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Handbook of Meta-Analysis in Ecology and Evolution
Contributor(s): Koricheva, Julia (Editor), Gurevitch, Jessica (Editor), Mengersen, Kerrie (Editor)
ISBN: 0691137293     ISBN-13: 9780691137292
Publisher: Princeton University Press
OUR PRICE:   $79.80  
Product Type: Paperback - Other Formats
Published: April 2013
Qty:
Additional Information
BISAC Categories:
- Science | Life Sciences - Ecology
- Science | Life Sciences - Evolution
- Mathematics | Probability & Statistics - General
Dewey: 576.8
LCCN: 2012041108
Physical Information: 1.3" H x 7" W x 9.8" (2.35 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