Rex B. Kline, PhD, is Professor of Psychology at Concordia University in MontrA (c)al, QuA (c)bec, 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.
Description
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 readersaEUR (TM) 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.
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