This book provides a very clear overview of meta-analysis — what it is, why we use it, how to perform and interpret a meta-analysis, and how to avoid common mistakes. It starts with basic ideas how to compute an effect size and perform a simple analysis but then moves on to more advanced issues subgroups analysis, meta-regression, multiple outcomes, publication bias.
While the book's approach is primarily conceptual, it includes formulas and worked examples that serve both to illustrate the concepts and to provide practical advice. The worked examples can be accessed here, both as Excel files and as CMA files. This book has been widely adopted as a text for meta-analysis courses, and has consistently been a best-seller on Amazon since its release.
Book details Download chapters Support files for book Order Online. 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. Harris Cooper, Larry V. Hedges, Jeffrey C. Valentine eds. New York: Russell Sage Foundation. This book is a classic. Recognized as the definitive resource for research synthesis when published in , updated in and again in , this work remains the first book many people turn to.
The book is arranged as an encyclopedia, with each chapter written by experts in a specific aspect of research synthesis. However, the editors also arranged for the chapter authors to use the same datasets and style throughout, so the book flows naturally from one section to the next, and offers an excellent overview of the field for statisticians and researchers alike.
In the third edition, the editors present updated versions of classic chapters and add new sections that evaluate cutting-edge developments in the field. This edition of the Handbook provides comprehensive instruction in the skills necessary to conduct research syntheses and represents the premier text on research synthesis.
Like "The Handbook", this book, published in , helped to open the field of meta-analysis to a generation of researchers, became an instant classic, and remains a valuable resource to this day. Where "The Handbook" covers many aspects of research synthesis, this volume focuses almost exclusively on meta-analysis the statistical component of research synthesis , and does so from a mathematical perspective.
Julian P. Page, Vivian A. Welch co-eds. Hoboken, NJ: Wiley-Blackwell. The fully updated second edition includes new and expanded content on avoiding common mistakes in meta-analysis, understanding heterogeneity in effects, publication bias, reporting the Knapp-Hartung Sidik-Jonkman adjustment, and more. Meta-analysis has become a critically important tool in fields as diverse as medicine, pharmacology, epidemiology, education, psychology, business, and ecology.
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.
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. 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. It is intended for quantitative researchers and students in the medical and social sciences who wish to learn how to perform meta-analysis with R. As such, the book introduces the key concepts and models used in meta-analysis.
It also includes chapters on the following advanced topics: publication bias and small study effects; missing data; multivariate meta-analysis, network meta-analysis; and meta-analysis of diagnostic studies. Carpenter introduces the concept of meta-analysis, a statistical approach to finding patterns and trends among research studies on the same topic.
Meta-analysis allows the researcher to weight study results based on size, moderating variables, and other factors. Virtually all of the testable terms, concepts, persons, places, and events from the textbook are included. Cram Just the FACTS studyguides give all of the outlines, highlights, notes, and quizzes for your textbook with optional online comprehensive practice tests.
Only Cram is Textbook Specific. 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. Comprehensive Meta-Analysis is a powerful computer program for meta-analysis. The program combines ease of use with a wide array of computational options and sophisticated graphics. Complex analyses can be conducted easily using this intuitive software.
The support staff are always helpful and respond quickly when questions arise. I highly recommend CMA. 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.
Its use and importance have exploded over the last 25 years as the need for a robust evidence base has become clear in many scientific areas, including medicine and health, social sciences, education, psychology, ecology, and economics. Recent years have seen an explosion of methods for handling complexities in meta-analysis, including explained and unexplained heterogeneity between studies, publication bias, and sparse data. At the same time, meta-analysis has been extended beyond simple two-group comparisons of continuous and binary outcomes to comparing and ranking the outcomes from multiple groups, to complex observational studies, to assessing heterogeneity of effects, and to survival and multivariate outcomes.
Many of these methods are statistically complex and are tailored to specific types of data. Key features Rigorous coverage of the full range of current statistical methodology used in meta-analysis Comprehensive, coherent, and unified overview of the statistical foundations behind meta-analysis Detailed description of the primary methods for both univariate and multivariate data Computer code to reproduce examples in chapters Thorough review of the literature with thousands of references Applications to specific types of biomedical and social science data This book is for a broad audience of graduate students, researchers, and practitioners interested in the theory and application of statistical methods for meta-analysis.
It is written at the level of graduate courses in statistics, but will be of interest to and readable for quantitative scientists from a range of disciplines. The book can be used as a graduate level textbook, as a general reference for methods, or as an introduction to specialized topics using state-of-the art methods.
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