Thematic analysis is a method of identifying patterns of meaning in qualitative datasets. It’s a powerful technique that’s particularly useful if you’re dealing with subjective information like people’s experiences and opinions.
Does your research method involve using surveys or interviews to gather data about participants’ experiences and perceptions? If so, thematic analysis offers a flexible approach to interpreting the information you’ve collected and aligning it with your research questions.
In this post, we’ll take you through the six parts of the reflexive thematic analysis process, developed by psychology academics Virginia Braun and Victoria Clarke:
Braun and Clarke emphasize that thematic analysis is more than just working through these stages one by one. For larger datasets, it may be necessary to revisit the coding and theme elements – possibly several times. For smaller datasets, some of the stages may be combined.
Read on to learn how to conduct a thematic analysis.
Start by thoroughly immersing yourself in your data. Read interview transcripts and survey responses over and over to give yourself a feel for each item and for the dataset as a whole. As you read, take note of any recurring features and your initial thoughts about how you might code the data.
“Coding” might sound a little technical, but it simply means creating meaningful labels that identify recurring ideas in the dataset. Scrutinize the data, highlighting any sections of text that seem relevant, and label them with succinct descriptions of their content (i.e., codes). In the following extract from an interview about people’s attitudes on recycling, the researcher has used different-colored highlighting to indicate the codes applied.
I try to recycle as much as I can. Sometimes it’s hard to tell if a container can be recycled or not. That winds me up! If I’m not sure, I throw it in the regular bin. I feel bad for that, but I don’t want everything to end up in a landfill.
Codes:
Good intentions
Uncertainty and confusion
Annoyance
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Feelings of guilt
When you’ve gone through the entire dataset at least twice, you can then collate all the codes you’ve created with the highlighted extracts:
The next step is to begin identifying potential themes. This may involve grouping some of your codes into a broader theme. On the other hand, you may find that some codes occur so frequently that they should be considered themes in themselves. Or you might find that some of your codes don’t appear enough to be relevant, so they can be discarded.
When deciding on your themes, you can take either an inductive or a deductive approach:
● With the inductive approach, you don’t have any preconceived ideas about what your themes will be. Instead, you allow the codes you’ve identified in the dataset to be your guide.
● Conversely, if you already have an idea of what your themes will be, you will take a deductive approach. This means your choice of themes will be influenced by existing knowledge.
In this phase, check each of your potential themes against the data and assess whether they’re relevant, useful, and distinct from the other themes. Again, you may decide to combine, split, or eliminate some of your candidate themes.
You should now be ready to name your themes and give each of them a clear and engaging description. In this stage of the process, you will identify a narrative for each theme, which will help people to understand the data.
Finally, you will document the findings of your thematic analysis. In your report, you should state your research question and show how it has been answered by the analysis.
You should set out the methodology of your research process and describe the codes and themes you used. Taking each theme in turn, explain what it means, backing up what you say with evidence from the data.
Before you submit your finished thematic analysis to your tutor or to a journal, be sure to have it proofread. Our editors work around the clock and return every document error-free within 24 hours. Why not try us out for free today?
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