| Handbook of Meta-Analysis in Ecology and Evolution Contributor(s): Koricheva, Julia (Editor), Gurevitch, Jessica (Editor), Mengersen, Kerrie (Editor) |
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ISBN: 0691137285 ISBN-13: 9780691137285 Publisher: Princeton University Press OUR PRICE: $125.40 Product Type: Hardcover - Other Formats Published: April 2013 |
| Additional Information |
| BISAC Categories: - Science | Life Sciences - Ecology - Science | Life Sciences - Evolution - Mathematics | Probability & Statistics - General |
| Dewey: 576.8 |
| LCCN: 2012041108 |
| Physical Information: 1.4" H x 7.1" W x 10.1" (2.80 lbs) 520 pages |
| Themes: - Topical - Ecology |
| Descriptions, Reviews, Etc. |
| Publisher Description: Meta-analysis is a powerful statistical methodology for synthesizing research evidence across independent studies. This is the first comprehensive handbook of meta-analysis written specifically for ecologists and evolutionary biologists, and it provides an invaluable introduction for beginners as well as an up-to-date guide for experienced meta-analysts. The chapters, written by renowned experts, walk readers through every step of meta-analysis, from problem formulation to the presentation of the results. The handbook identifies both the advantages of using meta-analysis for research synthesis and the potential pitfalls and limitations of meta-analysis (including when it should not be used). Different approaches to carrying out a meta-analysis are described, and include moment and least-square, maximum likelihood, and Bayesian approaches, all illustrated using worked examples based on real biological datasets. This one-of-a-kind resource is uniquely tailored to the biological sciences, and will provide an invaluable text for practitioners from graduate students and senior scientists to policymakers in conservation and environmental management.Walks you through every step of carrying out a meta-analysis in ecology and evolutionary biology, from problem formulation to result presentation Brings together experts from a broad range of fields Shows how to avoid, minimize, or resolve pitfalls such as missing data, publication bias, varying data quality, nonindependence of observations, and phylogenetic dependencies among species Helps you choose the right software Draws on numerous examples based on real biological datasets |