Hey y’all

This is the first post in a new series on causal inference. We will learn how to analyze data from true experiments with techniques from the contemporary causal inference literature. Unlike with my earlier series focused on the generalized linear model (GLM), all the data in this series will have more complicated structures, which we’ll capture with the generalized linear mixed model (GLMM). In this first post, we walk out the general framework using log(normal) reaction-time data collected from a within-person factorial experiment.

In this third post we explore different ways to fit a model with a mean-centered predictor, what this means for how we set our priors with brms, and what this all means for the underlying Stan code. Along the way we practice with the transformed data block, and get fancy with the model matrix.

Abstract A suicide epidemic exists among young U.S. veterans, with risk especially elevated in the first year of transition for the 200,000 servicemembers exiting the military annually. The VA Veteran Sponsorship Initiative (VSI) is a public–private-partnership between federal and community partners that aims to decrease suicides by providing a VA-certified volunteer peer sponsor and connection to community services. Onward Ops is a key community-based national program that enrolls, matches and manages the relationship between servicemembers and sponsors.

Abstract Objective: Potentially morally injurious events (PMIEs) involve transgressions of one’s deeply held sense of right and wrong. Race-related stress may be a source of PMIEs, but no studies have investigated how PMIE exposures differ by racial or ethnic identity. This study explored variations in reported rates of PMIEs among Black/African American, White, Hispanic/Latino/a/e, and non-Hispanic/Latino/a/e United States post-9/11 combat veterans. Methods: Secondary data analysis were conducted using observational cohort data from a repository on post-deployment functioning among combat veterans ($n = 487$).

Prerequisites We assume familiarity with R and RStudio. Outcomes After completing this course, the participants will: have a basic professional website, built primarily with Quarto, with files stored on GitHub, and hosted by Quarto Pub. Supporting materials You can find supporting materials from the workshop on the OSF at https://osf.io/es8bv/.

In partnership with Physalia Course, this will be my first introductory Bayesian statistics workshop since grad school.