Statistical Methods for Meta-Analysis
- Larry Hedges, University of Chicago
- Ingram Olkin, Stanford University, California
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.View full description
Upper-level undergraduate and graduate students studying mathematics.
- Published: July 1985
- Imprint: ACADEMIC PRESS
- ISBN: 978-0-12-336380-0
"This book brings a new level of statistical sophistication to the problem of comining quantitative measures from different studies. The book will be widely used and admired." AMERICAN SCIENTIST "Within its scope and for its intended audience, Statistical Methods for Meta-Analysis is... the best of several books on meta-analysis currently available. It belongs in the libraries of schools of education and of medicine, and in departments of economics, polictical science, psychology, sociology, and statistics. Anyone regularly engaged in meta-analysis will want to own it." JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
Table of ContentsPreface. Introduction. Data Sets. Tests of Statistical Significance of Combined Results. Vote-Counting Methods. Estimation of a Single Effect Size: Parametric and Nonparametric Methods. Parametric Estimation of Effect Size from a Series of Experiments. Fitting Parametric Fixed Effect Models to Effect Sizes: Categorical Methods. Fitting Parametric Fixed Effect Models to Effect Sizes: General Linear Models. Random Effects Models for Effect Sizes. Multivariate Models for Effect Sizes. Combining Estimates of Correlation Coefficients. Diagnostic Procedures for Research Synthesis Models. Clustering Estimates of Effect Magnitude. Estimation of Effect Size When Not All Study Outcomes Are Observed. Meta-Analysis in the Physical and Biological Sciences. Appendix. References. Index.