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.

Klappentext

Born from the teachings of a popular meta-analysis course, Introduction to Meta-Analysis provides a concise and clearly presented discussion of all the elements in a meta-analysis. Written by four of the leading names in meta-analysis research, many points are explained visually by using screenshots from Excel spreadsheets and computer programs. The text's user-friendly style allows researchers, graduate students, and advanced undergraduate students with no previous experience of meta-analysis to grasp its techniques, and utilize them with a hands-on, interactive approach.

Inhalt

Acknowledgements. Preface. An ethical imperative. From narrative reviews to systematic reviews. The systematic review and meta-analysis. Meta-analysis is used in many fields of research. Meta-analysis as part of the research process. The intended audience for this book. An outline of this book's contents. What this book does not cover. Web site. PART 1: INTRODUCTION. 1. How a meta-analysis works. 2. Why perform a meta-analysis. PART 2: EFFECT SIZE AND PRECISION. 3. Overview. 4. Effect sizes based on means. 5. Effect sizes based on binary data (2x2 tables). 6. Effect sizes based on correlations. 7. Converting among effect sizes. 8. Factors that affect precision. 9. Concluding remarks. PART 3: FIXED-EFFECT VERSUS RANDOM-EFFECTS MODELS. 10. Overview. 11. Fixed-effect model. 12. Random-effects model. 13. Fixed effect versus random-effects models. 14. Worked examples (Part 1). PART 4: HETEROGENEITY. 15. Overview. 16. Identifying and quantifying heterogeneity. 17. Prediction intervals. 18. Worked examples (Part 2). 19. Subgroup analyses. 20. Meta-regression. 21. Notes on subgroup analyses and meta-regression. PART 5: COMPLEX DATA STRUCTURES. 22. Overview. 23. Independent subgroups within a study. 24. Multiple outcomes or time points within a study. 25. Multiple comparisons within a study. 26. Notes on complex data structures. PART 6: OTHER ISSUES. 27. Overview. 28. Vote counting - a new name for an old problem. 29. Power Analysis for meta-analysis. 30. Publication bias. Part 7: Issues related to effect size. 31. Overview. 32. Effect sizes rather than p-values. 33. Simpson's paradox. 34. Generality of the basic inverse-variance method. PART 8: FURTHER METHODS. 35. Overview. 36. Meta-analysis methods based on direction and p-values. 37. Further methods for dichotomous data. 38. Psychometric meta-analysis. PART 9: META-ANALYSIS IN CONTEXT. 39. Overview. 40. When does it make sense to perform a meta-analysis. 41. Reporting the results of a meta-analysis. 42. Cumulative meta-analysis. 43. Criticisms of meta-analysis. PART 10: RESOURCES AND SOFTWARE. 44. Software. 45. Books, web sites and professional organizations. Web sites. References.