[MARMAM] Course on Structural equation modelling

Oliver Hooker oliverhooker at prstatistics.com
Tue May 30 12:25:54 PDT 2017


Structural Equation Modelling for Ecologists and Evolutionary Biologists 
(SEMR01)

https://www.prstatistics.com/course/structural-equation-modelling-for-ecologists-and-evolutionary-biologists-semr01/

This course will be delivered by Dr. Jarrett Byrnes and Dr. Jon Lefcheck 
(piecewise cran package)

23 October 2017 - 27 October 2017

Course Overview:

The course is a primer on structural equation modelling (SEM) and 
confirmatory path analysis, with an emphasis on practical skills and 
applications to real-world data.

Structural equation modelling is a rapidly growing technique in ecology 
and evolution that unites multiple hypotheses in a single causal 
network. It provides an intuitive graphical representation of 
relationships among variables, underpinned by well-described 
mathematical estimation procedures. Several advances in SEM over the 
past few years have expanded its utility for typical ecological 
datasets, which include count data, missing observations, and nested or 
hierarchical designs. This course is particularly suited to those 
studying complex interactions between animals and their environments 
making it extremely useful to those studying marine mammals.

We will cover the basic philosophy behind SEM, provide approachable 
mathematical explanations of the techniques, and cover recent extensions 
to mixed effects models and non-normal distributions. Along the way, we 
will work through many examples from the primary literature using the 
open-source statistical software R (www.r-project.org). We will draw on 
two popular R packages for conducting SEM, including lavaan and 
piecewiseSEM.

Participants are encouraged to bring their own data, as there will be 
opportunities throughout the course to plan, analyze, and receive 
feedback on structural equation models.

Monday 23rd
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)

Tuesday 24th
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)

Wednesday 25th
Piecewise SEM
Module 8: Introduction to piecewise approach
Module 9: Incorporation of random effects models
Model 10: Autocorrelation Reading: Shipley 2009; Lefcheck 2016

Thursday 26th
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

Friday 27th
Open Lab and Final Presentations
Please email any inquiries to oliverhooker at prstatistics.com or visit our 
website www.prstatistics.com
Please feel free to distribute this material anywhere you feel is 
suitable

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Oliver Hooker PhD. PR statistics
2017 publications -
Ecosystem size predicts eco-morphological variability in post-glacial 
diversification. Ecology and Evolution. In press.
The physiological costs of prey switching reinforce foraging 
specialization. Journal of animal ecology.
prstatistics.com

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