Modelo matemático para la transferencia de masa

One of the main aims of the project is to investigate how participants form perceptions of others’ trustworthiness and how they claim trustworthy personas for themselves discursively. The focus of the interviews is on the factors that seem to affect perceptions of trustworthiness in the given context; in the first instance, the focus of my analysis is on the content of the participants’ utterances. From this perspective thematic analysis was deemed an appropriate choice. I discuss this further below.

Thematic analysis consists of the identification, analysis and elaboration of the patterns, or themes that emerge within a set of data as being important to the description of the phenomenon studied (Braun and Clarke, 2006; Daly et al., 1997). Thematic analysis is made up of several steps, the first of which is the process of transcribing the tape-recorded data. Dornyei (2007) argues that while transcribing, the researcher already obtains a rough idea of the unprocessed information. The transcription of interview data does not include detailed interactional features (e.g.

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as per the conversation analysis transcription conventions), as the aim is content analysis. After transcription, the researcher needs to familiarises him/herself with the data. Familiarisation, although seemingly an obvious step, is of crucial importance, since it determines the integrity of the analysis (Ritchie and Lewis, 2003). Familiarisation is achieved by reading the transcribed data several times and by adding comments and reflections (Denscombe, 2010; Rice and Ezzy, 1999). During this process the qualitative researcher also aims to identify ‘patterns and processes, commonalities and differences’ in the data (Miles and Huberman 1994:9). This process is considered to be a pre-coding exercise as it includes the first identification of possible codes. A ‘code’ is a label that assigns meaning to a chunk of text and refers to the ‘most basic segment, or element, of the raw data or information that can be assessed in a meaningful way regarding the phenomenon’ (Boyatzis, 1998:63). Or as Dornyei puts it, the code is a label attached to a chunk of information that intends to make the specific bit of information ‘manageable and malleable’ (2007:250). Boyatzis (1998:1) describes as a ‘good code’ one that captures the qualitative richness of the phenomenon. What counts as code at this first stage is relative; it might be a word appearing many times or highlighting a special feature of the data that can be linked to broader concepts considered to be highly relevant and meaningful for the phenomenon (Denscombe, 2010). These codes are used at the next stage to form categories; these should be formulated in such way that they can all be classified under at least one of the categories (Strauss and Corbin, 1990). These first sets of codes and categories are subject to a continuous process of refinement during the whole analytic process (Denscombe, 2010).

Up to this stage, the researcher in practice organizes the data in such a way that can be manageable. The actual interpretation of the data, however, starts to take place in the next phase, where the researcher having developed a list with his/her codes and categories sorts them into broader labels, the themes, and starts to develop arguments about the phenomenon under examination (Braun and Clarke, 2006). Boyatzis (1998) defined a theme as ‘a pattern in the information that at minimum describes and organizes the possible observations and at maximum interprets aspects of the phenomenon’ (p.161). The identification of themes is supposed to

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offer explanations on the interconnections that recur between the codes and categories that have emerged, or in other words explain how the different units are related (Denscombe, 2010). The identification of certain themes at this stage can be assisted by the use of visual representations, such as mind-maps and memos (Braun and Clarke, 2006). The coding process, however, is not complete at this stage. It might be necessary for the researcher to refine his/her codes, categories and themes by going back to the data to ascertain that the themes work in relation to them, otherwise he/she will need to do a re-coding (Denscombe, 2010). After having completed this step, then the researcher should be able to produce a final thematic map with the main themes that emerged from the thematic analysis (Braun and Clarke, 2006). Once the thematic map is finalized, the researcher should form arguments to support the choice of the specific themes based on related literature (Aronson, 1995).

Despite the linear presentation of the process offered above, thematic analysis is understood as an iterative and reflexive process; i.e., the analysis process is not linear, but uses a ‘zig-zag’ pattern (Dornyei, 2007:243). This implies that the analyst goes back and forth between data collection, analysis and interpretation, depending on the emergent results, and reflects constantly on his/her actions/choices/decisions (Fereday and Muir-Cochrane, 2006). For example, it is not uncommon for the researcher to decide in the middle of the analysis that he/she needs to collect some additional data on a specific aspect, or to go back to the raw data and rearrange it according to some newly conceived categories (Dornyei, 2007). Regardless of the stage, when reflecting, it is also common practice and advisable to go back to the interviewees and ask for their feedback, either on the transcripts, or on the themes identified and the interpretations made (Aronson, 1995). I had the opportunity to meet with some of the interviewees more than once and in these cases, I asked them to elaborate on certain topics that were raised in our previous encounters and that I had found interesting after a first round of analysis.

Coding can be assisted by relevant software. In the case of my project though, the coding process took place manually, since the number of interviews (20) was manageable. The process was data driven and iterative; codes and categories were

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reviewed many times before being finalized. While coding, I was not always sure what to regard as a code or what to exclude, so I started underlining any key words, sentences and concepts that seemed significant for the participants or interesting, even if not directly linked to my immediate focus, in order to let new insights be generated (Dornyei, 2007). I also paid attention to words or concepts that were repeated across the data, as well as to parts of the text that reminded me of theories or other relevant results from previous reports on the topic. As a next step, I rewrote all the codes in an A2 blank poster, and started classifying them in possible categories, depending on their conceptual relevance. Boundaries of categories were fluid and revised multiple times. Issues of frequency and significance became particularly salient at this stage. More specifically, I was in doubt whether to develop categories for concepts mentioned by only one or very few participants or not, or keep to recurring ideas expressed by the majority. I decided to include both recurring concepts and concepts mentioned by fewer participants. This decision was because qualitative research is not preoccupied with the generalizability of the results, but aims at a better understanding of the phenomena (Dornyei, 2007). In order to reach this understanding, the researcher needs to pay attention to all relevant aspects of a phenomenon, not only the ones mentioned more often. For this reason, categories were also made for less common ideas that were given particular attention from participants and seemed to be significant to the topic. Contradictory data, especially those coming from the same participants, posed an extra challenge for me at this stage. Contradictory data though seem to be an implication of researching abstract notions. In order to cope with the frustration that contradictory data can cause, I identified inclusive categories and then themes that could also incorporate contradictions. The following table provides an example that illustrates the coding process.

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Chunk of text Code Categories

Interview with Nick

When you are ethical in your life, you do not miss friends. The snakes and worms go away and good

friends remain. These are only a few. But this is the

joy of life, you know, to say it is a big honour to have you as a friend Ethical-positive Friends (reward) A few (precious) Joy of life Big honour of having someone friend Friendship as honour

Interview with Bill

Me and my father, we are going to Turkey every fortnight and we have very good relations there, personal ones that have been developed, at a level of friendship’. Family Very good relations Personal relations developed (dynamic) friendship Personal relationship friendship

Table 3.3: Coding process sample

Once categories were formed, I drew a schematic representation inclusive of all of them and tried to point out possible connections between them. Visual representation of the data, according to Richards (2003) can prove quite insightful and reveal new aspects of the data. Its significance is also evident in Miles and Huberman’s words (1994:91) ‘You know what you can display’. The different features in the map were rearranged many times. Finally, the revision of the first analytic schema led to the identification of broader categories that explain the phenomenon.

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I will now turn to the discussion of interactional sociolinguistics, which informs the analysis of spoken interaction recorded.