An Introduction to Structural Equation Modeling for Ecology & Evolutionary Biology
Structural Equation Modeling (SEM) or path analysis is a multivariate technique that can test for the nature and magnitude of direct and indirect effects of multiple interacting factors. SEM is an approach that interprets information about the observed correlations among the traits of organisms or groups of organisms in order to evaluate complex causal relationships.
It is a rich technique that is particularly well suited for large-scale observational community or population data sets. Its intuitive connection to how we conceive of our study systems makes it a powerful and useful technique for ecologists and evolutionary biologists. The aim of this course is to familiarize with the basic techniques of SEM using the ‘lavaan’ package in R.
Before the course starts students are required to work through a preclass-excerise and tutorial.
Lectures: What is SEM? How can it be part of your research program, SEM as a process: Creating multivariate causal models, Fitting piecewise models
Exercises: Creating causal conceptual models, Piecewise model creation
Lectures: Fitting observed variable models with covariance structures, What does it mean to evaluate a multivariate hypothesis? ANCOVA revisited & nonlinearities.
Exercises: Fitting observed variable structural equation models in R
Lectures: Multigroup models, Latent variable models
Exercises: Multigroup analysis and the introduction of the latent variable
Lectures: Composite variables, Prediction using SEMs, Dealing with clustered data, space and time, How to fool yourself with SEM (sensu Kline)
Exercises: Composites & other advanced techniques
Open lab and student presentations