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Qualitative Data Analysis: 4 Fixes if ChatGpt did not work for you
Usage of ChatGPT and associated tools such as Co-Pilot on qualitative data such has shown mixed results. Though the promise of auto summary and thematic analysis is alluring, researchers have encountered hurdles around accuracy, transparency and researcher's time savings.

Looking forward, qualitative researchers are presented with five viable options:

Traditional Methods: Continue with conventional practices like listening to audio recordings, reading transcripts and using self-service QDA tools. This is a preferred approach when there is no time pressure and number of files to read are small. This approach also has the advantage for Researchers to immerse in customer stories.

Manual Use of ChatGPT: Researchers opting for this need to be aware of the limitations, including text length constraints, and develop skills in prompt engineering to obtain the best results. It also nee

ChatGPT API: For those who have established standardized prompts, using the ChatGPT API can be effective, particularly in large-scale studies. This method allows for more consistent and efficient data analysis, but pricing is dependent on number of prompts*number of words.

Outsourcing Data Services: Perhaps the most efficient alternative to Traditional methods is to outsource data analysis to specialized services. These services can provide researchers with AI coded and summarised coupled up by Analyst validated files, release precious time for data synthesis, story-telling and client management.

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