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Cause and Correlation in Biology: A User's Guide to Path Analysis, Structural Equations and Causal Inference with R 2nd Edition, Kindle Edition
- ISBN-13978-1107442597
- Edition2nd
- PublisherCambridge University Press
- Publication dateApril 18, 2016
- LanguageEnglish
- File size11136 KB
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Editorial Reviews
Review
"Addressing students and practising biologists, Shipley does a terrific job of making mathematical ideas accessible … Cause and Correlation in Biology is a nontechnical and honest introduction to statistical methods for testing causal hypotheses."
Johan Paulsson, Nature Cell Biology
"Bill Shipley has done an excellent job in tackling the fundamental issue of testing causality in biology and making it accessible to any biology student or scholar. This book is about statistics, but the storytelling is for biologists. When the first edition for this book came out, in 2000, path analyses were not a common tool for biologists. Although the first edition convinced us to use structural equation modelling, this second edition supplies the essential toolbox. This book is the best route to take if you want to master structural equation modelling in biology, and the very good news is that this second edition not only provides updates and extensions, it also offers R codes to run your analyses."
Anne Charmantier, Centre d’Écologie Fonctionnelle et Évolutive (CEFE), Montpellier
Review of previous edition:
"I highly recommend the book for those interested in multivariate approaches to biology."
Annals of Botany
Review of previous edition:
"… the perfect introduction to SEM. This book can be used as the primary text in a SEM course given within any discipline, and can be used by scholars and researchers from any area of science."
Structural Equation Modeling
"For a long time biologists have inferred causation only from carefully designed experiments. Shipley's book broadens horizons by showing how to use observational data to infer whether a causal model is plausible, and to estimate the variation in response due to competing causes."
David Warton, University of New South Wales, Sydney
About the Author
Product details
- ASIN : B01DPNK206
- Publisher : Cambridge University Press; 2nd edition (April 18, 2016)
- Publication date : April 18, 2016
- Language : English
- File size : 11136 KB
- Simultaneous device usage : Up to 4 simultaneous devices, per publisher limits
- Text-to-Speech : Enabled
- Screen Reader : Supported
- Enhanced typesetting : Enabled
- X-Ray : Not Enabled
- Word Wise : Enabled
- Print length : 312 pages
- Best Sellers Rank: #2,459,975 in Kindle Store (See Top 100 in Kindle Store)
- #441 in Science Education Research
- #446 in Science Methodology & Statistics
- #770 in Ecology (Kindle Store)
- Customer Reviews:
About the author
Videos of the first two chapters of my 2016 book (Cause and correlation in biology) are available at https://youtu.be/YeiwJxu380g, https://youtu.be/7gwlB7WDPDY. I offer an online course based on this book: https://billshipley45.wixsite.com/onlinesem.
I was born in a log-like cabin (i.e. a split-level bungalow) in the small village of Laskey (Ontario, Canada) in 1960. My intellectual Djourney went from Bishop's University (BSc. 1983), to the University of Ottawa (PhD. 1987) and passed through a post-doc at McGill University. I have been a professor at the Université de Sherbrooke since 1992 and have not yet reached senility. My research interests are in plant ecology (theoretical and empirical) and mathematical modelling. My hero is Albus Dumbledore.
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Learn more how customers reviews work on AmazonTop reviews from the United States
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- Reviewed in the United States on May 7, 2018This is a fantastic book. Causal inference (CI) and structural equation modeling (SEM) can be hard to get your head around; after years of education and experience in computer science and statistics, I was still wrestling with the concepts. Shipley does a great job of explaining the motivations and applications for CI along with just enough theory to ensure everything makes sense. As a bonus, he provides enough of the history and philosophy of the field to counter some of the common objections. (Anyone who thinks parroting "correlation does not imply causation" and thinks they're being clever is cordially invited to read the first couple of chapters and then come back when they're ready to talk with the grown-ups.) Before tackling foundational works like Causality: Models, Reasoning and Inference, read this book--you'll be glad you did.
- Reviewed in the United States on June 26, 2018It is a total cover of an esencial matter in all research that pretend to stay at vanguard
- Reviewed in the United States on December 31, 2017Super helpful description of how and why for causal analysis using path models.
- Reviewed in the United States on June 17, 2018Excellent introduction to causal, path, and latent variable models. Don't let the "biology" fool you; the book is accessable to people from any content background.
- Reviewed in the United States on July 25, 2022The general verbal content is excellent, discussing strengths and weaknesses of basic analysis, but the technical content is so full of errors as to make reading the book a chore. Example:
Figure 2.6 is wrong and the discussion regarding it in Table 2.1 is wrong. In Figure 2.6 the nodes S1 and S2 point in the wrong direction. And the summary of this node in Table 2.1 likewise is wrong. D-Separation is false for a collider given a causal causal child -- not causal ancestor.
On the same page, the R code for the DAG for the Figure 2.6 graph doesn't model the graph (in addition it's not legal R code) but at least it models what the author likely intended the graph to be.
I don't know if the rest of the book is as bad as Chapter 2 in these regards. Maybe all the book needs is to reissue Chapter 2 in its entirety.
As far as the Kindle and Apple Books versions, the digital versions not only reflect the chapter 2 flaws, and are flawed to the point of unreadable.
Top reviews from other countries
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g2gReviewed in Italy on September 4, 2023
5.0 out of 5 stars Chiarissimo
Un'introduzione all'argomento che riesce a bilanciare informatività e chiarezza espositiva, teoria ed esempi. Un libro prezioso.
- Diego Santiago AlarconReviewed in Mexico on July 19, 2020
5.0 out of 5 stars Best to learn SEM using R for biologists
This is the best book to learn the conceptual bases of path analysis and structural equation modelling. In this second edition, the author adds the benefit of R language to teach these themes. If you are working with complex systems like in ecological sciences and dealing with continuous variables, it would be worth your time to study this book.
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BAVUIReviewed in Canada on September 23, 2019
5.0 out of 5 stars Un essentiel
Très bon ouvrage pour ce qui débutent dans les analyses de pistes. Un excellent pas à pas pour réfléchir à ses hypothèses et construire ses propres analyses.