DATA ANALYSIS METHOD - THEMATIC

Mayer (2015) introduces the topic of qualitative data analysis with a famous quote from the late Albert Einstein, who said that “Not everything that can be counted counts, and not everything that counts can be counted” (Mayer, 2015). Culén (2010), quoting Marshall and Rossman (1990) explains how the process of data analysis involves bringing order, structure and meaning to the mass of data collected. It is being referred to as a messy, ambiguous, time consuming, creative and fascinating process that does not proceed in a linear fashion, nor is it neat. It is merely a search for general statements about relationships that exists amongst categories of data (Culén, 2010).

In qualitative research the method chosen for analysing the collected data is not only determined by the questions being asked and the type of data collected, but also based on the truth-seeking assumptions underlying the study (Burney, 2008).

Qualitative data analysis therefore involves a range of processes where a movement is made from the raw qualitative date that have been collected, towards forming an explanation and understanding or interpreting the phenomenon being investigated (Sunday, 2007). Sunday (2007) takes this a step further and explains how qualitative data analysis is usually based on an interpretative philosophy where the idea is to examine the meaningful and symbolic content of qualitative data (Sunday, 2007).

Thematic analysis is one of the most common forms of analysis used in qualitative research and involves an approach to extract meanings and concepts from data by pinpointing, examining, and recording patterns or themes (Javadi & Zarea, 2016).

Braun and Clarke (2006) defines thematic analysis as a method for identifying, analysing and reporting patterns within data (Braun, V. and Clarke, 2006).

Braun and Clarke (2006) discusses the diverse and complex nature of qualitative approaches, and then explains how thematic analysis can be viewed as a

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foundational method to qualitative analysis as it provides core skills required in conducting most other methods of qualitative analysis, of which some include discourse analysis, narrative analysis and content analysis (Braun, V. and Clarke, 2006). Although thematic analysis has only recently started to achieve the brand recognition held by other methods, they argue that most of these other analysis methods referred to above are essentially also thematic (Braun, V. and Clarke, 2006).

The simplicity associated with thematic analysis lends itself to be use by novice researchers who are unfamiliar with more complex types of qualitative analysis.

This simplicity comes in the form of flexibility in the researcher’s choice of theoretical framework, as opposed to other methods of analysis that are closely tied to specific theories. This flexibility allows thematic analysis to provide a rich, detailed and complex description of the data (Clarke & Braun, 2013).

The data analysis method that will be used for this research study is thematic analysis, and the rationale for selecting this method is supported by the views and benefits highlighted above. The most widely used process for conduction thematic analysis comes from Braun and Clarke (2006), where they provide a six-step process for identifying, analysing and reporting qualitative data using thematic analysis. It is important to note that these steps should not be viewed as a linear model where one cannot proceed to the next step without completing a previous step, but rather as a recursive process in relation to the research questions and the available data (Six Simple Steps to Conduct a Thematic Analysis, 2016). This six-step process that will be used as the foundation for data analysis in this research study is illustrated in table 3.3 below.

Table 3.3: Six phases of thematic analysis

Step Activity Description

1. Familiarising yourself with the data

This step provides the foundation for the subsequent analysis. The researcher is required

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to become actively engaged and familiarise him/her with all aspect of the data by listening to the audio recordings, transcribing the interactions, and re-reading the transcripts. The purpose of this step is to identify and note down initial analytical observations and ideas.

2. Generating initial codes

The researcher identifies preliminary codes to capture both the semantic and conceptual reading of the data. These are the features of the data that appears interesting and meaningful, and provide an indication of the context of the conversation relevant to the broad research question guiding the analysis. Every data item is coded and all codes and relevant data extracts are collated at the end of this step.

3. Searching for themes In this step the researcher performs an interpretive analysis of the collated codes by sorting the relevant coded extracts to construct overarching themes. The thought process in this step should allude to the relationships between codes, subthemes, and themes. This step is completed by collating all the coded data relevant to each theme.

4. Reviewing the themes A deeper review of the identified themes is conducted to verify meaningful coherence to both the coded extracts and the full data-set. There should be clear and identifiable distinctions between the themes and the researcher needs to question whether to combine, define, separate or discard initial themes. In this step the researcher begin to define the nature of each individual

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theme, and the relationship between the themes.

In closing a thematic map can be generated.

5. Defining and naming the themes

Refining and defining the themes involves an ongoing detailed analysis to further enhance the identified themes. The researcher should ask

“what story does this theme tell?” and “how does the theme fit into the overall story about the transform the analysis into an interpretable narrative using vivid and compelling extract examples that relate to the themes, research empirical evidence that addresses the research questions.

Source: (Clarke & Braun, 2013)