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Machine Learning for Social and Behavioral Research PDF

435 Pages·2023·8.971 MB·English
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by Ross Jacobucci, Kevin J. Grimm, Zhiyong Zhang| 2023| 435 pages| 8.971| English

About Machine Learning for Social and Behavioral Research

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 5 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.

Detailed Information

Author:Ross Jacobucci, Kevin J. Grimm, Zhiyong Zhang
Publication Year:2023
ISBN:9781462552931
Pages:435
Language:English
File Size:8.971
Format:PDF
Price:FREE
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