Descripción y Análisis de Impacto .1 Etapa de Construcción

In this section I discuss and justify the data analysis process used in my study. I discuss the participatory analysis that I employed, how I worked with the data to arrive at the findings, and how I recontextualised the findings in the literature.

Data analysis is described as “an ongoing process [that] involves a continual reflection about the data, asking analytical questions, and writing memos throughout the study” (Creswell, 2009, p. 184). Qualitative data analysis is therefore conducted concurrently with data generation and it

131 involves multiple levels of analysis, depending on the nature of the study. Braun and Clarke (2006) indicate two ways of conducting data analysis: data-driven and theory driven coding. In this study I drew on data-driven analysis within the chosen theoretical framework. Data driven analysis is also described as an inductive coding which develops the themes and depends on the data (Maree, 2007; Nieuwenhuis, 2007c; Tesch, 1990). Therefore inductive thematic analysis was used to analyse and to report on these secondary school children’s construction of care and support and how participatory arts-based methodologies could enable agency in the lives of the ‘vulnerable’ secondary school children in rural school in the age of HIV and AIDS.

Using a variety of data generation methods resulted in a voluminous amount of data that needed to be managed and kept safely. I started working with the data by organising all of it into manageable formats to allow for easy access when needed. To facilitate this process, I began by opening folders for all data generation methods and labeled the folders according to the research methods. All data was digitised and preserved electronically. I also kept original documents safely in a paper file.

I employed participatory oriented analytic procedures as a strategy for analysing and interpreting the data in my study. From a critical philosophical stance, the principles underlying participatory analysis are based on the recognition that people are not only “respondents, they are players, performers and presenters of their own play, performance and presentation” (Chambers, 1992, p. 300). The process of participatory analysis first seeks to amplify the voices of the participants – what Chambers (2013) refers to as “putting the last first” (p.168), especially marginalised groups and marginalised communities. In participatory analysis, participants co-construct data and provide their own analysis of their data. Although the prompts were used to guide the participants to generate data, the actual data production (i.e. the drawings, description of each artifact, and the interpretation) was done by the participants with little guidance from me as the researcher.

In this study, I used multiple layers of engagement (Creswell, 2009; Marshall & Rossman, 2011; Silverman, 2013) with the data in an on-going process. The first layer of data analysis and interpretation involved the school children who thought about, identified, and generated their

132 own drawings, photographs, and collages and explained all these in response to the issue under investigation. This became the first layer of data analysis.

The second layer of data analysis and interpretation involved taking a critical stance, with my encouraging the school children to talk about the what, the why and the how of their photographs, drawings or collages and what each represents in relation to this research study. During this stage of analysis I also drew from the work of various researchers working in a critical visual methodology framework (Olivier et al., 2009; Theron et al., 2011) by posing some questions to guide it. For example, such questions were adopted from Wang (2005) as cited in Olivier et al. (2009) and are as follows: “What is shown in the drawings/photographs/collage? What is represented here? What is really happening here? How does the issue raised relate to our lives? and What can you/we do about it?” (p. 16). The school children were active and the discussion embodied a dialogical and reciprocal relationship not only between me and them but also among the participants themselves. Through their explanations, they also provided clarity, posed and answered questions, and codified issues and themes that arose from the discussion, as Wang (2006) would suggest. This stage of analysis enabled the school children to reflect, re- construct, add, and re-pattern units of meanings in relation to the topic under investigation. I argue, like Mitchell (2011) and Croghan, Griffin, Hunter, and Phoenix (2008), that the dynamics inherent in participatory analysis have potential for social change because it is here that they could (re)construct their thinking about their own realities “through their experiences, their imagination and intuition, their thinking and action” (Reason, 1994, p.324).

The school children’s various responses involved a mass of data – visual and textual; written, spoken and recorded explanations; and their reflections in relation to their construction of care and support and how such arts-based methods (drawing, photographs, and collage) could be significant to secondary school children’s agency in the rural school in the context of HIV and AIDS. The data corpus formed a blended conversation that was transcribed and with which I engaged in a third layer of analysis. Participatory analysis articulates the potential of engaging participants in data analysis building from the bottom to the top, but also in a cyclical way. Each layer informs the other layer in an interactive manner since such inferences are based on the assumption of a democratic conversation (Van der Riet, 2008). However, there is a layer in which the qualitative inquirer, despite her analytical differences, and depending on her research

133 strategy, often uses a general procedure in order to generate units of meaning of the research (Creswell, 2009; Henning et al., 2004). Thus, she looks at qualitative data analysis as involving steps from specific to general in order to make a logical analysis. It is at this third layer of analysis that I looked at data (first and second layers of data analysis, my own field notes and reflections) to arrive at units of meaning of the topic under investigation. I employed a descriptive analysis technique outlined by Tesch (1990) to generate categories and themes. This is what I did.

 Reading through all data was the next step to obtain a general sense of the information and reflect on its overall meaning. This involved looking at both written and visual texts. What are participants saying in general? How are they representing these ideas visually? Why is it so? In this way I was still trying to become immersed in the data as advocated by De Vos, Strydom, Fouché and Delport (2011) and Marshall and Rossman (2011). I looked at the school children’s drawings, photographs and collages not only in terms of their visual content but also the explanations that accompanied them, as (De Lange, 2012; Mitchell, 2011; Theron et al., 2011) all advise.

 Data segments or chunks of similar topics were then clustered together to define conceptual similarities and discover patterns. This process involved taking written and visual texts gathered during data generation and putting the units of meaning into categories, labeling the categories using the actual language of the participants, a process that Creswell (2009, p. 186) refers to as being “in vivo”. Marshall and Rossman (2011), Creswell (2009) and Tesch (1990) agree that coding does not involve what the researcher expects to find based on her/his past experience, but includes an array of major topics, unique topics, leftovers, surprises and triggers.

 For Flick (2009); Gibson and Brown (2009); Marshall and Rossman (2011); Silverman (2010); and Tesch (1990) these categories and codes are woven together to generate themes. These themes are the ones that would appear as the major findings in response to the critical questions in the study. Themes are therefore informed by multiple perspectives from the participants and are supported by diverse evidence such as participants’ direct quotes, and their own drawings, photographs and collages, as adjuncts to the discussion. Themes can also be interconnected to develop a storyline,

134 which, according to Creswell (2009), is a demonstration of an additional layer of the complexity of the issue being studied. Flexibility is one feature embedded in qualitative research so the reporting of findings is not linear and could take many forms.

 The fourth and final layer of analysis is characterised by a deeper discussion of the broad themes beyond descriptive analysis where I respond to the research questions and provide a synthesis of the findings, drawing on the existing literature, theoretical frameworks of Bronfenbrenner’s bio-ecological systems theory and Giddens’s structuration theory as used in my study. I constantly moved between the existing theory and literature particularly in order to explore insights from the data in the context of established and reputable knowledge as suggested by Nieuwenhuis (2007c). This was to find aspects in the data that corroborate what is in the literature or contradict it. I therefore identified similarities and differences in existing literature in order to evaluate the significance and meaning of the findings. This process could also raise new questions that, as the researcher, I had not foreseen in the study and that need to be asked.