Hey y’all

I’m finally dipping my does into causal inference for quasi-experiments, and my first use case has missing data. In this post we practice propensity score matching with multiply-imputed data sets, and how to compute the average treatment effect for the treated (ATT) with g-computation.

In this ninth post of the causal inference + GLM series, we explore the beta likelihood for continuous data restricted within the range of 0 to 1.

Abstract Background: The USA is undergoing a suicide epidemic for its youngest Veterans (18-to-34-years-old) as their suicide rate has almost doubled since 2001. Veterans are at the highest risk during their first-year post-discharge, thus creating a “deadly gap.” In response, the nation has developed strategies that emphasize a preventive, universal, and public health approach and embrace the value of community interventions. The three-step theory of suicide suggests that community interventions that reduce reintegration difficulties and promote connectedness for Veterans as they transition to civilian life have the greatest likelihood of reducing suicide.

Abstract Research on moral elevation has steadily increased and identified several psychosocial benefits that bear relevance to both the general population and people with psychological distress. However, elevation measurement is inconsistent, and few state-level measures have been created and critically evaluated to date. To address this gap, the State Moral Elevation Scale (SMES) was developed and tested using an online sample (N = 930) including subsamples of general participants (nonclinical) and those who screened positive for mental health symptoms (clinical).

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