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<h1 class="title toc-ignore">Helpful and/or interesting material</h1>
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<p>This page contains links to journal articles, blog posts, webpages,
etc., that we (the instructors) believe may be useful to you, or at the
very least interesting. <strong>Bold</strong> articles are ones that we
consider <strong>must-reads</strong> but don’t have enough time to
actually assign. And to be clear, must-read means that people will
probably assume you’ve read this and know the basic points.</p>
<div id="cohen-cohen-west-aiken-2003" class="section level2">
<h2><a href="https://psycnet.apa.org/record/2002-18109-000">Cohen,
Cohen, West, & Aiken (2003)</a></h2>
<p>For additional review of correlation and regression beyond the
assigned textbook, here are chapters from another popular textbook
(edition 3).</p>
<p><a href="readings/CCWA_ch1.pdf">Chapter 1.
<strong>Introduction.</strong></a> - Multiple regression/correlation as
a general data-analytic system - A comparison of multiple
regression/correlation and analysis of variance approaches - Multiple
regression/correlation and the complexity of behavioral sciences -
Orientation of the book - Computation, the computer, and numerical
results - The spectrum of behavioral sciences</p>
<p><a href="readings/CCWA_ch2.pdf">Chapter 2. <strong>Bivariate
Correlation and Regression.</strong></a> - Tabular and graphic
representations of relationships - The index of linear correlation
between two variables - The Pearson Product-Moment Correlation
Coefficient - Alternative formulas - Regression coefficients -
Regression toward the mean - The standard error of the estimate and
measures of the strength of association - Statistical inference -
Precision and power - Factors affecting the size of <em>r</em></p>
<p><a href="readings/CCWA_ch3.pdf">Chapter 3. <strong>Multiple
Regression/Correlation with Two or More Independent
Variables</strong></a> - Causal models - Regression with two independent
variables - Measures of association - Patterns of association - Multiple
regression/correlation with <em>k</em> independent variables -
Statistical inference - Precision and power - Equations and
prediction</p>
</div>
<div id="on-coding-and-data-science" class="section level2">
<h2>On coding and data science</h2>
<p>Peters (2004) <a
href="https://www.python.org/dev/peps/pep-0020/">“The Zen of
Python.”</a> – A list of 19 principles for writing better Python code;
the principles also apply to writing better R code.</p>
<p>Hill (2019) <a
href="https://arm.rbind.io/slides/xaringan.html#1">“Meet xaringan.”</a>
– There are a ton of resources for learning how to make slides using R.
Too many to list here. I like this one because it’s accessible, funny,
visually appealing, and will help you make slides that you’re proud of.
(By the way, UO has theme you can use.)</p>
<p>Bryan (2019) <a href="https://happygitwithr.com">“Happy Git with
R.”</a> – Interested in using GitHub for version control? Jenny Bryan
will guide you through the process and metaphorically hand you a tissue
when you’re screaming with frustration.</p>
<p>Anderson (2021) <a href="https://www.sds.pub/index.html">“Social Data
Science with R”</a> – This book is currently being developed for the
data science sequence at the University of Oregon, taught in the
education department by Daniel Anderson. It is a helpful resource for
foundational coding skills and data visualization in R.</p>
<p>Robinson (2016) <a
href="https://www.youtube.com/watch?v=7VGPUBWGv6g">Broom: Converting
Statistical Models to Tidy Data Frames</a> – This YouTube video explains
how to use the <code>{Broom}</code> package to extract useful
information from model objects.</p>
</div>
<div id="on-creating-useful-graphics" class="section level2">
<h2>On creating useful graphics</h2>
<p>Patil (2020) <a
href="https://indrajeetpatil.github.io/ggstatsplot/index.html">“ggstatsplot”</a>
– A very useful R package for creating visuals to display some basic
univariate and bivariate descriptives.</p>
</div>
<div id="on-statistical-models" class="section level2">
<h2>On statistical models</h2>
<p><a
href="https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab">YouTube
series on linear algebra</a></p>
<p><a href="https://uopsych.github.io/psy611/">PSY 611</a> – Don’t
forget everything we covered last term!</p>
<p>Dunn & Smyth (2019) <a
href="https://link.springer.com/book/10.1007/978-1-4419-0118-7">Generalized
Linear Models With Examples in R</a> – if you’re looking for a
supplemental (free, online) textbook to cover general and generalized
linear models, I highly recommend this one. It includes useful tidbits,
like <a
href="https://link.springer.com/chapter/10.1007/978-1-4419-0118-7_1#Sec4">coding
factors</a>, an expansion on the <a
href="https://link.springer.com/chapter/10.1007/978-1-4419-0118-7_2#Sec13">matrix
algebra notation</a> for multiple regression, and an extended section on
the <a
href="https://link.springer.com/chapter/10.1007/978-1-4419-0118-7_2#Sec39">R
code</a> useful for linear models. Plus, it expands to cases we don’t
cover in class.</p>
<p>Gelman posted a document containing <a
href="https://statmodeling.stat.columbia.edu/wp-content/uploads/2020/07/raos_tips.pdf">Tips
for improving regression</a> on his blog, but I can’t find the original
citation. It may be from one of his own works or from someone else’s.
Let me know if you figure it out!</p>
</div>
<div id="on-causality-confounds-colliders-and-controls"
class="section level2">
<h2>On causality, confounds, colliders, and controls</h2>
<p>Simonsohn (2019) <a href="http://datacolada.org/80">Interaction
effects need interaction controls.</a></p>
<p>Wang et al. (2020) <a href="readings/wang_2020.pdf">Using independent
covariates in experimental designs: Quantifying the trade-off between
power boost and Type I error inflation</a></p>
<p>Westfall & Yarkoni (2016) <a
href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0152719">Statistically
Controlling for Confounding Constructs Is Harder than You Think</a></p>
<p>Wysocki, Lawson, & Rhemtulla (2020) <a
href="https://psyarxiv.com/j9vw4">Statistical Control Requires Causal
Justification</a></p>
</div>
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