Today's social and behavioral researchers increasingly need to know: "What do I do with all this data?" This book provides the skills needed to analyze and report large, complex data sets using machine learning tools, and to understand published machine learning articles. Techniques are demonstrated using actual data (Big Five Inventory, early childhood learning, and more), with a focus on the interplay of statistical algorithm, data, and theory. The identification of heterogeneity, measurement error, regularization, and decision trees are also emphasized. The book covers basic principles as well as a range of methods for analyzing univariate and multivariate data (factor analysis, structural equation models, and mixed-effects models). Analysis of text and social network data is also addressed. End-of-chapter "Computational Time and Resources" sections include discussions of key R packages; the companion website provides R programming scripts and data for the book's examples.

About the Author

Ross Jacobucci, PhD, is Assistant Professor in Quantitative Psychology in the Department of Psychology at the University of Notre Dame. His research interests include the development and application of machine learning for clinical research, with a focus on suicide and nonsuicidal self-injury. Dr. Jacobucci is an active developer of open-source software for the R statistical environment, with five packages that implement some form of machine learning. His website is www.rjacobucci.com.

Kevin J. Grimm, PhD, is Professor of Psychology at Arizona State University. His research interests include multivariate methods for the analysis of change, multiple group and latent class models for understanding divergent developmental processes, nonlinearity in development, machine learning techniques for psychological data, and mathematics and reading ability development. Dr. Grimm is a recipient of the Early Career Research Award and the Barbara Byrne Book Award (for Growth Modeling: Structural Equation and Multilevel Modeling Perspectives) from the Society of Multivariate Experimental Psychology.

Zhiyong Zhang, PhD, is Professor in Quantitative Psychology in the Department of Psychology at the University of Notre Dame, where he directs the Lab for Big Data Methodology. He has conducted research in the areas of Bayesian methods, structural equation modeling, longitudinal data analysis, and missing data and non-normal data analysis. His recent research involves the development of new methods and software for social network and text analysis. Dr. Zhang is the founding editor of the Journal of Behavioral Data Science. His website is https://bigdatalab.nd.edu.

Reviews

"Current, highly informative, and useful, this is a 'go-to' book for social science graduate students, faculty, and practitioners seeking a strong introduction to machine learning. Unlike typical, more technical machine learning books, this one is unique in providing the strong psychological measurement guidance required to apply these techniques most appropriately. It walks the reader through general principles of machine learning, regression- and tree-based predictive models, text- and network-based methods of clustering, and--most innovatively--machine learning-based psychometric approaches (CFA and SEM)."--Fred Oswald, PhD, Professor and Herbert S. Autrey Chair in Social Sciences, Department of Psychological Sciences, Rice University

"This book is very timely. Social scientists need to be educated about the pros and cons of machine learning methods and about how, when, and why these methods can be applied to their research topics. The book describes key techniques in enough detail to enable readers to subsequently digest more specialized journal articles or software applications, but not in so much detail as to lose momentum."--Sonya K. Sterba, PhD, Department of Psychology and Human Development, Vanderbilt University

"Jacobucci, Grimm, and Zhang's ambitious book takes the reader on an in-depth tour of machine learning methods. Its strength is that the authors link machine learning to more traditional topics of regression, structural equation modeling, factor analysis, and network analysis methods. This book should be required reading for the new generation of psychology graduate students who are interested in more advanced quantitative methods."--James W. Pennebaker, PhD, Regents Centennial Professor of Liberal Arts and Professor of Psychology, The University of Texas at Austin

"A 'must read' for social scientists who want to familiarize themselves with machine learning but don't know where to start. Understanding the practices and principles of machine learning is fundamental to modern data analysis. Many social scientists will be surprised by how well their traditional statistical training has prepared them to grasp the material in the book."--Alexander Christensen, PhD, Department of Psychology and Human Development, Vanderbilt University-

Book Information
ISBN 9781462552924
Author Ross Jacobucci
Format Paperback
Page Count 416
Imprint Guilford Press
Publisher Guilford Publications
Weight(grams) 740g