This study introduces a statistical estimator that can be used to examine disproportionate traffic stop behavior of police officers. This estimator can be employed in concert with internal benchmark data and a tree diagram algorithm to identify and classify disproportionate behavior. These methodologies are multilevel and can be used (a) at the macrolevel to examine disproportionality of a police department as an organization and (b) at the microlevel to draw inferences about reasons for individual officers’ disproportionate behavior. These statistical routines were tested using data from a medium sized midwestern community. Results suggest that the models are effective in detecting disproportionality in both a police organization and an individual officers’ traffic stop activity. Moreover, the methods may serve as an initial step in pointing toward the sources of the officers’ behavior.
Categories
Latest News
Polstops Newsletter n4 (June 2022)
At last, we have been able to meet again. And we can now begin to identify what we have missed…
Read moreSpecial issue on POLICE ENCOUNTERS
A Special Issue on POLICE ENCOUNTERS of the Journal of Organizational Ethnography guest edited by Megan O’Neill, Mike Rowe, Sofie…
Read moreDoctoral and Early Career Training School 'Writing about Police Stops' - Call for Expressions of Interest
Location: Florence Dates: 2 – 6 May 2022 The EU Cost Action on Police Stops (CA17102) invites applications from Doctoral…
Read morePolstops Newsletter n3 (December 2021)
Looking back at our last newsletter, we optimistically planned in person meetings in the autumn of 2021. Travel restrictions made…
Read moreTo know more or to become part of this Action
Contact UsSubscribe to our newsletter

COST Action COST (European Cooperation in Science and Technology) is a funding agency for research and innovation networks. Our Actions help connect research initiatives across Europe and enable scientists to grow their ideas by sharing them with their peers. This boosts their research, career and innovation.