Machine Learning for Social and Behavioral Research

Machine Learning for Social and Behavioral Research by Ross Jacobucci, Kevin J. Grimm, and Zhiyong Zhang delivers a modern, practical roadmap for applying machine learning to real-world social science problems. Ideal for scholars, policy analysts, and data-savvy practitioners, this book bridges rigorous methodology with hands-on insight to transform how you analyze people, institutions, and communities.

Start with confidence: clear explanations demystify core algorithms—classification, regression, clustering—and show how to validate models, avoid overfitting, and interpret results responsibly. The authors emphasize issues central to social and behavioral research: measurement, causal inference, fairness, and transparency, helping you move from black-box predictions to meaningful, actionable findings.

See impact across contexts: whether you’re studying educational outcomes in the United States, public health trends in Europe, survey data in Asia, or community interventions in local governments, the book’s examples and case studies are designed for global applicability. Learn to translate complex data into policy recommendations, program evaluation, and scholarly publications.

Build capability and credibility: the text balances theory with practical workflows so you can design reproducible analyses, select appropriate validation strategies, and communicate results to non-technical stakeholders. It’s an essential resource for graduate students, social scientists, behavioral researchers, and data scientists seeking to apply rigorous machine-learning techniques in applied settings.

Ready to elevate your research? Add Machine Learning for Social and Behavioral Research to your library and start converting rich social data into reliable insights that inform policy, practice, and scholarship worldwide.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.