3.6 Research Method
3.6.3 Approach to Data Analysis
The data collected was analysed using Braun & Clarke’s (2006) 6 Stages of Thematic Analysis. This method of analysis was selected because it was deemed to be the most suitable form of analysis for the data being used. Thematic analysis can be very useful for identifying commonalities across data through the identification of shared themes.
These commonalities would hopefully reveal new and interesting knowledge about adolescent male self-harm and hence was selected for use in this study. A key feature of thematic analysis is its flexibility as an analytical tool – it can be used to analyse qualitative data following systematic observations of a person, interaction, group, situation, organisation or culture (Boyatzis, 1998). Braun & Clarke (2006) argue that qualitative analytical methods can be allocated to one of two camps; those associated with a particular theoretical or epistemological position and those which are not. They cite techniques such as interpretive phenomenological analysis, narrative analysis and discourse analysis as approaches that are housed within a broadly interpretivist
theoretical framework. Although some may seek to mark it with a realist/experimental tag, Braun & Clark (2013) argue that thematic analysis is sited firmly in the second camp, not tethered to any epistemological position. As a result of this, it can be used with both positivist and interpretivist research paradigms. Boyatzis (1998) argues that as such, thematic analysis can break down the epistemological totalitarianism between positivistic and interpretivist science.
Although there are various approaches to conducting thematic analysis, the broad aim is to identify, analyse and report patterns or themes, in order to make sense out of unrelated data (Braun & Clarke, 2006). It can be used to analyse qualitative data following systematic observations of a person, interaction, group, situation,
organisation or culture (Boyatzis, 1998). Analysis starts with the generation of codes, which leads to the development of wider themes. A code is the most basic segment of the raw data that can be evaluated in a meaningful way in relation to a particular phenomenon; whilst a theme is a pattern found in the data that interprets aspects of the phenomenon (Braun & Clarke, 2006). The identification of themes comes as a result
of the researcher immersing himself/herself in the text through reading and re-reading. The analysis moves through three phases of inquiry – firstly ‘seeing’ - observing or identifying something of interest to the research question. The second involves ‘seeing as’ or encoding the information as something else, before finally ‘interpretation’ can take place. What is seen by one person may not necessarily be seen or agreed with by others (Boyatzis, 1998). This demonstrates an interpretive aspect to thematic analysis.
Whilst thematic analysis is increasingly a popular tool for data analysis, its accommodating epistemological flexibility has attracted some criticism. Braun & Clarke (2006) observe that thematic analysis can be viewed as an unreliable tool, used in different ways by different researchers.Whilst arguing that those choosing to use it can make active choices about the form they employ, Braun & Clarke (2006) propose a 6 phase guide to thematic analysis which offers a systematic and structured way to look for patterns in data, outlined in Table 8.
Table 8: Braun & Clarke (2006) 6 phase guide to thematic analysis
Familiarisation with the data
Immersion and familiarisation with the data through reading and re-reading the data (and listening to audio-recorded data at least once, if relevant) and noting any initial analytic
Coding Generating labels for important features of the data of relevant to the (broad) research question guiding the analysis. Coding is not simply about reduction, it is also an analytic process. Codes capture both a semantic and conceptual reading of the data. The researcher codes every data item and ends this phase by
collating all the codes and relevant data extracts
Searching for themes
A theme is a coherent and meaningful pattern in the data relevant to the research question. It is coding the codes to identify similarities in the data from which the researcher constructs themes. The researcher ends this phase by collating all the coded data relevant to each theme.
Reviewing themes Involves checking that the themes ‘work’ in relation to both the coded extracts and the full data-set. The researcher should reflect on whether the themes tell a convincing and compelling story about the data, and begin to define the nature of each individual theme, and the relationship between the themes. It may be necessary to collapse two themes together or to split a theme into two or more themes, or to discard the candidate themes altogether and begin again the process of theme development.
Defining and naming themes
The researcher conducts and writes a detailed analysis of each theme (‘what story does this theme tell?’ and ‘how does this theme fit into the overall story about the data?’), identifying the ‘essence’ of each theme and constructing a concise, punchy and informative name for each theme.
Writing up Writing is an integral element of the analytic process and involves weaving together the analytic narrative and data extracts to tell the reader a coherent and persuasive story about the data, and contextualising it in relation to existing literature
Note. Adapted from Teaching thematic Analysis: Over-coming challenges and developing strategies for effective learning, by V. Clarke and V. Braun, 2013, The Psychologist, 26 (2), 120-123.
Braun & Clarke argue that the process of thematic analysis should not be viewed as linear – rather the analysis is a recursive process.
Once chosen as the process of analysis, other methodological decisions have to be made in terms of whether to take an inductive or deductive approach to mining the data and whether to deduce meaning at a semantic or latent level. Just like steps executed in the chosen version of thematic analysis, these decisions should be explicitly stated. A good analysis is one where the researcher gives a clear understanding of where he/she stands in relation to the above options, provides justification for making the choices he/she has made and then consistent applies these choices throughout the analysis (Braun & Clarke, 2012).