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
12 - Modeling means in latent variable models of natural selection
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
This chapter describes theory and methods for measuring mean changes in phenotypes when the phenotypic variable is considered as a latent construct with multiple indicators. Data for exposition of the methods are generated using EQS simulation techniques. Two datasets are used, one simulating the population prior to a selection event, the pre-selection group, and another simulating the population after the selection event, the post-selection group. The example includes an environmental construct that may also influence phenotypic variable means. The example demonstrates how structural equation modeling (SEM) may be used to statistically control for such environmental influences to arrive at a more accurate estimate of the effect of the selection event. Data are analyzed with LISREL version 8.30. The analysis includes a measurement model and stacked model of covariances to test model assumptions, and a final means model to estimate the mean phenotypic response to selection.
- Type
- Chapter
- Information
- Structural Equation ModelingApplications in Ecological and Evolutionary Biology, pp. 297 - 311Publisher: Cambridge University PressPrint publication year: 2003
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