DATA-ANALYSIS PROCEDURES .1 Preparing and organising the data

After the researcher has disengaged and exited the field, it is important that he shifts to a more settled data analysis exercise that culminates in the reporting of findings to the relevant stakeholders (Drew et al., 2008:343). For this reason, preparing and organising the data is essential.

In this study, I first transcribed verbatim the audio taped data and the field notes into text and organised them systematically by type (Creswell, 2005:232). Verbatim data transcription included capturing colloquial language, so as to accommodate “in vivo”

codes, that is, codes derived from the participants’ own words, which could possibly carry important meanings (Hennink et al., 2011:215). In accordance with Berg’s (2009:53) recommendation, my organising of the raw data involved editing, correcting, capturing and saving the data on computer files. I labelled the data files for ease of access.

A pertinent question often raised is at what point data transcription should commence. Hennink et al. (2011:212) suggest the options of either doing this soon after completion of the first interview, or after all the data has been collected. I

issues from the first interview, which could be explored further in subsequent interviews. Secondly, I could identify new, interesting, and unexpected theoretical issues that might assist in the refinement of the interview schedule for collection of more accurate, deeper information in subsequent interviews (Hennink et al., 2011:213). Lastly, the cyclical analytical process of induction and deduction motivated me to transcribe the data immediately.

Leedy and Ormrod (2010:153) regard data organisation as an integral part of settled, ongoing data analysis, and they advise the researcher to file the transcribed data by creating and maintaining a permanent computer database. Transcribing the data entailed segmenting and organising it by typing the following as textual data: the transcribed document analysis and lesson observation data (field notes); the transcribed post-scheme/plan interview data; the transcribed post-lesson interview data; and the transcribed interview data on teachers’ perceptions on overall implementation.

I saved the data sets into computer files and labelled them (Berg, 2009:53; Leedy and Ormrod, 2010:153) by school site (case), by research question, and by data-collection instrument, addressing one specific research question at a time. I conducted manual data analysis, because I did not feel comfortable to use qualitative computer software, as I have not received training on how to use it, and so that I could be closer to the data and have a hands-on feel for it (Creswell, 2005:234).

4.10.2 The analysis of the data

The data-reduction process of collapsing large masses of data into smaller, manageable chunks for making theoretical sense of them (Berg, 2009:54) set me into transforming the textual data by inductive coding. More specifically, I employed the inductive content-analysis method of data analysis, using ideas borrowed from the grounded theory approach of analysing qualitative data. This study’s interest in systematically developing theoretical explanations of the processes entailed in the enactment of the AIDS curriculum in classrooms (Creswell, 2007:64) justifies the use of inductive content analysis. This method of data analysis enables discovery of theoretical patterns and categories embedded in the research data that explain

teachers’ implementation of this curriculum. Because of my specific objective of gaining a nuanced understanding of teachers’ experiences with an education phenomenon, and thereby discovering concepts that theoretically explain the phenomenon, I opted for the method of content analysis. Essentially, the content analysis process involved coding data, developing themes, and the abstraction of theory.

4.10.2.1 Data analysis of how teachers understand and implement the curriculum

As I was collecting data from the teacher schemes/plans, using the document analysis and lesson observation data through lesson observation protocols that I had designed, I wrote reflective notes as an initial process of interpreting the data I was collecting. I used the rubric on the document analysis and lesson observation instruments as the predefined codes onto which I assigned meanings to develop theoretical themes/categories. I was therefore wary of the inevitability of the use of pre-existing ideas and concepts, and the impossibility of completely eliminating prior frameworks (Gibbs, 2012:8).

The analysis of the document analysis data collected from the photocopied teacher schemes/plans involved objective analysis of the messages conveyed in the schemes/plans, and the analysis of the lesson observation field notes involved subjective analysis of the teaching practices of the teachers.

Analysing the document analysis data entailed the counting of units of analysis from the field notes under the predefined codes, and then developing these codes into themes. The counts from the document analysis data transcript were then used to quantify the frequency of occurrence of the key curriculum features that teachers had included in their schemes/plans, to produce a general picture of their codifications of the AIDS curriculum. The results would be presented on an information table, accompanied by a narrative description. Lesson observation data was also subjected to coding of data and development of themes. Thus, from both the document analysis and the lesson observation transcripts, I formulated emerging themes.

I felt a sense of desperation as to how best I could develop themes from document analysis and lesson observation transcript field notes, where the data was dispersed across two sources unlike the way it would be if I had only interview data. As I thought the matter through, an idea suddenly dawned on me: “What if I acted as if I was interviewing the participant from whose scheme/plan and lesson observation transcript I had collected the data? Would he or she not give me the same answers about the predefined codes, which I would then start coding, as I would do if I conducted an interview?” This was indeed a “eureka” moment for me.

Upon adopting the above strategy, I came up with several themes from the predefined codes of the document analysis transcript and clustered and collapsed them into very few broad themes. A similar content analysis process with the lesson observation transcript resulted in the codes being reduced to a few broad themes.

4.10.2.2 Data analysis of teachers’ perceptions of their implementation configurations and practices, and personal and contextual factors that play out on them, their experiences, and suggestions

I employed the same content analysis procedures used for developing categories in the scheme/plan and lesson observation data to analyse data on teachers’ views with regard to their implementation of the AIDS curriculum.

More specifically, I engaged in the content analysis procedures of (a) perusing each transcript for a broad picture of the data, (b) writing down hunches in the margins, (c) open coding, (d) clustering and collapsing codes carrying related meanings into themes, and (e) categorising the themes for conceptualisation. I thus adopted Creswell’s (2005:231) open coding strategy of collapsing data into broader themes by integrating overlapping and redundant codes, which was relevant to my study, as it enabled me to obtain a deeper, detailed understanding of the phenomenon under investigation, using a reasonable number of themes.

Axial coding was then done. I categorised and conceptualised the data by identifying those codes with similar characteristics and grouping them together into meaningful categories (Hennink et al., 2011:245). As a measure to move analysis to

a higher level, I tried to establish theoretical connections between and across categories (Hennink et al., 2011:245; Leedy and Ormrod, 2010:143).

Heeding Gibbs’s (2012:8) coding strategies, I also tried to move away from the description of codes based on participants’ terms to a more categorical, analytic level of coding. This was done by clustering codes which described common issues that occurred with or were reported by participants across the different school contexts.

Content analysis procedures with the interview transcript of each participant resulted in the production of numerous initial themes, which were then collapsed into a few broad themes.

Using the constant comparison method, I continually returned to the database to check whether the provisional themes I had formulated were supported by the actual data. The back-and-forth process of returning to the data and going back to my tentative theoretical themes helped me to reach data saturation. It was only when I was satisfied that all my themes had been conceptually fitted into the broader categories that I stopped clustering and integrating the themes. The result of the analysis was some themes that remained exclusive by virtue of their uniqueness, since they could not fit into any category. However, since they spoke to my research interests, they were not discarded.

The sub-themes of the developed broad categories were therefore used in the service of being the substantive content of the categories. This was done to enable a thick description and an analytical and theoretical appraisal of the findings.