Introduction to Meta-Analysis
  1. English
  2. PDF
  3. Available on iOS & Android
eBook - PDF

About this book

A clear and thorough introduction to meta-analysis, the process of synthesizing data from a series of separate studies

The first edition of this text was widely acclaimed for the clarity of the presentation, and quickly established itself as the definitive text in this field. The fully updated second edition includes new and expanded content on avoiding common mistakes in meta-analysis, understanding heterogeneity in effects, publication bias, and more. Several brand-new chapters provide a systematic "how to" approach to performing and reporting a meta-analysis from start to finish.

Written by four of the world's foremost authorities on all aspects of meta-analysis, the new edition:

  • Outlines the role of meta-analysis in the research process
  • Shows how to compute effects sizes and treatment effects
  • Explains the fixed-effect and random-effects models for synthesizing data
  • Demonstrates how to assess and interpret variation in effect size across studies
  • Explains how to avoid common mistakes in meta-analysis
  • Discusses controversies in meta-analysis
  • Includes access to a companion website containing videos, spreadsheets, data files, free software for prediction intervals, and step-by-step instructions for performing analyses using Comprehensive Meta-Analysis (CMA)

Download videos, class materials, and worked examples at www.Introduction-to-Meta-Analysis.com

"This book offers the reader a unified framework for thinking about meta-analysis, and then discusses all elements of the analysis within that framework. The authors address a series of common mistakes and explain how to avoid them. As the editor-in-chief of the American Psychologist and former editor of Psychological Bulletin, I can say without hesitation that the quality of manuscript submissions reporting meta-analyses would be vastly better if researchers read this book."
Harris Cooper, Hugo L. Blomquist Distinguished Professor Emeritus of Psychology and Neuroscience, Editor-in-chief of the American Psychologist, former editor of Psychological Bulletin

"A superb combination of lucid prose and informative graphics, the authors provide a refreshing departure from cookbook approaches with their clear explanations of the what and why of meta-analysis. The book is ideal as a course textbook or for self-study. My students raved about the clarity of the explanations and examples."
David Rindskopf, Distinguished Professor of Educational Psychology, City University of New York, Graduate School and University Center, & Editor of the Journal of Educational and Behavioral Statistics

"The approach taken by Introduction to Meta-analysis is intended to be primarily conceptual, and it is amazingly successful at achieving that goal. The reader can comfortably skip the formulas and still understand their application and underlying motivation. For the more statistically sophisticated reader, the relevant formulas and worked examples provide a superb practical guide to performing a meta-analysis. The book provides an eclectic mix of examples from education, social science, biomedical studies, and even ecology. For anyone considering leading a course in meta-analysis, or pursuing self-directed study, Introduction to Meta-analysis would be a clear first choice."
Jesse A. Berlin, SCD

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Yes, you can access Introduction to Meta-Analysis by Michael Borenstein,Larry V. Hedges,Julian P. T. Higgins,Hannah R. Rothstein in PDF and/or ePUB format, as well as other popular books in Medicine & Biostatistics. We have over one million books available in our catalogue for you to explore.

Information

Publisher
Wiley
Year
2021
Print ISBN
9781119558354
eBook ISBN
9781119558385
Edition
2

Table of contents

  1. Cover
  2. Title Page
  3. Copyright
  4. Contents
  5. List of Tables
  6. List of Figures
  7. Acknowledgements
  8. Preface
  9. Preface to the Second Edition
  10. Website
  11. PART 1 Introduction
  12. PART 2 Effect Size and Precision
  13. PART 3 Fixed-Effect Versus Random-Effects Models
  14. PART 4 Heterogeneity
  15. PART 5 Explaining Heterogeneity
  16. PART 6 Putting it all in Context
  17. PART 7 Complex Data Structures
  18. PART 8 Other Issues
  19. PART 9 Issues Related to Effect Size
  20. PART 10 Further Methods
  21. PART 11 Meta‐Analysis in Context
  22. PART 12 Resources
  23. References
  24. Index
  25. EULA