding was completed, I began writing up a first draft of each participant’s educational life histories. As I did this, I became more immersed in the data, and my understanding of what had happened to the participants’ beliefs and practices over time became clearer. When I had finished these first drafts, I sent them to each teacher so that they could verify my initial interpretations and make suggestions if necessary. This method of “participant validation” or “member checking” was one of the key ways of trying to ensure the “trustworthiness” of the study (Lincoln and Guba, 1985; Goodson and Sikes, 2001; Onwuegbuzie and Leech, 2006). I focus on “trustworthiness” in more detail in Section 3.4.

None of the participants reported any significant problems with the first drafts that I sent them. This may have been because my analysis was very accurate, or perhaps because they either did not have the time to look at it detail, or did not feel comfortable enough to point out any problems (Cohen et al., 2011). In any case, my research design provided a number of other opportunities for the participants to “member check” my analysis of their educational life histories. The next chance for this to happen would be during the following timeline activity, which would confirm and supplement the information obtained from the first life history interviews.