4.12 Mixed methods in data analysis
4.12.2 Qualitative thematic analysis
The qualitative research in this evaluation involved analysing textual material on the program from a range of sources, collected and shaped by the case managers. Commonly known as extant texts, this material is material which:
... the researcher had no hand in shaping. Researchers treat extant texts as data to address their research questions although these texts were produced for other-often very different purposes. Archival data such as letters from a historical figure or era are a major source of extant texts. We may use elicited and extant texts as either primary or supplementary sources of data (Charmaz, 2006:35).
As the case managers and not the researcher had shaped the text and content relating to the young adults, this helped to safeguard against data manipulation, thereby further reducing potential researcher conflict and tension. However, one of the limitations was that these texts were not entirely objective facts, but rather subjective interpretations of what the young adults understood to be true according to the case manager. These texts were also embedded within the social, economic, cultural, political and situational context in which the YCLP and the young adults existed (Prior, 2003). Despite this limitation, the content nonetheless revealed a comprehensive overview of the relational dynamics and processes between different players participating in the program.
After initially intending to use NVivo software to store and organise the rich data, it was felt that, given the information had already been collected and recorded in MS Excel, this software was sufficient as a repository to hold and organise the information. All data were of interest as they pertained to the lives of the young adult, how they articulated their issues, how the case manager interpreted those issues and how agreement was reached between the young adult and the case manager on how the young adult would need to act upon those issues. As the data were being analysed in preparation for their export to SPSS, particular attention was paid to the activities and social processes occurring throughout the data. This was optimised by using gerunds (action words ending in ‘ing’) (Glaser, 1998; Charmaz, 2006) as they kept the data dynamic and foregrounded the key relationship between the case managers and the young adults as vehicles for change. This enabled a greater depth of engagement in the rich data while, at the same time, being cautious to not be over-zealous in order to remain true to the interpretation of the data and to ensure that the themes where not coloured by the researcher’s notions of how the interpreted data should evolve. This
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required methodical and continual re-engagement with the original data to ensure analytic integrity.
When using a mixed-methods approach, thematic analysis is one of the most favoured and appropriate techniques for the qualitative component, enabling the researcher to engage with, and immerse themselves in, the data in order to identify recurrent patterns and themes (Gleeson, 2003:2). The technique is less often explicitly referred to as ‘thematic analysis’; however, it is a common method within qualitative research. Braun and Clarke (2006) maintain that thematic analysis should be considered as a method in its own right, rather than as a technique that presides in or is subsumed by other methods, for example, the grounded theory method (Braun and Clarke, 2006:4).
In order to start the thematic analysis process, the data were read and re-read to create basic codes, during which patterns and themes emerged through key words and phrases that helped to frame the analysis (Denzin and Lincoln, 2005:8
).
It is said that a theme encapsulates something significant in the data, with Braun and Clarke (2006) adding that “[a] theme captures something important about the data in relation to the research question and represents some level of patterned response or meaning within the data set” (Braun and Clarke, 2006:82).The themes were essentially constructs that were deduced or emerged from the data, where clusters of codes were similar or compatible in meaning. Charmaz (2006) poses a range of helpful questions for the researcher to consider during textual analysis that proved useful for this evaluation and assisted in mitigating any potential problems that might have arisen during the analysis stage (see Figure 4.3).
117 What are the parameters of the information?
On what and whose facts does this information rest?
What does the information mean to various participants or actors in the scene? What does the information leave out?
Who has access to the facts, records or sources of the information? Who is the intended audience for the information?
Who benefits from shaping and or interpreting this information in a particular way? How, if at all, does the information affect actions?
How was the text produced? By whom? What is the ostensible purpose of the text?
Might the text serve other unstated or assumed purposes? Which ones? How does the text represent what its author(s) assumed to exist? Which meanings are embedded within it?
How do those meanings reflect a particular social, historical and perhaps organisational context?
What is the structure of the text?
How does its structure shape what is said? Which categories can you discern in its structure? What can you glean from these categories?
Do the categories change in sequential texts over time? How so?
Figure 4.1: Questions used to guide the thematic analysis
Note: Questions suggested by Charmaz (2006) that were used to guide the thematic analysis process (Charmaz, 2006:38-39). Being cognisant of these questions enabled the commonly used techniques in thematic analysis to be achieved. Figure 4.3 shows how this process commenced manually and then led to clustering these codes to produce more meaningful categories and then themes.
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Figure 4.3: Structure of thematic networks to form clusters
Note: The basic codes where clustered into organising themes which were then categorised into global themes. This was a lengthy process
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The process of open coding developed the broad basic codes from the data and was completed when core categories had emerged. Themes connected the concepts within the codes and created relationships between the categories, thus enabling the primary themes to be constructed. Using a constant comparative method with the codes and categories constructed from the data enabled a description of the themes and patterns that arose from the data (Mills et al., 2006:3). While this inductive approach assisted in identifying themes and patterns from the data, the evaluation also used a deductive approach which entailed searching for themes and patterns in the data, with these then measured against a range of predetermined goals outlined in the theory-driven model (Konstantinos, 2003). In addition, to make sense of the data, the primary research question, instead of being viewed as a hypothesis, provided the conceptual baseline that enabled the interpretation of the data: it was therefore expected that the constructed themes would clearly represent meaning for this line of inquiry. Both techniques were used in this evaluation which could therefore be referred to as taking an abductive approach (Charmaz, 2006).
In terms of taking a qualitative interpretive approach through thematic analysis, the aim was to understand and describe the YCLP implementation and the impact the program had on the young adults. Furthermore, given that qualitative approaches are able to gather data in an ‘open-ended fashion’ and within natural settings, this suited the interaction and engagement between the young adults and the case managers, as this type of relationship necessitated the construction of linking social capital (Konstantinos, 2003). Writers Braun and Clarke (2006) describe a six-step method to guide the qualitative analysis of the data, as outlined in Table 4.2 below, which was used as a guide for the YCLP evaluation.
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Table 4.2: Six-step method (Braun and Clarke, 2006)
Six Steps Process
Step 1
Familiarising and immersing oneself with the data to get a good sense of their form and content by reading and re-reading “in an active way” (Braun and Clarke, 2006:87).
Step 2
Generating initial codes from the data which, according to Clarke and Braun (2013), is a process of highlighting elements of the data that relate directly to the research question. Given also that this research was a theory-driven evaluation, the data were approached (and therefore coded) on the pre- existing notions and assumptions of what the researcher expected to encounter in accordance with the model.
Step 3
Searching for themes by clustering the codes that were previously generated into categories. The way that clustering is undertaken occurs with a significant or larger code being encircled with spokes drawn from the code to create relationships with other neighbouring codes to form configurations of clusters that produce an image of how the codes fit together (Charmaz, 2006:88).
Step 4
Reviewing the themes to determine representativeness: What does this theme mean? What are the assumptions underpinning it? What are the implications of this theme? What conditions are likely to have given rise to it? Why do people talk about this thing in this particular way (as opposed to other ways)? What is the overall story the different themes reveal about the topic? (Braun and Clarke, 2006:24).
Step 5
Defining and labelling the themed categories which entail refining the final themes by cross-checking them with preliminary themes and ensuring that they both support and align with each other.
Step 6 Linking the themes to the existing literature and producing the report which involves weaving the themes together and reinforcing them with data extracts.
Note: The six-step process, as recommended by Braun and Clarke (2006:79), was used to assist in structuring the themed categories that emerged from the qualitative data.
The rich data were coded manually to get a sense of the activity and emotional engagement and connection that were generated within the relationship between the case manager and the young adult. After coding, these initial chunks or segments of data were then clustered into categories. It was useful to reference the work of Speilberger at al. (2009) to determine the four components that related to the self, the other, the program and the community, with each split into inhibitors, motivational drivers and self-actualisers to cluster the codes. The analytical process involved continually creating memos to interpret and analyse the data and to build categories that would eventuate in the elucidation of the research question (Charmaz, 2006). As recommended in the literature, I generated a methodological journal (or code book) that described the data variables, outlining each with a title, description,
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format, how it was collected, the group to which it related, and where it could be accessed for future reference: this provided a comprehensive catalogue for the research study.