Syllabi are one of the suspected culprits behind the gender citation gap. As syllabi tend to disproportionately reflect the research of men, and syllabi are where many young scholars initially find their footing in a literature, syllabi can lead to men being cited at higher rates than women. Yet even well-intentioned instructors may struggle to ascertain that they have gender balance in their syllabi: counting names by hand is tedious and prone to all sorts of error. (Ask yourself, how many times have you lost your place while counting things?) Further, instructors may not know the gender identity of every scholar on their syllabus. This tool aims to help instructors assess the gender balance of their syllabi by parsing out the syllabus into names and then assigning to each name the probability that it belongs to a woman. These probabilities can then be used to approximate what percentage of a syllabus (or a works cited list) is women scholars. In addition, the tool provides an estimate of the racial breakdown of syllabi based on author surnames.
Electoral rules are taught in many comparative politics classes, but it can be hard sometimes to make the abstract concepts really concrete for students. Sure, we can produce historical examples, but then sometimes students are unable to conceptually separate the electoral rule configuration from the country, and it's difficult to think about what other potential -- but not realized -- scenarios might look like. When will a PR system produce a legislature in which one party has a majority? When might a first-past-the-post system end up with three parties in the legislature? Many instructors do some version of this simulation on the board or using Excel --- this tool aims to make that a little bit easier, by allowing students and instructors to change all the parameters, see how the outcomes change, and even download a spreadsheet of the votes, whether in class or not.
How often do you find yourself thinking, "I'd love a visual depiction of this text document, but I wish I could do that while demonstrating my love for internet memes of the early 2010s"? All the time, I'm sure. For all those moments where you just can't resist meme-ifyng your data, the Word Doge package for R steps in to save the day. Visualize word frequencies and enjoy bright colors, Comic Sans, and a picture of a Shiba Inu. This, my friends, is how you live the dream.
The interactionTest package for R is meant to help practitioners implement the advice in Esarey and Sumner (2017). In short, in that article, we argue that interaction terms introduce a multiple comparison problem, and this can mean that we are may be either over- or underconfident when we assess their significance, depending upon the hypotheses that need to be tested. This package, which is available on CRAN, implements the adjustment procedures laid out in the paper.
There are a few interactive examples for understanding and visualizing mathematical and statistical concepts over at my teaching page. These are intended less for a broader audience, since they were developed specifically to help students taking Emory's POLS 508 and POLS 509 courses, but they may be of use to students who are struggling with understanding and visualizing certain concepts, including delta-epsilon proofs, Riemann sums, and the Central Limit Theorem.