Synopses & Reviews
This book goes beyond the truism that "correlation does not imply causation" and explores the logical and methodological relationships between correlation and causation. The author presents a series of statistical methods that can test and potentially discover the cause-effect relationships between variables in situations in which it is not possible to conduct randomized or experimentally controlled experiments. Many of the methods discussed in the book are quite new and generally unknown to biologists. In addition to describing how to conduct these statistical tests, the volume also puts the methods into historical context and explains when they can and can't justifiably be used to test or discover causal claims. The author writes in a conversational style that minimizes technical jargon and assumes the reader has only a very basic knowledge of introductory statistics.
Synopsis
Bill Shipley explores the logical and methodological relationships between correlation and causation. He presents a series of statistical methods that can test, and potentially discover, cause-effect relationships between variables in situations where it is not possible to conduct randomized, or experimentally controlled, studies. Many of these methods are quite new and most are generally unknown to biologists. Besides describing how to conduct these statistical tests, he also puts the methods into historical context and explains when they can and cannot justifiably be used to test causal claims. Hb ISBN (2000); 0-521-79153-7
Synopsis
Explores the relationship between correlation and causation using a series of novel statistical methods.
Synopsis
Written in a jargon-free, conversational style, this book explores the relationship between correlation and causation. It presents a series of statistical methods that can test, and potentially discover, cause-effect relationships between variables in situations in which it is not possible to conduct controlled experiments.
About the Author
Bill Shipley teaches plant ecology and biometry in the Department of Biology at the Universite de Sherbrooke, Canada.
Table of Contents
Preface; 1. Preliminaries; 2. From cause to correlation and back; 3. Sewall Wright, path analysis and d-separation; 4. Path analysis and maximum likelihood; 5. Measurement error and latent variables; 6. The structural equations model; 7. Nested models and multilevel models; 8. Exploration, discovery and equivalence; Appendix; References; Index.