The Political Scientist

In a hyper-partisan world, it can feel like even the most mundane of conversations suddenly can erupt into a politically charged jousting match. However, more often than not, those who engage in political discussions are more likely to do so with likeminded individuals in an echo chamber where congruent world views are reinforced (and even strengthened) and differing opinions are either nowhere to be seen or openly derided. It certainly can't help matters much that those who are most likely to engage in online political discussions also happen to have more extreme liberal or conservative ideological views. In 2010, Pew Research Center conducted a post midterm election tracking survey that contained a number of questions related to online political engagement. Whether or not respondents had ever engaged in an online political discussion was one of the questions asked in the survey. When broken down by ideological intensity, the share of respondents in each ideological category

During the (agonizingly) long period leading up to the 2016 presidential election, much ado was made about whether Republican Donald J. Trump's success was (partially) chalked up to his political outsider status. While it's hard to know for sure, some of my own research suggests, at the very least, that presidential candidates were motivated to spend much less time in 2016 focusing on their prior experience as government officials, at least when it comes to primary and general election debates. Political scientists who study presidential debates have shown that candidates both influence and are constrained by public opinion. Presidential hopefuls must cater their debate performances to shifts in public opinion in order appeal to voters, while at the same time candidates must try to influence the agenda of debates to suit their own purposes. It's a delicate balancing act. The content of presidential debates, therefore, can tell us a lot about public opinion at the time o

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, which is free by the way), you shou

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 date makes clear that the prom

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 analysis on larg