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Cause and Correlation in Biology

A User's Guide to Path Analysis, Structural Equations and Causal Inference with R

(Autor)

Buch | Hardcover
398 Seiten
2026 | 3rd Revised edition
Cambridge University Press (Verlag)
978-1-009-56039-9 (ISBN)
CHF 218,20 inkl. MwSt
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Written for graduate students and researchers in biology without advanced statistical training, this book explains the process of testing cause-and effect hypotheses using statistical tools. Combining both theory and practice, learners are provided with step-by-step explanations for analysing data, using the dedicated R packages.
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.

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.

Preface; 1. Cause from correlation?; 2. From cause to correlation and back; 3. Sewall Wright, path analysis and d-separation; 4. Covariance-based SEM without explicit latent variables; 5. Statistical power, AIC statistics and equivalent models; 6. Piecewise SEM with implicit latent variables; 7. Modelling explicit latent variables in covariance-based SEM; 8. Multigroup and multilevel structural equation models; 9. Exploratory structural equations modelling; 10. A cheat sheet of important R functions; References; Index.

Erscheint lt. Verlag 31.5.2026
Zusatzinfo Worked examples or Exercises
Verlagsort Cambridge
Sprache englisch
Themenwelt Studium Querschnittsbereiche Epidemiologie / Med. Biometrie
Naturwissenschaften Biologie Ökologie / Naturschutz
ISBN-10 1-009-56039-5 / 1009560395
ISBN-13 978-1-009-56039-9 / 9781009560399
Zustand Neuware
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