Advanced Statistics for the Behavioral Sciences: A Computational Approach with R
by Jonathon D. Brown (Author)

About the Author
Jonathon D. Brown is a social psychologist at the University of Washington. Since receiving his Ph.D. from UCLA in 1986, he has written three books, authored more than 75 journal articles and chapters, received a Presidential Young Investigator Award from the National Science Foundation, and been recognized as one of social psychology's most frequently-cited authors.   

About this book
This book demonstrates the importance of computer-generated statistical analyses in behavioral science research, particularly those using the R software environment. Statistical methods are being increasingly developed and refined by computer scientists, with expertise in writing efficient and elegant computer code.  Unfortunately, many researchers lack this programming background, leaving them to accept on faith the black-box output that emerges from the sophisticated statistical models they frequently use.  
Building on the author’s previous volume, Linear Models in Matrix Form, this text bridges the gap between computer science and research application, providing easy-to-follow computer code for many statistical analyses using the R software environment. The text opens with a foundational section on linear algebra, then covers a variety of advanced topics, including robust regression,  model selection based on bias and efficiency, nonlinear models and optimization routines, generalized linear models, and survival and time-series analysis.  Each section concludes with a presentation of the computer code used to illuminate the analysis, as well as pointers to packages in R that can be used for similar analyses and nonstandard cases.  The accessible code and breadth of topics make this book an ideal tool for graduate students or researchers in the behavioral sciences who are interested in performing advanced statistical analyses without having a sophisticated background in computer science and mathematics.

Brief contents
Part I Linear Algebra
1 Linear Equations 3
2 Least Squares Estimation 39
3 Linear Regression 77
4 Eigen Decomposition 117
5 Singular Value Decomposition 149
Part II Bias and Efficiency
6 Generalized Least Squares Estimation 189
7 Robust Regression 219
8 Model Selection and Biased Estimation 253
9 Cubic Splines and Additive Models 289
Part III Nonlinear Models
10 Nonlinear Regression and Optimization 323
11 Generalized Linear Models 361
12 Survival Analysis 399
13 Time Series Analysis 449
14 Mixed-Effects Models 495

Pages: 526 pages
Publisher: Springer; 1st ed. 2018 edition (May 1, 2019)
Language: English
ISBN-10: 331993547X
ISBN-13: 978-3319935478