CHAPTER 4 RESEARCH METHODS AND METHODLOGY
4.5 Data analysis and interpretation
4.5.1 Thematic analysis
There are multiple ways in which qualitative data can be analysed thematically (Braun &
Clarke, 2006). Dey's (1993) ‘omelette approach’ requires the researcher to identify patterns and relationships within the data. This approach was selected because it is prescriptive and offers a step-by-step guide as to how the interpretation of themes can be accomplished within qualitative research. The concern is with categorising data and identifying or establishing connections between these categories (Dey, 1993), and is similar to other thematic techniques that seek to systematically work through and analyse raw primary data (Braun & Clarke, 2006).
Moreover, it is based on the assumption that interpretation and analysis of data is a subjective process and therefore the researcher’s positionality is expected to play a role (Kitchin & Tate, 2000). A central assumption of this approach is that interpretation and analysis of data is only possible by first ‘breaking it up’ and then ‘putting it back together’ again (Dey, 1993). This section describes the steps followed in interpreting the primary data.
According to Dey (1993: 31), “the core of qualitative analysis lies in these related processes of describing phenomena, classifying it, and seeing how … concepts interconnect”. These three stages in interpretation and analysis are briefly described.
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The descriptive stage of data interpretation and analysis aims to “develop thorough and comprehensive descriptions of the phenomenon under study” (Dey, 1993: 32). In describing the situational context, the researcher provides a ‘thick’ description of the context within which the activity took place. This includes the spatial, temporal, and social context. It is important to provide a thorough situational context, because “contexts … [are] key to meaning, since meaning can be conveyed ‘correctly’ only if [the] context is also understood” (Dey, 1993).
Kitchin and Tate (2000: 233) agree on the potential for context to impact data findings, suggesting that, “it is well known that the social, spatial and temporal context can all significantly affect the data generated”.
Classification of meaning into categories (or themes) is the first step towards interpreting data obtained in research, in an attempt to make it understandable, both to the researcher and to others, since “without classifying the data, we have no way of knowing what it is that we are analysing” (Dey, 1993: 41). Classification of data is the ‘breaking it up’ stage of the ‘omelette’
analogy and is “a process of drawing distinctions within the data” (Dey, 1993: 139). It is referred to as the ‘splitting’ phase of the analytic process. Once ‘broken up’, data can then be systematically categorised or grouped, allowing for more effective capturing of individual participants’ answers (Dey, 1993; Kitchin & Tate, 2000). This stage of the analytic process is largely dependent on the researcher’s ability to interpret meaning. In this research, categories and sub-categories evident in the data will help the researcher to answer the questions, with strong backward and forward linkages. This is because the categories of meaning are co- produced in the interview during the engagement between the researcher and the participant, and will therefore align with the objectives.
The next step in the ‘omelette approach’ to interpretative analysis involves “identification and understanding of the relationships and associations between different classes” (Kitchin & Tate, 2000: 235). This is the point at which the data is ‘put back together again’ to create a pattern of meaning and is also termed ‘splicing’ (Dey, 1993). This final step in the process renders classified data more readily accessible, enabling the researcher to begin answering the aim and objectives of the study.
In order to interpret and understand the data gathered for this study a thematic technique was used, whereby participants’ answers were systematically ‘split’ and sorted into separate categories. Each category constituted a master theme which was informed by, and derived
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from, the objectives of the research. The interviews were structured in order to collect data in a systematic manner, so that it could be easily ‘split’ and ‘categorised, while also enabling the interviews to ‘flow’. The interview schedule was therefore systematically designed in relation to the objectives of the research.
An important first step in guiding the analysis, was to establish four separate categories, each focusing on addressing and satisfying one of the objectives. This is how the master themes for analysis were derived. Table 4.3, below, shows the derivation of master themes from the research objectives, into which all interview data was ‘split’.
Table 4.3. Table showing master themes derivation from objectives
Objective Master theme
1. Explore the understandings of water quality
held by a wide range of knowledge holders Discourses used to understand and interpret sea water quality
2. Explore the knowledge that forms the basis of the two different water quality management approaches employed by the municipality, and understand how this knowledge is used
Discourses used to understand and interpret water quality knowledge
3. Explore the role played by politics in relation to the adoption of these different management approaches within eThekwini
The political contestation of water quality management in eThekwini 4. Assess which of Callon's (1999) three models
of knowledge production is most applicable to each of the water quality management approaches adopted by eThekwini
Knowledge production and contestation through informal participation and engagement
The first two master themes aid interpretation and understanding of the knowledge of water quality, as expressed by participants, while the latter two master themes relate to the political dimension of water quality management, and to the contestation of water quality knowledge in the local eThekwini context. It was through this process of derivation that it became possible to begin to summarise participants’ answers, and to form an understanding of what was said, while also exploring possible connections between separate answers.
Once the master themes had been derived and the data ‘split’ and sorted into separate categories, data was then further ‘split’ and refined into sub-themes, based on patterns that became evident in each category. When performing a thematic analysis, it is necessary to consider both the internal homogeneity and external heterogeneity of the master themes, and their respective sub-themes (Braun & Clarke, 2006). This ensures that derived categories or
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themes accurately capture and reflect the meaning suggested in the primary data, and that the categories are distinct enough to limit, or eliminate, any overlaps between sub-themes (insert ref).
The final step in the ‘omelette approach’, referred to by Dey (1993) as ‘splicing’, is concerned with understanding connections between different categories (themes), and also between individual participant’s answers. This is the stage at which data is ‘put back together’, and was a critical interpretative step enabling identification of dominant environmental discourses held by interview participants. Discourse identification required creativity and intellect on the part of the researcher, because identifying environmental discourses is challenging as a result of there being no “well-defined boxes” within which to classify environmental issues (Dryzek, 2005: 8). Additionally, because discourses represent personal ways of apprehending or perceiving the world, there is potential for a wide variety of discourses held by participants (Dryzek, 2005). Identifying discourses is a subjective task influenced by the researcher’s own positionality. All themes and sub-themes emerging in the primary data are treated as discourses, and are discussed in-depth in Chapter Five, and Chapter Six. How each research objective was satisfied is now presented.