Structural Equation Modeling and Natural Systems

This book, first published in 2006, shows that much can be learnt by viewing ecological systems from a multivariate perspective.

James B. Grace (Author)

9780521546539, Cambridge University Press

Paperback, published 17 August 2006

378 pages, 131 b/w illus. 40 tables
22.6 x 15 x 2.3 cm, 0.5 kg

'… well suited to its intended readership.' Biometrics

This book, first published in 2006, presents an introduction to the methodology of structural equation modeling, illustrates its use, and goes on to argue that it has revolutionary implications for the study of natural systems. A major theme of this book is that we have, up to this point, attempted to study systems primarily using methods (such as the univariate model) that were designed only for considering individual processes. Understanding systems requires the capacity to examine simultaneous influences and responses. Structural equation modeling (SEM) has such capabilities. It also possesses many other traits that add strength to its utility as a means of making scientific progress. In light of the capabilities of SEM, it can be argued that much of ecological theory is currently locked in an immature state that impairs its relevance. It is further argued that the principles of SEM are capable of leading to the development and evaluation of multivariate theories of the sort vitally needed for the conservation of natural systems.

Part I. A Beginning: 1. Introduction
2. Illustration of structural equation modeling with observed variables: the temporal dynamics of a plant-insect interaction
Part II. Basic Principles of Structural Equation Modeling: 3. The anatomy of structural equation models I: overview and observed variable models
4. The anatomy of structural equation models II: latent variables
5. Principles of estimation and model assessment
Part III. Advanced Topics: 6. Composite variables and their use in representing concepts
7. Additional techniques for complex situations
Part IV. Applications and Illustrations: 8. Model evaluation in practice
9. Multivariate experiments
10. The systematic application of a multivariate perspective to understanding plant diversity patterns in ecological communities
11. Cautions and recommendations for the application of SEM
Part V. The Implications of Structural Equation Modeling for the Study of Natural Systems: 12. How can structural equation modeling contribute to the advancement of the natural sciences?
13. Tuning in to nature's symphony: frontiers in the study of multivariate relations
Appendix I. Example analyses
References.

Subject Areas: Data capture & analysis [UNC], Environmental science, engineering & technology [TQ], Ecological science, the Biosphere [PSAF]