Joint Statistical Meetings 29 July 2019 Calibration Weighting for Nonreporting Agencies in FBI’s National Incident Based Reporting System (NIBRS) Joint Statistical Meetings 29 July 2019 Philip Lee1 Dan Liao1 Marcus Berzofsky1 Alexia Cooper2 1RTI International; 2Bureau of Justice Statistics

What is National Incident Based Reporting System (NIBRS) NIBRS is an incident-based reporting system used by Law Enforcement Agencies (LEAs) in the U.S. that provides detailed characteristics of crimes such as victim, offender, their relationships, and many other details. It is an administrative data source that has been collected by the FBI and is readily available to the public. It is a low- cost source of data as data collection costs are excluded. We’ll show our method on how to use an administrative data source as a leverage to produce national and subnational crime estimates. This is Intro 2

Percentage of NIBRS Reporting LEAs by State: 2018* By 2018, approximately 8,000 out of 18.000 Law Enforcement Agencies (LEAs) have transitioned to NIBRS. As of 2018, approximately 8,000 Law Enforcement Agencies (LEAs) have transitioned to NIBRS. As seen in the map, participation rates vary from state to state. My college Sarah Zimmermann will present the Coverage of the NIBRS data in her presentation “Coverage Error in Administrative Data: An Assessment of the National Incident Based Reporting System” later on today at 11:30 a.m. *For more details, see Sarah Zimmermann’s presentation “Coverage Error in Administrative Data: An Assessment of the National Incident Based Reporting System” at 11:30 am today.

Improving the Coverage and Representativeness of NIBRS On-going NCS-X Implementation Assistance Program Recruiting a probability sample of 400 law enforcement agencies (LEAs) to supplement the existing NIBRS data All agencies with 750+ police officers were included in these 400 LEAs NCS-X NIBRS Estimation Project Combine data from these 400 agencies with data from the 8,000+ existing reporting agencies to produce national estimates Utilizing statistical weighting approach to make the reporting LEAs more representative of the target population (i.e. all LEAs in the US) This is Intro 2

General Weighting Strategy Let large agencies (i.e. LEAs who have 750 officers or more) to be self-representing (i.e. weight=1, only represent themselves) For example, NYPD should be only self representing. Stratify medium-size and small-size agencies By Agency Size / Agency Type / Geographic Locations / Other Factors Perform calibration weighting within each stratum to correct for nonresponse bias Create separate set of weights for estimation of different crime types (violent crime vs. property crime) Validating the final weights with external data sources Let large agencies to be self-representing (i.e. weight=1, only represent themselves): Unlike household survey or personal-level survey, the size of agencies can vary and we want to distinguish the large agencies from others. These agencies will be excluded from the weighting process, because they cannot represent any other LEAs or be represented by other LEAs. Stratify medium-size and small-size agencies: We stratified by Agency Size / Agency Type / Geographic Locations / Other Factors. Perform calibration weighting within each stratum to correct for nonresponse bias: We do this since nonresponse patterns and the impact of nonresponse on the final estimates can vary greatly by strata. Create separate set of weights for estimation of different crime types (violent crime vs. property crime): Since the functionality of LEAs can be different, weights should be created by crime type. Validating the final weights with external data sources: It is important for us to understand the quality of the weighted results. We consulted with our expert committee and identified several important data sources. We are going to compare our weighted results with these external data sources to validate the weights.

External Data Sources for Weighting and Weight Validation Center for Disease Control’s (CDC) National Vital Statistics System (NVSS) Uniform Crime Reporting’s (UCR) Supplementary Homicide Report (SHR) UCR’s Summary Reporting System (SRS) UCR’s Supplement A – Property Stolen and Recovered UCR’s Arrest - Arrests by Age, Sex, and Race, Summarized Yearly Criminal Justice Information Services’ (CJIS) Universe Data Here are some datasets that we will use either in the weighting procedure or in the validation procedure. Because we have some many potential variables from these external data sources. When selecting useful weight variables, we will adopt some variable selection methods such as regression tree method to identify variables and interactions. I can provide more details of each of the datasets in the e-poster portion of the presentation.

RTI International Philip Lee pklee@rti.org 301-816-4601 Top priority is to produce national estimates, but subnational estimates are also important. In the e-poster portion of the presentation, we considered 3 different estimation option to develop the weights to produce national and subnational estimates.