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The book describes multivariate analyses for several indices commonly used in meta-analysis (e.g., correlations, effect sizes, proportions and/or odds ratios)
It outlines how to do power analysis for meta-analysis for each of the different kinds of study outcome indices
Features examination of issues around research quality and research design and their roles in synthesis
Meta-analysis is used increasingly in the social sciences to synthesize research results. As both primary research and the questions addressed by meta-analysis have been grown more complex, meta-analysis techniques have evolved to address these issues. This book covers a number of advances in meta-analysis that are not covered in detail in many introductory books on research synthesis. More specifically, this book discusses the planning of a meta-analysis for complex questions, computing power for tests in meta-analysis, handling missing data in meta-analysis, integrating individual data into a traditional meta-analysis and generalizing from the results of a meta-analysis. For each topic, a fully annotated example is provided with sample computer programs for the major statistical packages. This book assumes a familiarity with basic meta-analytic techniques. The goal of the book is to provide researchers with advanced strategies for strengthening the planning, conduct and interpretations of meta-analysis with complex data.
Terri D. Pigott is a Professor of Research Methodology in Loyola University Chicago’s School of Education. Her research interests are in developing new methods for meta-analysis, and the use of meta-analysis in public policy..
Content Level »Research
Keywords »ANOVA Model - Complex Data - Meta-Analysis - Meta-Regression - Variance Component
Rationale and overview of the book.- Complexities of primary research and meta-analysis.- Fixed, random, and mixed models.- Correlations.- Effect sizes.- Proportion/odds ratio data.- Dependence in primary research and meta-analysis.- Correlations.- Effect sizes (group comparisons and change measures).- Proportion/odds ratio data.- Bayesian approaches.- Missing data.- Combining across designs.- Qualitative studies in synthesis.- Quality assessments.- Studies on quality weighting.- Summary/Conclusions.