RT Article
T1 Using Bayesian Mixed Effect Generalized Linear Models to Evaluate Criminological Interventions: An Application to Firearm Seizures during Directed Patrol
JF Journal of quantitative criminology
VO 41
IS 4
SP 599
OP 622
A1 Rydberg, Jason
A1 Greene-Colozzi, Emily Ann
A1 McGarrell, Edmund F. 1956-
A1 Perry, Sean 1968-
A2 Greene-Colozzi, Emily Ann
A2 McGarrell, Edmund F. 1956-
A2 Perry, Sean 1968-
LA English
YR 2025
UL https://krimdok.uni-tuebingen.de/Record/1942594143
AB PurposeTo provide guidance on the use of Bayesian mixed-effect modeling for inference in criminological applications involving complex longitudinal data, and a demonstration involving police firearm seizures and gun violence.MethodsWe outline a Bayesian strategy for specifying and fitting mixed-effects models using the brms package in R, including procedures for prior predictive simulation, model selection, and posterior comparison during sensitivity analyses.ResultsTo demonstrate the utility of the Bayesian approach, we apply it to complex longitudinal data from a police directed patrol intervention in Flint Michigan, estimating the impact of gun seizures on firearm violence using a three-level mixed effects zero-inflated negative binomial regression model. The demonstration suggested a small and fragile reduction in reported gun violence associated with firearm seizures, which was not sufficiently distinguishable from placebo-based sensitivity analyses.ConclusionsBayesian methods are underutilized compared to frequentist approaches in criminological applications. This manuscript outlines an approach to expanding the criminological "toolkit" for mixed-effects modeling that leverages recent advances in software for Bayesian inference.
K1 Bayesian regression
K1 gun violence
K1 Illegal firearms
K1 Multilevel models
DO 10.1007/s10940-025-09610-0