As someone whose about to analysis a mass of public comments on a major development planning application, this has be incredibly helpful and enlightening! So thank you!
That's amazing to hear! Congratulations on completing your first qualitative analysis, and in just 2 hours-that's quite an achievement! I'm glad the video was helpful. Keep up the great work! 🎉
Great tutorial, thank you for taking the time to put this together. If you have transcripts more than 2,500 words to analyse then would you recommend Claude or the paid version of ChatGPT?
Hi, congratulations on your tutorials. I have a question to ask you. How do you insert focus group videos into gpt or claude chat, because there is also a need to analyze body language.
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)? Thanks for your attention.
Thank you for your insights on the application of AI in analyzing qualitative data. Emphasizing the use of AI tools in research is not a call for researchers to become overly dependent on these tools for their analysis. Instead, it offers an opportunity to enhance the quality of data analysis. For instance, AI can uncover themes previously unnoticed or extract potentially relevant excerpts, thereby broadening the analytical scope. However, integrating AI into the research process doesn't absolve researchers from the critical task of scrutinizing AI-generated outputs, due to the possibility of inaccuracies. Your reflection prompted a comparison between AI tools and SPSS, a software used for statistical analysis. The credibility of quantitative findings using SPSS isn't automatically granted by the tool itself but depends on the quality of data input, the appropriate analyses conducted, and an awareness of the underlying assumptions of these analyses. Similarly, AI tools, when utilized transparently and ethically, can provide valuable insights that aid the data analysis process. Moreover, researchers must possess the necessary knowledge and skills for data analysis, ensuring they can competently evaluate AI-generated results. Regarding your queries, I see no inherent issue in employing AI tools to facilitate the data analysis process, provided researchers do not become entirely reliant on these tools. Concerning your second question, the time required to complete data analysis varies widely, depending on factors such as data volume, type, and the specific insights sought. Similarly, the time saved through the use of AI tools will vary based on several factors. I believe that AI tools can support high-quality qualitative data analysis, depending on how they are incorporated into the research process. It's crucial to recognize that manual qualitative analysis does not automatically equate to a high-quality, robust process. The act of line-by-line examination by a researcher does not guarantee superior qualitative analysis. AI has the potential to significantly contribute to conducting comprehensive studies. I hope this clarification is beneficial!
Thanks for the interesting cues. I do believe that employing AI (or even traditional software suck as SPSS) for quantitative analysis is essentially different in terms of process, due to the different importance of epistemological perspectives (context and personal tacit knowledge) but I think you raised important points to reflect about how we as researcher can make the best of this major revolution. I am not sure about the potential benefits and disadvantages compared to manual coding (not only line-by-line). Hope to have the chance to discuss again in the future. Thanks a lot!
As someone whose about to analysis a mass of public comments on a major development planning application, this has be incredibly helpful and enlightening! So thank you!
Thank you @poppyheneghan-pickard5350! I'm glad it was helpful!
Thanks to this video, I just did a qualitative analysis for the first time in 2 hours
That's amazing to hear! Congratulations on completing your first qualitative analysis, and in just 2 hours-that's quite an achievement! I'm glad the video was helpful. Keep up the great work! 🎉
Thanks, very helpful!
Thank you Paula!!
Great tutorial, thank you for taking the time to put this together. If you have transcripts more than 2,500 words to analyse then would you recommend Claude or the paid version of ChatGPT?
You ca use both for free with even 3,000 words. Now GPT 4o can be used for free.
Hi, congratulations on your tutorials. I have a question to ask you. How do you insert focus group videos into gpt or claude chat, because there is also a need to analyze body language.
Thank you! Unfortunately, the AI tools don't analyze videos. Maybe one day there will an option to upload videos.
You did a great job here I must say.
Thank you!!
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)? Thanks for your attention.
Thank you for your insights on the application of AI in analyzing qualitative data. Emphasizing the use of AI tools in research is not a call for researchers to become overly dependent on these tools for their analysis. Instead, it offers an opportunity to enhance the quality of data analysis. For instance, AI can uncover themes previously unnoticed or extract potentially relevant excerpts, thereby broadening the analytical scope. However, integrating AI into the research process doesn't absolve researchers from the critical task of scrutinizing AI-generated outputs, due to the possibility of inaccuracies.
Your reflection prompted a comparison between AI tools and SPSS, a software used for statistical analysis. The credibility of quantitative findings using SPSS isn't automatically granted by the tool itself but depends on the quality of data input, the appropriate analyses conducted, and an awareness of the underlying assumptions of these analyses. Similarly, AI tools, when utilized transparently and ethically, can provide valuable insights that aid the data analysis process.
Moreover, researchers must possess the necessary knowledge and skills for data analysis, ensuring they can competently evaluate AI-generated results.
Regarding your queries, I see no inherent issue in employing AI tools to facilitate the data analysis process, provided researchers do not become entirely reliant on these tools.
Concerning your second question, the time required to complete data analysis varies widely, depending on factors such as data volume, type, and the specific insights sought. Similarly, the time saved through the use of AI tools will vary based on several factors.
I believe that AI tools can support high-quality qualitative data analysis, depending on how they are incorporated into the research process.
It's crucial to recognize that manual qualitative analysis does not automatically equate to a high-quality, robust process. The act of line-by-line examination by a researcher does not guarantee superior qualitative analysis.
AI has the potential to significantly contribute to conducting comprehensive studies.
I hope this clarification is beneficial!
Thanks for the interesting cues. I do believe that employing AI (or even traditional software suck as SPSS) for quantitative analysis is essentially different in terms of process, due to the different importance of epistemological perspectives (context and personal tacit knowledge) but I think you raised important points to reflect about how we as researcher can make the best of this major revolution. I am not sure about the potential benefits and disadvantages compared to manual coding (not only line-by-line). Hope to have the chance to discuss again in the future. Thanks a lot!
How can we use it for interpretation of the data in details?
This video would help: ruclips.net/video/L1WelrcgLGM/видео.htmlsi=Fe9xUyi6u1yw9mAO