Jacob A. Long

Assistant Professor of Mass Communications

University of South Carolina

I am (starting in fall 2020) 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.

Interests

  • Political communication
  • Social identity
  • Data science
  • Longitudinal data
  • Social networks

Education

Publications

Software

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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.

This is an interactive dashboard created to visualize the spread of COVID-19 in the state of South Carolina. It updates daily.

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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.

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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.

DOI

This is the Ruby-based command line tool I wrote to collect the music-related data that were content-analyzed in Long & Eveland (2019).

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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.

Teaching

JOUR 400
COMM 4820 Syllabus
COMM 3597 Syllabus
COMM 3554 Syllabus
COMM 2367 Syllabus

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