What is Thematic Analysis?
Thematic analysis is a qualitative data analysis method that involves identifying common themes (topics, ideas, and patterns) that come up repeatedly in a dataset. It is utilized to identify patterns of meaning across a set of data to provide answers to the research questions being addressed. Thematic analysis is particularly suited to questions related to people’s perceptions, views, and experiences, as well as understanding, representation, and construction of meaning. It is used in many different disciplines and research fields, including social, behavioral, and applied sciences, but the steps are always the same. The steps were originally developed for psychology research by Virginia Braun and Victoria Clarke in a famous seminal article titled “Using Thematic Analysis in Psychology” published in 2006 in Qualitative Research in Psychology. There are six steps involved in thematic analysis which are sequential and each builds on the previous one. However, the analysis is an iterative process with movement back and forth between the different steps.
Approaches to Thematic Analysis
There are different approaches to thematic analysis that a researcher may adopt depending on the purpose of the study.
Inductive v. Deductive approach: In the inductive approach, the content of the data determines coding and theme development. On the other hand, in the deductive approach, one comes with preconceived concepts or ideas based on existing knowledge or theory which direct coding and theme development.
Sematic v. Latent approach: In the sematic approach, coding and theme development reflect the explicit content of the data while coding and theme development in the latent approach report concepts and assumptions underpinning the data. For instance, if you are interested in people’s stated opinions, this is the semantic approach. However, if you are interested in what people’s statements reveal about their assumptions and social context, this is the latent approach.
Realist v. Constructivist approach: In a (critical) realist or essentialist approach, analysis is focused on reporting an assumed reality evident in the data. For instance, how are Saudi women represented in American magazines? In a constructionist approach, the data is used to construct a certain reality, e.g. how is race constructed in workplace diversity?
Please note that the most important aspect of your thematic analysis is that it should be coherent and consistent since the differences between the different approaches are not always that rigid.
Steps in Doing Thematic Analysis
Step 1: Familiarization
This step is all about getting to know your data. It involves reading and re-reading your data to become intimately familiar with its contents. If you have audio recordings, you will need to transcribe them in order to work with the data. You can pay someone to do the transcription for you but some researchers prefer to do it themselves because it allows them to start making sense of the data as they transcribe. In this step, you should go through the transcripts to become immersed and get a thorough overview of all the data collected. Also, start taking notes to help mark preliminary ideas for codes that can describe your content.
Step 2: Coding
This step entails generating initial codes that identify important information in the data which may be relevant in answering the research questions. Coding involves highlighting sections of the data (phrases or sentences) each time you note something interesting in your data and assigning succinct labels or codes describing their content. Coding will depend on whether you are performing a deductive analysis whereby you are searching for specific themes or inductive analysis where the themes are determined by the content of the data. You can code by taking notes on the transcript or use a table in a Word document. Also, you can use specific software for coding such as NVivo and RQDA. Once you have coded the entire dataset, collate together all the codes and all relevant data extracts/sections that fit in each code for later analysis stages.
Step 3: Generating Initial Themes
In this step, you examine all the codes and the collated data extracts to identify patterns among them and sort them into broader patterns of meaning (potential themes). Look through all your codes and their associated extracts and combine related codes into broader themes that tell you something helpful about the data. Most of the time, you will combine several codes into a single theme. The search for themes is an iterative process whereby you move codes back and forth while trying to form different themes. You may find that not all codes fit together with others or seem to fit anywhere and these might become themes in their own right. Also, some of the codes might become redundant, too vague, or not relevant enough because they don’t appear very often in the data and will need to be discarded. It is also alright to form subthemes to other themes when you find some codes that seem related but cannot be incorporated into a single theme. Again, theme development will be directed by whether you are doing inductive or deductive analysis.
Step 4: Reviewing Themes
The next step involves checking and refining the themes developed in step 3 to ensure they are useful and provide an accurate representation of the data. Individual themes are checked against the data to determine if they are really present in the data if there is anything missing, and what can be changed to make the themes work better. Also, the themes are refined if there are contradictions, overlapping, or become too broad. You can combine themes, split them up, create new ones, or discard them to make the themes more accurate and useful to your research questions. Also, you may need to move some of the codes and extracts into themes where they fit better. This step is an iterative process where you go back and forth between codes, extracts, and themes until you feel that all relevant data has been coded and you have a coherent and distinctive set of themes that accurately represent your data.
Step 5: Defining and Naming Themes
This step involves a detailed analysis of each of the final list of themes by describing what the theme is about, what is interesting about the theme, and why it is interesting. You should define what exactly you mean by each theme, identify which “story” the theme tells (its scope and focus), and how this “story” relates to other themes and your research questions. If you find that a theme is too complex or diverse to tell a coherent story, you will need to go back to step 4 and rework your themes. After defining your themes, you should come up with a succinct and easily understandable name for each theme.
Step 6: Writing Up
The final step involves writing up your results. You should start with an introduction to your research project to establish your research problem, aims, objectives, and questions. Then, you should provide background information about how you did your research (e.g. interviews, focus groups, open-ended survey) and the full analysis. This allows the reader to evaluate the quality of your research and enhance its validity. Next, you should provide a summary of the findings. Use the description of your themes in step 5 as the basis for the final report. Present themes, in turn, describing how often they came up and what they mean. Also, use quotes of what the participants said to demonstrate your findings for each theme. However, use pseudonyms or numbers (such as P01, P02, P03, and so on) to conceal their identities. If you have multiple research objectives or questions, organize your themes by research question or objective. Lastly, write a conclusion explaining the main takeaways and how the analysis answered the research questions.