Day 1
Introduction to SEM
Module 1: What is Structural Equation Modeling? Why would I use it?
Module 2: Creating multivariate causal models
Module 3: Fitting piecewise models
Readings: Grace 2010 (overview), Whalen et al. 2013 (example)
Day 2
SEM Using Likelihood
Module 4: Fitting Observed Variable models with covariance structures Module 5: What does it mean to evaluate a multivariate hypothesis?
Module 6: Latent Variable models Module 7: ANCOVA revisited & Nonlinearities
Readings: Grace & Bollen 2005, Shipley 2004
Optional Reading: Pearl 2012, Pearl 2009 (causality)
Day 3
Piecewise SEM
Module 8: Introduction to piecewise approach
Module 9: Incorporation of random effects models
Model 10: Autocorrelation Reading: Shipley 2009; Lefcheck 2016
Day 4
Advanced Topics with Likelihood and Piecewise SEM
Module 11: Multigroup models and non-linearities
Module 12: Composite Variables
Module 13: Phylogenetically-correlated data
Module 14: Prediction using SEM
Module 15: How To Reject A Paper That Uses SEM
Readings: Grace & Julia 1999, von Hardenberg & Gonzalez‐Voyer 2013
Day 5
Open Lab and Final Presentations
Please email any questions to oliverhooker@prstatistics.com
Structural Equation Modelling for Ecologists and Evolutionary Biologists (SEMR05)
https://www.prstatistics.com/course/structural-equation-modelling-for-ecologists-and-evolutionary-biologists-semr05/
6th - 10th March 2023
Day 1
Introduction to SEM
Module 1: What is Structural Equation Modeling? Why would I use it?
Module 2: Creating multivariate causal models
Module 3: Fitting piecewise models
Readings: Grace 2010 (overview), Whalen et al. 2013 (example)
Day 2
SEM Using Likelihood
Module 4: Fitting Observed Variable models with covariance structures Module 5: What does it mean to evaluate a multivariate hypothesis?
Module 6: Latent Variable models Module 7: ANCOVA revisited & Nonlinearities
Readings: Grace & Bollen 2005, Shipley 2004
Optional Reading: Pearl 2012, Pearl 2009 (causality)
Day 3
Piecewise SEM
Module 8: Introduction to piecewise approach
Module 9: Incorporation of random effects models
Model 10: Autocorrelation Reading: Shipley 2009; Lefcheck 2016
Day 4
Advanced Topics with Likelihood and Piecewise SEM
Module 11: Multigroup models and non-linearities
Module 12: Composite Variables
Module 13: Phylogenetically-correlated data
Module 14: Prediction using SEM
Module 15: How To Reject A Paper That Uses SEM
Readings: Grace & Julia 1999, von Hardenberg & Gonzalez‐Voyer 2013
Day 5
Open Lab and Final Presentations
Please email any questions to oliverhooker@prstatistics.com
Upcoming courses can be found here - https://www.prstatistics.com/live-courses/
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