Cause And Correlation In Biology: A User's Guide To Path Analysis, Structural Equations And Causal Inference With R

Shipley, Bill (Universite De Sherbrooke, Canada)

ISBN 10: 1009560395 / ISBN 13: 9781009560399

Published by Cambridge University Press, 2026

About this Item

3rd revised edition edition. 398 pages. 6.00x0.94x9.00 inches. In Stock.

Seller Inventory # x-1009560395

Bibliographic Details

• Title: Cause And Correlation In Biology: A User's Guide To Path Analysis, Structural Equations And Causal Inference With R
• Publisher: Cambridge University Press
• Publication Date: 2026
• Language: English
• ISBN 10: 1009560395
• ISBN 13: 9781009560399
• Binding: Hardcover
• Edition: 3rd Edition
• Condition: Brand New
• Weight: 0.77 kilograms

About this title

Synopsis

Aimed at practising biologists, especially graduate students and researchers in ecology, this revised and expanded 3rd edition continues to explore cause-effect relationships through a series of robust statistical methods. Every chapter has been updated, and two brand-new chapters cover statistical power, Akaike information criterion statistics and equivalent models, and piecewise structural equation modelling with implicit latent variables. A new R package (pwSEM) is included to assist with the latter. The book offers advanced coverage of essential topics, including d-separation tests and path analysis, and equips biologists with the tools needed to carry out analyses in the open-source R statistical environment. Writing in a conversational style that minimises technical jargon, Shipley offers an accessible text that assumes only a very basic knowledge of introductory statistics, incorporating real-world examples that allow readers to make connections between biological phenomena and the underlying statistical concepts.

About the Author

Bill Shipley is Associate Professor within the Department of Biology at Université de Sherbrooke, Canada. He is the author of From Plant Traits to Vegetation Structure: Chance and Selection in the Assembly of Ecological Communities (2012) and continues to make significant contributions to statistical methodology in ecology.