Book contents
- Frontmatter
- Contents
- List of contributors
- Preface
- Section 1 Theory
- Section 2 Applications
- 6 Modeling intraindividual variability and change in bio-behavioral developmental processes
- 7 Examining the relationship between environmental variables and ordination axes using latent variables and structural equation modeling
- 8 From biological hypotheses to structural equation models: the imperfection of causal translation
- 9 Analyzing dynamic systems: a comparison of structural equation modeling and system dynamics modeling
- 10 Estimating analysis of variance models as structural equation models
- 11 Comparing groups using structural equations
- 12 Modeling means in latent variable models of natural selection
- 13 Modeling manifest variables in longitudinal designs – a two-stage approach
- Section 3 Computing
- Index
9 - Analyzing dynamic systems: a comparison of structural equation modeling and system dynamics modeling
Published online by Cambridge University Press: 14 October 2009
- Frontmatter
- Contents
- List of contributors
- Preface
- Section 1 Theory
- Section 2 Applications
- 6 Modeling intraindividual variability and change in bio-behavioral developmental processes
- 7 Examining the relationship between environmental variables and ordination axes using latent variables and structural equation modeling
- 8 From biological hypotheses to structural equation models: the imperfection of causal translation
- 9 Analyzing dynamic systems: a comparison of structural equation modeling and system dynamics modeling
- 10 Estimating analysis of variance models as structural equation models
- 11 Comparing groups using structural equations
- 12 Modeling means in latent variable models of natural selection
- 13 Modeling manifest variables in longitudinal designs – a two-stage approach
- Section 3 Computing
- Index
Summary
Abstract
Structural equation modeling (SEM) is a method of testing systems of equations against observed covariance matrices while system dynamic modeling (SDM) simulates systems of differential equations representing nonlinear feedback loops. SEM has typically been constrained to linear systems of equations, but more recent developments have allowed SEM to represent nonlinear as well as nonrecursive (feedback) relationships. In this sense, SEM path diagrams with nonrecursive relationships and SDM causal loop diagrams look similar. This chapter analyzes the relationship between system dynamics modeling and structural equation modeling. A formal notion of equivalence between system dynamic models and structural equation models is developed. This is then analyzed through an illustration comparing the implied covariance matrices from one system dynamics model against the implied covariance matrices of two “equivalent” LISREL models. The first LISREL model tests whether an “equivalent” model can explain the entire family of covariance matrices generated by a system dynamics model, while the second LISREL model is developed to fit the data. The results suggest that structural equation models are able to explain the covariance matrices of a system dynamics model with changes in feedback loop dominance, but only by sacrificing a substantial portion of the latent structure representing feedback loops.
- Type
- Chapter
- Information
- Structural Equation ModelingApplications in Ecological and Evolutionary Biology, pp. 212 - 234Publisher: Cambridge University PressPrint publication year: 2003
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