Handbook of Meta-analysis in Ecology and Evolution
Language: English
Published by Princeton University Press, 2013
- Softcover
- Used
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Condition: Used - Very good
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The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.
Seller Inventory # GOR009916464
- Title
- Handbook of Meta-analysis in Ecology and Evolution
- Author
- Gurevitch, Jessica
- Publisher
- Princeton University Press
- Publication year
- 2013
- Condition
- Very Good
- Binding
- Paperback
- Language
- English
- ISBN 10
- 0691137293
- ISBN 13
- 9780691137292
- Item weight
- 921 grams
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
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