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
7 - Examining the relationship between environmental variables and ordination axes using latent variables and structural equation 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
Examinations of the relationships between environmental variables and ordination results often give little consideration to the complex relationships among environmental factors. In this chapter I consider the utility of structural equation modeling (SEM) with latent variables for evaluating the relationships among environmental variables and ordination axis scores. Using an example data set, I compare the efficiency of three approaches – (1) multiple regression, (2) principle component analysis, and (3) SEM – as methods for extracting information from multivariate data. All three approaches were found to be equivalent in their ability to explain variance in response variables but differ in their ability to explain the covariation among predictor variables. In general, when sufficient theoretical knowledge exists to permit the formulation of hypotheses about the relationships among variables, structural equation modeling can provide for a more comprehensive analysis. It is suggested that the analysis of latent variables using SEM may advance our understanding of environmental effects on vegetation data in many cases.
- Type
- Chapter
- Information
- Structural Equation ModelingApplications in Ecological and Evolutionary Biology, pp. 171 - 193Publisher: Cambridge University PressPrint publication year: 2003
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