I am an Assistant Professor at the University of South Carolina’s School of Journalism and Mass Communications.
My research interests are broadly in the area of political communication, with special interests in social identity, social environments, and the maintenance thereof. I also spend a good deal of my time thinking about the research designs and analytic methods we can use to learn more about these things.
PhD in Communication, 2020
MA in Communication, 2019
BA with Honors in Political Science, 2014
An expanding collection of tools I originally created to aid in my own research. The unifying theme is the ability to report and visualize the results of regression models. Most popular are functions that provide a streamlined, customizable summary of regressions (including robust standard error support) in the console, HTML/LaTeX/Word tables, and coefficient plots. A few other tools have been described in my blog and elsewhere.
Previously part of the
jtools package, this provides a set of functions that aid the analysis of statistical interactions. It implement simple slopes analysis, the calculation of Johnson-Neyman intervals, and plots for understanding interaction effects.
This is an R package that contains tools for the management and analysis of panel data. The main contributions are a
panel_data object class designed to make panel-specific functions easier to handle and
wbm, a procedure for fitting within-between regression models.
This R package implements a technique from Allison, Williams, and Moral-Benito (2017) and the Stata command
xtdpdml. It combines maximum likelihood estimation, the logic of cross-lagged panel models, and the robustness to spuriousness of fixed effects estimators into
dpm, dynamic panel models. Written with help from Richard Williams and Paul Allison.
This is an interactive dashboard created to visualize the spread of COVID-19 in the state of South Carolina. It updates daily.
This is an interactive data visualization created to look at geographic variation in polling error for recent US elections.
This is a Shiny app to demonstrate to students how much randomly assigned groups can differ on some measure without it actually being a significant difference.
This is the Ruby-based command line tool I wrote to collect the music-related data that were content-analyzed in Long & Eveland (2021; published online 2018).
A template for writing reports in APA format using the LaTeX typesetting engine. The heavy lifting is done by the
apa6 package, but this saves the user some time writing out code to get started.
Some folks like to look at current polls and poll averages and make a mental adjustment to the results and see how they’d look if the polls missed the way they did in the recent past. But I think even for people who follow this stuff pretty closely, it can be hard to remember all those details. My goal here is to help with that kind of thing while also showing that the narrative about polling errors is probably an overgeneralization.
I was thinking of sending some links to graduate students at my school, but realized the work of compiling these into a document probably meant I should share more widely. My experience on the job market was that there was simultaneously a lot but never enough information. What I’m sharing here is what I could track down from my bookmarks and so on from my job search 3 years ago.
When you want to find out who the NFL’s best kickers are, the tools available to you tend to be pretty crude, with not much more than a record of makes and misses. We all know from watching the games that some kicks are harder than others, but how do we synthesize that into a few easy to understand statistics?
Here I discuss statistical and logistical details involved in my attempts at modeling kicker talent. This includes model selection as well as a brief history of research. I use some quantitative assessments to compare my approach to previous attempts and it does quite well.
Observers note that in the 2020 Democratic primary, candidate support in polls is closely correlated with the amount of attention candidates get in the news. This begs the question of whether and how these two things might be causally related, particularly whether news coverage is helpful to candidates. Here I show some evidence that news coverage does indeed help.