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  • Cited by 4
  • Print publication year: 2003
  • Online publication date: October 2009

7 - Examining the relationship between environmental variables and ordination axes using latent variables and structural equation modeling

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.

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