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-<title>13&nbsp; Additional Resources – Field Guide to the R Mixed Model Wilderness</title>
+<title>14&nbsp; Additional Resources – Field Guide to the R Mixed Model Wilderness</title>
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-  <li><a href="#further-reading" id="toc-further-reading" class="nav-link active" data-scroll-target="#further-reading"><span class="header-section-number">13.1</span> Further Reading</a></li>
-  <li><a href="#other-resources" id="toc-other-resources" class="nav-link" data-scroll-target="#other-resources"><span class="header-section-number">13.2</span> Other Resources</a></li>
+  <li><a href="#further-reading" id="toc-further-reading" class="nav-link active" data-scroll-target="#further-reading"><span class="header-section-number">14.1</span> Further Reading</a></li>
+  <li><a href="#other-resources" id="toc-other-resources" class="nav-link" data-scroll-target="#other-resources"><span class="header-section-number">14.2</span> Other Resources</a></li>
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-<h1 class="title"><span class="chapter-number">13</span>&nbsp; <span class="chapter-title">Additional Resources</span></h1>
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-<section id="further-reading" class="level2" data-number="13.1">
-<h2 data-number="13.1" class="anchored" data-anchor-id="further-reading"><span class="header-section-number">13.1</span> Further Reading</h2>
+<section id="further-reading" class="level2" data-number="14.1">
+<h2 data-number="14.1" class="anchored" data-anchor-id="further-reading"><span class="header-section-number">14.1</span> Further Reading</h2>
 <ul>
 <li><p><a href="https://cran.r-project.org/web/packages/lme4/vignettes/lmer.pdf">lme4 vignette for fitting linear mixed models</a></p></li>
-<li><p><a href="https://link.springer.com/book/10.1007/b98882"><em>Mixed-Effects Models in S and S-PLUS</em></a> thee book for nlme, by José C. Pinheiro and Douglas M. Bates</p></li>
-<li><p><a href="https://link.springer.com/book/10.1007/978-0-387-87458-6"><em>Mixed Effects Models and Extensions in Ecology with R</em></a> by Alain F. Zuur, Elena N. Ieno, Neil Walker, Anatoly A. Saveliev, and Graham M. Smith</p></li>
+<li><p><a href="https://link.springer.com/book/10.1007/b98882"><em>Mixed-Effects Models in S and S-PLUS</em></a> <em>thee book</em> for <strong>nlme</strong>, by José C. Pinheiro and Douglas M. Bates. We used this book extensively for developing this guide. Sadly, it’s both out of print and we could not find a free copy online. However, there are affordable used copies available.</p></li>
+<li><p><a href="https://link.springer.com/book/10.1007/978-0-387-87458-6"><em>Mixed Effects Models and Extensions in Ecology with R</em></a> by Alain F. Zuur, Elena N. Ieno, Neil Walker, Anatoly A. Saveliev, and Graham M. Smith.</p></li>
 </ul>
 </section>
-<section id="other-resources" class="level2" data-number="13.2">
-<h2 data-number="13.2" class="anchored" data-anchor-id="other-resources"><span class="header-section-number">13.2</span> Other Resources</h2>
+<section id="other-resources" class="level2" data-number="14.2">
+<h2 data-number="14.2" class="anchored" data-anchor-id="other-resources"><span class="header-section-number">14.2</span> Other Resources</h2>
 <ul>
-<li><p><a href="https://easystats.github.io/easystats/">Easy Stats</a> a collection of R packages to assist in statistical modelling, with a big focus on linear models</p></li>
-<li><p><a href="https://mirror.oit.ncsu.edu/cran/web/views/MixedModels.html">Mixed Model CRAN Task View</a> a curated list of R packages relevant to mixed modelling</p></li>
+<li><p><a href="https://easystats.github.io/easystats/">Easy Stats</a> a collection of R packages to assist in statistical modelling, with a big focus on linear models.</p></li>
+<li><p><a href="https://mirror.oit.ncsu.edu/cran/web/views/MixedModels.html">Mixed Model CRAN Task View</a> a curated list of R packages relevant to mixed modelling. This is a great place to start</p></li>
 <li><p><a href="https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models">R-SIG-mixed-models</a> mailing list for help and discussion of mixed-model-related questions, course announcements, etc</p></li>
+<li><p><a href="https://emitanaka.org/edibble-book/">Grammar of Experimental Designs</a> by Emi Tanaka. This has a great description of basic principles of experimental design.</p></li>
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   <li><a href="#think-about-your-analytical-goals" id="toc-think-about-your-analytical-goals" class="nav-link active" data-scroll-target="#think-about-your-analytical-goals"><span class="header-section-number">2.0.1</span> Think About Your Analytical Goals</a></li>
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-  <li><a href="#comments-on-hypothesis-testing-and-usage-of-treatment-letters" id="toc-comments-on-hypothesis-testing-and-usage-of-treatment-letters" class="nav-link" data-scroll-target="#comments-on-hypothesis-testing-and-usage-of-treatment-letters"><span class="header-section-number">2.0.4</span> Comments on Hypothesis Testing and Usage of Treatment Letters</a></li>
+  <li><a href="#cld-warning" id="toc-cld-warning" class="nav-link" data-scroll-target="#cld-warning"><span class="header-section-number">2.0.4</span> Comments on Hypothesis Testing and Usage of Treatment Letters</a></li>
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@@ -337,8 +343,8 @@ <h3 data-number="2.0.3" class="anchored" data-anchor-id="interpret-anova-and-p-v
 <p>A better option would be to discuss the the results of the analysis and how they addressed the research questions: how did the dependent variable change (or not change) as a result of the treatments/interventions/independent variables? What are the parameters or treatment predictions and what do they tell us with regard to the research goals? And to bolster those estimates, what are the confidence intervals on those estimates? What are the p-values for the statistical tests? P-values can support the results and conclusions, but the main results desired by a researcher are usually the estimates themselves - so lead with that!</p>
 <p>To learn more about common pitfalls in interpreting p-values, check out our <a href="https://agstats.io/posts/p-values/">blog post</a> on the subject and/or this paper<a href="#fn3" class="footnote-ref" id="fnref3" role="doc-noteref"><sup>3</sup></a> on the subject.</p>
 <div class="no-row-height column-margin column-container"><div id="fn3"><p><sup>3</sup>&nbsp;Greenland S, Senn SJ, Rothman KJ, Carlin JB, Poole C, Goodman SN, Altman DG. (2016) <a href="https://doi.org/10.1007/s10654-016-0149-3">Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations</a>. <em>Eur J Epidemiol</em>. 31(4):337-50.</p></div></div></section>
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-<h3 data-number="2.0.4" class="anchored" data-anchor-id="comments-on-hypothesis-testing-and-usage-of-treatment-letters"><span class="header-section-number">2.0.4</span> Comments on Hypothesis Testing and Usage of Treatment Letters</h3>
+<section id="cld-warning" class="level3 page-columns page-full" data-number="2.0.4">
+<h3 data-number="2.0.4" class="anchored" data-anchor-id="cld-warning"><span class="header-section-number">2.0.4</span> Comments on Hypothesis Testing and Usage of Treatment Letters</h3>
 <p>Often, I see researchers use <a href="https://en.wikipedia.org/wiki/Compact_letter_display">compact letter display</a> (e.g.&nbsp;“A”, “B”, “C”, ….) for indicating differences among treatments. This makes for concise presentation of results in tables and figures, but it can both kill statistical power and misses nuance in the results.</p>
 
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+ <span class="menu-text"><span class="chapter-number">12</span>&nbsp; <span class="chapter-title">Marginal Means and Contrasts</span></span></a>
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