By Ronal W. Serpas, Chief of Police; and Eric Cardinal, Research Analyst II, Metropolitan Nashville, Tennessee, Police Department
ecisions made by police patrol supervisors on a daily, sometimes minute-by-minute basis can serve as the underpinnings of a successful U.S. police department or provide clear indications that the department is out of touch with the residents of the community it serves. This article emphasizes the vital importance of midlevel-manager decision making in police work and shows how the processes of accountability-driven leadership can greatly influence positive decision outcomes.
Accountability-driven leadership was developed and continues to evolve as an extension and refinement of the CompStat model. More specifically, this philosophy seeks agency-wide integration and synergy in the pursuit of the goals and objectives common to an entire agency. Accountability-driven leadership goes beyond the tenets of CompStat and its valuable focus on crime fighting through data-driven decisions by middle and upper management across all the demands placed on a modern police department.1 Over the last seven years, this philosophy has been implemented with documented success in both a statewide law enforcement/public safety agency and in a large municipal policing agency. It has also successfully added many auditing procedures to ensure the reliability and validity of the outcome measures of an organization.2
Central to the CompStat model is the unrelenting analysis and monitoring of the activities, or quantity, of police leaders’ and officers’ decisions and actions. In other words, CompStat revolutionized the idea of counting the things that police do, or should do, to reduce crime and disorder in the United States. This design raises the inevitable question of whether it is the quantity or the quality of work performed that makes a greater difference in achieving organizational strategic and tactical goals and objectives. Quantity and quality are not mutually exclusive; rather, they actually can and should complement each other.
As originally offered in the accountability-driven leadership philosophy, it is the linkage of enhanced efficiencies to desired effectiveness that serves to drive agency-wide synergy and accountability—whether the effectiveness desired is in crime fighting, quality-of-life initiatives, community policing, budget management and accountability, crime laboratory efficiencies and outcomes, detective investigative outcomes, or other related matters. The Metropolitan Nashville, Tennessee, Police Department (MNPD) uses a strategy to assess the quality outcomes of quantitative data outputs to assess the decision making and deployment strategies of patrol supervisory personnel. Simply put, the MNPD measures the quality of its patrol leadership’s decisions and deployment strategies in achieving the department’s goals and objectives.
The MNPD’s Strategic Development Division Crime Analysis Unit (SDD) has created an array of tools to monitor the effectiveness and efficiency of the entire organization. Recently, an internal study called the Patrol Shift Lieutenant Effectiveness Report, using a fairly standard method of analysis called technique for ordered preference by similarity to ideal solution (TOPSIS), was undertaken to specifically address the question of quality versus quantity in police field patrol work. Essentially, the question the MNPD sought to answer was clear: are field commanders balancing quantity of police actions (Terry stops, vehicle stops, warrant service, and so on) with desired quality outcomes, or is quantity alone driving decisions or goals?
To begin to answer this question, it was first necessary to define what the desired operational quality outcomes were, as opposed to mere quantities of outputs. For example, if the use of Terry stops is thought to have an impact on street-level crime by officers fully engaged in proactive work in high crime areas, then a ratio of Terry stops conducted to warrants cleared or arrests made would be a quality outcome, not a mere number of stops recorded and later reported at a CompStat meeting.3 Similarly, if a basic function of policing is to use traffic law enforcement to enhance visibility, ferret out criminal behavior, and reduce crashes, then the quality outcome of the issuance of citations or the lawful stopping of vehicles would be the reduction of collisions, collisions with injuries, and crime reports, with perhaps an increase in warrants served around areas suspected of or known for crime and disorder. In other words, during the weekly CompStat meeting, it is not sufficient to simply report that a particular number of vehicle stops or Terry stops was made in a particular area. The key question is whether such stops relate to fewer collisions with injuries or the identification of criminal suspects and behavior. If this extra step is not undertaken, it is far too easy for an agency to let its perceived need for quantity override its more fundamental missions, which can result in officers “fishing where the fishing is good,” setting “speed traps,” or meeting a “ticket quota.” Quantity without quality is a detriment to professions worldwide, law enforcement included.
Critics of policing often argue or assert that police are not focused on the activities that the critics think are useful. This complaint generally comes following traffic initiatives, when a loud group of drivers—typically those who have been ticketed—roundly criticizes officers for going after speeders instead of criminals. Such an argument dies quickly when an agency shows that (1) quality-driven decisions result in officers dedicated specifically to traffic enforcement working areas known for aggressive driving and collisions or areas known for high rates of crime and (2) that police enforcement strategies have had a demonstrated positive impact on numbers of injuries and property damage accidents as well as on crime fighting. For example, over the last several years, 20 to 25 percent of all MNPD arrest actions have started with a vehicle stop. By driving the decision-making process of patrol leaders to achieve quality outcomes, significant data are captured to address the concerns of the community or to answer the agency’s critics, be they external or internal.
This line of analysis may also figure in well with the emerging literature concerning the concepts of “place-based policing,” wherein focusing on policing problem places, as opposed to problem people, is offered as a more efficient and effective way to police U.S. cities.4 Within the MNPD, a critical component of the crime-fighting and quality-of-life enhancement strategies is to focus on the places in which crime and disorder occur. This focus necessitates a “quality” analysis of the efforts, activities, goals, and strategies of MNPD precinct commanders and their leadership teams. Analyzing quality against quantity as done in the MNPD’s Lieutenant Effectiveness Report is a concrete step in reinforcing the agency’s stated value of finding solutions to problem places through the use of quality-driven decisions.
MNPD leadership decided that field shift commanders (police lieutenants) would be measured on and held accountable for quality decisions, not merely quantity outputs, in the assignment of and follow-up on officer activity. Nashville has six police precincts, all of which operate under the traditional eight-hour shift configuration, with each shift led by a police lieutenant. Therefore, there are 18 patrol lieutenants total. These personnel are the primary police leaders in patrol during their hours of duty. For the last five years, the MNPD has directed that each precinct institutionalize the CompStat process and conduct a precinct-based CompStat meeting weekly. At these meetings, precinct commanders essentially play the role of CEOs while questioning, challenging, and encouraging their precinct leadership teams. More importantly, patrol shift lieutenants stay fully informed of all CompStat-related data and collaborate with their precinct commanders on priorities and strategies to reduce crime and disorder within their precincts during their tours of duty. Here, in these weekly meetings, precinct commanders establish the quality and quantity expectations of the patrol shifts.
The MNPD has incorporated the accountability-driven leadership and CompStat models as essential components of its tripartite strategy of fighting crime, enhancing residents’ quality of life, and embracing the community policing philosophy. To achieve these strategic goals, precinct commanders use accurate and timely data, which have been extensively audited, to direct quality decisions in the pursuit of organizational goals. The following sections explain the process by which the MNPD precinct shift lieutenants’ quality decisions are monitored in the furtherance of the agency’s goals.
Structure of the Patrol Shift Lieutenant Effectiveness Report
The Lieutenant Effectiveness Report (see table 1) was created to establish a statistically sound and objective process to determine the effectiveness of a given patrol shift. Each shift is then ranked by a score in furtherance of one of the central tenets of accountability-driven leadership: namely, that desired effectiveness is linked to enhanced efficiencies. The Lieutenant Effectiveness Report combines many variables of interest with the goal of providing a valuable assessment of the quality outcomes produced through decision making by shift lieutenants. Data are collected based on an individual police officer’s work effort when assigned to a given patrol shift. This is accomplished by setting forth predetermined benchmarks, in this case the desired quality outcomes of policing activity, not merely the quantity of that activity, and the use of the TOPSIS formula to rate and rank multiple defined criteria using a quantitative approach. Rankings are based on the highest score for a given category; the highest score is given the rank of 1, while the lowest is given the rank of 18. These rankings change from week to week, resulting in an ebb and flow in the rankings. This method was established because the score is based on results over time as a unit’s performance changes. To ensure that the data are more useful and relevant in accommodating these issues, the analysis is conducted on the last eight weeks of data in four-week intervals. The Lieutenant Effectiveness Report appears in the MNPD’s weekly CompStat booklet. In this way, the weekly CompStat booklet contains the last rolling eight-week analysis of the Lieutenant Effectiveness Report.
The MNPD chose the TOPSIS approach as the best method of analysis, given the type of data and quality measures to be analyzed. TOPSIS is a decision-making technique that uses a quantitative approach rather than a qualitative approach, such as the qualitative Pugh method. Pugh is a decision matrix in which processes are scored relative to criteria using a symbolic approach, normally one symbol each for good, neutral, and bad. These scores are then converted and combined into a matrix that yields an overall score for each process. The Pugh approach does not fit the kind of data used in the law enforcement profession.
The basic concept of the TOPSIS method is that the selected score should have the shortest distance from the positive-ideal solution and the farthest from the negative-ideal solution (in a geometric sense). TOPSIS assumes that each score “wants” to be either maximized or minimized, so the positive-ideal solution for a score that wants to be maximized is the maximum value of all the design options considered, and the negative-ideal solution is the minimum values of all the design options considered.
Patrol Shift Areas of Measure
The next step in developing the report was to consider and determine what measures of quality outcomes could be derived from the daily decisions of shift lieutenants and how they distributed, monitored, and directed the police resource under their control. At the outset, the MNPD desired to reinforce the overarching message of quality versus quantity while simultaneously monitoring and demonstrating that increasing quantity outputs in areas linked to quality outcomes was valued organizationally. As a result, five areas of measure were developed: proactive, reactive, quality, results, and correction. Each of these measures has a corresponding weight.
Proactive: The proactive measure is the percent change from the most recent four weeks of self-initiated calls for service to the previous four weeks of self-initiated calls for service. In the MNPD, self-initiated activity is calculated based on individual officer activity recorded (for example, Terry stops, vehicle stops, warrant service attempts, business checks, and so on) that is not the outcome of being directed by a dispatched call for service or while performing administrative duties. This information is collected from the computer-aided dispatch (CAD) system. If self-initiated activity increases over the previous four weeks, it is considered a positive result in this category. The MNPD, based on current staffing models, expects an average of 19 percent unallocated time (that is, time available less calls for service and administrative duties) for patrol officers. It is during this unallocated time that patrol shift lieutenants, sergeants, and officers are making decisions that are monitored through the Lieutenant Effectiveness Report and other analyses. This data point, predominately within the control of shift commanders and their officers, serves as a key indicator of how patrol shift lieutenants are leading, deploying, and monitoring the resources under their control.
Reactive: The reactive measure is the percent change from the most recent four weeks of dispatched calls for service to the previous four weeks of dispatched calls for service. If dispatched activity increases over the previous four weeks, it is considered a positive result in this category. This data point is primarily out of a patrol shift commander’s control but is included because it affects the amount of time officers have available for proactive initiatives established or directed by the shift lieutenant. It is also likely that a decrease in reactive calls for service is an indication that there has been some amelioration of the underlying crime or disorder issues in the patrol area under review.
Quality: Quality is measured by a series of ratios that result in a total score. This category measures the effectiveness, or quality outcomes of quantity actions, of several categories. Scores in the individual categories are represented mostly in the form of “batting averages,” or ratios. The numbers that the quality measure takes into account are as follows:
- The number of vehicle stops that result in an arrest divided by the total number of vehicle stops
- The number of Terry stops that result in an arrest divided by the total number of Terry stops
- The number of warrants served divided by the total number of warrant service attempts
- The percent change from the number of persons arrested in the most recent four weeks to the number arrested in the previous four weeks
- The number of accidents that result in injury divided by the total number of accidents
Results: For the purposes of this report, results are defined as the percent change from the most recent four weeks of Federal Bureau of Investigation Uniform Crime Report (UCR) Part I reports to the previous four weeks of UCR Part I reports.
Correction: The correction measure is the total actual minutes worked by individual officers (aggregated) in a given patrol shift divided by the total expected minutes worked for the most recent four-week period. The correction factor is a method to adjust for the reality of available personnel versus the perfect staffing state, so that the comparisons among the 18 patrol shift lieutenants can be meaningful.
The data used to construct this report come from multiple sources, such as the following:
- MNPD CAD system
- Warrant audit tables
- Incident-reporting system
- Warrant database
- Arrest database
- Human resources database
When a law enforcement agency embraces the philosophy of accountability-driven leadership in a manner built on strong, reliable data combined with a CompStat model, the agency’s efficiency, effectiveness, and credibility in the community it serves is significantly enhanced. Quality decision making at the midmanagement level is critical to this process. The creation of the Patrol Shift Lieutenant Effectiveness Report in the MNPD gives top management the quality assurance that finite resources are being deployed and directed in a strategic, purposeful way. The Patrol Shift Lieutenant Effectiveness Report provides a valuable and carefully constructed overview of the complex daily duties and responsibilities of patrol shift supervisors. This overview assists in holding these important leaders accountable to the agency’s goals and objectives. Patrol shift lieutenants, after all, are ideally situated in the organizational scheme to influence and drive cultural change and expectations in the areas of crime fighting, quality-of-life initiatives/enforcement, and community policing. By directly tying quality decision making to the performance measures of a shift lieutenant—not just the output data of arrests, citations, and so on—the MNPD has further advanced the operational theories of accountability-driven leadership.
The MNPD continues to enjoy significantly positive approval rankings from the residents and business owners of Nashville as the city experiences its fifth consecutive year of overall major crime reduction, both in incidents and crime rates. Moreover, the Nashville community is experiencing its fourth year in a row of fewer accidents with injuries. The sustained achievements in Nashville, including increasing officer productivity and success, go hand-in-hand with progressively enhanced quality decision making at the field lieutenant level. Accountability-driven leadership provides a strategy for personal and agency-wide success—clearly a win-win formula for the law enforcement profession as a whole, its employees, and the communities served. ■
1See Ronal W. Serpas, “Beyond CompStat: Accountability-Driven Leadership,” The Police Chief 71, no. 1 (January 2004): 24–31.
2See Ronal W. Serpas and Matthew Morley, “The Next Step in Accountability-Driven Leadership: ‘CompStating’ the CompStat Data,” The Police Chief 75, no. 5 (May 2008): 60–70.
3See Terry v. Ohio, 392 U.S. 1, 88 S.Ct. 1868, 20 L.Ed.2d 889 (1968).
4See generally David Weisburd, “Place-Based Policing,” Ideas in American Policing (Police Foundation), no. 9 (January 2008), http://www.policefoundation.org/pdf/placebasedpolicing.pdf (accessed October 23, 2008).
From The Police Chief, vol. LXXV, no. 12, December 2008. Copyright held by the International Association of Chiefs of Police, 515 North Washington Street, Alexandria, VA 22314 USA.