Law Enforcement Agency Roster (LEAR), 2016 (ICPSR 36697)

Alternate Title:   LEAR 2016

Principal Investigator(s): United States Department of Justice. Office of Justice Programs. Bureau of Justice Statistics

Summary:

In the past several years, BJS has made efforts to develop a national roster of publicly funded law enforcement agencies. The Census of State and Local Law Enforcement Agencies (CSLLEA) represents the core of the BJS's law enforcement statistics program, and is currently used as the primary universe for all BJS law enforcement collections. The CSLLEA was last conducted in 2014 but encountered data collection issues. Since the last law enforcement universe list was the 2008 CSLLEA, BJS decided further work was needed to have a reliable and complete roster of law enforcement agencies. Using the 2008 and 2014 CSLLEA universe as the base, the LEAR integrated multiple datasets in an effort to compile a complete list of active general purpose law enforcement agencies. The goal of the LEAR was to serve as the universe list for which the Law Enforcement Management and Administrative Statistics (LEMAS) core and supplement samples could be pulled. The 2016 LEAR contains a census of 15,810 general purpose law enforcement agencies, including 12,695 local and county police departments, 3,066 sheriffs' offices and 49 primary state police departments. Staffing size from multiple datasets has also been merged into the LEAR file.

Access Notes

  • The public-use data files in this collection are available for access by the general public. Access does not require affiliation with an ICPSR member institution.

Dataset(s)

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Study Description

Citation

United States Department of Justice. Office of Justice Programs. Bureau of Justice Statistics. Law Enforcement Agency Roster (LEAR), 2016. ICPSR36697-v1. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2017-03-31. https://doi.org/10.3886/ICPSR36697.v1

Persistent URL: https://doi.org/10.3886/ICPSR36697.v1

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Funding

This study was funded by:

  • United States Department of Justice. Office of Justice Programs. Bureau of Justice Statistics

Scope of Study

Subject Terms:    administration, law enforcement, law enforcement agencies, management, personnel, police, police departments, statistical data, workers

Smallest Geographic Unit:    states, cities, zip codes

Geographic Coverage:    United States

Time Period:   

  • 2015--2016

Date of Collection:   

  • 2015--2016

Unit of Observation:    Law enforcement agencies

Universe:    All state and local law enforcement agencies that were publicly funded and employed at least one full-time or part-time sworn officer with general arrest powers.

Data Type(s):    census/enumeration data

Data Collection Notes:

The LEAR is a working file that is updated as new agency information is received. The 2016 LEAR reflects what was deemed current as of December 2016. With each new data collection, BJS receives new information and updates the law enforcement agency's record. BJS continues work on LEAR to identify any potential errors with agency records. Users are encouraged to contact BJS if they find errors so they can be remedied.

Users should be aware that some variables have missing values which have been assigned. Some variables also have system missing values.

Methodology

Sample:   

The 2016 LEAR file was created by RTI international through seven stages:

Stage 1. The 2014 CSLLEA universe file was linked to the 2008 CSLLEA universe file. The combined 2014 and 2008 CSLLEA file served as the core of the LEAR dataset. These files were merged using the 2008 CSLLEA ID, which was contained in both datasets.

Stage 2. Agencies from 2014 state-supplied Peace Officer Standards and Training (POST) lists and 2015 UCR state lists (see Note 1) were appended in an effort to supplement the core LEAR dataset. The state POST lists were matched using agency name and address. The majority of this matching was done automatically with text matching scripts. Automatic matching required names and addresses to be nearly identical (minor variations in capitalization and abbreviations were ignored). Matches that could not be made automatically were identified manually by a reviewer. Additional information was sourced from the FBI's Uniform Crime Reporting (UCR) program. The FBI obtained state-sourced datasets with information on law enforcement agencies that reported crime data to the state UCR coordinator program. This information was manually merged to LEAR due to the varying formats of the state-level datasets. This information was used as an additional source of validation but, by itself, was not used to rule an agency as out of service. Additionally, the 2012 Law Enforcement Agency Identifiers Crosswalk (LEAIC) was used as a source to provide and confirm agency information but not contribute additional agencies.

Stage 3. The merged datasets were de-duplicated. Some source datasets did not contain sufficiently unique identifiers (e.g., had same CSLLEA IDs but were unique agencies). This resulted in duplicate records. Records were reviewed and assigned duplicate identifiers where appropriate for further follow-up in Stage 4.

Stage 4. Cases were reviewed for validity. The LEAR file was divided into 51 state files (including the District of Columbia) and assigned to a set of reviewers. Reviewers flagged cases when there were any irregularities in the record. Issues that were commonly flagged included (1) incomplete contact information, (2) city and agency name mismatch, (3) agency not included in all datasets, (4) agency associated with a very small population (i.e., jurisdiction populations of less than 1,000), and (5) duplication or potential duplication with another agency. Reviewers were instructed to be very liberal with flagging and highlight any potential discrepancy in the data file.

Once this primary review was completed, a meeting was convened to discuss the reasons for agency flags. Cases were assigned to one of two conditions. Some issues could be resolved with no further follow-up (e.g., duplicate records where all information matched). Other cases were passed on for further review which included a combination of online research and direct agency contact. Agency outreach and review included determining agency type, in-service status, law enforcement function and staffing size.

Stage 5. Staffing data from the 2008 CSLLEA, 2013 LEMAS and 2014 FBI's Police Employee Data (see Note 2) were merged into the file using 2008 CSLLEA ID or ORI. Additionally, the number of full-time and part-time sworn officers were obtained for a subset of agencies during LEAR Stage 4. Logic checks were conducted on agency information and identifying records that failed basic consistency checks (e.g., large discrepancies in sworn officer staffing size between different datasets) and only corrected if there were potential errors in matching identified.

Stage 6. Two additional files from the 2014 CSLLEA were verified. First, a set of cases had disposition changes between the intermediate 2014 CSLLEA file and the final 2014 CSLLEA file. Cases with scope changes between the interim and final datasets were reviewed for accuracy. Second, a subset of cases were marked as out of scope (n=444). Agencies marked as out-of-scope were reviewed to determine if this disposition was accurate and consistent with the inclusion criteria for the LEAR file. As a result of this review, 34 agencies were moved back in scope.

Stage 7. In order to pull out a unique set of general purpose agencies for the final LEAR file, various filters were applied to remove non-qualifying agencies (Table 1). Agencies were first selected by type as the CSLLEA universe files also contain special purpose agencies (see Note 3) not considered in scope for LEAR. This resulted in 3,136 sheriff's departments, 13,316 local and county police departments, and 66 primary state police agencies before filtering. Duplicates, agencies not providing law enforcement services, out of service, contract cities and non-primary state agencies were then dropped from the file. The final 2016 LEAR includes 15,810 general purpose agencies, with 3,066 sheriff's departments, 12,695 local and county police departments, and 49 primary state police agencies (see Note 4).

Notes:

1) During the LEAR creation process agencies were also validated through communications with the FBI national UCR Program and state UCR Programs. This information was manually merged to LEAR due to the varying formats of the state-level datasets. This information was used as an additional source of validation but, by itself, was not used to rule an agency as out of service.

2) United States Department of Justice. Federal Bureau of Investigation. Uniform Crime Reporting Program Data: Police Employee (LEOKA) Data, 2014. ICPSR36395-v1. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2016-03-24. http://doi.org/10.3886/ICPSR36395.v1.

3) Special purpose agencies included constables, marshal offices that could not be confirmed to have a primary law enforcement jurisdiction, tribal agencies, criminal investigation agencies, special enforcement (e.g., alcohol, gaming, narcotics, racing) and agencies providing law enforcement services for transportation systems/facilities, natural resources/parks, public buildings/facilities (e.g., primary schools, colleges/universities, state buildings, and public housing).

4) Hawaii Department of Public Safety was included in the 2013 LEMAS as a primary state police agency. However, through the LEAR development it was determined that this agency does not provide primary law enforcement services throughout the state and was reclassified as a special purpose agency.

Time Method:    Cross-sectional

Description of Variables:    Variables include name and location of the law enforcement agency, Originating Agency Identifier (ORI), Federal Information Processing Standard Code (FIPS), and the number of male and female full-time/part-time sworn/civilian employees the agency had.

Extent of Processing:   ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection:

  • Standardized missing values.
  • Checked for undocumented or out-of-range codes.

Version(s)

Original ICPSR Release:   2017-03-31

Variables

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