1.

Record Nr.

UNINA9910449786803321

Autore

Shipley Bill <1960->

Titolo

Cause and correlation in biology : a user's guide to path analysis, structural equations, and causal inference / / Bill Shipley [[electronic resource]]

Pubbl/distr/stampa

Cambridge : , : Cambridge University Press, , 2000

ISBN

1-107-12154-X

1-280-43001-X

9786610430017

0-511-17348-2

0-511-01772-3

0-511-15255-8

0-511-32339-5

0-511-60594-3

0-511-04681-2

Descrizione fisica

1 online resource (xii, 317 pages) : digital, PDF file(s)

Disciplina

570/.1/5195

Soggetti

Biometry

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Title from publisher's bibliographic system (viewed on 05 Oct 2015).

Nota di bibliografia

Includes bibliographical references (p. 308-315) and index.

Nota di contenuto

Cover; Half-title; Title; Copyright; Dedication; 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

Sommario/riassunto

This book goes beyond the truism that 'correlation does not imply causation' and explores the logical and methodological relationships 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 randomised or experimentally controlled experiments. Many of these methods are quite new and most are generally unknown to



biologists. In addition to describing how to conduct these statistical tests, the book also puts the methods into historical context and explains when they can and cannot justifiably be used to test or discover causal claims. Written in a conversational style that minimises technical jargon, the book is aimed at practising biologists and advanced students, and assumes only a very basic knowledge of introductory statistics.