I am a PhD candidate at the School of Communication at [The] Ohio State University. 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 (expected), 2020
Ohio State University
MA in Communication, 2019
Ohio State University
BA with Honors in Political Science, 2014
An expanding collection of tools I've created to aid in my own research. 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 is the Ruby-based command line tool I wrote to collect the music-related data that were content-analyzed in Long & Eveland (2019).
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.
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.
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.
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.
It has been just a little more than a day since I announced my new R package,
panelr, to the wider world. There's at least one comparable package for R, called
plm, which is very good and should be particularly appealing for economists. This leads to the understandable question as to how
panelr differs from
It has been a long time coming, but my R package panelr is now on CRAN. Since I started work on it well over a year ago, it has become essential to my own workflow and I hope it can be useful for others. panel_data object class One key contribution, that I hope can help other developers, is the creation of a panel_data object class. It is a modified tibble, which is itself a modified data.