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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="on-data-collection-measuring-constructs-scales"
class="section level2">
<h2>On data collection, measuring constructs, scales</h2>
<p>Lord (1953) <a href="readings/Lord_1953.pdf">“On the statistical
treatment of football numbers.”</a> – This one is worth reading several
times, ideally with a friend. Don’t worry, it’s quite short. A small
note: “Tchebycheff” is sometimes written as “Chebyshev” and in either
case is pronounced Cheb-ee-sheve.</p>
<p>Cox (1980) <a
href="https://www.jstor.org/stable/pdf/3150495.pdf?casa_token=pnuy8XQLHTIAAAAA:QOUc17Ic0cquCBXuqyRvfCSBUiXkFwDoVjuOxHB8qjJHtyJXGSlam7Zxcd2RIABVOXMy-YARHJ6dHhuGc_ABJaTBk8Lf8FLF9EKX9wlrn99yB5CWPgg">“The
optimal number of response alternatives for a scale: A revew”</a> – For
those developing new measures, it’s worth spending some time reading
about psychometrics. As with everything, there’s no one-size-fits-all
answer to how many response options, whether to include a midpoint, and
how to label your scales. But you should understand the implications of
these choices before you write your own items.</p>
<p>Malone & Goldstone (2019) <a
href="https://www.npr.org/2019/08/28/755191639/episode-936-the-modal-american">“Episode
936: The Modal American.”</a> – This episode of NPR’s podcast,
<em>Planet Money</em>, is a great discussion of what we mean when we say
“average,” why the mean can be a bad measure of central tendency, and
how complicated calculating simple statistics are. They don’t use the
words “artifact” or “collinearity”, but they discuss both and how they
can lead to bad answers.</p>
<p>Rohrer (2019) <a
href="http://www.the100.ci/2019/10/03/indirect-effect-ex-machina">“Indirect
Effect <em>Ex Machina</em>.”</a> – come for the ice cream and
sauerkraut, stay for the creeping dread that many mediation studies are
probably wrong.</p>
<p>Chester & Lasko (2019) <a
href="https://psyarxiv.com/t7ev9">“Construct Validation of Experimental
Manipulations in Social Psychology: Current Practices and
Recommendations for the Future.”</a> – a totally brand new preprint
about construct validity in social psychology. They do a great job of
explaining different kinds of validity in addition to empirically
describing the extent to which social psychologists check for construct
validity. Here is a lovely quote: “In the context of archery, construct
validity is the practice of checking that the arrow hit the bullseye and
internal validity is the practice of preventing the wind and your own
breathing from influencing your aim.”</p>
<!-- ## On (matrix) algebra -->
<!-- Kahn Academy ["Linear algebra."](https://www.khanacademy.org/math/linear-algebra) For those looking for tutorials to practice matrix algebra. I've linked to the broader page because it includes linear combinations. -->
<!-- 3Blue1Brown's [YouTube channel.](https://www.youtube.com/channel/UCYO_jab_esuFRV4b17AJtAw) Thanks to Scott Wallner for recommending these! There are playlists for [linear algebra](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab), [calculus](https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr), and [fractals](https://www.youtube.com/watch?v=gB9n2gHsHN4), among others. -->
</div>
<div id="on-sampling-distributions" class="section level2">
<h2>On sampling distributions</h2>
<p><a
href="https://onlinestatbook.com/stat_sim/sampling_dist/">Simulation on
the Rice Virtual Lab in Statistics</a> See a population, take a sample.
Or take 5 samples. Or 10,000. Build your sampling distribution by hand.
Compare sampling distributions with different sample sizes. See how the
population distribution changes the sampling distribution.</p>
<p><a href="https://rpsychologist.com/d3/ci/">Confidence Interval
Demo</a> by Kristoffer Magnusson. Respect sampling variability! This
demo shows that not only does the mean change with each sample, but so
does the width of the confidence interval (gasp!). But seriously, spend
some time on this site. It will change the way you see statistics,
literally speaking.</p>
</div>
<div id="on-probability-and-its-use-and-misuse" class="section level2">
<h2>On probability and its use and misuse</h2>
<p>Fry (2019) <a
href="https://www.newyorker.com/magazine/2019/09/09/what-statistics-can-and-cant-tell-us-about-ourselves">“What
statistics can and can’t tell us about ourselves.”</a> This article in
the <em>New Yorker</em> explains issues of (frequentist) probability in
terms you can explain to your grandma, as well as makes clear the issues
of the Fisher tradition of reducing experiments to p-values.</p>
<p>Resnick (2017) <a
href="https://www.vox.com/science-and-health/2017/7/31/16021654/p-values-statistical-significance-redefine-0005">“What
a nerdy debate about p-values shows about science – and how to fix
it.”</a> Another great article, this time on <em>Vox</em>, about
p-values</p>
</div>
<div
id="on-the-credibility-revolution-aka-replication-crisis-or-reproducibility-crisis"
class="section level2">
<h2>On the credibility revolution (aka replication crisis or
reproducibility crisis)</h2>
<p><strong>Ioannidis (2005) <a href="readings/Ioannidis_2005.pdf">“Why
most published findings are false.”</a></strong> – This is one of the
articles that kick-started the concern.</p>
<p><strong>Bem (2011) <a href="readings/Bem_2011.pdf">“Feeling the
future: Experimental evidence for anomalous retroactive influences on
cognition and affect.”</a></strong> – It’s true, ESP is real. Or is it?
What (if anything) in this article makes you skeptical of the results?
Try to articulate the weaknesses.</p>
<p>Bem (2004) <a href="readings/Bem_2004.pdf">“Writing the empirical
journal article.”</a> – This was assigned to me when I was a graduate
student. Some of the advice is actually very good, although most of the
best stuff can be found in Strunk and White. The part of this article
that’s really worth reading is the “Analyzing Data” and “Reporting the
Findings” subsections of “Planning your article.”</p>
<p><strong>Szollosi et al. (2019). <a
href="https://psyarxiv.com/x36pz/">“Preregistration is redundant, at
best.”</a></strong> Preprint.</p>
<p><strong>Anonymous et al. (2021). <a
href="http://datacolada.org/98">Evidence of Fraud in an Influential
Field Experiment about Dishonesty</a></strong> – This is not only a case
study of the issues of fraud in psychology, but it also serves as an
excellent example of how examining descriptive statistics and
distributions can reveal a lot about data.</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 resource 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>
</div>
<div
id="on-being-a-grad-student-being-a-scientist-balancing-life-and-work"
class="section level2">
<h2>On being a grad student, being a scientist, balancing life and
work</h2>
<p><a href="https://www.cns.nyu.edu/events/growingupinscience/">Growing
up in Science</a> – a set of online, global, free events about
mentorship, being a student, challenges, successes, failures, and
more.</p>
</div>
</div>
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