Statistical Methods for Meta-Analysis

Capa
Academic Press, 28 de jun de 2014 - 369 páginas
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.
 

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Conteúdo

Chapter 1 Introduction
1
Chapter 2 Data Sets
15
Chapter 3 Tests of Statistical Significance of Combined Results
27
Chapter 4 VoteCounting Methods
47
Parametric and Nonparametric Methods
75
Chapter 6 Parametric Estimation of Effect Size From a Series of Experiments
107
Categorical Models
147
General Linear Models
167
Chapter 11 Combining Estimates of Correlation Coefficients
223
Chapter 12 Diagnostic Procedures for Research Synthesis Models
247
Chapter 13 Clustering Estimates of Effect Magnitude
265
Chapter 14 Estimation of Effect Size When Not All Study Outcomes Are Observed
285
Chapter 15 MetaAnalysis in the Physical and Biological Sciences
311
Appendix
327
References
347
Author Index
361

Chapter 9 Random Effects Models for Effect Sizes
189
Chapter 10 Multivariate Models for Effect Sizes
205

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Sobre o autor (2014)

Albert W. Marshall, Professor Emeritus of Statistics at the University of British Colombia, previously served on the faculty of the University of Rochester and on the staff of the Boeing Scientific Research Laboratories. His fundamental contributions to reliability theory have had a profound effect in furthering its development.

Ingram Olkin is Professor Emeritus of Statistics and Education at Stanford University, after having served on the faculties of Michigan State University and the University of Minnesota. He has made significant contributions in multivariate analysis and in the development of statistical methods in meta-analysis, which has resulted in its use in many applications.

Professors Marshall and Olkin, coauthors of papers on inequalities, multivariate distributions, and matrix analysis, are about to celebrate 50 years of collaborations. Their basic book on majorization has promoted awareness of the subject, and led to new applications in such fields as economics, combinatorics, statistics, probability, matrix theory, chemistry, and political science.

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