Software

.js-id-RPackages
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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.

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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.

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 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.

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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

COMM 4820
COMM 3597
COMM 3554
COMM 2367
COMM 2367

Recent Posts

Zotero is a major part of my workflow from gathering research to the final, written output. One major annoyance, though, is its interpretation of APA reference style. It’s all correct, of course, with one exception: It adds the issue number to every journal citation.

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The survey package is one of R’s best tools for those working in the social sciences. For many, it saves you from needing to use commercial software for research that uses survey data. However, it lacks one function that many academic researchers often need to report in publications: correlations. The svycor function in jtools (more info) helps to fill that gap.

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There are a lot of reasons to use R instead of my field’s standby software, SPSS. With that said, I won’t get into them here. Instead, I just want to talk about a few things in R that might help a beginner get the hang of it.

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