Data analysis

McMillan and Schumacher (2010:367) explain qualitative data analysis as an inductive process of organising data into categories and identifying patterns and relationships among the categories. The researcher synthesises and makes meaning from the data, starting with the specific data and ending with categories and patterns. According to Krueger and Casey (2000), in Greeff in De Vos et al. (2005:311) analysis begins by going back to the purpose of the study. Welman et al.

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(2005:211) report that in order to analyse the raw field notes, they have to be processed and this entails converting the notes into write-ups which must be intelligible products that can be read.

According to Maree (2010:99) qualitative data analysis tends to be an ongoing and iterative (non-linear) process, implying that data collection, processing, analysis and reporting are intertwined, and not merely a number of successive steps.

Leedy and Ormrod (2010:153) comment that in most qualitative research data analysis and interpretation are closely interwoven, and both are often enmeshed with data collection as well. De Vos in De Vos et al. (2005:333) describe data analysis as a process of bringing order, structure and meaning to the mass of collected data or textual information. According to these authors the aim of data analysis is to look for trends and patterns that reappear within a single data sheet or among various data sheets. They further state that the sources for analysis can be transcripts, tapes, notes and memory. When analysing the data the researcher needs to be creative and explicit.

McMillan and Schumacher (2010:370) highlight that there are mainly three kinds of data in qualitative studies - notes taken during observation and interviewing, audiotape-recorded interviews and visual images. These authors further describe transcription as a process of converting recordings into a format that will facilitate analysis (such as typed text). Maree (2010) presents the model of Seidel (1998) which consists of three essential elements: noticing, collecting and reflecting. Creswell (1998), in De Vos in De Vos et el. (2005:334) believes that the process of data analysis and interpretation can best be represented by a spiral image. This study utilizes the data analysis spiral of Creswell (1998) as presented by Leedy and Ormrod (2010:153). Data processing was done following the following steps:

I organised the data; mainly using index cards. Large bodies of text were broken down into smaller units, such as sentences or individual words.

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I went through the entire data set several times to get a sense of the whole. In the process I jotted down a few memos that suggested possible categories or interpretations.

I identified general categories or themes, and some subcategories or subthemes as well, and then classified each piece of data accordingly. At this stage I already got a general sense of patterns.

I integrated and summarized the data.

This spiral data analysis was integrated with the process as described by Marshall and Rossman (1999) in De Vos in De Vos et al. (2005:334). Initially, planning was done for recording of data, followed by data collection and preliminary analyses, managing and organizing the data, reading and writing memos, generating categories, themes and patterns, coding the data, testing the emergent understandings, then searching for alternative explanations and finally representing in writing the report.

I made sure that the data was well organised for analysis. In analysing, I considered the words, context, the internal consistency, frequency of comments, extensiveness of comments, specificity of comments and what was not said, as well as finding the “big idea”(Morgan & Krueger 1998 in De Vos et al. 2005). Barbour and Kitzinger (1999) in Greeff in De Vos et al. (2005:312) also mention that data analysis involves, at the very least, drawing together and comparing discussions of similar themes, and examining how these relate to the variation between individuals and between groups. Segmenting involves dividing the data into meaningful analytical units whereby the text is read line by line while the researcher keeps on posing a question to himself/herself about this segment of text.

Maree (2010:101) mentions different data analyses such as content analysis which identifies and summarizes message content and focuses on things like books, brochures, written documents such as minutes, transcripts, news reports and visual media, conversation analysis which is the study of talk in interaction, discourse

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narrative analysis which refers to a variety of procedures for interpreting narratives

generated in research in search of narrative strings. In this study as a researcher I have almost used all the above modes of data analysis. Documents that were analysed in this study included the SGB constitution, code of conduct for the learners and the minutes of the meetings. I checked on the manner in which the minutes were written and read. These documents provided clues on how the SGB members interacted with each other. I listened to the conversations and analysed their responses.

According to De Vos in De Vos et al. (2005:335) data analysis in a qualitative inquiry necessitates a twofold approach. The first aspect involves data analysis at the

research site during data collection and the second aspect involves data analysis away from the site, following on a period of data collection. These authors continue to say that data collection and analysis work hand in glove in order to build a

coherent interpretation of the data. The researcher alternates between data collection and analysis to create meaning from raw data. The researcher is responsible for data collection and analysis. At this stage the analysis is of an interim nature. Memoing is considered to be a helpful tool for recording ideas generated during data analysis.

The researcher should always be flexible in data analysis; there is no right or wrong approach. Patton (2002), in De Vos in De Vos et al. (2005:336) highlights that analysis begins when ideas that make sense of the data emerge while still in the field. According to Patton (2002) the researcher needs to revisit the fieldwork if there are ambiguities and gaps that are identified. The researcher can go back to the site to recollect additional information for clarity purposes. In the case of my research, this was not necessary.