Speaker 1: You've come across thematic analysis and are wondering what this qualitative research method is all about? No problem. In this video, you will learn the six steps of thematic analysis according to Brown and Clark, the different types of thematic analysis you can do, the difference between thematic analysis and qualitative content analysis, and the types of research projects for which thematic analysis is particularly well suited. And now, without further ado, welcome to Shribe. Thematic analysis is one of the most popular qualitative research methods out there. Since Brown and Clark published their paper using thematic analysis in psychology in 2006, it has been cited over 150,000 times. Therefore, the method has gained recognition far beyond the realms of psychology and is used across various disciplines. The reasons for the popularity of thematic analysis are manifold. Unlike grounded theory, it represents a specific method rather than a methodological approach. This means that there are concrete steps for its execution that have been clearly and explicitly defined. Moreover, thematic analysis still offers a certain flexibility, which is essential for qualitative approaches. The method has evolved very little since 2006, meaning the guidelines by Brown and Clark are still highly relevant. However, since then, the duo has distinguished between three different kinds of thematic analysis. This differentiation arose because the method was sometimes interpreted in ways different from what they originally intended. The three types are as follows. Reflexive thematic analysis. This is the method as Brown and Clark envisioned it. It's based on a constructivist mindset, meaning subjective interpretations of the qualitative data are at the forefront. Positivist thematic analysis. In this variant, researchers compute a reliability measure to check for agreement among them or check the frequency of categories. This version of thematic analysis follows more of a positivist mindset and isn't quite what the two authors originally had in mind. Thematic analysis with a codebook. This third variant is neither one extreme nor the other. It involves working with a codebook, which can contain predefined categories, but can also be expanded spontaneously. Now, what is a theme? A theme is either a summary of the content or a central concept that encapsulates the meaning of similar contents. Themes cannot be discovered or found within the content. They have to be generated by you as the researcher. So never write, I identified five themes. But instead say, I developed five themes. What now follows are the six steps of reflexive thematic analysis as proposed by Brown and Clark. Step one. Familiarize yourself with the data. First, transcribe your data, if you have it only available in audio or video format. Then read the entire data set twice from start to finish. This gives you a good overview of all your material. It's better than starting to evaluate a transcript without knowing the rest. Try to fully immerse yourself in the situation described in the transcripts. However, always maintain an analytical perspective. Take notes as you read. You can also take notes right after conducting an interview or while visiting a company on site, if that's your research context. All your notes are for your personal use. You don't need to share them later. However, they will assist you in the evaluation later on. In your notes, jot down your initial reactions. These can be analytical or purely intuitive. Step two. Generate initial codes. Now it's time to start coding. The codes that emerge at this stage are not yet themes, but categories. What are categories? Try to code all your data according to the same schema. That is, find categories on a consistently similar level of abstraction. An example of a category would be democratic decision-making within the team. Another category on the same level could be open discussion about the integration of new technologies. Two categories that are not on the same level might be hierarchy, which is too abstract at this point, and weekly meetings where hiring decisions are made, which is not abstract enough. With the categories, you can certainly venture a preliminary interpretation like democratic decision-making. This exact phrase wasn't really in the data. It's something that you interpreted. But what's the purpose of the categories? They reduce the volume of your data and group your analytical units. What's essential for thematic analysis, as per Brown and Clark, is that you don't code everything. Instead, you should only form categories that are relevant to your research question. In the data, you will find many sections that just aren't interesting and won't help answer your question. You don't need to code these sections. The naming of your categories should be chosen such that they precisely describe what's relevant to your question. A category doesn't have to consist of just one word. It can be a bit longer, like three to six words. How to code? You can either work digitally with software like Amvivo or use pen and sticky notes. I'm more of a software person, but everyone has their own preferences. Even while coding, you can and should continue to take notes that you can use later on. Step 3. Generate the first themes. The reflexive thematic analysis by Brown and Clark operates inductively. Your themes should arise exclusively based on your data and at this point based on your categories. Now for that, group the categories and codes that you have. Which ones are thematically related? This will lead to clusters of categories. Each cluster will then become a theme. Here you can also work with mind maps and visually develop the clusters. It's also possible that within a larger cluster, you have smaller clusters or sub-themes. However, try not to make it too complicated. In the end, having three to six main themes is a good amount to work with. The biggest mistake in coding and also in generating themes is the use of so-called buckets. A classic bucket includes categories or themes like advantages, disadvantages, barriers or challenges. It's crucial to steer clear of these. Step 4. Review your themes. Once you've finalized all the themes, create a final mind map featuring all the themes, potential sub-themes and categories. Check if everything forms a coherent overall picture and accurately reflects the content of the data. Ask yourself the following questions for each theme. Is this more than just a category? Does this theme encapsulate multiple categories? How does the theme relate to the research question? Are there overlaps between themes? Is there sufficient data supporting the theme? And is the theme too broad or too specific? If you encounter issues with these questions, such as overlapping themes, take a step back and rephrase the themes or rearrange the structure. Thematic analysis by Brown and Clark isn't a linear process. You can always move forward and backward as needed. Now, before we get to step 5, please consider to give this video a like and subscribe to the channel if you want to see more of this type of content. Step 5. Define and name your themes. Now write a detailed description for each theme, comprising 5-6 sentences. Also finalize the specific designation for each theme. If you encounter issues while describing or naming, it typically indicates that the theme isn't distinct enough yet. In such cases, revert a step or two and reconsider. Step 6. Write down your findings. The final step for your thematic analysis is drafting your report. In most cases, this will be an academic paper. Now you will integrate your findings with existing literature and align the motivation, research question, results and discussion. In your methodology section, ensure to cite Brown and Clark and explain how you approached your thematic analysis. In the results section, introduce all the themes at a glance and then delve deeper into each specific theme. Provide quotes from your data that represent each theme. Certainly, let the quotes speak for themselves. They can even be a bit lengthy. However, simply stringing together quotes isn't sufficient. Between them, you must jot down your interpretation and establish the connection between the data and the theme. Now what's the difference between Brown and Clark's thematic analysis and qualitative content analysis? Answering this question is not that straightforward. The approach suggested by Brown and Clark is simply their perspective on systematically analyzing qualitative data. With qualitative content analysis, there are different variations too. For example, by the German social scientists Philipp Meyring or Udo Kuckertz. A content analysis would probably be more suited if you have a big qualitative data set and would like to count your categories or if you want to develop a codebook for other researchers to use. The procedures of thematic analysis and inductive content analysis are quite similar and differ by maybe one or two steps and their respective labels. When selecting your method, consider the target audience for your research. For more interpretive research and an English-speaking audience, choose thematic analysis. For a more structured approach and some quantification of your qualitative data, choose content analysis.