Introduction to Meta-AnalysisA clear and thorough introduction to meta-analysis, the process of synthesizing data from a series of separate studies The first edition of this text was widely acclaimed for the clarity of the presentation, and quickly established itself as the definitive text in this field. The fully updated second edition includes new and expanded content on avoiding common mistakes in meta-analysis, understanding heterogeneity in effects, publication bias, and more. Several brand-new chapters provide a systematic "how to" approach to performing and reporting a meta-analysis from start to finish. Written by four of the world's foremost authorities on all aspects of meta-analysis, the new edition:
Download videos, class materials, and worked examples at www.Introduction-to-Meta-Analysis.com "This book offers the reader a unified framework for thinking about meta-analysis, and then discusses all elements of the analysis within that framework. The authors address a series of common mistakes and explain how to avoid them. As the editor-in-chief of the American Psychologist and former editor of Psychological Bulletin, I can say without hesitation that the quality of manuscript submissions reporting meta-analyses would be vastly better if researchers read this book." "A superb combination of lucid prose and informative graphics, the authors provide a refreshing departure from cookbook approaches with their clear explanations of the what and why of meta-analysis. The book is ideal as a course textbook or for self-study. My students raved about the clarity of the explanations and examples." "The approach taken by Introduction to Meta-analysis is intended to be primarily conceptual, and it is amazingly successful at achieving that goal. The reader can comfortably skip the formulas and still understand their application and underlying motivation. For the more statistically sophisticated reader, the relevant formulas and worked examples provide a superb practical guide to performing a meta-analysis. The book provides an eclectic mix of examples from education, social science, biomedical studies, and even ecology. For anyone considering leading a course in meta-analysis, or pursuing self-directed study, Introduction to Meta-analysis would be a clear first choice." |
Contents
WHY PERFORM A METAANALYSIS | 9 |
EFFECT SIZE AND PRECISION | 17 |
3 | 34 |
EFFECT SIZES BASED ON CORRELATIONS | 39 |
9 | 46 |
10 | 52 |
CONCLUDING REMARKS | 55 |
11 | 61 |
ΚΝΑΡPHARTUNG ADJUSTMENT | 243 |
COMPLEX DATA STRUCTURES | 253 |
MULTIPLE OUTCOMES OR TIMEPOINTS WITHIN A STUDY | 263 |
MULTIPLE COMPARISONS WITHIN A STUDY | 277 |
METAANALYSIS IN CONTEXT | 287 |
VOTE COUNTING A NEW NAME FOR AN OLD PROBLEM | 289 |
POWER ANALYSIS FOR METAANALYSIS | 295 |
Is there evidence of any bias? | 320 |
Summary points | 64 |
RANDOMEFFECTS MODEL | 65 |
13 | 70 |
FIXEDEFFECT VERSUS RANDOMEFFECTS MODELS | 71 |
WORKED EXAMPLES PART 1 | 81 |
17 | 82 |
HETEROGENEITY | 97 |
21 | 98 |
25 | 110 |
PREDICTION INTERVALS | 119 |
WORKED EXAMPLES PART 2 | 127 |
AN INTUITIVE LOOK AT HETEROGENEITY | 139 |
CLASSIFYING HETEROGENEITY AS LOW MODERATE OR HIGH | 155 |
EXPLAINING HETEROGENEITY | 161 |
METAREGRESSION | 197 |
NOTES ON SUBGROUP ANALYSES AND METAREGRESSION | 213 |
PUTTING IT ALL IN CONTEXT | 223 |
LIMITATIONS OF THE RANDOMEFFECTS MODEL | 233 |
Using logic to disentangle bias from smallstudy effects | 326 |
EFFECT SIZES RATHER THAN pVALUES | 337 |
SIMPSONS PARADOX | 343 |
GENERALITY OF THE BASIC INVERSEVARIANCE METHOD | 349 |
ISSUES RELATED TO EFFECT SIZE | 359 |
FURTHER METHODS | 361 |
FURTHER METHODS FOR DICHOTOMOUS DATA | 369 |
PSYCHOMETRIC METAANALYSIS | 377 |
WHEN DOES IT MAKE SENSE TO PERFORM A METAANALYSIS? | 393 |
REPORTING THE RESULTS OF A METAANALYSIS | 401 |
CUMULATIVE METAANALYSIS | 408 |
Garbage in garbage | 416 |
Features in | 425 |
Plot showing distribution of effects | 431 |
Publication bias | 438 |
Metafor | 471 |
REFERENCES | 479 |