This book provides a clear and thorough introduction to meta-analysis, the process of synthesizing data from a series of separate studies. Meta-analysis has become a critically important tool in fields as diverse as medicine, pharmacology, epidemiology, education, psychology, business, and ecology. Introduction to Meta-Analysis:  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 Clarifies concepts using text and figures, followed by formulas and examples Explains how to avoid common mistakes in meta-analysis Discusses controversies in meta-analysis Features a web site with additional material and exercises  A superb combination of lucid prose and informative graphics, written by four of the world’s leading experts on all aspects of meta-analysis. Borenstein, Hedges, Higgins, and Rothstein 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, who used pre-publication versions of some of the chapters, 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  Introduction to Meta-Analysis is an excellent resource for novices and experts alike. The book provides a clear and comprehensive presentation of all basic and most advanced approaches to meta-analysis. This book will be referenced for decades. Michael A. McDaniel, Professor of Human Resources and Organizational Behavior, Virginia Commonwealth University

Introduction to Meta-Analysis

Science & Math

Michael Borenstein

Larry V. Hedges

Hannah R. Rothstein

Julian P.T. Higgins

Biological Sciences

wiley

Common Mistakes in Meta-Analysis and How to Avoid Them

<p>Among the thousands of meta-analyses that have been published over the past several decades, there are a number of mistakes that appear on a fairly regular basis. This book outlines the most common mistakes, using examples in medicine, epidemiology, education, psychology, criminal justice, and other fields. For each, it explains why it is a mistake, the implications of the mistake, and how to correct the mistake. The book is intended primarily for researchers, and so the discussion is conceptual rather than statistical. The examples show the real-world consequences of the mistakes, explaining (for example) how the mistakes can lead to the adoption of interventions that may actually be harmful in some populations. The book includes a section with examples that show how to report the results of an analysis correctly. These examples can serve as templates for reporting an analysis, while avoiding the mistakes discussed in earlier chapters. The book's author is the co-author of the text Introduction to Meta-Analysis, the best-selling text in this field. In the current volume he draws on his experience teaching meta-analysis to thousands of researchers as well as his experience as a reviewer of meta-analyses for numerous journals.</p>

Biostat, Inc.

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Books by Michael Borenstein