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portada Handbook of Meta-Analysis in Ecology and Evolution
Type
Physical Book
Illustrated by
Publisher
Author
Year
2013
Language
English
Pages
520
Format
Paperback
Dimensions
24.9 x 17.8 x 3.3 cm
Weight
1.07 kg.
ISBN
0691137293
ISBN13
9780691137292
Edition No.
2013
Categories

Handbook of Meta-Analysis in Ecology and Evolution

Julia Koricheva (Illustrated by) · Jessica Gurevitch (Illustrated by) · Kerrie Mengersen (Illustrated by) · Princeton University Press · Paperback

Handbook of Meta-Analysis in Ecology and Evolution - Koricheva, Julia ; Gurevitch, Jessica ; Mengersen, Kerrie

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Synopsis "Handbook of Meta-Analysis in Ecology and Evolution "

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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