Qualitative Data Analysis With AI

Susanne Friese is a scholar of qualitative methods with a long track record in research, teaching, and methodological development. Her work spans interpretive approaches, and the evolution of computer assisted analysis. In recent years, she has become a leading voice in rethinking how qualitative analysis is done in an era shaped by artificial intelligence. Her focus lies on dialogue-based inquiry, transparency, and the integration of AI in ways that strengthen rather than replace human interpretation. David L. Morgan received his PhD in sociology from the University of Michigan, and is currently an emeritus professor in the Department of Sociology at Portland State University. He is an inter-disciplinary research methodologist, working in both qualitative research and mixed methods research. In addition to artificial intelligence, his research interests include focus groups and mixed methods research. He is the author of more than fifty peer-reviewed articles and author or editor of nine books on research methods; he is currently the series editor for the Qualitative Research Methods Series from Sage (the "little blue books").

Preface Acknowledgement Editors List of Contributors Introduction The AI-Cyborg Researcher: A Human-Centered Approach to Qualitative Data Analysis in the Era of Artificial Intelligence, - Sharlene Hesse-Biber Introduction The Coming of a New Renaissance: The Rise of Artificial Intelligence and Generative Technologies The Paradigm Shift from Manual Coding and Computer-Assisted Coding to Prompting The Rise of the AI-Cyborg Researcher The Cyborg Researcher Guides AI in Feminist Principles of Praxis Future Directions: Expanding the AI-Cyborg Researcher Model of Meaning-Making Framework. Conclusion AI Sandbox: Reflection References Chapter 2: The Five-Level QDA Method in the Gen-AI Era: Rethinking Qualitative Pedagogy and Practice - Christina Silver CAQDAS Pedagogy: The Five-Level QDA Method Experiences and Ethos Learners' Uncertainties and Expectations Pedagogic Aims and Instructional Frameworks The Whether-When-How Debate Encouraging Critical Reflection Contexts Framing Discussion of GenAI for QDA Enacting Analytic Tasks via the use of GenAI Tools GenAI Conversing as an Example of Tactics Informing Strategies Discussion Conclusion AI Sandbox: Reflection References Chapter 3: Integrating AI into QDA Software: The Example of MAXQDA - Stefan Raediker and Udo Kuckartz Introduction Software and AI in Qualitative Data Analysis Overview of AI Features in MAXQDA AI in Practice: Support for Qualitative Content Analysis and Grounded Theory Integrated AI in MAXQDA vs. External AI Tools like ChatGPT Conclusion AI Sandbox: Practice References Chapter 4: An Experiment: Can Consumer Chatbots Analyze Open-Ended Survey Responses? - Jessica Parker, Veronika Richard and Susanne Friese Introduction Traditional Coding Workflows in Qualitative Survey Analysis The value and limits of traditional approaches From Human Coding to AI Assisted Coding Why This Is Not a Straw-Man Experiment The Sample Data Set Why Automated Coding Falls Short Implications: From Coding to Dialogic Analysis Conclusion AI Sandbox: Practice References Appendix: Initial prompt for code frame development Chapter 5: Beyond Coding: Conversational AI for Qualitative Analysis with QInsights - Susanne Friese Towards a New Perspective on Qualitative Analysis The Origins of Coding: A Historical Perspective The Emergence of AI and LLMs in Qualitative Analysis Understanding and Working with LLMs A New Workflow: Engaging with Data Through Questions Exemplary Analysis with QInsights Methodological Adaptation Discussion AI Sandbox: Practice References Chapter 6: Productivity and Quality of using AI for Qualitative Data Analysis in One Research Project - Jonas Wibowo & Hendrik Wiese Introduction Productivity Promises of Generative AI Problematic Dimensions in QDA using GenAI Project Description Categorical Qualitative Data Analysis as an Analytic Framework Study Design for Testing GenAI Supported Categorical QDA The Final Procedure A Framework for GenAI-Assisted Categorical QDA Discussion AI Sandbox: Reflection References Appendix Chapter 7: Hybrid interpretation of text-based data with dialogically integrated LLMs. On the use of generative AI in qualitative research - Uwe Kraehnke, Thorsten Dresing, and Thorsten Pehl Introduction Fundamentals, Potentials and Current Developments of AI-supported Analysis of Text-based Empirical Data Hybrid Text Interpretation with Multiple, Dialogically Integrated LLMs Application Example: Functional Segmentation as a Coping Strategy Discussion: Opportunities and Challenges of AI-assisted Qualitative Analysis Epistemological Clarification Data Protection Compliance and Research Ethics Critical Reflection AI Sandbox: Practice References Chapter 8: AI and the Co-Creation of Meaning: Using Large Language Models in Grounded Theory Research - Kai Droege Introduction Grounded Theory and AI - An Overview The Role of AI in the Research Process Sycophancy: Bias Towards User Confirmation Common Sense Orientation and Bias The Fluid Positionality of AI Putting It into Practice: Integrating AI into Grounded Theory Research Coding and Memo Writing in the Age of AI Close Reading and "Open Data Exploration" Memos AI Assisted "Horizontal" Coding Consolidating the Emerging Theory and Writing a Report Conclusion AI Sandbox: Practice References Chapter 9: Modular Prompting with the Documentary Method: Rethinking Interpretation with AI in Reconstructive Social Research - Fabio Roman Lieder Introduction Some Theoretical Considerations Agency of LLMs in Distributed Interpretation Meaning-Making through Modular Prompting Some Basics on the Documentary Method A Practical Example of Distributed Interpretation via Modular Prompting Resulting Hybrid Interpretation Evaluating the Result Discussion and Outlook AI Sandbox: Practice References Chapter 10: The MERIT Framework: Guiding responsible innovation in qualitative methods - Jessica Nina Lester and Trena M. Paulus Introduction Defining generative AI AI and Qualitative Data Analysis Software Guidelines for Responsible AI Use Reporting Guidelines for Qualitative Researchers A Heuristic for Generating Reporting Guidelines for Qualitative Data Analysis Future Directions AI Sandbox: Reflection References Chapter 11: Understanding the Adoption of an Innovation: The Case of AI in Analyzing Qualitative Data - David Morgan Diffusion of Innovations Conclusions References Glossary References

Qualitative Data Analysis With AI the power of critical thinking and transformative insight through generative AI to challenge convention and inspire innovation. -- Minghe Sun * Minghe Sun, The University of Texas at San Antonio * This book is a great go-to for understanding the implications and use of AI in our qualitative world. Not only do I need this information for my students, but I need this information for myself as a researcher. -- Kristen M. Curry This book promises to be an indispensable resource for the burgeoning field of AI in qualitative data analysis. -- Steven A. Harvey As an expert and teacher of qualitative research to doctoral level students, I was pleasantly surprised to learn new things from this text. It is on the cutting edge of the AI curve, answering some questions, while posing many more. -- Helen Runyan This is a great beginning for students who are at the cusp of technological advancement and learning the "traditional" fundamentals of qualitative research! -- Hannah Nario Lopez