According to Braun & Clarke (2013:204) data analysis in qualitative research normally only occurs once all data have been collected. However, the authors argue that that may not always be the case in qualitative research. There is not always a clean separation between data collection and data analysis. In some instances, data coding may begin while data is being collected and reviewed to identify patterns to refine subsequent data collection. Qualitative research designs
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are known for their advantages of flexibility. Narrative techniques may employ different analytical methods (Davies, 2018:1121).
The study followed a combination of data analyses tools, namely; content, matrix, and thematic analyses. Each is described in turn.
3.9.1 Content Analysis
Wright (2017:1) describes content analysis as a process of examining various cultural texts or non-living material “as a means of gaining insight into a social world, ideologies, or dominant worldviews.” O’Connor (2017:499) citing (Krippendorff, 2004) argues that content analysis is mostly used for analysing textual data, which uses the combination of quantitative and qualitative techniques. However, Zhao, et al. (2018:35) suggests that content analysis is the most effective method to find patterns from texts using codes. Du Plooy (2009:219) argues that qualitative content analysis requires the research problem to be about themes, values or styles and differed ideological levels of meanings.
In this study, the researcher created participant codes and they are found in 3.5.2 table 3.1. In terms of application regarding primary data, codes create a crucial link between data collection and data interpretation. This allowed the researcher to use a set of guidelines to make sense of data systematically. The researcher performed data analysis after following a step-by-step procedure to analyse multiple data from interviews, documents and archives through content.
The researcher created word tables to analyse data in conjunction with matrix analysis Yin (2014:165) argues that it is possible to create word tables for individual units of analysis that show data in accordance with one or more uniform classifications.
In addition to qualitative content analysis, the researcher also used matrix analysis, which is discussed next.
3.9.2 Matrix Analysis
Neale (2016:1097) suggests that qualitative data analysis is a systematic and rigorous process however it is underpinned by ‘creativity and inspiration.’ Matrix analysis, in particular, allows data to be summed up and reviewed across and within cases looking for concepts, patterns, associations and or explanations through mapping and interpreting data within a matrix. Matrix analysis permits the researcher to create matrices which according to Neale (2016:1100) facilitates data reduction and data display.
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The Researcher used ‘Ruffin’s Guidelines on Matrix Development for Qualitative Research Data Analysis’ for data reduction and shared ideas for data reduction with the research collective (Ruffin, 2019). The researcher first familiarized himself with data. This included listening to audiotaped interviews and reading the transcripts of the interview to familiarize the researcher with the information provided by the participants and forming impressions.
Participant codes were then generated and assigned to each participant. Data relevant to each code was colour coded and extracted into a table where data with similar colours were grouped together in a table to form matrices. The excerpts extracted from transcripts along with participant codes were categorized in relation to the objectives of the study. Data reduction ensued while preserving the participants’ meanings consistent with the constructivist philosophical worldview.
Subcategories were formed in the matrices which ultimately led to the researcher teasing out the themes (see 3.9.3) from the subcategories. The cross-narrative analysis was employed to analyse the four different subunits of analysis. The point of these projects being sub-units of analysis is highlighted in section 4.2.3. that is, the research problem and questions literature review etcetera which led to a narrative technique. Matrix analysis complemented the narrative strategy employed in the study.
3.9.3 Thematic Analysis
Chen & Lawless (2019:92) note that “thematic analysis is a handy methodological tool that researchers use to code and interpret in-depth, qualitative interview discourses.” Quoting Braun and Clarke (2006), Chen & Lawless (2019:93) cite a six-step process to thematic analysis, namely, (i) familiarity with data; (ii) generation of codes; (iii) searching for themes; (iv) reviewing themes; (v) defining and naming the themes; and (vi) production of a report. The authors describe thematic analysis as a “much more recognized and used as a tool for inductively analyzing qualitative, empirical data.”
Thematic analysis according to Maguire & Delanunt (2017:3352) and Clarke & Braun (2017:297) is a process of identification of themes and patterns within a qualitative data. Braun & Clarke (2016) cited in Nowell, et al. (2017) argue that thematic analysis can produce insightful and trustworthy findings. This study adopted thematic analysis to analyse data in the matrices after developing subcategories in the matrices which are relevant to the research objectives.
As mentioned in 3.9.2 the researcher teased out the themes from the subcategories following
‘Ruffin’s Guidelines on Matrix Development for Qualitative Research Data Analysis,’ (Ruffin, 2019). The researcher analysed from a categorical dimension. According to Wright (2017:1), researchers can examine a holistic versus a categorical aspect of narratives. The researcher went
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beyond the superficial or more obvious elements to the underlying meanings possible in the categorical dimension mention earlier. A detailed process is discussed in 3.9.2 above. A method of ongoing analysis to refine the themes was put in place followed by a process of a clear definition of themes representing the entire story through an analytical lens. The themes generated by the study appear in Chapter Four, section 4.5.
3.9.4 Analysis of Secondary Data
Rosinger & Ice (2018:1) refer to secondary data analysis as the analysis of existing datasets with which the researcher was not involved in the design, compilation or collaboration of the original researcher. According to Schutts (2016), data collected for one purpose or the other may be re- analyzed by the same researcher or another researcher to answer different research questions.
In this study, the researcher used Excel listings provided by the Department to create a chart in Microsoft Excel to compare and contrast the number of projects in the Provinces of South Africa.
This provided a narrative of the 1hh1ha programme. Section 4.2.1 provides a full narrative of the outcome of secondary data analysis.