![]() You may have thoughts about what might be discussed (e.g., academic matters because it’s an academic setting), but these cannot be noted as themes ahead of analysing your data. In other words, you’d dive into your analysis without an idea of what themes will emerge, and thus allow themes to be determined by the data – to emerge from the data.įor example, if you’re investigating typical lunchtime conversational topics in a university faculty, you’d enter the research without any preconceived themes or expected outcomes. The inductive approach involves deriving meaning and creating themes from data without any preconceptions. Let’s have a look at the main approaches to thematic analysis. The approach you take will depend on what is most suitable for your research design, and it is possible to take more than one approach. There are several overarching approaches to thematic analysis. In short, thematic analysis is a good choice when you are wanting to categorise large bodies of data (although the data doesn’t necessarily have to be large), and particularly when you are interested in subjective experiences. These examples are all research questions centering on the subjective experiences of participants and aim to assess experiences, views, and opinions, and thus thematic analysis is a possible approach. How is gender constructed in a high school classroom setting?.What opinions do health professionals hold about the Hippocratic code?.What are students’ experiences with the shift to online learning?.How do dog walkers perceive rules and regulations on dog-friendly beaches?. ![]() For example, if your research questions were to be along the lines of: Your research questions can also give you an idea of whether you should use thematic analysis or not. Thematic analysis is particularly useful when looking for subjective information such as a participant’s experiences, views, and opinions, which is why it is usually conducted on data derived from, for example, surveys, social media posts, interviews, and conversations. When working with large bodies of data, thematic analysis is highly beneficial as it allows you to divide and categorise large amounts of data in a way that makes it far easier to digest. For example, by using content analysis, discourse analysis, or narrative analysis. There are several ways that you can analyse a set of data. With that basic terminology out of the way, let’s jump into the wonderful world of thematic analysis… Grouping your codes into themes serves as a way of summarising sections of your data in a useful way that helps you answer your research question(s) and achieve your research aim(s) and objective(s). In other words, it’s a topic or concept that pops up repeatedly throughout your data. But what exactly is a theme? A theme is a pattern that can be identified within a data set. If this is a new concept to you, be sure to check out our detailed post about qualitative coding.Ĭodes are vital as they are a foundation for themes. ![]() The process of assigning codes is called coding, where you categorise data in a way that allows you to derive themes and patterns. A code is a label assigned to a piece of text, and the aim of using a code is to identify and summarise important concepts within a set of data, such as an interview transcript.įor example, if you had the sentence, “My rabbit ate my shoes”, you could use the codes rabbit or shoes to highlight these two concepts. In thematic analysis, you’ll make use of codes. Before we begin, let’s first lay down some terminology.
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