Welcome!

I am a quantitative social scientist with expertise in statistical modeling, data analysis, data visualization, research design, and in communicating complex topics to a broad audience. In my academic research, I have fielded large-scale surveys, survey experiments, and worked with large-N observational data to model the behavior of both politicians and voters in the United States.

I have two peer-reviewed publications, two pre-print publications, and have presented my work at several academic conferences. I also have extensive experience in the classroom as the instructor of record and as a teaching assistant.

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Interests
  • Statistics
  • Bayesian Inference
  • Causal Inference
  • Data analysis
  • Machine learning
  • Data visualization
  • Research
  • Teaching
Education

  • Ph.D. in Political Science, exp. 2023

    University of California, Riverside

  • M.A. in Economics, 2017

    California State University, Long Beach

  • B.A. in Business Administration, 2015

    California State University, Fullerton

Recent Posts

Tidy Time Series Analysis with R
This post will teach you the basics of working with times series data in R as well as how to build simple forecasting models and evaluate their performance.
April 14, 2022 42 min read
Visualizing Data and Statistical Models in R with ggplot2
Exploratory data analysis (EDA) is an important part of the data science process. EDA is helpful for identifying patterns in data, examining the relationship between variables, and ultimately generating testable hypotheses and producing visualizations is one of the most effective ways to accomplish these tasks.
February 24, 2022 29 min read
Getting Started with Git and GitHub the Easy Way
What is Git? Git helps you avoid being this guy. Git is a version control system that maintains a library of changes made to a file to help you keep track of everything that you edit.
January 13, 2022 10 min read
Bayesian Data Analysis with brms
In this post, we’ll walk through the Bayesian workflow for data analysis using the R package brms. To get started, we need to install Stan and brms. This involves a couple steps.
October 14, 2021 18 min read
A General Guide to Generalized Linear Models
This post will walk you through how to use some of the most common generalized linear model and explain what problems they help solve and why. Before going through this code, I suggest that you take some time to review the presentation slides that I put together here.
Last updated on May 12, 2022 17 min read

Recent Publications

These published projects use a range of quantitative methods and research designs.

Nicholas R. Jenkins, Michelangelo Landgrave (2021). Do candidates benefit from rejecting PAC contributions? Evidence from a pre-registered candidate evaluation survey experiment. SocArXiv.

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Nicholas R. Jenkins (2021). Minimizing Whose Influence? How Rejecting PAC Contributions Affects Contribution Patterns. SocArXiv.

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Nicholas R. Jenkins, Michelangelo Landgrave, Gabriel E. Martinez (2020). Do campaign contributions facilitate access to government information? Evidence from a FOIA compliance field experiment with US municipalities. Journal of Behavioral Public Administration.

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Jacob M. Meyer, Nicholas R. Jenkins (2019). Interest Groups, Policy Responses to Global Shocks, and the Relative Likelihood of Currency Crashes Versus Banking Crises. Journal of International Commerce, Economics and Policy.

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Popular Topics

Bayesian brms data analysis data manipulation data visualization generalized linear models ggplot Git GitHub pandas Python R tidyverse time series