Proceso de Mejorar Posicionamiento en el Mercado

Thematic analysis is defined as ‗a method for identifying, analysing and reporting patterns (themes) within data‘ (Braun and Clarke, 2006: 79). It ‗minimally organizes and describes your data set in (rich) detail‘ (ibid.). The reason why I adopted thematic analysis in this study is that it ‗offers an accessible and theoretically flexible approach to analysing qualitative data‘ (ibid. 77). In the table below, Braun and Clarke identify the main phases involved in thematic analysis:

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Phase Description of the process

1. Familiarizing yourself with your data:

2. Generating initial codes:

3. Searching for themes:

4. Reviewing themes:

5. Defining and naming themes:

6. Producing the report:

Transcribing data (if necessary), reading and re-reading the data, noting down initial ideas.

Coding interesting features of the data in a systematic fashion across the entire data set, collating data relevant to each code.

Collating codes into potential themes, gathering all data relevant to each potential theme.

Checking if the themes work in relation to the coded extracts (Level 1) and the entire data set (Level 2), generating a thematic ‗map‘ of the analysis.

Ongoing analysis to refine the specifics of each theme, and the overall story the analysis tells, generating clear definitions and names for each theme.

The final opportunity for analysis. Selection of vivid, compelling extract examples, final analysis of selected extracts, relating back of the analysis to the research question and literature, producing a scholarly report of the analysis.

Table 4-4 Phases of thematic analysis (Braun and Clarke, 2006: 87)

The process, therefore, starts with taking notes and identifying interesting features while reading or transcribing the data. Trying to label elements of the data is usually known as coding. Then, gathering similar codes helps in developing patterns or themes to represent parts of the data in a way that would answer the questions that may have already been defined by the researcher or redefined in the process of the data analysis. Braun and Clarke point out that ‗a theme captures something important about the data in relation to the research question‘ (ibid. 82).

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In thematic analysis, themes can be identified in two ways: ‗in an inductive or ‗bottom up‘ way … or in a theoretical or deductive or ‗top down‘ way‘ (ibid. 83). ‗An inductive approach means the themes identified are strongly linked to the data themselves … (as such, this form of thematic analysis bears some similarity to grounded theory)‘ (ibid.) in the sense that themes emerge from the data. According to Braun and Clarke,

In this approach, if the data have been collected specifically for the research (eg, via interview or focus group), the themes identified may bear little relation to the specific questions that were asked of the participants. They would also not be driven by the researcher‘s theoretical interest in the area or topic. Inductive analysis is therefore a process of coding the data without trying to fit it into a pre-existing coding frame, or the researcher‘s analytic preconceptions. In this sense, this form of thematic analysis is data-driven (ibid.).

In contrast, ‗a ‗theoretical‘ thematic analysis would tend to be driven by the researcher‘s theoretical or analytic interest in the area, and is thus more explicitly analyst-driven‘ (ibid. 84).

Rather than perceiving them as opposites, it may be better to think of the inductive and the deductive thematic analyses as complementary to each other in the sense that one‘s drawbacks would be enhanced by the strengths of the other. The way it worked in this research exemplifies how they could complement each other. When I started analysing the qualitative data in the field, I adopted the inductive approach trying to find out about the appropriateness and the effectiveness of using supplementary internet materials through examining the participants‘ daily experiences and perceptions. Adopting the inductive approach to analysis helped me in developing

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appropriate internet methodology. But, with regard to the effectiveness of the materials used, the process was very complicated. The inductive approach seemed to take me in no particular direction. I started to get lost in the data which were showing evidence of the effectiveness of the internet materials in different areas such as active participation, motivation, and autonomy. Focusing on only one of the emerging areas was not only unfair to present what was coming out from the data but also impossible as even when I tried to focus my questions and observations on one particular area the data I got were not telling what I wanted to hear. This was not an easy process. On the contrary, it was very difficult, time-consuming, frustrating, and even worrying sometimes as I could see how the research was losing focus and direction. So, I decided to go back to the literature to see where all these emerging areas could lead to. While the internet literature did not help me find a theoretical framework to bring all the different parts together, the literature on motivation did. Engagement as a multidimensional construct had emerged in this back and forth process (between the inductive process and categories derived from my reading). Exploring the literature, I could identify recent and comprehensive models of learners‘ engagement (Svalberg, 2009; Caulfield, 2010) that account for the behavioural, cognitive, affective, and also social aspects of individual responses to certain activities. The analytical framework I used in this research was developed from these already existing models and, therefore, applied deductively (or at least in the back and forth manner described above). Making use of existing models helped me compensate for the drawbacks that resulted from merely relying on the inductive approach.

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The analytical framework provided the main themes for engagement under which deductively- as well as inductively-developed sub-themes fell. Braun and Clarke argue that:

You will need to identify whether or not a theme contains any sub-themes. Sub-themes are essentially themes-within-a-theme. They can be useful for giving structure to a particularly large and complex theme, and also for demonstrating the hierarchy of meaning within the data (2006: 92).

Richards et al argue that thematic analysis ‗is based on the identification of themes in a text at different levels‘ (2012: 79); i.e., themes and sub-themes. Taking the main themes and some sub-themes from the engagement literature and adding others that were inductively developed from the data did not only result in enriching the existing models on learners‘ engagement but also in applying them to the area of Internet- Assisted Language Learning.

After doing the thematic analysis, the presentation of themes in the final analysis needs to be supported with extracts from the data. A ‗data extract refers to an individual coded chunk of data‘ (ibid. 79) which has been extracted from a data item. A ‗data item is used to refer to each individual piece of data collected, which together make up the data set‘ (ibid.). Using more than one extract and from different data items and sets to exemplify a theme would reflect how common it is in the data and reduce the risk of engaging in ‗cherry-picking‘ (Morse, 2010). Braun and Clarke point out that ‗there are various ‗conventions‘ for representing prevalence in thematic (and other qualitative) analysis that does not provide a quantified measure‘ (2006: 83), for example, ‗the majority of participants‘, ‗many participants‘, or ‗a number of participants‘. ‗Such descriptors work rhetorically to suggest a theme

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really existed in the data, and to convince us they are reporting truthfully about the data‘ (ibid.). Although it is important to represent the prevalence, ‗the ‗keyness‘ of a theme is not necessarily dependent on quantifiable measures _ but rather on whether it captures something important in relation to the overall research question‘ (ibid. 82).

In short, in spite of all the advantages that using thematic analysis brought to this research, some of the disadvantage were that unlike narrative analysis, in thematic analysis ‗you are unable to retain a sense of continuity and contradiction through any one individual account, and these contradictions and consistencies across individual accounts may be revealing‘ (ibid. 97). Also, ‗in contrast to methods similar to DA and CA, a simple thematic analysis does not allow the researcher to make claims about language use, or the fine-grained functionality of talk‘ (ibid.). That was why I needed to draw on other types of analyses to compensate for the disadvantages specified above.