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Dave Armstrong's Website


As you’ll see below, my research interests are pretty broad and are often driven by collaborative projects where my contributions are largely methodological in nature. You can see a pdf version of my CV here or view my publications on Google Scholar

Books

Presenting Statistical Results Effectively: Perfect for any statistics student or researcher, this book offers hands-on guidance on how to interpret and discuss your results in a way that not only gives them meaning, but also achieves maximum impact on your target audience. No matter what variables your data involves, it offers a roadmap for analysis and presentation that can be extended to other models and contexts. Focused on best practices for building statistical models and effectively communicating their results, this book helps you:

  • Find the right analytic and presentation techniques for your type of data
  • Understand the cognitive processes involved in decoding information
  • Assess distributions and relationships among variables
  • Know when and how to choose tables or graphs
  • Build, compare, and present results for linear and non-linear models
  • Work with univariate, bivariate, and multivariate distributions
  • Communicate the processes involved in and importance of your results.

We have developed an R package that contains all the data, some helper functions and all the code to replicate the visualizations in the book. You can also download the R code and data from the book’s dataverse.

Analyzing Spatial Models of Choice and Judgment with R, 2nd ed: With recent advances in computing power and the widespread availability of political choice data, such as legislative roll call and public opinion survey data, the empirical estimation of spatial models has never been easier or more popular. The book demonstrates how to estimate and interpret spatial models using a variety of methods with the popular, open-source programming language R. The second edition is re-organized so all of the Bayesian content is presented in one chapter in a more coherent way. We have also improved the R package and moved to ggplot2 to make all of the plots.


  • Clone or install the R package asmcjr from github. The package includes all of the data for the book in addition to the functions that we wrote to estimate and interrogate the models.
  • Download all the R code

Peer Reviewed Articles:

Published Works (Not Peer Reviewed):