Amazon cover image
Image from Amazon.com

Comparing groups : randomization and bootstrap methods using R / Andrew S. Zieffler, Jeffrey R. Harring, Jeffrey D. Long.

By: Contributor(s): Material type: TextTextPublication details: Hoboken, N.J. : Wiley, ©2011.Description: 1 online resource (xxxii, 298 pages) : illustrationsContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781118063682
  • 1118063686
  • 9780470621691
  • 0470621699
  • 111806366X
  • 9781118063668
Subject(s): Genre/Form: Additional physical formats: Print version:: Comparing groups.DDC classification:
  • 519.5/4 22
LOC classification:
  • QA276.8 .Z53 2011
Other classification:
  • SOC027000
Online resources:
Contents:
Front Matter -- An Introduction to R -- Data Representation and Preparation -- Data Exploration: One Variable -- Exploration of Multivariate Data: Comparing two Groups -- Exploration of Multivariate Data: Comparing Many Groups -- Randomization and Permutation Tests -- Bootstrap Tests -- Philosophical Considerations -- Bootstrap Intervals and Effect Sizes -- Dependent Samples -- Planned Contrasts -- Unplanned Contrasts -- References.
Summary: "This book, written by three behavioral scientists for other behavioral scientists, addresses common issues in statistical analysis for the behavioral and educational sciences. Modern Statistical & Computing Methods for the Behavioral and Educational Sciences using R emphasizes the direct link between scientific research questions and data analysis. Purposeful attention is paid to the integration of design, statistical methodology, and computation to propose answers to specific research questions. Furthermore, practical suggestions for the analysis and presentation of results, in prose, tables and/or figures, are included. Optional sections for each chapter include methodological extensions for readers desiring additional technical details. Rather than focus on mathematical calculations like so many other introductory texts in the behavioral sciences, the authors focus on conceptual explanations and the use of statistical computing. Statistical computing is an integral part of statistical work, and to support student learning in this area, examples using the R computer program are provided throughout the book. Rather than relegate examples to the end of chapters, the authors interweave computer examples with the narrative of the book. Topical coverage includes an introduction to R, data exploration of one variable, data exploration of multivariate data - comparing two groups and many groups, permutation and randomization tests, the independent samples t-Test, the Bootstrap test, interval estimates and effect sizes, power, and dependent samples"-- Provided by publisher.
List(s) this item appears in: Sofware Engineering & Computer Science
Tags from this library: No tags from this library for this title. Log in to add tags.
No physical items for this record

Front Matter -- An Introduction to R -- Data Representation and Preparation -- Data Exploration: One Variable -- Exploration of Multivariate Data: Comparing two Groups -- Exploration of Multivariate Data: Comparing Many Groups -- Randomization and Permutation Tests -- Bootstrap Tests -- Philosophical Considerations -- Bootstrap Intervals and Effect Sizes -- Dependent Samples -- Planned Contrasts -- Unplanned Contrasts -- References.

Includes bibliographical references (pages 287-298).

"This book, written by three behavioral scientists for other behavioral scientists, addresses common issues in statistical analysis for the behavioral and educational sciences. Modern Statistical & Computing Methods for the Behavioral and Educational Sciences using R emphasizes the direct link between scientific research questions and data analysis. Purposeful attention is paid to the integration of design, statistical methodology, and computation to propose answers to specific research questions. Furthermore, practical suggestions for the analysis and presentation of results, in prose, tables and/or figures, are included. Optional sections for each chapter include methodological extensions for readers desiring additional technical details. Rather than focus on mathematical calculations like so many other introductory texts in the behavioral sciences, the authors focus on conceptual explanations and the use of statistical computing. Statistical computing is an integral part of statistical work, and to support student learning in this area, examples using the R computer program are provided throughout the book. Rather than relegate examples to the end of chapters, the authors interweave computer examples with the narrative of the book. Topical coverage includes an introduction to R, data exploration of one variable, data exploration of multivariate data - comparing two groups and many groups, permutation and randomization tests, the independent samples t-Test, the Bootstrap test, interval estimates and effect sizes, power, and dependent samples"-- Provided by publisher.

Print version record.

Social Sciences and Humanities