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Course on Structural Equation Modelling In Ecology Oct 23-27


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#1 Melanie Colón

Melanie Colón
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  • Texas A&M University, College Station, TX,
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Posted 06 July 2017 - 07:28 AM

"Structural Equation Modelling for Ecologists and Evolutionary Biologists”

Delivered by Dr. Jarret Byrnes and Dr. Jon Lefcheck

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

This course will run from 23rd - 27th October 2017 at Margam Park
Discovery Centre, Wales, UK

Course only and all inclusive packages are available.

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 (SEM) 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.
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, analyse, and receive feedback
on structural equation models.

Course content is as follows

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 inquiries to oliverhooker@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.

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