605
Views
44
CrossRef citations to date
0
Altmetric
 

Abstract

Using domestic violence incidence and arrest data from Maryland (1991–1997), this research examines whether the proportion of incidents that result in arrest increased due to a legislative initiative implemented in 1994 and, if so, whether this change is uniform across different types of offenders (race and gender) and offense characteristics. Using interrupted time‐series analysis (ARIMA), we observe an increase in both the number of incidents reported to police and the percent of reported cases resulting in arrest. The legislative intervention has a significant positive impact on arrest likelihood above and beyond the increase over time for the state as a whole. While arrest probabilities increased across the board for males and females, African American and Whites, the ARIMA models do not suggest that the legislation differentially impacted arrest probabilities for these groups.

Acknowledgments

This research was supported by a grant from the state of Maryland Governor’s Office of Crime Control and Prevention with support from the Grants to Encourage Arrest Program of the Violence Against Women Grant Office of the US Department of Justice. Points of view are those of the authors and do not necessarily reflect those of the Governor’s Office, the US Department of Justice, or the University of Maryland.

Notes

1. The formula for the coefficient difference z‐test is: . See Paternoster, Brame, Mazerolle, and Piquero (Citation1998), for a discussion of coefficient difference tests.

2. There is no global test for differences between more than two coefficients (Paternoster, 2005, personal communication). In this analysis, we use the standard coefficient comparison z‐test (Paternoster et al., Citation1998). With multiple comparisons, this does raise a concern about alpha inflation. To account for this possibility, we have used a higher level of significance (.01).

Additional information

Notes on contributors

Sally S. Simpson

Sally S. Simpson is Professor and Chair of Criminology and Criminal Justice at the University of Maryland, College Park. Her research interests include corporate crime, criminological theory, and the intersection between gender, race, class, and crime. She is author of Corporate Crime, Law and Social Control (2002, Cambridge University Press) and Of Crime & Criminality (2000, Pine Forge Press). Her recent articles have appeared in Criminology, Justice Quarterly, and Law & Society Review.

Leana Allen Bouffard

Dr Bouffard is an Assistant Professor of Criminal Justice at Washington State University. Her Research Interests include violence against women, quantitative methods, and criminology theory.

Joel Garner

Joel H. Garner is the Director of Research at the Joint Centers for Justice Studies, Inc. His research interests include the effectiveness of criminal justice responses to intimate partner violence, police use of force, federal firearm regulation, racial profiling, and alternative forms of research synthesis.

Laura Hickman

Laura J. Hickman is a Behavioral Scientist at the RAND Corporation, Associate Professor of Criminal Justice at the Pardee RAND Graduate School, and Adjunct Professor of Sociology at University of Massachusetts Amherst. Her work focuses on evaluating programs and policy responses to crime and victimization, including an evaluation of a 15‐site federal initiative to implement programs intended to ameliorate the negative impacts of children’s exposure to violence.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 53.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 386.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.