The Political Scientist

With election time coming up soon (Nov. 8th!), and with the headlines obsessed with the presidential race, I wanted to take a moment to focus on down ballot races--in particular, congressional district races for the House of Representatives. I have zero interest in telling anyone how to vote, but I would like to help any interested parties figure out where the incumbent in their district stands in terms of ideology and overall congressional leadership relative to other House members. Knowing this information might, at the very least, help a given voter to know whether the incumbent in their district is performing they way they want them to perform. I'm also going to use this as an opportunity to demonstrate how to use R (a great tool for doing an analysis like this) to determine where the incumbent in your congressional district stands relative to other House members. If you follow the steps outlined below (and if you download R and R Studio, ...

I recently had the wonderful opportunity to present some of my research at the 87th annual conference of the Indiana Academy of the Social Sciences. It was an amazing experience, and I was so thrilled that my fiancée and parents were able to come support me. The following is a shortened version of the paper I presented in a panel session alongside three other researchers who also discussed their own, very interesting, research projects: Introduction Evidence from numerous surveys over the years clearly shows that there is a growing chasm between Democrats and Republicans, not only ideologically, but also in terms of animosity.   At the same time, as we’ve traversed headlong into the digital age, social media use has increased exponentially.   While, according to more optimistic scholars, social media offers the promise of a public sphere where people with varying perspectives, ideologies, and ideas can engage in productive dialogue, the literature to d...

If you had told me a year or two ago that I would one day learn how to estimate a structural topic model (STM) -- an unsupervised machine learning based form of text analysis that incorporates “metadata” (i.e., a matrix of covariates) in the estimation of a generalized linear model of topical prevalence and content -- I would have either laughed until I cried or stared blankly at you wondering if either you or I had just had a stroke because I didn't understand a word of the gibberish that just came out of your mouth. And yet, here I am, taking my first steps into the wild world of text analysis. Unlike other, more traditional quantitative methods, topic models allow researchers to conduct statistical analysis on textual data -- open-ended survey questions, newspaper articles, blogs, transcripts, etc.  -- where "topics" (i.e., sets of highly associated words) are identified, not by a person, but by an algorithm, allowing users to conduct statistical a...