As TA was selected as the chosen research method and the themes that transpire from the coding process serve as a vehicle for the analysis aspect of TA, the analysis used in this research study aimed to be shaped uniquely (being moulded by the thematic data), and not by a pre- existing model (Braun & Clarke, 2006). However, it must be noted that, although themes that shape the analysis allow the approach to be flexible, there is still a general consensus with regards to how TA must be implemented once all the qualitative data is collected. That is, the coding of transcripts comes first; modifications to initial coding and the final analysis come thereafter (Braun & Clarke, 2006). Also, as the emergence of themes directs the analysis, this gives the researcher an opportunity to judge what excerpts are most salient and of high importance to the overall research question. Simply put, the researcher’s interpretation and analysis are just as important as the thoughts and opinions shared by the participants during interview. It was important to be aware of Braun and Clarke’s (2013) notion that personal experiences shape how one reads data, meaning that, although the researcher’s interpretation of the data is a valuable source during analysis, it is also possible that personal experience may
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influence what one sees in the data. For this reason, the researcher was reflective throughout the analytic process and made a particular effort to look beyond his personal views and experiences in order to extract the most valuable points from the data. According to Braun and Clarke’s (2006) criteria for the analysis phase, it is good practice to make a real interpretation of the data in order to make sense of it; that the analysis matches the data extracts; the data tells a convincing and well-organised story about the research; and finally, that there is a good balance between the data extracts and the narration used to interpret them.
Stages of the Coding Process
Braun and Clarke (2006) have briefly outlined how to conduct a TA in a logical sequence by giving mention to six key phases. The researcher ought to:
(1) Familiarise him/her with the data; (2) Generate initial codes by interest;
(3) Search for and collate themes within the data with respect to its relevance; (4) Review the themes on two levels (e.g. within the coded extracts such as those in
the codebook, and with the data set as a whole);
(5) Make some refinements where necessary in order to provide specific names for themes;
(6) Produce a report based on a final analysis of the defined themes.
The basis of TA typically requires the researcher to identify useful pieces of data from seemingly large amounts of undistinguished data (i.e. in the form of interview transcripts). Identification is then developed into codes and subsequently into themes which provide the framework for interpretation and eventually the final analysis. A sound interpretation requires a dependable coding process and so the first step taken before interpretation is to recognise an
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important moment and perceive it as useful (Boyatzis, 1998). Furthermore, as TA requires recognition of significant patterns, a good coding rule is needed to differentiate between what is relevant and what is irrelevant to the overall analysis. Boyatzis (1998, p. 1) further explains thematic coding by defining a “good code” as having the following five elements:
(1) A label.
(2) A definition of what the theme concerns.
(3) A description of how to know when the theme occurs (i.e. how to flag it).
(4) A description of any qualifications or exclusions to the identification of the theme (i.e. criteria to determine the theme).
(5) Examples, both positive and negative, to eliminate possible confusion when looking for the theme (i.e. like a guide).
Following these steps ensures that a systematic and comprehensive TA is performed which moves beyond a simple description of what is there, to an interpretation of what it means (Braun & Clarke, 2013). Rigour can thus be achieved through this deeper level of interpretation (e.g. of underlying meanings rather than the explicit descriptions) using Braun and Clarke’s (2006) guidance on conducting an effective TA.
In order to conduct a plausible analysis, each item of data was given equal amounts of consideration and themes were generated from a thorough overview of the data rather than from a brief and ‘vivid’ impression. The relevant extracts used for each theme were collated in a codebook format and themes were checked against themselves and the rest of the data set. They were also checked for internal coherence, consistency and distinctiveness in line with Braun and Clarke’s (2006) summarised guidance for the coding process. Although quality of content is considered throughout this research study, the analysis itself did not include an axiological evaluation using methods like the Jefferson system. Taking the axiological route
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was considered to be unnecessary for a project that predominantly looks at content containing emotion, rather than the assessment of emotion as a separate dimension.
Discovering Themes
Themes are thought to be developed on different structural levels where they complement similarities within the data (i.e. other similar themes) and help to identify any dissimilarities (i.e. data that may contradict the general consensus).
Themes are derived from initial codes set by the researcher. The researcher reviews the codes and identifies ones that are similar or overlap; these are grouped into a centralised theme (Braun & Clarke, 2013). Some codes may be turned into themes if they are considered to be large, rich and complex enough (Charmaz, 2006). Whilst it is important that themes are identified across a proportion of the data, the themes do not necessarily have to be present in every piece of data or stated by every participant. What is more important is the quality of the theme and whether it adds something meaningful and important to the research (Braun & Clarke, 2013).
Differing explanations on what themes are, help to explain what they represent. Though some of the definitions may sound quite vague, Ryan and Bernard (2003) provide a little more clarity: “You know you have found a theme when you can answer the question, ‘What is this expression an example of?” (p. 87) and again explaining the extent that themes have on providing meaningfulness to the data: “Themes come in all shapes and sizes. Some themes are broad and sweeping constructs that link many different kinds of expressions” (p. 87). Codes function like a ‘rough draft’, whereas actual themes are deemed to be more concrete. Themes develop as a result of codes and Braun and Clarke (2013) offer a useful analogy by stating, “a theme is like the wall or roof panel of a house, made up of many individual bricks or tiles (codes)” (p. 224). Therefore, the initial coding process is vitally important as it helps to form the themes of the research. A code tends to capture one idea, whereas a theme acts as the
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“central organising concept” (Braun & Clarke, 2013, p. 224) which is usually made up of lots of different aspects relating to that concept (i.e. codes).
When themes have been developed, there are steps that can be taken to ensure that they are ‘good themes’ for the data set. Braun and Clarke (2013) state that themes should be considered both on their own and as part of a group. They state that it is important that the theme makes sense individually (meaning they are distinctive) and that themes fit together to form the overall analysis (p. 231).
Equally as important as the development of themes, is knowing when to disregard codes which are not relevant. Braun and Clarke (2013) offer a suggestion that codes which do not seem to fit into any obvious themes should be grouped into a ‘miscellaneous’ category during the analysis process, as they may start to fit as the analysis progresses. According to Braun and Clarke (2013), an important part of qualitative research is being able to let go of material that does not fit into the aims of the research. They state that it is important to “tell a particular story about the data” and not to “represent everything that was said in the data” (Braun & Clarke, 2013, p. 230).
Sub-themes
The researcher is free to manipulate the data to form personalised thematic categories in which the themes are grouped. After the data is gathered and the themes are developed, there may be sub-themes that also emerge to further specify, or categorise, a particular pattern that exists within the relationship between various themes. The themes may represent different levels of arrangement within the data, hence why they become classed as main themes and sub-themes, although all are important. Braun and Clarke (2006) represent the existence of “main overarching themes and the sub-themes within them” using an initial thematic map (see Figure
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2, p. 98). Therefore by following the guidance in Braun and Clarke (2006) the themes and sub- themes were identified. A thematic codebook or a thematic map being devised to record useful themes and their extracts. It must be noted, however, that these themes may be subject to change and are not necessarily set in stone.
Semantic vs. Latent Themes
As previously mentioned, themes can be looked at as main overarching themes or the more specific sub-themes. Furthermore, as Boyatzis (1998) identifies, greater specificity can also be achieved through examining themes at different ‘levels’, the semantic (or explicit) level and the latent (or interpretive) level. Braun and Clarke (2006) use a fitting analogy to help illustrate how the semantic and latent levels work in synergy. They elucidate that: “if we imagine our data three-dimensionally as an uneven blob of jelly, the semantic approach would seek to describe the surface of the jelly, its form and meaning, while the latent approach would seek to identify the features that gave it that particular form and meaning” (p. 84).
When using the semantic approach, usually the meaning is quite clear, hence its alternative term ‘explicit’. What the researcher typically requires when using semantic themes, are the responses from the participants and nothing that goes further than the participant’s view. Therefore, the researcher does not examine anything implicit here, such as generalising or making assumptions. It is then essential that within the analytic phase the semantic patterns and descriptions are evolved and interpreted. As part of interpretation, the semantic content is linked in with theory to conceptualise what the patterns mean, their importance and their potential implications for the research (Patton, 1990). On the other hand, a more in-depth tactic takes the form of the latent approach. It aims to explore the underlying ideas behind the participants’ responses along with assumptions, conceptualisations and ideologies that have
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influenced the formation of this semantic matter. This research adopted a latent approach since it appeared important to seek out the meaning behind the words.
The Use of a ‘Codebook’ or ‘Thematic Map’
A codebook was used to systematically keep a record of the codes and themes found within the data. The researcher was able to use this for reference and have examples of what the codes relate to. For example, definitions would encapsulate the use of codes that the researcher has suggested for the analysis (i.e. when to use the code and what it refers to within the data). A table format was used to structure the codebook for easy reference. It was important for the table to maintain organisation, so that the codes could be categorised to provide clarity from the large amounts of data and the consequential number of codes used. Preparation of a codebook ensured consistency and improved efficiency due to its logical structure. This saved the researcher time during the analysis and functioned as a universal tool for the process.
A thematic map on the other hand is quite similar to the thematic codebook, though it visually represents the data to show associations between the codes, themes and categories, much like a ‘thematic network’ (Attride-Stirling, 2001). It also illustrates the multidimensional links between the qualitative data extracts and serves as an efficient tool in the “systematisation and presentation of qualitative analyses” (Attride-Stirling, 2001, p. 385).