Skip to main content
Log in

Reported Crime Frequencies: A Statistical Comparison of State Crime Reports and the UCR

  • Published:
American Journal of Criminal Justice Aims and scope Submit manuscript

Abstract

Prior research suggests there are inconsistencies between the UCR and other National-level crime data sources. Less research has tested whether similar inconsistencies exist between UCR and State annual crime reports. The current study compared 48 U.S. State’s Part I offense counts to the FBI’s UCR’s Part I offense counts for the years 2000–2018. Paired samples t-tests revealed significant differences for specific Part I offense counts as reported by individual States and the UCR. Percentage differences further indicated that the magnitudes of differences were substantively meaningful. Pairwise correlations indicated strong linear associations and convergence between State and UCR Part I offenses Nationally, but convergence diminished when assessing individual States. Frequency and percentage differences were treated as dependent variables in multivariate models. Results from OLS regressions suggest certain State-level factors significantly predict the observed differences between State and UCR reported Part I offenses. These results reveal that inconsistencies exist between two official data sources which have the same origin.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Notes

  1. For example, one content-analysis of 1,211 articles published in three top criminal justice journals between 2000 and 2010 found that over 60% of all articles based their findings on secondary data sources (Nelson et al., 2014). Elsewhere, content analyses of seven criminology/criminal justice journals revealed that of 609 published studies, 39.1% used secondary data (Woodward et al., 2016; see also Kleck et al., 2006).

  2. Part I offenses refer to eight major crime types which include murder/non-negligent homicide, forcible rape, robbery, aggravated assault, burglary, larceny/theft, motor-vehicle theft, and arson.

  3. For example, see all Alabama crime reports from 1977 to 2019, available through the Alabama Law Enforcement Agency (ALEA) Criminal Justice Service Division (found at https://www.alea.gov/sbi/criminal-justice-services/alabama-crime-statistics).

  4. Data provided by the FBI’s UCR Crime Data Explorer tool (found at https://crime-data-explorer.fr.cloud.gov/downloads-and-docs).

  5. The NCS is now known as the NCVS.

  6. For the current analysis and consistency purposes, we used the legacy definition of rape for all years of UCR data. The FBI began using the revised definition of rape in 2013. Although the FBI rape definition is therefore consistent across all years assessed, the same cannot be said for State reported rape counts which may reflect different definitional criterion and thus impact the comparisons made in the current study for that Part I offense.

  7. The Part I offense arson was excluded from the current analysis due to substantial missingness across years both in the UCR and within individual state reports.

  8. A document with links to where we obtained each State’s crime reports/data is available upon request.

  9. Crime reports for Indiana and Mississippi for the years 2000 through 2018 could not be located.

  10. Including this variable was a novel approach that has not been included in prior research in this area. We thought it could be useful and chose to include it in the regression models for exploratory purposes. We entertained the possibility that states that experience higher percentages of lethal violence could possibly have fewer resources dedicated to accurate crime reporting since addressing violent crime takes precedence over filing paperwork.

  11. Tables with correlational values at the National and individual State levels are not included given limited space but are available upon request.

References

  • Akiyama, Y., & Propheter, S. K. (2005). Methods of data quality control for uniform crime reporting programs. Federal Bureau of Investigation, Criminal Justice Information Services Division.

  • Ansari, S., & He, N. (2015). Convergence revisited: A multi-definition, multi-method analysis of the UCR and the NCVS crime series (1973–2008). Justice Quarterly, 32(1), 1–31.

    Article  Google Scholar 

  • Barnett-Ryan, C., & Swanson, G. (2008). The role of state programs in NIBRS data quality: A case study of two states. Journal of Contemporary Criminal Justice, 24(1), 18–31.

    Article  Google Scholar 

  • 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(1), 61–87.

    Article  Google Scholar 

  • Bibel, D. (2015). Considerations and cautions regarding NIBRS data: A view from the field. Justice Research and Policy, 16(2), 185–194.

    Article  Google Scholar 

  • Blumstein, A., Cohen, J., & Rosenfeld, R. (1991). Trend and deviation in crime rates: Comparison of UCR and NCS data for burglary and robbery. Criminology, 29(2), 237–264.

    Article  Google Scholar 

  • Boylan, R. T. (2019). Imputation methods make crime studies suspect: Detecting biases via regression discontinuity. https://cear.gsu.edu/files/2019/05/Boylan-3.pdf

  • Chambliss, W. J., & Nagasawa, R. H. (1969). On the validity of official statistics: A comparative study of white, black, and Japanese high-school boys. Journal of Research in Crime and Delinquency, 6(1), 71–77.

    Article  Google Scholar 

  • Cork, D., Cohen, M., Rand, M. R., & Rennison, C. M. (2002). Window on Washington: True crime stories? Accounting for differences in our national crime indicators. Chance, 15(1), 47–51.

    Article  Google Scholar 

  • Cronin, S. W., McDevitt, J., Farrell, A., & Nolan, J. J., III. (2007). Bias-crime reporting: Organizational responses to ambiguity, uncertainty, and infrequency in eight police departments. American Behavioral Scientist, 51(2), 213–231.

    Article  Google Scholar 

  • Donohue, J. J., Aneja, A., & Weber, K. D. (2019). Right-to-Carry Laws and Violent Crime: A Comprehensive Assessment Using Panel Data and a State-Level Synthetic Control Analysis. Journal of Empirical Legal Studies, 16(2), 198–247.

    Article  Google Scholar 

  • Drukker, D. M. (2003). Testing for serial correlation in linear panel-data models. The Stata Journal, 3(2), 168–177.

    Article  Google Scholar 

  • Eterno, J. A., Verma, A., & Silverman, E. B. (2016). Police manipulations of crime reporting: Insiders’ revelations. Justice Quarterly, 33(5), 811–835.

    Article  Google Scholar 

  • Federal Bureau of Investigation. (2016). Uniform crime reporting program: National incident-based reporting system. https://ucr.fbi.gov/nibrs/2015/resource-pages/methodology-2015_final-1.pdf

  • Hickman, M. J., & Rice, S. K. (2010). Digital analysis of crime statistics: Does crime conform to benford’s law? Journal of Quantitative Criminology, 26(3), 333–349.

    Article  Google Scholar 

  • James, N. (2008). How crime in the United States is measured. Library of congress Washington DC congressional research service.

  • King, W. R., Cihan, A., & Heinonen, J. A. (2011). The reliability of police employee counts: Comparing FBI and ICMA data, 1954–2008. Journal of Criminal Justice, 39(5), 445–451.

    Article  Google Scholar 

  • Kleck, G., Tark, J., & Bellows, J. J. (2006). What methods are most frequently used in research in criminology and criminal justice? Journal of Criminal Justice, 34(2), 147–152.

    Article  Google Scholar 

  • Kovandzic, T. V., Marvell, T. B., & Vieraitis, L. M. (2005). Impact of “shall-issue” concealed handgun laws on violent crime rates: Evidence from panel data for large urban cities. Homicide Studies, 9(4), 292.

    Article  Google Scholar 

  • Lauritsen, J. L., & Cork, D. L. (2017). Expanding our understanding of crime: The National Academies Report on the future of crime statistics and measurement. Criminology & Public Policy, 16(4), 1075–1098.

    Article  Google Scholar 

  • Loftin, C., & McDowall, D. (2010). The use of official records to measure crime and delinquency. Journal of Quantitative Criminology, 26(4), 527–532.

    Article  Google Scholar 

  • Ludwig, A., & Marshall, M. (2015). Using crime data in academic research: Issues of comparability and integrity. Records Management Journal, 25(3), 228–247.

    Article  Google Scholar 

  • Lynch, J. P., & Jarvis, J. P. (2008). Missing data and imputation in the uniform crime reports and the effects on national estimates. Journal of Contemporary Criminal Justice, 24(1), 69–85.

    Article  Google Scholar 

  • MacDonald, Z. (2002). Official crime statistics: Their use and interpretation. The Economic Journal, 112(477), 85–106.

    Article  Google Scholar 

  • Maltz, M. D. (1999). Bridging gaps in police crime data: A discussion paper from the BJS Fellows Program. US Department of Justice, Office of Justice Programs, Bureau of Justice Statistics.

  • Maltz, M. D. (2006). Analysis of missingness in UCR crime data. Criminal Justice Research Center, Ohio State University.

    Google Scholar 

  • Maltz, M. D. (2019). Can we trust the FBI’s crime estimation procedures? The Criminologist, 44(3), 6–8.

    Google Scholar 

  • Maltz, M. D., & Targonski, J. (2002). A note on the use of county-level UCR data. Journal of Quantitative Criminology, 18(3), 297–318.

    Article  Google Scholar 

  • Maltz, M. D., & Targonski, J. (2003). Measurement and other errors in county-level UCR data: A reply to Lott and Whitley. Journal of Quantitative Criminology, 19(2), 199–206.

    Article  Google Scholar 

  • Maltz, M. D., & Targonski, J. (2004). Making UCR Data Useful and Accessible. US Department of Justice. Document205171.

  • McCleary, R., Nienstedt, B. C., & Erven, J. M. (1982). Uniform crime reports as organizational outcomes: Three time series experiments. Social Problems, 29(4), 361–372.

    Article  Google Scholar 

  • McCormack, P. D., Pattavina, A., & Tracy, P. E. (2017). Assessing the coverage and representativeness of the National Incident-Based Reporting System. Crime & Delinquency, 63(4), 493–516.

    Article  Google Scholar 

  • McDowall, D., & Loftin, C. (1992). Comparing the UCR and NCS over time. Criminology, 30(1), 125–132.

    Article  Google Scholar 

  • McDowall, D., & Loftin, C. (2007). What is convergence, and what do we know about it? In J. P. Lynch & L. A. Addington (Eds.), Understanding crime statistics: Revisiting the divergence of the NCVS and the UCR (pp. 93–124). Cambridge University Press.

    Google Scholar 

  • Menard, S. (1987). Short-term trends in crime and delinquency: Comparison of UCR, NCS, and self-report data. Justice Quarterly, 4(3), 455–474.

    Article  Google Scholar 

  • Messner, S. F., & Rosenfeld, R. (2017). The present and future of institutional-anomie theory. In Taking Stock (pp. 127–148). Routledge.

  • Milwaukee Journal-Sentinel. (2013, April). FBI’s national crime data found to be flawed, manipulated. Prison Legal News. https://www.prisonlegalnews.org/news/2013/apr/15/fbis-national-crime-data-found-to-be-flawed-manipulated/

  • Mosher, C. J., Miethe, T. D., & Phillips, D. M. (2002). The mismeasure of crime. Sage Publications.

    Google Scholar 

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

  • National Academies of Sciences, Engineering, and Medicine. (2018). Modernizing crime statistics: Report 2: New systems for measuring crime. National Academies Press.

  • Nelson, M. S., Wooditch, A., & Gabbidon, S. L. (2014). Is criminology out-of-date? A research note on the use of common types of data. Journal of Criminal Justice Education, 25(1), 16–33.

    Article  Google Scholar 

  • Nolan, J. J., III. (2004). Establishing the statistical relationship between population size and UCR crime rate: Its impact and implications. Journal of Criminal Justice, 32(6), 547–555.

    Article  Google Scholar 

  • Nolan, J. J., Haas, S. M., & Napier, J. S. (2011). Estimating the impact of classification error on the “statistical accuracy” of uniform crime reports. Journal of Quantitative Criminology, 27(4), 497–519.

    Article  Google Scholar 

  • Nolan, J. J., Haas, S. M., Turley, E., Stump, J., & LaValle, C. R. (2015). Assessing the “statistical accuracy” of the National Incident-Based Reporting System hate crime data. American Behavioral Scientist, 59(12), 1562–1587.

    Article  Google Scholar 

  • O’Brien, R. M. (1996). Police productivity and crime rates: 1973–1992. Criminology, 34(2), 183–207.

    Google Scholar 

  • O’Brien, R. M., Shichor, D., & Decker, D. L. (1980). An empirical comparison of the validity of UCR and NCS crime rates. The Sociological Quarterly, 21(3), 391–401.

    Article  Google Scholar 

  • Pepper, J., Petrie, C., & Sullivan, S. (2010). Measurement error in criminal justice data. In Handbook of quantitative criminology (pp. 353–374). Springer, New York, NY.

  • Pridemore, W. A. (2005). A cautionary note on using county-level crime and homicide data. Homicide Studies, 9(3), 256–268.

    Article  Google Scholar 

  • Rosenfeld, R. (2007). Transfer the uniform crime reporting program from the FBI to the Bureau of Justice Statistics. Criminology & Public Policy, 6(4), 825–833.

    Article  Google Scholar 

  • Ruback, R. B., & Menard, K. S. (2001). Rural-urban differences in sexual victimization and reporting: Analyses using UCR and crisis center data. Criminal Justice and Behavior, 28(2), 131–155.

    Article  Google Scholar 

  • Sampson, R. J., Raudenbush, S. W., & Earls, F. (1997). Neighborhoods and violent crime: A multilevel study of collective efficacy. Science, 277(5328), 918–924.

    Article  Google Scholar 

  • Sampson, R. J. (2012). Great American City. University of Chicago Press.

    Book  Google Scholar 

  • Skogan, W. G. (1974). The validity of official crime statistics: An empirical investigation. Social Science Quarterly, 25–38.

  • Speir, J., Meredith, T., Johnson, S., & Hull, H. (2003). Georgia UCR arrest statistics: Assessing accuracy using computerized criminal history records. Criminal Justice Coordinating Council, Georgia Statistical Analysis Center. https://www.jrsa.org/awards/winners/04_Georgia_UCR_Arrest_Statistics.pdf

  • Strom, K. J., & Smith, E. L. (2017). The future of crime data: The case for the National Incident-Based Reporting System (NIBRS) as a primary data source for policy evaluation and crime analysis. Criminology & Public Policy, 16(4), 1027–1048.

    Article  Google Scholar 

  • Woodward, V. H., Webb, M. E., Griffin, O. H., III., & Copes, H. (2016). The current state of criminological research in the United States: An examination of research methodologies in criminology and criminal justice journals. Journal of Criminal Justice Education, 27(3), 340–361.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Benjamin P. Comer.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Comer, B.P., Jorgensen, C. & Carter, D. Reported Crime Frequencies: A Statistical Comparison of State Crime Reports and the UCR. Am J Crim Just 48, 151–175 (2023). https://doi.org/10.1007/s12103-021-09623-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12103-021-09623-y

Keywords

Navigation