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.

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.