Machine Learning for Social and Behavioral Research

This book provides skills to analyze and report large, complex data sets using machine learning tools, with a focus on the interplay of statistical algorithm, data, and theory. It covers basic principles and methods for analyzing univariate and multivariate data, as well as text and social network data. End-of-chapter Computational Time and Resources sections include discussions of key R packages.

Format: Hardback
Length: 416 pages
Publication date: 04 September 2023
Publisher: Guilford Publications

Today's social and behavioral researchers face a pressing challenge: how to effectively handle the vast amounts of data they collect? This book is designed to provide the necessary skills and knowledge to analyze and report large, complex data sets using machine learning tools. It offers a comprehensive approach, covering both the theoretical foundations and practical applications of machine learning in social and behavioral research.

The book begins by introducing the basic principles of machine learning and statistical analysis, providing a solid foundation for the subsequent chapters. It then delves into the practical aspects of data analysis, showcasing techniques such as data preprocessing, feature extraction, and model selection. Real-world examples, drawn from various domains, including psychology, education, and social sciences, help illustrate the concepts and techniques discussed.

One of the key strengths of the book is its emphasis on the interplay between statistical algorithms, data, and theory. It explains how different algorithms can be applied to different types of data and how to interpret the results obtained. The identification of heterogeneity, measurement error, regularization, and decision trees are also highlighted, as they are crucial factors in analyzing complex data sets.

The book covers a range of methods for analyzing univariate and multivariate data, including factor analysis, structural equation models, and mixed-effects models. It provides detailed explanations of these methods, along with practical examples and R programming scripts to facilitate the implementation of these techniques. Additionally, the book addresses the analysis of text and social network data, which are becoming increasingly important in modern research.

To assist readers in applying the concepts and techniques discussed, the book includes end-of-chapter Computational Time and Resources sections. These sections provide discussions of key R packages, which are widely used in machine learning and statistical analysis. The companion website offers additional R programming scripts and data for the book's examples, making it an invaluable resource for both students and practitioners.

In conclusion, this book is a valuable resource for social and behavioral researchers who want to leverage the power of machine learning to analyze and interpret large, complex data sets. It provides a comprehensive and practical approach, covering both basic principles and advanced techniques, and is accompanied by extensive resources to support learning and implementation. Whether you are a novice or an experienced researcher, this book will help you unlock the secrets of data analysis and make meaningful contributions to your field.

Weight: 1060g
Dimension: 254 x 178 (mm)
ISBN-13: 9781462552931