Ethical Thematic Analysis with ChatGPT: Step-by-step Tutorial

Step-by-Step explanation of how to use MS Word and Chat-GPT to deliver a high-quality analysis that is academically robust and adheres to ethical standards
I demonstrate how to create codes (initial, open coding), using ChatGPT and Microsoft Word and what to do next, switching between these tools until we develop the final themes.
#qualitativeresearch #academia #researchpaper
👉 My Ebook on how to use ChatGPT for thematic analysis (including a list of GPT prompts) payhip.com/b/KmzOL
➡️ Feel free to ask me questions in the comments, and if you feel that you may require a more guided assistance, you can book a lesson with me through the following link drkriukow.com/qualitative-res...
00:00-02:52 Overview before we start - watch this!
02:52-11:05 Ground work in ChatGPT
11:05-14:41 Using Microsoft Word
14:41-17:24 Focused Coding
17:24-20:59 Developing themes

[@qualitativeresearcher]

👉 My Ebook on how to use ChatGPT for thematic analysis (including a list of GPT prompts) payhip.com/b/KmzOL

[@heatherhatchett7817]

This got me started at coding a new project. Easy to follow.

[@qualitativeresearcher]

Thank you for taking the time to share this! Really glad I could help

[@walterantoniocanu9356]

This is great food for thought! Let me share some reflections: I think that going through the transcripts and reading, coding, re-coding, etc. is actually the most fundamental part of the whole qualitative process when coding is involved (there are some other qualitative methods which do not involve coding), and using AI sometimes seems like encouraging the researcher to skip such coding process, getting directly to the results (that should be checked, of course). A diligent or curious researcher could also go through the traditional deep reading-and-coding process and THEN use AI, but it would look like a double effort. I think the process is more important than the results, because it brings you a better understanding, which is crucial for developing themes, besides guaranteeing high quality codes. However, maybe the time saved with AI can balance the cons of skipping the hard part and its benefits. So my questions for you: 1) What do you think about the reading-and-coding process with AI? ; 2) How much time would you save using AI for a high quality thematic analysis (following a robust iterative process such as that outlined in your recent e-book)? Thanks for your attention and congratulations for your work.

[@qualitativeresearcher]

I agree with what you said - by reading the transcripts by yourself, you are developing understanding of your data that will also contribute to the subsequent iterations of your codes and, finally, theme development. I can still imagine doing this without having read the transcripts, and relying solely on the codes, however - for if these are good, descriptive codes, in principle they are essentially "summaries of the data" as I often say. This is provided, however, that you can trust ChatGPT completely (that it indeed generated extremely accurate and detailed codes), which of course is the main concern - can we trust it that much? In short, at the moment I would definitely NOT trust ChatGPT to do the work for me like this, unless it is a very small dataset and I can easily control what it does, but as you said, what is the point then? I can definitely see it as a useful second coder, as you suggested above. In the future, I do think that (very soon in fact) we will be able to hand our data over to such tools completely and not worry about our own understanding at all - because if you think about the goals of data analysis, it is not for us to understand the data, but rather to answer the questions about the data that we have. We only need all these processes (coding, subsequent coding, etc) to make sure that we manage this amount of data with our simple human brains :) When we can trust AI completely, we won't need to ask it to create, organize and list codes - it will be enough to upload the data and ask the research questions. I believe we are very close to this point at the moment, possibly even there already, and the only reason we are not Officially there is simply that academia is not ready to accept the fact that this aspect of research has changed forever

[@walterantoniocanu9356]

​​​​​​@@qualitativeresearcherInteresting perspective! It seems like you are saying that reading-coding-recoding is a means for an end/result (answering the research question) and I agree with that. Anyway, I also think that such process lead to different results if done manually, allowing reflections and research questions to develop inductively and abductively, thus changing the end/result. As they say: the journey sometimes is more important than the final destination (i.e. you learn new things and know better where to go once in the field yourself). Furthermore, contextual / personal knowledge of the researcher makes the process of qualitative analysis essentially different from what happens in quantitative analysis, making AI contribution inherently different. But that's a story for another discussion. Thanks again!

[@rollintroll]

Jumping in quickly here (have not watched the video yet) but as a business school academic, this is the way forward. However like you I suspect there will be some degree of "expertise" loss, similar to knowing how to manually calculate the square root of large numbers vs. asking your phone. LLM will shortcut a lot of our accumulated expertise, but just like the @qualitativeresearcher says, maybe the magic is not in the tools (coding) but gaining insight from the data analysis itself. Ok off to watch the video!

[@user-re3en9su7z]

How can we do this when we must maintain confidentiality and our subjects have signed privacy documents? I've always been told NOT to input anything into GPT because it scrapes the data.

[@qualitativeresearcher]

I have commented to this under another video - you would need to double check it, but I am pretty confident that no data is being kept in ChatGPT, OpenAI explains that the text you input into the chat is not used to train the model. privacy and data protection is therefore not an issue, but whether it is ethical to "share" the information that you promised not to share, with ChatGPT, will largely depend on the wording of the individual NDA agreement.

[@Xerses999]

So this will not be red flag in Turnitin for plagiarism?

[@qualitativeresearcher]

According to Open Ai, the data from these chats is not stored anywhere, which means that it wouldn't be flagged as plagiarism later.

[@rajanvk939]

is this ethical to do ??

[@qualitativeresearcher]

Of course it is - as I stress in the video, this approach specifically prioritizes research integrity. I am constantly reviewing what ChatGPT gives me, then structuring my codes and themes, etc - there is human involvement and ChatGPT only serves as an additional tool that helps us maximize our own effectiveness and save time - just like a calculator does. ChatGPT is not interpreting anything for me, not developing any findings, etc. - notice that I am only employing it for the relatively automatic and objective task of doing the initial coding, which is essentially summarizing the text

[@rajanvk939]

Thank you for your insight. You are really an amazing teacher. I love to learn from your videos and highly appreciate your valuable time and efforts to educate us.@@qualitativeresearcher