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”).

Qualitative Data Analysis With AI the power of critical thinking and transformative insight through generative AI to challenge convention and inspire innovation.

• PrefaceAcknowledgementEditorsList of ContributorsIntroductionThe AI-Cyborg Researcher: A Human-Centered Approach to Qualitative Data Analysis in the Era of Artificial Intelligence, - Sharlene Hesse-BiberIntroductionThe Coming of a New Renaissance: The Rise of Artificial Intelligence and Generative TechnologiesThe Paradigm Shift from Manual Coding and Computer-Assisted Coding to PromptingThe Rise of the AI-Cyborg ResearcherThe Cyborg Researcher Guides AI in Feminist Principles of PraxisFuture Directions: Expanding the AI-Cyborg Researcher Model of Meaning-Making Framework.ConclusionAI Sandbox: ReflectionReferencesChapter 2: The Five-Level QDA Method in the Gen-AI Era: Rethinking Qualitative Pedagogy and Practice - Christina SilverCAQDAS Pedagogy: The Five-Level QDA MethodExperiences and EthosLearners’ Uncertainties and ExpectationsPedagogic Aims and Instructional FrameworksThe Whether-When-How DebateEncouraging Critical ReflectionContexts Framing Discussion of GenAI for QDAEnacting Analytic Tasks via the use of GenAI ToolsGenAI Conversing as an Example of Tactics Informing StrategiesDiscussionConclusionAI Sandbox: ReflectionReferencesChapter 3: Integrating AI into QDA Software: The Example of MAXQDA - Stefan Rädiker and Udo KuckartzIntroductionSoftware and AI in Qualitative Data AnalysisOverview of AI Features in MAXQDAAI in Practice: Support for Qualitative Content Analysis and Grounded TheoryIntegrated AI in MAXQDA vs. External AI Tools like ChatGPTConclusionAI Sandbox: PracticeReferencesChapter 4: Can Consumer Chatbots Analyze Open-Ended Survey Responses?: An Experiment - Jessica L. Parker, Veronica M. Richard, and Susanne FrieseIntroductionTraditional Coding Workflows in Qualitative Survey AnalysisThe value and limits of traditional approachesFrom Human Coding to AI Assisted CodingWhy This Is Not a Straw-Man ExperimentThe Sample Data SetWhy Automated Coding Falls ShortImplications: From Coding to Dialogic AnalysisConclusionAI Sandbox: PracticeReferencesAppendix: Initial prompt for code frame developmentChapter 5: Beyond Coding: Conversational AI for Qualitative Analysis with QInsights - Susanne FrieseTowards a New Perspective on Qualitative AnalysisThe Origins of Coding: A Historical PerspectiveThe Emergence of AI and LLMs in Qualitative AnalysisUnderstanding and Working with LLMsA New Workflow: Engaging with Data Through QuestionsExemplary Analysis with QInsightsMethodological AdaptationDiscussionAI Sandbox: PracticeReferencesChapter 6: Productivity and Quality of using AI for Qualitative Data Analysis in One Research Project - Jonas Wibowo and Hendrik WieseIntroductionProductivity Promises of Generative AIProblematic Dimensions in QDA using GenAIProject DescriptionCategorical Qualitative Data Analysis as an Analytic FrameworkStudy Design for Testing GenAI Supported Categorical QDAThe Final ProcedureA Framework for GenAI-Assisted Categorical QDADiscussionAI Sandbox: ReflectionReferencesAppendixChapter 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 PehlIntroductionFundamentals, Potentials and Current Developments of AI-supported Analysis of Text-based Empirical DataHybrid Text Interpretation with Multiple, Dialogically Integrated LLMsApplication Example: Functional Segmentation as a Coping StrategyDiscussion: Opportunities and Challenges of AI-assisted Qualitative AnalysisEpistemological ClarificationData Protection Compliance and Research EthicsCritical ReflectionAI Sandbox: PracticeReferencesChapter 8: AI and the Co-Creation of Meaning: Using Large Language Models in Grounded Theory Research - Kai DrögeIntroductionGrounded Theory and AI – An OverviewThe Role of AI in the Research ProcessSycophancy: Bias Towards User ConfirmationCommon Sense Orientation and BiasThe Fluid Positionality of AIPutting It into Practice: Integrating AI into Grounded Theory ResearchCoding and Memo Writing in the Age of AIClose Reading and “Open Data Exploration” MemosAI Assisted “Horizontal” CodingConsolidating the Emerging Theory and Writing a ReportConclusionAI Sandbox: PracticeReferencesChapter 9: Modular Prompting with the Documentary Method: Rethinking Interpretation with AI in Reconstructive Social Research - Fabio Roman LiederIntroductionSome Theoretical ConsiderationsAgency of LLMs in Distributed InterpretationMeaning-Making through Modular PromptingSome Basics on the Documentary MethodA Practical Example of Distributed Interpretation via Modular PromptingResulting Hybrid InterpretationEvaluating the ResultDiscussion and OutlookAI Sandbox: PracticeReferencesChapter 10: The MERIT Framework: Guiding responsible innovation in qualitative methods - Jessica Nina Lester and Trena M. PaulusIntroductionDefining generative AIAI and Qualitative Data Analysis SoftwareGuidelines for Responsible AI UseReporting Guidelines for Qualitative ResearchersA Heuristic for Generating Reporting Guidelines for Qualitative Data AnalysisFuture DirectionsAI Sandbox: ReflectionReferencesChapter 11: Understanding the Adoption of an Innovation: The Case of AI in Analyzing Qualitative Data - David MorganDiffusion of InnovationsConclusionsReferencesGlossaryReferences