Full Description
AI in Social Sciences Research explains how artificial intelligence is reshaping social inquiry, from data collection and research design through analysis, interpretation, and theory-building. Treating AI as both a toolkit and a methodological shift, the volume offers a clear route into machine learning, natural language processing, simulation, and generative AI for social researchers, while keeping ethics, bias, transparency, and interpretability in view.
Key features include:
· Practical guidance on integrating AI into social science research workflows, without losing theoretical and contextual judgement
· Coverage of core techniques, including machine learning for prediction and pattern discovery, and NLP for large-scale textual and qualitative data
· Discussion of explainability and "black box" risks, with approaches for interpretability and responsible use
· Cross-disciplinary examples spanning sociology, psychology, anthropology, political science, economics, and management
· A critical account of research integrity issues: data quality, bias, privacy, accountability, and the limits of automation
Designed for postgraduate students, early-career researchers, and instructors teaching digital and computational social science methods, the book supports readers who want to use AI with methodological care, not just technical speed.
Contents
Introduction, 1. Introduction - AI and the Evolving Landscape of Social Science Research, 2. Machine Learning and Big Data Analytics in Social Sciences, 3. Natural Language Processing for Qualitative and Textual Data, 4. AI-Enhanced Research Design and Data Collection, 5. AI in Management and Business Research, 6. Psychology: AI-Assisted Paths to Mind and Behavior, 7. Anthropology - Digital Ethnography and Algorithmic Thick Description, 8. Sociology: Algorithmic Sociology and the Macro-Micro Bridge, 9. Conclusion, 10. References
