Using Victimization Reporting Rates to Estimate the Dark Figure of Crime: A Case Study of Domestic Violence

Abstract

This paper illustrates how factoring in variables associated with reporting and underreporting can be used to adjust official statistics and generate an estimate of the true prevalence of domestic violence in a community. Combining data from the FBI National Incident Based Reporting System as well as the BJS National Crime Victimization Survey, we show several jurisdictions that have increased aggravated domestic rates in recent NIBRS data. Our up-adjusted total crime counts suggest using only NIBRS data is on average 40% smaller, with the underreporting varying from 23% to 76% across larger NIBRS reporting agencies. Combining law enforcement reports and victimization survey data, we provide more accurate estimates of the number of aggravated domestic violence incidents that occur in individual jurisdictions.

Keywords

domestic violence National Crime Victimization Survey National Incident Based Reporting System

Get full access to this article

View all access options for this article.

References

Agresti

(2012). Categorical data analysis (Vol. 792). John Wiley & Sons.

Baumer

E. P.

Xie

(2025). Neighborhood immigrant concentration, interview language, and survey nonresponse in the National Crime Victimization Survey (NCVS). Journal of Contemporary Criminal Justice, 41(1), 71–95.

Berg

(2024). Issues associated with the formulation of a small area model for estimation of state-level crime victimization rates. Journal of Contemporary Criminal Justice, 41(1), 30–50.

Berg

M. T.

Lauritsen

J. L.

(2016). Telling a similar story twice? NCVS/UCR convergence in serious violent crime rates in rural, suburban, and urban places (1973–2010). Journal of Quantitative Criminology, 32, 61–87.

Berk

MacDonald

J. M.

(2008). Overdispersion and Poisson regression. Journal of Quantitative Criminology, 24, 269–284.

Blumstein

Cohen

Rosenfeld

(1991). Trend and deviation in crime rates: A comparison of UCR and NCS data for burglary and robbery. Criminology, 29(2), 237–263.

Brunton-Smith

Cernat

Pina-Sánchez

Buil-Gil

(2024). Estimating the reliability of crime data in geographic areas. The British Journal of Criminology, 64, azae018.

Bureau of Justice Statistics. (n.d.). NCVS dashboard (N-Dash). Terms and definitions. https://ncvs.bjs.ojp.gov/terms#terms

Bureau of Justice Statistics. (2023). National Crime Victimization Survey, Concatenated File, [United States], 1992-2022. https://doi.org/10.3886/ICPSR38963.v1.

10.

Circo

Wheeler

(2023). Using every door direct mail web push surveys and multi-level modelling with post stratification to estimate perceptions of police at small geographies. CrimRxiv. https://www.crimrxiv.com/pub/p2pxki1g/release/1.

11.

Circo

Wheeler

(2025). An open source replication of a winning recidivism prediction model. International Journal of Offender Therapy and Comparative Criminology, 69(5), 438—453.

12.

Council on Criminal Justice. (2024). Better crime data, better crime policy.

13.

Criminal Justice Interagency Working Group. (2023). Equity and law enforcement data collection, use, and transparency. National Science and Technology Council.

14.

Desmond

Papachristos

A. V.

Kirk

D. S.

(2016). Police violence and citizen crime reporting in the black community. American Sociological Review, 81(5), 857–876.

15.

Fay

R. E.

Planty

Diallo

M. S.

(2013, August 3–8). Small area estimates from the National Crime Victimization Survey [Conference session]. Proceedings of the Section on Survey Research Methods. American Statistical Association (pp. 1544–1557).

16.

Federal Bureau of Investigation. (2019). NIBRS offense definitions. https://ucr.fbi.gov/nibrs/2019/resource-pages/nibrs_offense_definitions-2019.pdf

17.

Gelman

Goel

Rothschild

Wang

(2016). High frequency polling with non-representative data. In Schill

Kirk

Jasperson

A. E.

(Eds.), Political communication in real time (pp. 89–105). Routledge.

18.

Harrell

F. E.

(2001). Regression modeling strategies: With applications to linear models, logistic regression, and survival analysis (Vol. 608). Springer.

19.

Johnson

T. H.

Snowden

A. J.

(2024). Neighborhood ecological models of alcohol outlet density and male–on–female domestic violence: Accounting for adjacent place and neighborhood characteristics. Journal of Drug Issues, 54(2), 185–201.

20.

Kaplan

(2024). Jacob Kaplan’s concatenated files: National incident-based reporting system (NIBRS) data 1991–2022 V10. https://www.openicpsr.org/openicpsr/project/118281/version/V10/view

21.

Kourti

Stavridou

Panagouli

Psaltopoulou

Spiliopoulou

Tsolia

Sergentanis

T. N.

Tsitsika

(2023). Domestic violence during the COVID-19 pandemic: A systematic review. Trauma, Violence, & Abuse, 24(2), 719–745. https://doi.org/10.1177/15248380211038690

22.

Lantz

(2022). Why more agencies and researchers should embrace the upcoming NIBRS transition: Contributions and Promise of the NIBRS data. American Journal of Criminal Justice, 47, 462–484.

23.

Lauritsen

J. L.

Rezey

M. L.

(2013). Measuring the prevalence of crime with the National Crime Victimization Survey. US Department of Justice, Office of Justice Programs, Bureau of Justice Statistics.

24.

Maltz

M. D.

(2010). Look before you analyze: Visualizing data in criminal justice. In Piquero

Weisburd

(Eds.), Handbook of quantitative criminology (pp. 25–52). Springer.

25.

McDowall

Loftin

(1992). Comparing the UCR and NCS over time. Criminology, 30, 125–132.

26.

Mills

J. M.

Lantz

Wenger

M. R.

(2024). Understanding community hate crimes as an incorrigible proposition: Local political attitudes, path dependence, and the ceremonious reporting of hate crime statistics. Criminology, 62, 381–411.

27.

Morgan

R. E.

Smith

E. L.

(2023). The National Crime Victimization Survey and national incident-based reporting system: A complementary picture of crime in 2022. US Department of Justice, Office of Justice Programs, Bureau of Justice Statistics.

28.

National Academies of Sciences, Engineering, and Medicine. (2016). Modernizing crime statistics: Report 1: Defining and classifying crime. The National Academies Press.

29.

Piatkowska

S. J.

Stevens

(2025). Unveiling the intersection of race, lynching legacy, and hate crime victimization: Insights from the national crime victimization survey. Unpublished Manuscript.

30.

Piquero

A. R.

Jennings

W. G.

Jemison

Kaukinen

Knaul

F. M.

(2021). Domestic violence during COVID-19: Evidence from a systematic review and meta-analysis. Journal of Criminal Justice, 74, May-June 2021, 101806.

31.

Powers

R. A.

Bleeker

(2023). Self-defense and police reporting of intimate partner violent victimization: A comparison of White, Black, and Hispanic women victims. Journal of Interpersonal Violence, 38(3–4), 4189–4214.

32.

R Core Team. (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/

33.

Rezey

M. L.

Lauritsen

J. L.

(2023). Crime reporting in Chicago: A comparison of police and victim survey data, 1999–2018. Journal of Research in Crime and Delinquency, 60(5), 664–699.

34.

Schechter

(1982). Women and male violence: The visions and struggles of the battered women's movement. South End Press.

35.

Wheeler

A. P.

Kovandzic

T. V.

(2018). Monitoring volatile homicide trends across US cities. Homicide Studies, 22(2), 119–144.

36.

Wickham

(2016). Ggplot2: Elegant graphics for data analysis. Springer-Verlag New York. https://ggplot2.tidyverse.org

37.

Wright

E. M.

Benson

M. L.

(2011). Clarifying the effects of neighborhood context on violence ‘behind closed doors’. Justice Quarterly, 28(5), 775–798.

38.

Xie

Baumer

E. P.

(2019). Neighborhood immigrant concentration and violent crime reporting to the police: A multilevel analysis of data from the National Crime Victimization Survey. Criminology, 57(2), 237–267.

39.

Zoorob

(2020). Do police brutality stories reduce 911 calls? Reassessing an important criminological finding. American Sociological Review, 85(1), 176–183.