The process of data analysis

this invitation, probably because the interviews were quite extensive).

The duration of the interviews amounted on average to about 90 minutes. The first pilot interview with teacher A54 took one hour and a half, but the interview guideline was at that time only a draft. She was contacted a second time¹⁰⁶, which took another one and a half hours. In her case the interview took three hours.

All interviews had a positive course, were pleasant and were characterized by a very friendly attitude towards the researcher and the questions (in one case a teacher took a very long time to answer all the questions and in another case another teacher did not seem as relaxed as the others, but in both cases their openness and willingness was manifest). In all cases their seriousness, their openness and desire to express themselves freely and honestly were impressive.

Post-interview script

After the interview was conducted, relevant information was annotated for each interview, regarding place, time and duration of the interview, comments about any incidents and impressions about their willingness, openness and attitude and about the global atmosphere.

After having described the methods, the following section offers a documentation of some aspects of the process of data analysis for this study.

coding design and the transcription of data.

Coding design

The analysis of data began when developing and coding the first pilot interviews (n = 5) with teachers who had not participated in the programme, and continued until the final research questions and the guideline for the semi-structured interviews were produced.

The process of data analysis was inevitably a blend of deductive and inductive categories. As Kruse (2010: 227) illustrates:

Auch wenn man nach dem Verfahrensprinzip der “Grounded Theory“ arbeitet (bottom-up gesteuerte Auswertung) […] verfolgt man Analyseheuristiken, mit denen man die Analysearbeit strukturiert. Diese orientieren sich an den Forschungsfragestellungen und an die Gesprächsleitfäden. Eine völlig

“voraussetzungslose” Analyse ist niemals gegeben und auch nicht möglich.

The categories were initially influenced by “sensitising concepts” (Blumer 1954; Bowen 2006; Flick 2009: 12), derived from the literature and unavoidably reflected in the research questions. The term refers to concepts “that suggest directions along which to look” (Flick 2009: 473) and are “required” to approach the issue under study (ibid. 12). They therefore initially helped to guide the analysis. However, as the process of analysis began, the categories were developed inductively by focusing on the concepts and the meanings emerging from the data. Grounded categories then evolved, as that they were “grounded in the data” (Freeman 1996: 371; Glaser & Strauss 1967). The points of views of the participants were thus included in the analysis, which was guided by the meanings they expressed in the interviews.

This process of defining the categories and identifying the dimensions occurred in a continuous interaction with the data. As a consequence, the process of data analysis, following an iterative approach (Freeman 1996: 371), required returning cyclically to the data to look for patterns and for associations.

The following figure is an example extracted from some of the categories which were developed:

Figure 3.4 – Sample of categories used to code the data

The data anaylsis process was supported by a computer assisted qualitative data analsysis software (CAQ-DAS). All interviews and entries from the questionnaires were coded to be processed by a software programme, (MAXQDA¹⁰⁹), a tool specifically designed for qualitative data analysis. The use of computer programmes to assist in the analysis of qualitative data has recently increased, due to the flexibility and versatility of these tools (Kuckartz 2008: 10; 15), and is thought to provide “invaluable assistance” (Mason 2002:

160). The integration of the categories in the software programme corresponds to the traditional paper-and-pencil procedure of annotating key words or themes on cards and does not exempt one from the “dull desk work” (Kuckartz 2007: 9) of reading the materials several times until patterns emerge from the data analysis task. The use of technology applied to the

109 MAXQDA, software for qualitative data analysis, 1989-2010, VERBI Software. Consult. Sozialforschung GmbH, Berlin-Marburg-Amöneburg, Germany.

process of data analysis is merely a facilitation in “structuring and organising the data” (Kelle 2008: 488), in that all operations of highlighting related concepts under superordinate headings, looking for parallels or inserting comments can be executed more flexibly by marking with different colours the various relationships. Their main advantage of using such a tool is to assist the operations of exploring the materials and retrieving text passages, concepts or categories and the representation of the data (Flick 2009: 362). Codes can be assigned by dragging a marked passage onto the appropriate code in the corresponding category system.

The task of retrieving data is also assisted by comparing items or associating them more easily than in handwritten notes. As a consequence, all the analytical tasks are facilitated.

Transcription of data

Since the primary focus of the interviews was on the content of the utterances and not on the mechanisms involved in the interaction between the researcher and the participants (Bergmann 2008: 535) or on the “system of talk” (Seedhouse 2011: 359), as in conversational analysis (CA), the adoption of a specific CA methodology was not applicable to the transcription of the data. However, the rules followed for the transcription of the interviews attempted to capture and render peculiar features when they were notably emphasised by the participants during the interview, in the assumption that this information belongs to the face-to-face event and does add relevant details to the transcription. Four of these main features were considered relevant:

1. emphasising utterances which were expressed in a peculiar mode, such as laughter or in a tone which was strikingly different from the normal speech of the teacher

2. highlighting references to contextual (discourse) information, such as indications of the topic being addressed, when the whole answer was not reported entirely because of its length

3. adding non-verbal communication, when it was an integral part of the discourse, such as in the case of one Spanish-speaking teacher, who tended to express her thoughts through gestures. This para-textual information was considered complementary to the face-to-face communication in course.

4. anonymising names, organisations or places to protect the speaker’s identities and replacing them with a description.

To differentiate between these descriptions and the texts themselves (Kowal & O’Connell 2008: 444), all the precautions above are provided in italics and in square brackets [ ] in the quotations.

Furthermore, the following procedure was adopted to refer to the answers of the participants:

The quotations of the teachers from the interviews are signalled with the label “Interview” in square brackets, followed by the teacher’s identification number, followed by the number of the row in the transcription of the interviews, as in the following example: [Interview D243:

197-9]. The researcher as interviewer is identifiable through the letter: “Q.”, abbreviation for

“Question”. If it is not a question, but only a remark, it will be signalled by the initials “EG”.

The quotations from the questionnaire are identified as entries and are structured in the following way: they are first denoted as “Entry” (to distinguish them from the interviews), are then followed by the teacher’s identification number, followed by the number of the questionnaire and lastly by the number of the rows in which the transcription appears, as in the following example: [Entry P73, Q177-172: 53-4]