Cause and Correlation in Biology

Many problems in biology require an understanding of the relationships among variables in a multivariate causal context. Exploring such cause-effect relationships through a series of statistical methods, this book explains how to test causal hypotheses when randomised experiments cannot be performed. This completely revised and updated edition features detailed explanations for carrying out statistical methods using the popular and freely available R statistical language. Sections on d-sep tests, latent constructs that are common in biology, missing values, phylogenetic constraints, and multilevel models are also an important feature of this new edition. Written for biologists and using a minimum of statistical jargon, the concept of testing multivariate causal hypotheses using structural equations and path analysis is demystified. Assuming only a basic understanding of statistical analysis, this new edition is a valuable resource for both students and practising biologists.

Written for biologists and students, this practical guide underlies the principle methods for analysing cause-effect relationships. Featuring extensive sections on the use of R statistical language to apply statistical methods to biological data, this completely revised new edition is a valuable resource for practising biologists.

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. Multigroup models, multilevel models, and corrections for non-independence of observations; 8. Exploration, discovery and equivalence; Index.