Maguire and Delahunt, 2017 thematic analysis

 The qualitative researcher is often described as the research instrument due to their ability to understand, describe and interpret experiences for analysis (Maguire and Delahunt, 2017). 

Thematic analysis is the process of identifying patterns or themes within qualitative data (Maguire and Delahunt, 2017; p.3353).

Thematic analysis isn’t tied to specific epistemological or theoretical perspectives which makes it an ideal and flexible method for the diversity of work and research with the context of learning and teaching (Maguire and Delahunt, 2017).

The six step method by Braun and Clarke (2006) is argued as the most influential, clear and usable framework for doing thematic analysis (Maguire and Delahunt, 2017).

The goal of thematic analysis is to identify themes and patterns in the data that are of importance and interest to highlight key issues and answer the research question (Maguire and Delahunt, 2017).

A common pitfall is using interview questions as the themes (Clarke and Braun, 2013), doing this means the data has only been collated and summarised, rather than analysed (Maguire and Delahunt, 2017; p.3353).

Braun and Clarke (2006) explain two ways to theme data, known as semantic and latent. In semantic themes, the researcher or analyst is not looking for anything more than what participants have said to be able to analyse and interpret the data to explain findings and their meaning within the theme. Latent theming looks beyond what has been said to further examine in more depth to explore underlying ideas, assumptions, conceptualisations and ideologies to theorise. (Maguire and Delahunt, 2017; p.3353).

Realist research questions are those e.g. interested in students own accounts of their experiences and points of view.

Braun and Clark (2006) explain the difference between theoretical theming that is either ‘top down’ and driven by a specific research question or a ‘bottom up’ approach that is inductive and driven by what specifically emerges from the data. 

The first step in any qualitative analysis is reading and rereading the transcripts. It is advised that researchers should become familiar with the entire body of data or the ‘corpus’ before any coding begins. But instead jot down some initial thoughts and notes as you get a feel for the data as a whole (Maguire and Delahunt, 2017; p.3355).

In phase two researchers start to organise data in a meaningful and systematic way by generating codes, that reduces lots of data into small chunks of meaning. There are many different ways to code and the method will be determined by the researcher’s perspective or research questions.

Analysing data with research questions in mind or capturing something interesting in relation to the research question or questions is known as theoretical thematic analysis (p.3355).

Qualitative data analysis software such as ATLAS or Nvivo can be very useful, particularly with larger data sets (p.3356).

Following on from coding, the researcher searches for themes that capture something significant towards the research question, the codes get grouped together to develop preliminary themes (p.3356).

Themes get reviewed, modified and developed from preliminary themes, it’s a chance to consider if themes really work in the context of the entire data set. At this point themes might be broken down hierarchically into main themes with sub-themes beneath them.

The final refinement of themes identity the essence of what each theme is about (Braun and Clarke, 2006; p.92). The thematic map will help to illustrate what they overarching themes are, what the theme is saying, the sub-themes and the relationships of how they interact and interrelate with other themes (p.33511).