Effect Sizes Based on Means

Abstract

This text provides a concise and clearly presented discussion of all the elements in a meta-analysis. It is illustrated with worked examples throughout, with visual explanations, using screenshots from Excel spreadsheets and computer programs such as Comprehensive Meta-Analysis (CMA) or Strata. This book provides a clear and thorough introduction to meta-analysis, the process of synthesizing data from a series of separate studies. Meta-analysis has become a critically important tool in fields as diverse as medicine, pharmacology, epidemiology, education, psychology, business, and ecology. Introduction to Meta-Analysis: Outlines the role of meta-analysis in the research process; Shows how to compute effects sizes and treatment effects; Explains the fixed-effect and random-effects models for synthesizing data; Demonstrates how to assess and interpret variation in effect size across studies; Clarifies concepts using text and figures, followed by formulas and samples; Explains how to avoid common mistakes in meta-analysis; Discusses controversies in meta-analysis; Features a web site with additional materials and exercises. -- Part 1: Introduction -- How a meta-analysis works -- Why perform a meta-analysis -- Part 2: Effect size and precision -- Overview -- Effect sizes based on means -- Effect sizes based on binary data (2 x 2 tables) -- Effect sizes based on correlations -- Converting among effect sizes -- Factors that affect precision -- Concluding remarks -- Part 3: Fixed-effect versus random-effects models -- Overview -- Fixed-effect model -- Random-effects model -- Fixed-effect versus random-effects models -- Worked examples (part 1) -- Part 4: Heterogeneity -- Overview -- Identifying and quantifying heterogeneity -- Prediction intervals -- Worked examples (part 2) -- Subgroup analyses -- Meta-regression -- Notes on subgroup analyses and meta-regression -- Part 5: Complex data structures -- Overview -- Independent subgroups within a study -- Multiple outcomes or time-points within a study -- Multiple comparisons within a study -- Notes on complex data structures -- Part 6: Other issues -- Overview -- Vote counting: a new name for an old problem -- Power analysis for meta-analysis -- Publication bias -- Part 7: Issues related to effect size -- Overview -- Effect sizes rather than p-values -- Simpson's paradox -- Generality of the basic inverse-variance method -- Part 8: Further methods -- Overview -- Meta-analysis methods based on direction and p-values -- Further methods for dichotomous data -- Psychometric meta-analysis -- Part 9: Meta-analysis in context -- Overview -- When does it make sense to perform a meta-analysis? -- Reporting the results of a meta-analysis -- Cumulative meta-analysis -- Criticisms of meta-analysis -- Part 10: Resources and software -- Software -- Books, web sites and professional organizations.