Advances in Meta-Analysis

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..

Auteur

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.

Résumé

The subject of the book is advanced statistical analyses for quantitative research synthesis (meta-analysis), and selected practical issues relating to research synthesis that are not covered in detail in the many existing introductory books on research synthesis (or meta-analysis). Complex statistical issues are arising more frequently as the primary research that is summarized in quantitative syntheses itself becomes more complex, and as researchers who are conducting meta-analyses become more ambitious in the questions they wish to address. Also as researchers have gained more experience in conducting research syntheses, several key issues have persisted and now appear fundamental to the enterprise of summarizing research.

Specifically the book describes multivariate analyses for several indices commonly used in meta-analysis (e.g., correlations, effect sizes, proportions and/or odds ratios), will outline how to do power analysis for meta-analysis (again for each of the different kinds of study outcome indices), and examines issues around research quality and research design and their roles in synthesis. For each of the statistical topics we will examine the different possible statistical models (i.e., fixed, random, and mixed models) that could be adopted by a researcher. In dealing with the issues of study quality and research design it covers a number of specific topics that are of broad concern to research synthesists. In many fields a current issue is how to make sense of results when studies using several different designs appear in a research literature (e.g., Morris & Deshon, 1997, 2002). In education and other social sciences a critical aspect of this issue is how one might incorporate qualitative (e.g., case study) research within a synthesis. In medicine, related issues concern whether and how to summarize observational studies, and whether they should be combined with randomized controlled trials (or even if they should becombined at all).

For each topic, included is a worked example (e.g., for the statistical analyses) and/or a detailed description of a published research synthesis that deals with the practical (non-statistical) issues covered.

Contenu

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.