Streamlined workflow for qualitative data analysis with Whisper and ATLAS.ti

• Main Authors: RATMELIA, Bella, LEOW, Bryan, DONG, Danping
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Whisper; ATLAS.ti; QDA; interview transcript; Library and Information Science; Social and Behavioral Sciences
• Online Access: https://ink.library.smu.edu.sg/library_research/219; https://ink.library.smu.edu.sg/context/library_research/article/1224/viewcontent/whisper_atlasti_slides_v1_participants.pdf
• Institution: Singapore Management University

Qualitative research, often reliant on interviews and focus groups, stands to benefit significantly from the latest advancements and innovations in AI. This workshop presents a practical and efficient research workflow with two powerful tools—Whisper and ATLAS.ti—to streamline the process of qualitative data analysis. Whisper is an open-source machine learning model for speech recognition and transcription by OpenAI. With the ability to achieve human-level accuracy in speech recognition, it significantly reduces the time and effort required for transcribing, saving your time for more valuable work. Moving beyond transcription, the workshop presents ATLAS.ti, a well-established qualitative data analysis tool. Learn how to code data, extract insights, create visualizations, and explore the tool's latest AI features. Join us to discover the new tools to improve your qualitative research workflow.