Structural Equation Modeling: Applications in Ecological and Evolutionary Biology

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Structural equation modeling (SEM) is a technique that is used to estimate, analyze, 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 a 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 web site to assist the learning process.
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Product Details

  • ISBN-13: 9780521104029
  • Publisher: Cambridge University Press
  • Publication date: 3/19/2009
  • Pages: 424
  • Product dimensions: 6.00 (w) x 9.00 (h) x 1.20 (d)

Meet the Author

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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Table of Contents

List of contributors
1 Structural equation modeling: an introduction 3
2 Concepts of structural equation modeling in biological research 42
3 Modeling a complex conceptual theory of population change in the Shiras moose: history and recasting as a structural equation model 60
4 A short history of structural equation models 85
5 Guidelines for the implementation and publication of structural equation models 125
6 Modeling intraindividual variability and change in bio-behavioral developmental processes 143
7 Examining the relationship between environmental variables and ordination axes using latent variables and structural equation modeling 171
8 From biological hypotheses to structural equation models: the imperfection of causal translation 194
9 Analyzing dynamic systems: a comparison of structural equation modeling and system dynamics modeling 212
10 Estimating analysis of variance models as structural equation models 235
11 Comparing groups using structural equations 281
12 Modeling means in latent variable models of natural selection 297
13 Modeling manifest variables in longitudinal designs - a two-stage approach 312
14 A comparison of the SEM software packages Amos, EQS, and LISREL 355
Index 392
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