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: <ul>
<li>Data exploration of one variable and multivariate data</li> <li>Comparing two groups and many groups</li> <li>Permutation tests, randomization tests, and the independent samples t-Test</li> <li>Bootstrap tests and bootstrap intervals</li> <li>Interval estimates and effect sizes</li> </ul>
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.

Comparing Groups: Randomization and Bootstrap Methods Using R

Science & Math

Long, Jeffrey D.

Zieffler, Andrew S.

Harring, Jeffrey R.

Mathematics

wiley

Learning statistics is sexy. Almost every person on earth will benefit from learning some foundational ideas of statistics. This is true because statistics forms the basis of our everyday world just as much as do science, technology, and politics. Google, Netflix, Twitter, Facebook, OKCupid, Match.com, Amazon, iTunes, and the Federal Government are just a handful of the companies and organizations that use statistics on a daily basis. Journalism, political science, biology, sociology, psychology, graphic design, economics, sports science, and dance are all disciplines that have made use of statistical methodology. The materials in this book will introduce you to the seminal ideas underlying the discipline of statistics. In addition, they have been designed with your learning in mind. As you engage in and use the skills, concepts and ideas introduced in the material, you will find yourself thinking about data and evidence in a different way.

Statistical Thinking: A Simulation Approach to Modeling Uncertainty

Catalyst Press

The research presented in this volume is the culmination of a two-year process that began in August 2013. That summer, 26 statistics education researchers met in Two Harbors, Minnesota for the Eighth International Research Forum on Statistical Reasoning, Thinking, and Literacy (SRTL-8). The theme for the forum was reasoning about uncertainty in the context of making informal statistical inferences.Over a period of seven days, this group of researchers presented, discussed, and examined research related to the forums theme of reasoning about uncertainty in the context of making informal statistical inferences. The research at SRTL-8 covered many different facets (e.g., use of technology and students' classroom articulation) and explored several different populations of students (primary, secondary, and tertiary levels), adults, and even teachers of statistics. The chapters in this book constitute a subset of that research, and reflect the variation in both the populations and topics studied.

Reasoning about Uncertainty: Learning and Teaching Informal Inferential Reasoning

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Books by Zieffler, Andrew S.