• Presenting Statistical Results Effectively (Sage Press, Forthcoming, December 2021) 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.
  • 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Published Works (Not Peer Reviewed):

Works Under Review

Works in Progress:

Statistics and Software

  • A Bayesian Analog to the Altnerating Least Squares Optimal Scaling Algorithm (with William Jacoby)

  • The Costs and Benefits of Conditioning on Covariates in Models for Measurement

  • Machine Learning Tools for Multiple Imputation of Corss-Sectional and Panel/TSCS Data (With Chris Schwarz)

  • Machine Learning Tools as Model Diagnostics (with William Jacoby and Chris Schwarz)

  • Flexible Control Variable Modeling using Machine Learning Tools (with Tyler Girard)

  • Missing Data and the Gibbs Sampler: A Simple Approach to Estimating Models With Missing Data (with Ryan Bakker and Johannes Karreth)

Democracy and Repression

  • The Unexpected Effect of Elections on Repression

  • Causal Mechanisms and the Democracy-Repression Nexus