Structural equation modelling (SEM) is a technique that is used to estimate, analyse and test models that specify relationships among variables. The ability to conduct such analyses is essential for many problems in ecology and evolutionary biology. This book begins by explaining the theory behind the statistical methodology, including chapters on conceptual issues, the implementation of an SEM study and the history of the development of SEM. The second section provides examples of analyses on biological data including multi-group models, means models, P-technique and time-series. The final section of the book deals with computer applications and contrasts three popular SEM software packages. Aimed specifically at biological researchers and graduate students, this book will serve as valuable resource for both learning and teaching the SEM methodology. Moreover, data sets and programs that are presented in the book can also be downloaded from a website to assist the learning process.
Part I. Theory: 1. Structural equation modelling: an introduction Scott L. Hershberger, George A. Marcoulides and Makeba M. Parramore; 2. Concepts of structural equation modelling in biological research Bruce H. Pugesek; 3. Modelling 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. Modelling intra-individual 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 modelling James B. Grace; 8. From biological hypotheses to structural equation models: the imperfection of causal translation Bill Shipley; 9. Analysing dynamic systems: a comparison of structural equation modelling and system dynamics modelling 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. Modelling 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; Philip K. Wood and Bobby D. Keeland; Part III. Computing: 14. A comparison of the SEM software packages Amos, EQS and LISREL Alexander von Eye and Bret E. Fuller; Index.
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.
