Computer-Assisted Qualitative Data Analysis with Automation & AI - A Short Course

Demonstrations and exercises feature an interactive data set, integrating the offline language-model tools in ATLAS.ti, as well as free open-source resources that run in a browser or Python environment. Each session combines short explanations with hands-on practice as we build a sample data set and walk-through common procedures. You will learn to:

• • Chart the full workflow of qualitative data analysis, identifying contemporary tools and strategies for each task.
  • Use ATLAS.ti 25 to compile and code diverse data types—interviews, ethnographic fieldnotes, focus-group transcripts, open-ended survey questions, PDFs, images, audio, and video—then analyze results inside ATLAS.ti or export them for other software.
  • Use automation and deep-readings of data to index, code, and write memos to develop confidence in findings and produce research.
  • Use first-pass indexing with regular-expression search, named-entity recognition, and synonym suggestions to ‘chunk’ data, while maintaining the depth of original data.
  • Build or refine codebooks and dictionaries for targeted indexing, with help from large-language-model output (does not require sharing data!).
  • Create interactive visuals in ATLAS.ti.
  • Export coded segments and use free browser-based Python tools to create data visualizations and validation checks.
  • Organize comparative analysis across groups, cases, or time points.
  • Automate de-identification before any public sharing of data.
  • Consider the promise—and cautions—of AI for qualitative work, including bias, misclassification, and responsible deployment.
  • Manage multi-researcher projects, track coder agreement, and integrate Computer-Assisted Qualitative Data Analysis (CAQDA) results into mixed-methods studies.

Throughout the seminar we will introduce free and open-source utilities that complement ATLAS.ti for file management, project comparison, and other common tasks.

Blog Posts

Read Qualitative Coding Simplified, a blog post by Corey M. Abramson, to learn what qualitative coding is and how it can be used in a multitude of ways to enhance researchers’ data and help make sense of texts. 

In Sub-setting Qualitative Data for Machine Learning or Export, Abramson discusses ‘sets’ for analyzing qualitative data, including what sets are, how they can be used, and examples of how to sub-set data in Atlas.ti.

No CAQDA or coding background is required. Social scientists, clinicians, public-health specialists, mixed-methods teams, and others working with text-rich data (or data types that can be transcribed) will benefit. The seminar also serves quantitative analysts who want a grounded entry into qualitative techniques, and qualitative researchers interested in responsible uses of automation and AI.

Foundations & Interface

• Core CAQDA concepts
• Contemporary, software-agnostic workflow, scalable for small or large projects
• ATLAS.ti overview
• Entering and structuring data

Coding & Data Management

• De-identification before indexing
• Search-based indexing
• Building and revising codebooks
• Memo strategies
• GREP, named-entity recognition, synonym support

Comparative Analysis & Scaling

• Working with multiple researchers
• Comparative analysis across cases or time
• Exporting to browser-based Python tools
• Quick visuals
• File-management tips

Retrieval, Representation & Ethics

• Advanced queries
• Network and co-occurrence visuals
• Integrating results into writing
• Promise-and-cautions discussion on AI
• Resources for next steps

“The discussions of simple things like naming conventions and other organizational tools that can be used across different platforms will be helpful! I appreciated how knowledgeable the instructor was and his real-world examples! The resources provided during the course and on Slack were also very helpful.” 
  Chloe Sierka, Temple University

“Corey was extremely knowledgeable. He answered questions immediately and would show or repeat anything that was asked. I really liked how real-world examples/projects were used and the hands on/following along part was great!” 
  Caitlin Brady, Georgia Southern University

“Very interactive and good use of technology to facilitate communication. I appreciated that the instructor responded to questions real-time and and the real-time sharing of information.” 
  Anne Skalicky, Evidera

“Corey was fantastic. His training and experience make him perfect for this course. I appreciated the context and examples that he consistently provided.”
  James Sutton, Hobart and William Smith Colleges

“Corey is funny, patient, flexible, and very experienced. He provided a nuanced story full of helpful examples from his own experience. I furthermore liked the material he provided; very concrete, clear, worth keeping for future reference.”
  Marrije Prins, Vrije Universiteit Amsterdam

“I liked the quantitative aspects within qualitative work. I was especially happy to see I can use the software to run frequencies and utilization of network visualization. The instructor was passionate about his stuff and has awesome understanding as a subject matter expert. I loved his passion and mastery of qualitative content.”
  Odylia Muhenje, CDC

“I have had opportunity to follow through this virtual training on computer assisted qualitative data analysis using ATLAS.ti. I have used the training to navigate my qualitative data and managed to perform some preliminary analysis. Though this was my first time and I was not able to attend the live sessions, Corey made it doable by answering all the questions that I had. I also found the contributions from colleagues who attended the course very helpful. The recorded sessions and videos together with the shared publications and other materials were instrumental in making sure I continued learning in order to achieve my goals.”
  Joyce S. Nalugya, Makerere University