Structural Equation Modelling for Ecologists and Evolutionary Biologists (SEMR01)
23 October 2017 - 27 October 2017£260.00 - £670.00
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.
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.
This course is orientated to PhD and MSc students, as well as persons in research or industry working on ecological data.
We offer COURSE ONLY and ACCOMMODATION PACKAGES;
• COURSE ONLY – Includes lunch and refreshments.
• ACCOMMODATION PACKAGE (to be purchased in addition to the course only option) – Includes breakfast, lunch, dinner, refreshments, minibus to and from meeting point and accommodation. Accommodation is multiple occupancy (max 3 people) single sex en-suite rooms. Arrival Sunday 22nd October and departure Friday 27th PM.
To book ‘COURSE ONLY’ with the option to add the additional ‘ACCOMMODATION PACKAGE’ please scroll to the bottom of this page.
Other payment options are available please email firstname.lastname@example.org
Cancellation policy: Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact email@example.com Failure to attend will result in the full cost of the course being charged. In the unfortunate event that PRstatistics must cancel this course due to unforeseen circumstances a full refund for the course will be credited. However PRstatistics cannot be held responsible for any travel fees, accommodation or other expenses incurred to you as a result of the cancellation.
Introductory lectures on the concepts and mathematics of SEM; practical lectures demonstrating the application to real datasets; computer labs to expand on practical lecture materials. Participants are encouraged to bring their own data and develop their own models. Time will be set aside at the end of each day to work with participants on their models. Datasets will be made available for those who do not have existing data to bring.
Assumed quantitative knowledge
Basic knowledge of linear modelling.
Assumed computer background
Proficiency with R programming language, including: importing/exporting data; manipulating data in the R environment; constructing and evaluating basic statistical models (e.g., lm()).
Equipment and software requirements
A laptop/personal computer with a working version or R and RStudio installed. R and RStudio are supported by both PC and MAC and can be downloaded for free by following these links
It is essential that you come with all necessary software and packages already installed (you will be sent a list of packages prior to the course) internet access may not always be available.
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Meet at Margam Discovery Centre at approximately 18:30 (Download directions PDF)
Monday 23rd – Classes from 09:00 to 17:00
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 – Classes from 09:00 to 17:00
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 – Classes from 09:00 to 17:00
Module 8: Introduction to piecewise approach
Module 9: Incorporation of random effects models
Model 10: Autocorrelation Reading: Shipley 2009; Lefcheck 2016
Thursday 26th – Classes from 09:00 to 17:00
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 – Classes from 09:00 to 16:00
Open Lab and Final Presentations