Get Ready for Data Science A Primer for Social and Behavioral Scientists

by Kline, Rex B.

• ISBN13: 9781462563494
• ISBN10: 146256349X
• eBook ISBN(s): 9781462563500
• Format: Hardcover
• Copyright: 2026-08-20
• Publisher: The Guilford Press
• More Book Details

Summary

Filling a key need, this book provides a gateway to modern data analytics written expressly for social researchers and students. Rex B. Kline builds on readers’ statistical strengths and fills in the gaps from their traditional training. Chapters on programming, data visualization, big data, and supervised and unsupervised machine learning emphasize concepts over equations and feature rich graphics, including 11 color plates. Throughout, worked-through examples with real and simulated data, along with detailed interpretation of the R code and output, prepare readers to apply the techniques. Kline also provides pointers on the pitfalls and advantages of different statistical and data science techniques and explores incorrect interpretations. The companion website supplies R-generated syntax, output, and graphics files for the book's examples; complete data sets; chapter appendices with explanations of the syntax files; and primers on bivariate and multiple regression fundamentals, standard errors and classical significance tests, and data screening and preparation.

Pedagogical Features

• Chapter-opening objectives and key concepts. 
• End-of-chapter summaries. 
• Exercises with end-of-book answers. 
• Annotated suggested readings. 
• Key terms for review.
   

Author Biography

Rex B. Kline, PhD, is Professor of Psychology at Concordia University in Montréal, Québec, Canada. Since earning a doctorate in clinical psychology, Dr. Kline has conducted research on the psychometric evaluation of cognitive abilities, behavioral and scholastic assessment of children, structural equation modeling, training of researchers, statistics reform in the behavioral sciences, and usability engineering in computer science. He has published a number of chapters, journal articles, and books in these areas.

Table of Contents

Introduction
I. Classical Statistics and Data Science
1. Ready Set Go
2. Classical Statistics
3. Data Science
II. Dispelling Myth and Alternatives
4. Estimating Uncertainty
5. Avoiding Insignificance
6. Lessons from Regression
7. Bayesian Basics
III. Programming and Data
8. Programming
9 Data Visualization
10. Big Data
11. Data Reduction
IV. Machine Learning and Beyond
12. Supervised Machine Learning: Core Methods
13. Supervised Machine Learning: Hyperparameter Tuning
14. Supervised Machine Learning: Complex Effects
15. Unsupervised Machine Learning
16. Other Data Science Horizons
Suggested Answers to Exercises
References
Author Index
Subject Index
About the Author