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.
关于作者
Andrew S. Zieffler, Ph D, 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, Ph D, 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, Ph D, 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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