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
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.
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