Qualitative Data Analysis With AI

Theory, Methods, and Practice
1. Auflage 2027
ISBN: 978-1-0718-6839-3
Verlag: SAGE Publications Inc

Buch, Englisch, 304 Seiten, Format (B × H): 187 mm x 231 mm

This text provides a broad, interdisciplinary overview of the emerging field of artificial intelligence (AI) in qualitative research. By combining conceptual reflection with detailed accounts of practice, the chapters offer both an overview of new possibilities and a realistic understanding of how AI-supported analysis works in research settings.

Autoren/Hrsg.

Friese, Susanne

Morgan, David L

Inhaltsverzeichnis

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 Rädiker 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 Krähnke, 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 Dröge
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

Über Autor(innen)

Friese, Susanne
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.

Morgan, David L
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”).