Reflexive Thematic Analysis

One of the most widely used approaches to qualitative data analysis in Social Science research is Reflexive Thematic Analysis (RTA). Developed by Virginia Braun and Victoria Clarke as a variation of thematic analysis, RTA incorporates an explicit focus on the researcher’s reflexivity.

Thematic Analysis involves identifying and analyzing patterns or themes within qualitative data, such as interview transcripts, focus group discussions, or textual documents. It aims to uncover the underlying meaning and structure of the data.

Reflexive Thematic Analysis goes beyond this by emphasizing the importance of the researcher’s active engagement and reflection throughout the analysis process.

Reflexivity, the key feature of RTA, refers to the researcher’s awareness of their own biases, assumptions, and preconceptions that may influence the interpretation of the data. RTA encourages researchers to critically examine their own role in filtering the literature review and shaping the analysis of the data in hand. It encourages the researcher to consider how their personal characteristics, experiences, and values may influence the interpretations they make.

The reflexive aspect of this approach acknowledges that researchers’ interpretations are influenced by their own subjectivity, assumptions, and experiences. It encourages researchers to reflect on their own positionality and the potential impact it may have on the analysis process and findings. By incorporating reflexivity, researchers aim to improve the rigour and transparency of their analysis.

The process of conducting RTA typically involves several iterative stages. Here is a general outline:

1. Familiarization with the Data: Researchers start by becoming familiar with the dataset, which typically consists of transcribed interviews, focus group discussions, or other textual sources. They read and re-read the data to gain a holistic understanding of the content. Researchers become immersed in the data by reading and re-reading the material to gain a comprehensive understanding of its content.
2. Initial Coding: Researchers begin the coding process by identifying meaningful units of text, which are then assigned initial codes. This stage involves breaking down the data into smaller parts and attaching descriptive labels or codes to them. Codes are usually applied in an inductive manner, allowing themes to emerge from the data rather than imposing preconceived categories. Researchers start the coding process by identifying initial themes or patterns in the data. They mark segments of text that relate to these themes with descriptive codes.
3. Theme Development: Researchers search for patterns, similarities, and differences across the codes to identify potential themes. This involves comparing codes, looking for connections, and clustering related codes into preliminary themes. It is important to maintain openness to unexpected or divergent themes during this stage. Researchers begin to group related codes into potential themes. They explore the relationships between different codes and consider how they may fit together to form coherent themes.
4. Reviewing Themes: Researchers review and refine the identified themes by going back to the original data to ensure their accuracy and fit. They consider the coherence of each theme, its relevance to the research questions, and whether it captures important aspects of the data. This process may involve further iterations of coding and theme development. Researchers critically review and refine the identified themes, ensuring they are coherent, meaningful, and reflective of the data. They may revise, rename, merge, or split themes as needed.
5. Reflecting on Reflexivity: Researchers engage in reflexive processes throughout the analysis, considering their own assumptions, perspectives, and potential biases. They reflect on how their interpretations may be influenced by these factors.
6. Defining Themes: Once the themes are reviewed and refined, researchers define and name each theme. They create clear and concise descriptions that capture the essence of each theme and distinguish it from others. Researchers define the final themes in a clear and concise manner, supported by illustrative examples from the data. They also consider how the themes relate to the research objectives or broader theoretical frameworks.
7. Writing the Analysis: Reflexive thematic analysis involves critical reflection on the role of the researcher in shaping the analysis. Researchers consider their assumptions, biases, and personal experiences that may have influenced the interpretation of the data. This reflexivity helps to increase transparency and rigour in the analysis. Researchers write a narrative account of the themes, providing a coherent and compelling story that captures the essence of the data. They may use quotes or excerpts from the data to illustrate each theme.
8. Reporting the Findings: Finally, researchers report their findings, providing a comprehensive account of the themes, supporting evidence from the data, and an interpretation that acknowledges the researchers’ reflexive stance. The analysis should be transparent, providing readers with insights into the analytical process and potential limitations. Researchers engage in discussions with peers or participants to validate the findings and interpretations. This helps ensure that the analysis accurately represents the data and provides a robust account of participants’ experiences.

The thematic analysis component involves systematically identifying, analyzing, and organizing themes or patterns within the data. Researchers engage in a process of coding, categorizing, and synthesizing the data to extract meaningful themes. The themes are rationalised to represent underlying concepts or ideas present in the dataset. These themes are then further refined and interpreted in relation to the research objectives and the researcher’s reflexivity.

It’s important to note that RTA is an iterative and flexible process. This means that researchers may move back and forth between the different stages as they refine and develop their analysis. The emphasis on reflexivity distinguishes Reflexive Thematic Analysis from other Thematic Analysis approaches. It is this distinguishing characteristic that highlights the importance of critically considering the influence of the researcher’s subconscious perceptions and personal experiences, if any, on the research data. Reflexive Thematic Analysis simply acknowledges the subjectivity inherent in qualitative research.

Note: This article is based on what we have been reading about the topic from various sources and may be updated with increased understanding and learning. The infographic has been designed by our Squad.