Machine Learning for Social and Behavioral Research (Methodology in the Social Sciences Series)

This comprehensive guidebook provides a thorough understanding of machine learning techniques and their applications in social science research.

With a focus on practical implementation, the book covers essential topics such as data analysis, measurement error, regularization, and decision trees.

The author demonstrates various methods for analyzing univariate and multivariate data using real-world examples, including factor analysis, structural equation models, and mixed-effects models.

Additionally, the book addresses text and social network data analysis.

Whether you're a graduate student or a seasoned researcher, this book is an invaluable resource for anyone looking to harness the power of machine learning in their work.