A Multi-Site Assessment of Police Consolidation: California, Michigan, Minnesota, Pennsylvania, 2014-2015 (ICPSR 36951)

Version Date: Oct 25, 2018 View help for published

Principal Investigator(s): View help for Principal Investigator(s)
Jeremy M. Wilson, Michigan State University. College of Social Science; Steven M. Chermak, Michigan State University. College of Social Science

https://doi.org/10.3886/ICPSR36951.v1

Version V1

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed.

The study gathered information from police officers and residents of four different community areas that had undergone some form of police consolidation or contracting. The communities were the city of Pontiac in Michigan; the cities of Chisago and Lindstrom in Minnesota; York and Windsor Townships and the boroughs of Felton, Jacobus, Yoe, Red Lion, and Windsor in Pennsylvania; and the city of Compton in California. Surveys were administered to gauge the implementation and effectiveness of three models of police consolidation: merger of agencies, regionalization under which two or more agencies join to provide services in a broader area, and contracting by municipalities with other organizations for police services.

The collection includes 5 SPSS files:

  • ComptonFinal_Masked-by-ICPSR.sav (176 cases / 99 variables)
  • MinnesotaFinal_Masked-by-ICPSR.sav (228 cases / 99 variables)
  • PontiacFinal_Masked-by-ICPSR.sav (230 cases / 99 variables)
  • YorkFinal_Masked-by-ICPSR.sav (219 cases / 99 variables)
  • OfficerWebFINALrecodesaug2015revised_Masked-by-ICPSR.sav (139 cases / 88 variables)

Wilson, Jeremy M., and Chermak, Steven M. A Multi-Site Assessment of Police Consolidation: California, Michigan, Minnesota, Pennsylvania, 2014-2015. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2018-10-25. https://doi.org/10.3886/ICPSR36951.v1

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United States Department of Justice. Office of Justice Programs. National Institute of Justice (2013-IJ-CX-0019)

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Access to these data is restricted. Users interested in obtaining these data must complete a Restricted Data Use Agreement, specify the reason for the request, and obtain IRB approval or notice of exemption for their research.

Inter-university Consortium for Political and Social Research
2015-02-01 -- 2015-07-01 (Officer Dataset), 2014-12-02 -- 2015-06-17 (Compton Dataset), 2014-11-06 -- 2015-04-08 (Minnesota Dataset), 2014-11-03 -- 2015-08-14 (Pontiac Dataset), 2014-11-19 -- 2015-08-30 (York Dataset)
2014-11-03 -- 2015-07-19 (Primary Data Collection), 2015-07-28 -- 2015-08-30 (Additional data collection in York)

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed.

Qualitative data collected for this study are not available as part of the data collection at this time.

The purpose of the study was to conduct an assessment of three popular types of police department consolidation schemes that are in use in policing today: mergers, regionalization, and contracting. Information was gathered from police officers and residents in four different community areas that had undergone some form of police consolidation. These areas were examined: implementation, effectiveness and cost-efficiency of police departments.

The four communities chosen were:

  • Compton, California
  • Pontiac, Michigan
  • Chisago and Lindstrom, Minnesota
  • York and Windsor townships (and the boroughs of Felton, Jacobus, Yoe, Red Lion, and Windsor), Pennsylvania

To develop practical resources for policymakers and practitioners, a multi-method approach was used to examine the implementation and effectiveness of three models of police consolidation: merger of agencies, regionalization under which two or more agencies join to provide services in a broader area, and contracting by municipalities with other organizations for police services. Specifically, field visits were conducted, residents and police officers were surveyed, and crime data was analyzed in four communities that had consolidated their police services.

The study sampled residents of four different community areas that had undergone some form of police consolidation. The communities were chosen by the Principal Investigators and were the city of Pontiac in Michigan; the cities of Chisago and Lindstrom in Minnesota; York and Windsor Townships and the boroughs of Felton, Jacobus, Yoe, Red Lion, and Windsor in Pennsylvania; and the city of Compton in California. The telephone-based survey questions asked about residents' interaction with police, satisfaction with local police, impressions of local consolidation, attitudes towards their neighborhood, and demographics.

The state surveys used a dual frame random digit dialing (RDD) design with landline and cellphone samples purchased from Survey Sampling International, Inc. (SSI) for each site. All sample orders were deduped against each other to ensure that numbers were not replicated.

California: Landline sample: The first landline RDD sample order for California contained 500 address-matched landline numbers, and 275 landline RDD numbers that were not address matched. The second landline RDD sample order for Pontiac contained 3,250 address-matched landline numbers, and 1,800 landline RDD numbers that were not address matched. Cell Sample: The first cell phone RDD sample order for California contained 1,000 cellular telephone numbers. The second cell phone RDD sample order for California contained 3,000 cellular telephone numbers. All cell numbers for both orders were drawn from the Compton, CA rate center. No cell phone numbers could be addressed matched.

Michigan: Landline sample: The first landline RDD sample order for Pontiac contained 500 RDD address-matched landline phone numbers and 275 landline RDD numbers that were not address matched. The second landline sample order for Pontiac contained 2,500 address-matched RDD landline numbers, and 1,375 RDD landline numbers that were not address matched. Cell Sample: The first cell phone RDD sample order for Pontiac contained 1,000 RDD cellular telephone numbers. The second cell phone sample order for Pontiac contained 3,000 RDD cellular telephone numbers. All cell numbers for both orders were drawn from the Pontiac, MI rate center. No cell phone numbers could be addressed matched.

Minnesota: Landline sample: The first landline RDD sample order for Minnesota contained 500 address-matched phone numbers, and 275 landline RDD numbers that were not address matched. The second landline RDD sample order for Minnesota contained 1,500 address-matched phone numbers, and 825 landline RDD numbers that were not address matched. Cell Sample: The first cell phone RDD sample order for Minnesota contained 800 cellular RDD telephone numbers. Numbers for this order were drawn from the Cambridge, MN, Osceola, WI, and St. Croix falls, WI rate centers. The second cell phone RDD sample order for Minnesota contained 2,400 cellular RDD telephone numbers. Numbers for this order were also drawn from the Cambridge, MN, Osceola, WI, and St. Croix falls, WI rate centers. The Third cell phone sample order for Minnesota contained 5,000 cellular RDD telephone numbers. All numbers for the third order were drawn from the Twin Cities rate center. No cell phone numbers could be addressed matched.

Pennsylvania: Landline sample: The first landline RDD sample order for Pennsylvania contained 500 address-matched landline numbers, and 275 landline RDD numbers that were not address matched. The second landline sample order for Pennsylvania contained 2,500 address-matched landline RDD numbers, and 1,375 landline RDD numbers that were not address matched. Cell Sample: The first cell phone RDD sample order for Pennsylvania contained 1,000 cellular RDD telephone numbers. The second cell phone sample order for Pennsylvania contained 2,000 cellular RDD telephone numbers. All cell numbers for both orders were drawn from the York, PA and Red Lion, PA rate centers. No cell phone numbers could be addressed matched.

Officer dataset:

  • Compton, California: 163 Officers in the Los Angeles County Sheriff's Department
  • Minnesota: 12 Officers in the Lakes Area Police Department
  • Pontiac, Michigan: 97 Officers in the Oakland County Sheriff's Department - Pontiac Patrol Division
  • York, Pennsylvania: 42 Officers in the York Area Regional Police Department

Cross-sectional

  • English-speaking and Spanish-speaking adult residents (18 years of age or older) living within the cities of Compton, California; Pontiac, Michigan; Chisago and Lindstrom, Minnesota; York or Windsor Townships, (including the boroughs of Felton, Jacobus, Yoe, Red Lion, and Windsor), Pennsylvania.
  • Policer officers employed at all levels in Compton, California; Pontiac, Michigan; Chisago or Lindstrom, Minnesota; or York or Windsor Townships, Pennsylvania.

Individuals
survey data

The variables in each of the four community datasets are responses from the questionnaire named Four Communities Survey and are categorized in these sections:

  • Residents' interaction with police
  • Satisfaction with local police
  • Impressions of local consolidation
  • Attitudes towards their neighborhood
  • Respondent demographics

The variables in the officers dataset are responses from the questionnaire on police department consolidation (including mergers, regionalization, and contracting) included the following sections:

  • Officer's assessment of management, staffing, and community involvement
  • Consolidation/contracting process, implementation and outcomes
  • Job satisfaction, pre and post consolidation
  • Respondent demographics

  • California dataset: 16.5 percent
  • Michigan dataset: 22.8 percent
  • Minnesota dataset: 25.2 percent
  • Pennsylvania dataset: 19.2 percent
  • Officer dataset: 44.2 percent
  • nominal and Likert-type scales

    2018-10-25

    State datasets: FINALWT

    Notes

    • These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed.

    • 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.

    • One or more files in this data collection have special restrictions. Restricted data files are not available for direct download from the website; click on the Restricted Data button to learn more.

    • The citation of this study may have changed due to the new version control system that has been implemented.
    NACJD logo

    This dataset is maintained and distributed by the National Archive of Criminal Justice Data (NACJD), the criminal justice archive within ICPSR. NACJD is primarily sponsored by three agencies within the U.S. Department of Justice: the Bureau of Justice Statistics, the National Institute of Justice, and the Office of Juvenile Justice and Delinquency Prevention.