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Structural Equation Modeling: Applications in Ecological and Evolutionary Biology

Edited By: Bruce Pugesek, Adrian Tomer and Alexander Von Eye

409 pages, Figs, tabs

Cambridge University Press

Hardback | Jan 2003 | #128079 | ISBN: 0521781337
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NHBS Price: £103.00 $134/€117 approx

About this book

Structural Equation Modeling (SEM) is a technique that is used to estimate, analyse and test models that specify relationships among variables. This volume provides an introduction to statistical methodology, including chapters on conceptual issues, the implementation of an SEM study, and the history of the development of SEM. It also provides examples of analyses on biological data including multi-group models, means models, p-technique and time-series, and contrasts three popular SEM software packages.


Preface; Part I. Theory: 1. Structural equation modeling: an introduction Scott L. Hershberger, George A. Marcoulides and Makeba M. Parramore; 2. Concepts of structural equation modeling in biological research Bruce H. Pugesek; 3. Modeling a complex conceptual theory of population change in the shiras moose: history and recasting as a structural equation model Bruce H. Pugesek; 4. A short history of structural equation models Adrian Tomer; 5. Guidelines for the implementation and publication of structural equation models Adrian Tomer and Bruce H. Pugesek; Part II. Applications: 6. Modeling intraindividual variability and change in bio-behavioural developmental processes Patricia H. Hawley and Todd D. Little; 7. Examining the relationship between environmental variables and ordination axes using latent variables and structural equation modeling James B. Grace; 8. From biological hypothesis to structural equation models: the imperfection of causal translation Bill Shipley; 9. Analyzing dynamic systems: a comparison of structural equation modeling and system dynamics modeling Peter S. Hovmand; 10. Estimating analysis of variance models as structural equation models Michael J. Rovine and Peter C. M. Molenaar; 11. Comparing groups using structural equations James B. Grace; 12. Modeling means in latent variable models of natural selection Bruce H. Pugesek; 13. Modeling manifest variables in longitudinal designs - a two-stage approach Bret E. Fuller, Alexander von Eye, Phillip K. Wood and Bob Keeland; Part III. Computing: 14. A comparison of the SEM software packages Amos, EQS and LISREL Alexander von Eye and Bret E. Fuller.

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Bruce Pugesek is a research statistician in the U.S. Department of the Interior and is adjunct professor in the Department of Comparative Biomedical Sciences at The Louisiana State University. Adrian Tomer is an Associate Professor at the Department of Psychology at Shippensburg University, Pennsylvania, where he teaches the psychology of aging and developmental psychology. Alexander von Eye is a Professor in the Department of Psychology at Michigan State University.

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