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Advanced Statistics for the Behavioral Sciences

A Computational Approach with R

  • Textbook
  • © 2018

Overview

Authors:
  1. Jonathon D. Brown

    1. Department of Psychology, University of Washington, Seattle, USA

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  • Teaches core subjects students need to both understand and perform computer-generated statistical analyses
  • Builds on Brown's earlier book on common statistical analyses, Linear Models in Matrix Form? (Springer, 2014)
  • Utilizes R as standard tool for algebraic calculations

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Table of contents (14 chapters)

  1. Front Matter

    Pages i-xxi
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  2. Linear Algebra

    1. Front Matter

      Pages 1-1
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    2. Linear Equations

      • Jonathon D. Brown
      Pages 3-38

    3. Least Squares Estimation

      • Jonathon D. Brown
      Pages 39-76

    4. Linear Regression

      • Jonathon D. Brown
      Pages 77-115

    5. Eigen Decomposition

      • Jonathon D. Brown
      Pages 117-148

    6. Singular Value Decomposition

      • Jonathon D. Brown
      Pages 149-186

  3. Bias and Efficiency

    1. Front Matter

      Pages 187-187
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    2. Generalized Least Squares Estimation

      • Jonathon D. Brown
      Pages 189-217

    3. Robust Regression

      • Jonathon D. Brown
      Pages 219-252

    4. Model Selection and Biased Estimation

      • Jonathon D. Brown
      Pages 253-288

    5. Cubic Splines and Additive Models

      • Jonathon D. Brown
      Pages 289-319

  4. Nonlinear Models

    1. Front Matter

      Pages 321-321
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    2. Nonlinear Regression and Optimization

      • Jonathon D. Brown
      Pages 323-360

    3. Generalized Linear Models

      • Jonathon D. Brown
      Pages 361-398

    4. Survival Analysis

      • Jonathon D. Brown
      Pages 399-447

    5. Time Series Analysis

      • Jonathon D. Brown
      Pages 449-494

    6. Mixed-Effects Models

      • Jonathon D. Brown
      Pages 495-526
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Keywords

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.


Authors and Affiliations

  • Department of Psychology, University of Washington, Seattle, USA

    Jonathon D. Brown

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.   

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