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Statistical Methods For Meta Analysis

Author: Larry V. Hedges
Publisher: Academic Press
ISBN: 0080570658
Size: 49.54 MB
Format: PDF, Kindle
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The main purpose of this book is to address the statistical issues for integrating independent studies. There exist a number of papers and books that discuss the mechanics of collecting, coding, and preparing data for a meta-analysis , and we do not deal with these. Because this book concerns methodology, the content necessarily is statistical, and at times mathematical. In order to make the material accessible to a wider audience, we have not provided proofs in the text. Where proofs are given, they are placed as commentary at the end of a chapter. These can be omitted at the discretion of the reader. Throughout the book we describe computational procedures whenever required. Many computations can be completed on a hand calculator, whereas some require the use of a standard statistical package such as SAS, SPSS, or BMD. Readers with experience using a statistical package or who conduct analyses such as multiple regression or analysis of variance should be able to carry out the analyses described with the aid of a statistical package.

Statistical Methods For Meta Analysis

Author: Larry V. Hedges
Publisher: Academic Press
ISBN:
Size: 41.21 MB
Format: PDF, Docs
View: 1980
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The main purpose of this book is to address the statistical issues for integrating independent studies. There exist a number of papers and books that discuss the mechanics of collecting, coding, and preparing data for a meta-analysis , and we do not deal with these. Because this book concerns methodology, the content necessarily is statistical, and at times mathematical. In order to make the material accessible to a wider audience, we have not provided proofs in the text. Where proofs are given, they are placed as commentary at the end of a chapter. These can be omitted at the discretion of the reader. Throughout the book we describe computational procedures whenever required. Many computations can be completed on a hand calculator, whereas some require the use of a standard statistical package such as SAS, SPSS, or BMD. Readers with experience using a statistical package or who conduct analyses such as multiple regression or analysis of variance should be able to carry out the analyses described with the aid of a statistical package.

Methods Of Meta Analysis

Author: Frank L. Schmidt
Publisher: SAGE Publications
ISBN: 1483324516
Size: 17.70 MB
Format: PDF
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Designed to provide researchers clear and informative insight into techniques of meta-analysis, the Third Edition of Methods of Meta-Analysis: Correcting Error and Bias in Research Findings is the most comprehensive text on meta-analysis available today. It is the only book that presents a full and usable treatment of the role of study artifacts in distorting study results, as well as methods for correcting results for such biases and errors. Meta-analysis is arguably the most important methodological innovation in the last thirty-five years, due to its immense impact on the development of cumulative knowledge and professional practice. This text, now in its updated Third Edition, has been revised to cover the newest developments in meta-analysis methods, evaluation, correction, and more. This reader-friendly book is the definitive resource on meta-analysis. “This text is the primary source text for psychometric meta-analysis methods.” —Emily E. Tanner-Smith, Vanderbilt University “The key strength of the book is the complete and thorough coverage of psychometric meta-analysis. This technique is not covered in any other meta-analysis text, and is a major contribution to the literature…The meta-analysis field needs to find ways to integrate Hunter and Schmidt’s methods into current meta-analysis practice.” —Terri D. Pigott, Loyola University of Chicago “This is an important text. It is the only book that presents adequate coverage of psychometric meta-analysis. In addition to its use as a textbook, it is an invaluable resource for anyone involved in meta-analytic studies.” —Steven Pulos, University of Northern Colorado

Statistical Meta Analysis With Applications

Author: Joachim Hartung
Publisher: John Wiley & Sons
ISBN: 1118210964
Size: 65.13 MB
Format: PDF
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An accessible introduction to performing meta-analysis across various areas of research The practice of meta-analysis allows researchers to obtain findings from various studies and compile them to verify and form one overall conclusion. Statistical Meta-Analysis with Applications presents the necessary statistical methodologies that allow readers to tackle the four main stages of meta-analysis: problem formulation, data collection, data evaluation, and data analysis and interpretation. Combining the authors' expertise on the topic with a wealth of up-to-date information, this book successfully introduces the essential statistical practices for making thorough and accurate discoveries across a wide array of diverse fields, such as business, public health, biostatistics, and environmental studies. Two main types of statistical analysis serve as the foundation of the methods and techniques: combining tests of effect size and combining estimates of effect size. Additional topics covered include: Meta-analysis regression procedures Multiple-endpoint and multiple-treatment studies The Bayesian approach to meta-analysis Publication bias Vote counting procedures Methods for combining individual tests and combining individual estimates Using meta-analysis to analyze binary and ordinal categorical data Numerous worked-out examples in each chapter provide the reader with a step-by-step understanding of the presented methods. All exercises can be computed using the R and SAS software packages, which are both available via the book's related Web site. Extensive references are also included, outlining additional sources for further study. Requiring only a working knowledge of statistics, Statistical Meta-Analysis with Applications is a valuable supplement for courses in biostatistics, business, public health, and social research at the upper-undergraduate and graduate levels. It is also an excellent reference for applied statisticians working in industry, academia, and government.

Meta Analysis

Author: Fredric Marc Wolf
Publisher: SAGE
ISBN: 9780803927568
Size: 46.75 MB
Format: PDF, ePub, Mobi
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Meta-Analysis shows concisely, yet comprehensively, how to apply statistical methods to achieve a literature review of a common research domain. It demonstrates the use of combined tests and measures of effect size to synthesize quantitatively the results of independent studies for both group differences and correlations. Strengths and weaknesses of alternative approaches, as well as of meta-analysis in general, are presented.

Methods For Meta Analysis In Medical Research

Author: A. J. Sutton
Publisher: John Wiley & Sons Incorporated
ISBN:
Size: 35.70 MB
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With meta-analysis methods playing a crucial role in health research in recent years, this important and clearly-written book provides a much-needed survey of the field. Meta-analysis provides a framework for combining the results of several clinical trials and drawing inferences about the effectiveness of medical treatments. The move towards evidence-based health care and practice is underpinned by the use of meta-analysis. This book: * Provides a thorough criticism and an up-to-date survey of meta-analysis methods * Emphasises the practical approach, and illustrates the methods by numerous examples * Describes the use of Bayesian methods in meta-analysis * Includes discussion of appropriate software for each analysis * Includes numerous references to more advanced treatment of specialist topics * Refers to software code used in the examples available on the authors' Web site Practising statisticians, statistically-minded clinicians and health research professionals will benefit greatly from the clear presentation and numerous examples. Medical researchers will grasp the basic principles of meta-analysis, and learn how to apply the various methods.

Systematic Reviews In Health Care

Author: Matthias Egger
Publisher: John Wiley & Sons
ISBN: 0470693142
Size: 35.78 MB
Format: PDF, ePub, Docs
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The second edition of this best-selling book has been thoroughly revised and expanded to reflect the significant changes and advances made in systematic reviewing. New features include discussion on the rationale, meta-analyses of prognostic and diagnostic studies and software, and the use of systematic reviews in practice.

Introduction To Meta Analysis

Author: Michael Borenstein
Publisher: John Wiley & Sons
ISBN: 1119964377
Size: 73.85 MB
Format: PDF, ePub, Mobi
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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

Meta Analysis

Author: Mike W.-L. Cheung
Publisher: John Wiley & Sons
ISBN: 1119993431
Size: 23.80 MB
Format: PDF, ePub, Mobi
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Presents a novel approach to conducting meta–analysis using structural equation modeling. Structural equation modeling (SEM) and meta–analysis are two powerful statistical methods in the educational, social, behavioral, and medical sciences. They are often treated as two unrelated topics in the literature. This book presents a unified framework on analyzing meta–analytic data within the SEM framework, and illustrates how to conduct meta–analysis using the metaSEM package in the R statistical environment. Meta–Analysis: A Structural Equation Modeling Approach begins by introducing the importance of SEM and meta–analysis in answering research questions. Key ideas in meta–analysis and SEM are briefly reviewed, and various meta–analytic models are then introduced and linked to the SEM framework. Fixed–, random–, and mixed–effects models in univariate and multivariate meta–analyses, three–level meta–analysis, and meta–analytic structural equation modeling, are introduced. Advanced topics, such as using restricted maximum likelihood estimation method and handling missing covariates, are also covered. Readers will learn a single framework to apply both meta–analysis and SEM. Examples in R and in Mplus are included. This book will be a valuable resource for statistical and academic researchers and graduate students carrying out meta–analyses, and will also be useful to researchers and statisticians using SEM in biostatistics. Basic knowledge of either SEM or meta–analysis will be helpful in understanding the materials in this book.