Use ELGA to Simplify Thematic Data Analysis (Full Transcript)

[00:00:00] Speaker 1: I'm going to show you a framework that you can use that will 10x your understanding of qualitative analysis and you'll be able to easily explain how you analyze your data to your audience to better understand. Think about it. You already have your transcript. You are going through the transcript. You have familiarized yourself with the data. So the next stage is for you to start the data analysis, especially if you are using thematic analysis. So the first stage is to extract information that is significant. So how do you do that? With the research question in mind, ask yourself what are the statements that are addressing the research question. It may directly or indirectly address the research question. So what you have to do is to identify those significant information. And sometimes if you don't have your research question, you can also think about the purpose of your study or the objective of your study or your topic. Which of the statement participant gave you is really addressing the topic or your research question that you have. That is extract. So you extract all the information that is significant. The next stage is to label the significant information. How do you label? You look at what participant is telling you. Ask yourself what is this participant saying? Because before you do the label, you have to really understand what the participant is telling you. So now if you understand the significant information, you come up with a phrase, which is in between two to five words. It can be two to six or seven, but having two to five words would be better. Within two to five words, representing the significant information. And that is the label. You label all the significant information, which is, we call it code, right? After coding, what do you have to do? Because your goal is to reduce the data, not only reducing, but meaningfully reducing the data. You have to examine each of the codes so that you can group them based on the similarities, based on the things that they have in common. And then what you have to do, you give them labels. Each group you're going to label addressing the research question also represent the codes that are in the group, right? So those labels will be themes. And then what you have to think about is the last stage is to answer, right? You have to make sure that the themes are addressing the research question. You have to examine each of the themes. Is this theme really addressing the research question? And then you are done. So think about ELGA whenever you are explaining data analysis process. If you want to simplify the data analysis process, this is what you have to think about. This simple framework. You extract, you label, you group, and you answer. This is a very simple framework that will 10x how you explain or articulate how you analyze your data to people so that you can really understand what you're doing. I hope this one is clear. If you have any questions, let me know. I'll be happy to address them for you.