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<h1 class="title toc-ignore">Course Schedule</h1>
</div>
<p>The general schedule for the course is below. Please note, for readings the u/p is semclass for both. For code, please see <a href="https://www.dropbox.com/sh/objbunt4wxdlnxb/AAB38yRdxi62BlMC1ca_SYPJa?dl=0">this dropbox</a> for a static view. During the course itself, I’ll give you share access so that you can update files as things change. Please <a href="./files/scripts/Install_Required_Packages.R">run this script</a> in R to install the packages you will need for the course before day 1.<br />
<br />
For the course, we’ll also use a number of common data sets which you can download (and put into a data folder) <a href="files/data.zip">here</a>.<br />
<br />
## Day 1. Introduction to SEM <strong>Overview:</strong> We discuss just what is SEM. Along the way we’ll discuss it’s origins, give a general example of moving from a traditional ANOVA-esque framework, and finally discuss how to build a well-justified causal model. Causality will be central, and we won’t pull any punches! We’ll end the day by starting to talk about how to fit SEMs using likelihood using covariance-based estimation with likelihood.<br />
<strong>Lectures:</strong> <a href="lecture_pdfs/SEM_Intro_And_History.pdf">What is SEM? A Practical and Historical Overview</a><br />
<a href="lecture_pdfs/Anatomy_of_SEM.pdf">Anatomy of SEM</a><br />
<a href="lecture_pdfs/Multivariate_Causal_Models.pdf">Building Multivariate Causal Models</a>, <a href="files/scripts/multivariate_causal_models.R">code</a><br />
<strong>Readings:</strong> <a href="http://byrneslab.net/classes/sem_pdfs/General/Grace%20et%20al%202010%20Ecological%20Monographs.pdf">Grace 2010</a> (overview), <a href="http://byrneslab.net/classes/sem_pdfs/examples/Whalen_et_al_2013_Ecology.pdf">Whalen et al. 2013</a> (example) (note: semclass/semclass for pdfs)<br />
<strong>Optional Reading:</strong> <a href="http://byrneslab.net/classes/sem_pdfs/General/Matsueda%202012%20history.pdf">Matsueda 2012</a> (history), <a href="http://byrneslab.net/classes/sem_pdfs/causality/pearl-causality-review.pdf">Pearl 2012a</a> (history of causality)<br />
<br />
</p>
<div id="day-2.-covariance-based-sem" class="section level2">
<h2>Day 2. Covariance Based SEM</h2>
<p><strong>Overview:</strong> We’ll pick up where we left off discussing covariance-based estimation. We’ll spend time discussing how we evaluate an SEM and what we report. We’ll end the day exploring the concept of latent variables - variables for which we do not actually have a measured variable, but for which we have one or more indicators.<br />
<strong>Lectures:</strong> <a href="lecture_pdfs/Intro_to_Likelihood_Fitting.pdf">Engines of SEM: Covariance-Based Estimation</a>, <a href="files/scripts/Likelihood_Fitting.R">code</a><br />
<a href="lecture_pdfs/Assesing_Likelihood_Fits.pdf">What does it mean to evaluate a multivariate hypothesis?</a>, <a href="files/scripts/Assessing_Likelihood.R">code</a>, <a href="files/scripts/fitted_lavaan.R">fitted_lavaan.R</a><br />
<a href="lecture_pdfs/Latent_Variables.pdf">Latent Variable models</a>, <a href="files/scripts/Latent_variables.R">code</a><br />
<strong>Readings:</strong> <a href="http://byrneslab.net/classes/sem_pdfs/General/Grace%20&%20Bollen%202005%20Bull%20Ecol%20Soc%20Amer.pdf">Grace and Bollen 2005</a><br />
<br />
</p>
</div>
<div id="day-3.-piecewise-sem" class="section level2">
<h2>Day 3. Piecewise SEM</h2>
<p><strong>Overview:</strong> We move beyond covariance based techniques into local estimation techniques that embrace the graph theoretic approach to SEM via the <code>piecewiseSEM</code> package by Jon Lefcheck.<br />
<strong>Lectures:</strong> <a href="lecture_pdfs/Local_Estimation.pdf">Engines of SEM: Local Estimation with piecewiseSEM</a>, <a href="files/scripts/Local_Estimation.R">code</a><br />
<a href="lecture_pdfs/Nonlinear_nonnormal.pdf">A Nonlinear, Non-normal world</a>, <a href="files/scripts/nonlinear_nonnormal.R">code</a><br />
<a href="lecture_pdfs/Categorical_Variables.pdf">Categorical Variables and Multigroup Analysis</a>, <a href="files/scripts/Categorical_Vars.R">code</a>, <a href="files/scripts/multigroup_margins.R">multigroup_margins.R</a><br />
<a href="lecture_pdfs/Mixed_Models.pdf">Mixed Models in SEM</a>, <a href="files/scripts/Mixed_Models.R">code</a><br />
<strong>Readings:</strong> <a href="http://jslefche.github.io/piecewiseSEM/articles/piecewiseSEM.html">piecewiseSEM vignette</a>, <a href="http://byrneslab.net//classes/sem_pdfs/d-sep/Shipley%202009%20dsep%20with%20multilevel%20models.pdf">Shipley 2009</a>, <a href="http://byrneslab.net//classes/sem_pdfs/d-sep/Lefcheck_2016_piecewiseSEM.pdf">Lefcheck 2016</a><br />
<br />
</p>
</div>
<div id="day-4.-special-topics-in-sem" class="section level2">
<h2>Day 4. Special Topics in SEM</h2>
<p><strong>Overview:</strong> This day varies quite a bit, depending on the current class. It’s a potpurri of more advances topics, not all of which will be covered.<br />
<strong>Lectures:</strong> <a href="lecture_pdfs/bayesian_sem.pdf">Engines of SEM: Bayesian SEM</a>, <a href="files/scripts/bayesian_sem.R">code</a><br />
<a href="lecture_pdfs/Spatial_Autocorrelation.pdf">Spatial Data in SEM</a>, <a href="files/scripts/Spatial_autocorrelation.R">code</a>, <a href="files/scripts/residuals.R">helper functions</a><br />
<a href="lecture_pdfs/Temporal_Autocorrelation.pdf">Timseries and Temporal Autocorrelation in SEM</a>, <a href="files/scripts/Temporal_Autocorrelation.R">code</a><br />
<a href="lecture_pdfs/Composite_Variables.pdf">Composite Variables</a>, <a href="files/scripts/Composites.R">code</a><br />
Whole-System Prediction with SEM<br />
<strong>Readings:</strong> <a href="https://rpubs.com/jebyrnes/brms_bayes_sem">brms and SEM</a>. <a href="https://rpubs.com/jebyrnes/causal_mods">causal model structure and random effects</a><br />
<br />
</p>
</div>
<div id="day-5.-open-lab-and-presentations" class="section level2">
<h2>Day 5. Open Lab and Presentations</h2>
<p><strong>Overview:</strong> In the morning, we’ll have an open lab. Students will work on their own data and projects with an aim to building a 2-3 slide powerpoint presentation detailing 1. The problem/system, 2. The model they built, and 3. The Final result and any challenges. We’ll present these in the afternoon, hopefully in a convivial atmosphere, after a brief primer on warming up for a talk you are nervous about!<br />
<strong>Lectures:</strong> How to fool yourself with SEM!<br />
<br />
</p>
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