Comparing Groups: Randomization and Bootstrap Methods Using R (Google eBook)

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John Wiley & Sons, Jul 8, 2011 - Social Science - 384 pages
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A hands-on guide to using R to carry out key statistical practices in educational and behavioral sciences research

Computing has become an essential part of the day-to-day practice of statistical work, broadening the types of questions that can now be addressed by research scientists applying newly derived data analytic techniques. Comparing Groups: Randomization and Bootstrap Methods Using R emphasizes the direct link between scientific research questions and data analysis. Rather than relying on mathematical calculations, this book focus on conceptual explanations and the use of statistical computing in an effort to guide readers through the integration of design, statistical methodology, and computation to answer specific research questions regarding group differences.

Utilizing the widely-used, freely accessible R software, the authors introduce a modern approach to promote methods that provide a more complete understanding of statistical concepts. Following an introduction to R, each chapter is driven by a research question, and empirical data analysis is used to provide answers to that question. These examples are data-driven inquiries that promote interaction between statistical methods and ideas and computer application. Computer code and output are interwoven in the book to illustrate exactly how each analysis is carried out and how output is interpreted. Additional topical coverage includes:

  • Data exploration of one variable and multivariate data
  • Comparing two groups and many groups
  • Permutation tests, randomization tests, and the independent samples t-Test
  • Bootstrap tests and bootstrap intervals
  • Interval estimates and effect sizes

Throughout the book, the authors incorporate data from real-world research studies as well aschapter problems that provide a platform to perform data analyses. A related Web site features a complete collection of the book's datasets along with the accompanying codebooks and the R script files and commands, allowing readers to reproduce the presented output and plots.

Comparing Groups: Randomization and Bootstrap Methods Using R is an excellent book for upper-undergraduate and graduate level courses on statistical methods, particularlyin the educational and behavioral sciences. The book also serves as a valuable resource for researchers who need a practical guide to modern data analytic and computational methods.

  

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Contents

Randomization and Bootstrap Methods Using R 1 An Introduction to R
1
Randomization and Bootstrap Methods Using R 2 Data Representation and Preparation
21
Randomization and Bootstrap Methods Using R 3 Data Exploration One Variable
49
Randomization and Bootstrap Methods Using R 4 Exploration of Multivariate Data Comparing Two Groups
67
Randomization and Bootstrap Methods Using R 5 Exploration of Multivariate Data Comparing Many Groups
95
Randomization and Bootstrap Methods Using R 6 Randomization and Permutation Tests
117
Randomization and Bootstrap Methods Using R 7 Bootstrap Tests
139
Randomization and Bootstrap Methods Using R 8 Philosophical Considerations
173
Randomization and Bootstrap Methods Using R 9 Bootstrap Intervals and Effect Sizes
181
Randomization and Bootstrap Methods Using R 10 Dependent Samples
207
Randomization and Bootstrap Methods Using R 11 Planned Contrasts
229
Randomization and Bootstrap Methods Using R 12 Unplanned Contrasts
255
Randomization and Bootstrap Methods Using R References
287
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About the author (2011)

Andrew S. Zieffler, PhD, is Lecturer in the Department of Educational Psychology at the University of Minnesota. Dr. Zieffler has published numerous articles in his areas of research interest, which include the measurement and assessment in statistics education research and statistical computing.

Jeffrey R. Harring, PhD, is Assistant Professor in the Department of Measurement, Statistics, and Evaluation at the University of Maryland. Dr. Harring currently focuses his research on statistical models for repeated measures data and nonlinear structural equation models.

Jeffrey D. Long, PhD, is Professor of Psychiatry in the Carver College of Medicine at The University of Iowa and Head Statistician for Neurobiological Predictors of Huntington's Disease (PREDICT-HD), a longitudinal NIH-funded study of early detection of Huntington's disease. His interests include the analysis of longitudinal and time-to-event data and ordinal data.

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