Cooper (2010) posits that narrative data analysis attempts to systematically relate the narrative means deployed for the function of laying out and making sense of particular kinds of, if not totally unique, experiences, which in this study were experiences of at-riskiness. The two fundamental approaches to analysing qualitative data possible in this research were the deductive approach and the inductive approach (Burnard et al, 2008). These two approaches differ in that, in the first approach, I would use a structure or a predetermined framework to analyse data by imposing that structure or theories on the data, and then use that to analyse the interviews. I wanted the data to ‘talk for itself’, and for that reason I therefore adopted the inductive approach, appraised for its enabling data analysis with little or no predetermined theory or structure but the actual data itself to derive the structure of analysis (Burnard et al,
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2008). Making sense of the data involved my first listening to the recorded interviews and then remembering the context and how I had captured the recordings. I further analysed transcripts, identified themes within the data and gathered together examples of those themes from the texts that I had transcribed verbatim. I coded the texts by making notes in the margin of words, labelling themes or adding short phrases to sum up what the text said to me in the hope of making a summarizing statement or word for each element that I had identified as being significant in the transcript (Burnard et al, 2008).
I achieved this by focusing on interview responses and narrative text for my thematic data analysis (Miles, Huberman & Saldana, 2014) under the framework method with two goals in mind:
1. To understand what participants ‘really’ thought, felt or did in some situations or at some point in time for the richness of real-life experiences of at-riskiness.
2. To adopt a hermeneutic perspective on texts in which I viewed texts as an interpretation that I could never judge as true or false.
Narrative data analysis is both the means and the way in which these means are used to arrive at presentations and interpretations of meaningful experiences (Cooper, 2010). This qualitative research method generated words, rather than numbers, as data for analysis (Brikci & Green, 2007; Hollway & Jefferson, 2008). They further suggest that, in order to generate narrative data for analysis, the researcher needs to regard the whole narrative as a unit of analysis and in an ongoing exercise starting with the first interview. To make this convenient and efficient, I adopted Wells’ (2011) framework methodology and data analysis because this method offered practical guidelines which I considered suitable for both simultaneous data collection and analysis during and after at least two interview sessions per participant. I did this, as guided by McLaughlin (Sa), who suggested the following combinations in order to capture the themes:
1. Constant comparative content analysis. 2. Themes generated from the literature review. 3. Themes embedded in instrument questions. 4. Themes embedded in research questions.
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Themes are defined as something important about the data in relation to the research question, and representing some level of patterned response or meaning within the data set, irrespective of size of prevalence across the entire data set, even if there will be a number of instances of the theme across the data set (Braun & Clarke, 2006). In this study, themes were regarded as belonging to phenomenological epistemology, gave experience primacy, and sought to understand the participants’ everyday experience of reality in greater detail so that I could gain an understanding of the phenomenon of at-riskiness (Holloway & Todres, 2003; McLeod, 2001; Smith, Jarman & Osborn, 1999; Smith & Osborn, 2003).
4.6.1 Constant Comparative Content Analysis
With regard to constant comparative content analysis, the literature advised that the family composition of disadvantaged children also indicated disadvantage through the socio-economic activities of the respective members. Therefore, in my probing questions, I included non- ambiguous content aspects that sought confirmation comments relating to the family status of the household head, ownership of a home and personal space for the participant. This was consistent with Holsti’s (1969: 14) broad definition of content analysis when he states that it is a technique for making inferences by objectively and systematically identifying specified characteristics of messages. I also did not want a restrictive definition but one applicable to other areas, such as the coding of actions and gestures during the interviews, in order to replicate the criteria from participant to participant. The probing questions I presented here were therefore designed to elicit definite characteristics of the definition of content analysis, according to Krippendorff (2004), whereby content is about the characteristics of educational disadvantage, by answering the following questions:
1. Which data are analysed?
2. How are they defined?
3. What is the population from which they are drawn?
4. What is the context relative to which the data are analysed? 5. What are the boundaries of the analysis?
6. What is the target of the inference?
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I then added the responses to these questions to my handwritten analysis charts filled with cut and pasted notes of various representative colours for each participant. The charts made it easy to make comparisons, for example, I could identify which child lived with both parents, lived in a single room, had five or more siblings, what birth number they were in the family, how this compared with the rest of the participants from the other school, and also by gender. I simplified my boundaries by asking for straightforward responses. Either the participant lived with their father or not, and, if not, how often they met up, and I would also ask if they had an amicable relationship relating to their educational access and what words they used to explain such relationships, which indicated a participant perspective as central to content analysis coding. This fulfilled the posited argument that, with the inductive approach, themes are generated from the data through open (unrestricted) coding, followed by refinement of themes, using the framework method for the analysis of qualitative data in multi-disciplinary health research (Gale et al, 2013).
4.6.2 Themes Generated from the Literature Review
Literature relating to Zimbabwe’s socio-economic disadvantages for children carried out by researchers, including Robson (2004), in general had earlier indicated to me the possible themes of family sickness, poverty, child carers, poor working conditions and child labour at the expense of the children’s education. I then used this background in my analysis to appreciate the participants’ responses and to probe for time use management, whereby participants would indicate their economic activities’ themes, such as work both at home or to raise supplementary income for the home, which then confirmed the theme of child labour and the sub-theme of time shortages.
4.6.3 Themes Embedded in Instrument Questions
The themes were embedded in the following instrument questions:
1. Tell me about your secondary school education, highlighting issues you regard to be very significant (educational life journey, plot, positive and negative influencing factors, for example, bereavement, unemployment, poverty, single parent, economic climate).
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2. What words can you use to describe fellow students who are likely to be expelled from this school? Why? (behaviour, drugs, etc.).
3. How does this school minimize at-riskiness? (counselling, prefect system).
4. Why do we not have adequate resources in this school or at home? (poverty, unemployment).
4.6.4 Themes Embedded in Research Questions
The themes were embedded in the following research questions:
Research Question 1a: The needs; 1b: The characteristics of at-risk students. Research Question 2: The behaviour of at-risk students.
Research Question 3: How at-riskiness appeared in the Zimbabwean education system. Research Question 4: The characteristics of school-based programmes to reduce at-riskiness. Research Question 5: The contextual factors contributing to the incidence of at-riskiness.
In the sessions, I also wrote down affective expressions, such as sighing, crying or laughing, and I found it helpful to immediately listen to the recordings and relate to how the narratives had been presented while it was still fresh in my mind. I also asked the interviewee to give me a title for each chapter relating to their educational lives:
Tell me about a significant episode or a memory that you remember from this stage.
What kind of a person were you during this stage?
Who were significant people for you during this stage, and why? What is your reason for choosing to terminate this stage when you did?
The recorded data were then processed according to the following seven-stage framework method for analysis procedure (Gale et al, 2013):
Stage 1: Transcription
Stage 2: Familiarization with the interview Stage 3: Coding
Stage 4: Developing a working analytical framework Stage 5: Applying the analytical framework
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Stage 6: Charting data into the framework matrix Stage 7: Interpreting the data
The above steps for qualitative data analysis also roughly correspond to the five steps of data analysis compiled by Wells (2011) and O’Connor and Gibson (2003: 9) for my framework. A framework in qualitative data analysis served to establish the highest degree of clarity of the conceptual methods that I applied in order to follow the principles of formal logic (Schutz 1967), which I merged in order to come up with the plan below.