Applied Meta Analysis For Social Science Research

Offering pragmatic guidance for planning and conducting a meta-analytic review, this book is written in an engaging, nontechnical style that makes it ideal for graduate course use or self-study. The author shows how to identify questions that can be answered using meta-analysis, retrieve both published and unpublished studies, create a coding manual, use traditional and unique effect size indices, and write a meta-analytic review. An ongoing example illustrates meta-analytic techniques. In addition to the fundamentals, the book discusses more advanced topics, such as artifact correction, random- and mixed-effects models, structural equation representations, and multivariate procedures. User-friendly features include annotated equations; discussions of alternative approaches; and "Practical Matters" sections that give advice on topics not often discussed in other books, such as linking meta-analytic results with theory and the utility of meta-analysis software programs. ÿ

Praised in the first edition for the clarity of his general framework for conceptualizing meta-analysis, Rosenthal's revised edition covers the latest techniques in the field, such as a new effect size indicator for one size data, a new coefficient of robustness of replication, new procedures for combining and comparing effect sizes for multiple dependent variables, and new data on the magnitude of the problem of incomplete retrieval (the file drawer problem).

"This book offers readers the best of both worlds: technical sophistication coupled with user-friendly, practical information for doing meta-analysis." -- Page 4 of cover.

The Fifth Edition of Harris Cooper′s bestselling text offers practical advice on how to conduct a synthesis of research in the social, behavioral, and health sciences. The book is written in plain language with four running examples drawn from psychology, education, and health science. With ample coverage of literature searching and the technical aspects of meta-analysis, this one-of-a-kind book applies the basic principles of sound data gathering to the task of producing a comprehensive assessment of existing research.

Covering the most important developments in meta-analysis from 1990 to 2004, this text presents new patterns in research findings as well as updated information on existing topics.

Scientific progress often begins with the difficult task of preparing informed, conclusive reviews of existing research. Since the 1970s, the traditional "subjective" approach to research reviewing in the social sciences has been challenged by a statistical alternative known as meta-analysis. Meta-analysis provides a principled method of distilling reliable generalizations from previous studies on a single topic, thereby providing a quantitative and objective background for future research. The Future of Meta-Analysis brings together expert researchers for an in-depth examination of this new methodology—not to promote a consensus view but rather to explore from several perspectives the theories, tensions, and concerns of meta-analysis, and to illustrate through concrete examples the rationale behind meta-analytic decisions. In a meta-analysis prepared especially for this volume, a statistician and a psychologist review the existing literature on aphasia treatment. In a second study, experts analyze six still-unpublished meta-analyses sponsored by the National Institute of Education to investigate the effects of school desegregation on the academic achievement of black children. This unique case study approach provides valuable discussion of the process of meta-analysis and of the current implications of meta-analysis for policy assessment. Prepared under the auspices of the National Research Council, The Future of Meta-Analysis presents a forum for leaders in this rapidly evolving field to discuss salient conceptual and technical issues and to offer a new theoretical framework, further methodological guidance, and statistical innovations that anticipate a future in which meta-analysis will play an even more effective and valuable role in social science research.

Author Fredric M. Wolf explains how to use combined statistical tests and measures of effect size to synthesize the results of independent studies of a common research question.

In biostatistical research and courses, practitioners and students often lack a thorough understanding of how to apply statistical methods to synthesize biomedical and clinical trial data. Filling this knowledge gap, Applied Meta-Analysis with R shows how to implement statistical meta-analysis methods to real data using R. Drawing on their extensive research and teaching experiences, the authors provide detailed, step-by-step explanations of the implementation of meta-analysis methods using R. Each chapter gives examples of real studies compiled from the literature. After presenting the data and necessary background for understanding the applications, various methods for analyzing meta-data are introduced. The authors then develop analysis code using the appropriate R packages and functions. This systematic approach helps readers thoroughly understand the analysis methods and R implementation, enabling them to use R and the methods to analyze their own meta-data. Suitable as a graduate-level text for a meta-data analysis course, the book is also a valuable reference for practitioners and biostatisticians (even those with little or no experience in using R) in public health, medical research, governmental agencies, and the pharmaceutical industry.