quantitative research in law enforcement

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Springer Nature - PMC COVID-19 Collection

Police Stress and Deleterious Outcomes: Efforts Towards Improving Police Mental Health

Tina b. craddock.

1 Department of Social Science, 344 Gilchrist Education and Psychology Complex (Office), Division of Academic Affairs, Elizabeth City State University, 1704 Weeksville Road, Elizabeth City, NC 27909 USA

Grace Telesco

2 Abraham S. Fischler College of Education, School of Criminal Justice, Nova Southeastern University, 3301 College Ave, Davie, FL 33314 USA

Associated Data

SPSS data electronically stored and available.

Police officers are subjected, daily, to critical incidents and work-related stressors that negatively impact nearly every aspect of their personal and professional lives. They have resisted openly acknowledging this for fear of being labeled. This research examined the deleterious outcomes on the mental health of police officers, specifically on the correlation between years of service and change in worldviews, perception of others, and the correlation between repeated exposure to critical events and experiencing Post-Traumatic Symptoms. The Cumulative Career Traumatic Stress Questionnaire- Revised (Marshall in J Police Crim Psychol 21(1):62−71, 2006 ) was administered to 408 current and prior law enforcement officers across the United States. Significant correlations were found between years of service and traumatic events; traumatic events and post-traumatic stress symptoms; and traumatic events and worldview/perception of others. The findings from this study support the literature that perpetual long-term exposure to critical incidents and traumatic events, within the scope of the duties of a law enforcement officer, have negative implications that can impact both their physical and mental wellbeing. These symptoms become exacerbated when the officer perceives that receiving any type of service to address these issues would not be supported by law enforcement hierarchy and could, in fact, lead to the officer being declared unfit for duty. Finally, this research discusses early findings associated with the 2017 Law Enforcement Mental Health and Wellness Act and other proactive measures being implemented within law enforcement agencies who are actively working to remove the stigma associated with mental health in law enforcement.

Introduction

On December 9, 2015, Nicole Rikard, a crime scene investigator for the Asheville Police Department in North Carolina received word that her 38-year-old husband, Sergeant John Rikard, also of the Ashville Police Department, had been found dead in their home of an apparent self-inflicted gunshot wound (Balaban and Doubek  2019 ). In October 2017, Sergeant Michael Borland, a 44-year-old sergeant who spent 21 years with the Pinellas County Sheriff’s Office, was found deceased in the parking lot of the St. Petersburg College-Veterinary Technology Center, also from a self-inflicted gunshot wound (Pinellas Co. Sheriff’s Office press release  2017 ). Shortly after 6 pm on August 14, 2019, veteran NYPD officer, Robert Echeverria, died at his home from a self-inflicted gunshot wound. Officer Echeverria was only 56 years old and the ninth NYPD officer to commit suicide in 2019 following a fellow NYPD officer who killed himself the previous day (Moore and Celona  2019 ).

Law enforcement officers have historically been required to perform many functions within the scope of their jobs. They protect and serve the public, which at times requires them to assume the role of social worker, guidance counselor, and impartial peacekeeper. These roles, however, come with an emotional price tag. They are front-row witnesses to horrific atrocities including catastrophic natural disasters, acts of terrorism that results in mass casualties, suicides, motor vehicle accidents that result in trauma and/or death, child abuse or neglect, and acts of domestic violence.

These cumulative traumatic events have the potential to impact the individual and result in post-traumatic stress disorder (PTSD), as defined by the American Psychiatric Association. A study by Stephens and Long ( 1999 ) noted that between 12 and 35% of police officers suffered from PTSD. Another, examining the impact of suicide exposure on law enforcement found that nearly all their study participants (95%) indicated they had been exposed to an average of 30 career suicide scenes with two occurring over the 12 months prior to the study (Cerel et al.  2019 ). Additionally, it was noted that over 20% of those who responded indicated they experienced difficulties after the exposure including nightmares. After years of research, planning, and debate, the most recent version of the Diagnostic and Statistical Manual of Mental Disorders, the DSM-5, revised how PTSD was defined. It was removed from the anxiety disorder category, in part, because of the multiple emotions associated with PTSD (i.e., guilt, shame, and anger). It was subsequently placed in a new category, appropriately named “Trauma and Stressor-related Disorders”. This diagnostic category is distinctive among psychiatric disorders in the requirement of exposure to a stressful event as a “precondition” (Pai et al.  2017 p. 2.). This is significant and relevant to law enforcement because it encapsulates the symptoms they experience, often daily, over the course of their career.

It was decades after American troops returned from Vietnam that the mental health community openly acknowledged that soldiers deployed into war zones were emotionally traumatized by what they had witnessed and been exposed to. Regrettably, Vietnam veterans with PTSD symptomology did not receive proper mental health services upon their return. It was not until 1980 that the diagnosis of PTSD made its first appearance in the third edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-lll) published by the American Psychiatric Association. Similarly, in the quasi-military world of policing, officers too often find themselves exposed to traumatic events, but fear of being labeled as “not tough enough for the job”, “weak”, or even declared to be “unfit for duty” makes it difficult for them to display emotion and admit to experiencing stress in order to safeguard their career (Bonifacio  1991 ; Brown et al.  1994 ).

Research indicates that police officers suffer from higher-than-average instances of substance abuse and suicide when compared to the general population (Barron  2010 ; Cross and Ashley  2004 ; O’Hara et al.  2013 ). While the overall suicide rate in the USA is 13 per 100,000, the number for law enforcement is closer to 17 for every 100,000 (Hillard  2019 ). Between January 2016 and December 2019, there were over 700 reported current or former law enforcement officer deaths by suicide ( www.bluehelp.org ). In fact, it was noted that a law enforcement officer was more likely to die by suicide than to be killed in the line of duty ( www.bluehelp.org ). This work presents a summary of the literature on the most prevalent police stressors, the impact these stressors have on an officer’s physical and emotional well-being, and strategies for assistance. It further provides the findings of a descriptive, cross-sectional, and quantitative study, conducted by the authors, that examined how the variables of tenure (years of service), exposure to traumatic events, and gender and race of officer are associated with post-traumatic stress symptoms and change in officer’s worldview and perception of others.

Literature Review

Impact of long-term exposure.

There are copious amounts of research supporting the deleterious effects of police stress (Cerel et al.  2019 ; Chopko et al. 2018 ; Morash et al.  2006 ; Soomro and Yanos  2018 ; Stinchcomb  2004 ). Many of these studies focus on stress arising from one of two areas related to policing: operational stressors that would include stress from the demands and duties of the occupation and organizational stressors that include things such as a perceived lack of support, pressure from administration or a lack of opportunities to move up the hierarchy (Shane  2010 ; Stinchcomb  2004 ; Violanti  2011 ). Symptoms of long-term traumatic stress exposure, according to Marshall ( 2006 ), may appear without warning, leaving an officer confused and unprepared to cope. There is additional research, however, that focuses on the impact of law enforcement officers who have experienced mass casualty situations, did not feel comfortable seeking out any type of meaningful long-term mental health services, due to potential stigmas, and then returned to police work (Substance Abuse and Mental Health Services Administration  2018 ). The image that comes to mind is of a juggler attempting to maintain multiple fire sticks and having a double-edge sword thrown in when they were unprepared.

Witt ( 2005 ), speaking of decorated Oklahoma police officer turned convicted drug felon Jim Ramsey, summed it up best by saying:

There’s a dark underside to the heroics performed by rescue workers that is little noticed by citizens they protect: Long after the smoke clears and the last bodies are retrieved, massive disasters and terrorist attacks routinely claim additional casualties among the first responders who rush in to help, only to succumb to alcoholism, broken families and post-traumatic stress disorder (PTSD). (n.p.)

Following the September 11, 2001, attacks on the World Trade Center, Lowell et al. ( 2017 ) examined longitudinal studies of PTSD among those most exposed populations between October 2001 and May 2016. The findings suggested a significant burden of 9/11-related PTSD among those most exposed, and while most studies they examined indicated a decline in rates of the prevalence of PTSD, those of rescue/recovery workers showed an increase over time. Many of those serving in the capacity of rescue/recovery at both the Alfred P. Murrah Federal Building and the twin towers of the World Trade Center were police officers and other first responders. Telesco ( 2019 ) noted that the WTC Health Registry, who followed up on individuals directly impacted by the events of 9/11 including police officers, found an “elevated prevalence of post-traumatic stress disorder (PTSD) and physical and mental health burdens among 9/11-exposed individual’s years after exposure” (p. 10). Many of the police officers, who were physically able, went back out on the streets and to the daily stressors of being a police officer that were compounded by what they had been exposed to at ground zero.

There has been a steady stream of research conducted, over the past several decades, pertaining to the psychological impact of critical incidents on law enforcement officers. Spielberger et al. ( 1981a , b ) were early pioneers in this area focused their research specifically on areas including salary, shift work, administrative hierarchy, and job-related conflicts, as well as crises that occurred within the scope of their job were all examined.

Sheehan and Van Hasselt ( 2003 ) noted that “among law enforcement officers, job-related stress frequently contributes to the ultimate maladaptive response to stress: suicide” (p.16). Police organizations are beginning to acknowledge that they are now facing a mental health crisis of epic proportion and to combat this crisis a fundamental change in the police culture must occur. This change involves implementing proactive and ongoing mental healthcare practices with a focus on addressing the psychological equilibrium of law enforcement officers.

Similar findings were noted by Mumford et al. ( 2021 ), whose study focused on the physical and mental health of law enforcement officers as well as Price ( 2017 ), who acknowledged there had been an increase in wellness programs whose goal is to mitigate the effects of job-related stress. Finally, Price ( 2017 ) also reiterates previous findings that job-related stressors and/or exposure to critical incidents have a greater likelihood to manifest into symptoms including PTSD-like symptoms, increased alcohol abuse, increased suicide risk, relationship problems, depression, and aggressive conduct.

Law enforcement officers are subjected to not only critical incidents that occur within the day-to-day function of the job, but are also subject to other, more subtle, factors including organizational stressors (i.e., inadequate training, poor supervision, perceived inequality); additional job stressors (i.e., long hours in addition to “on call” status); public scrutiny; and specialized duties (i.e., undercover assignments, hostage rescue, or crisis negotiation). This is compounded by the normal personal problems that can occur including the physical changes that occur in our bodies as we begin to get older; increased likelihood of injury or illness; or psychological factors (Sheehan and Van Hasselt  2003 ; Wagner et al.  2020 ). These researchers noted that it was not a situation where either critical incidents or cumulative stressors alone would cause law enforcement officers undue stress, but rather the convergence of these factors.

Several studies have confirmed a relationship between the frequency by which law enforcement officers were exposed to critical incidents and PTSD symptom variables (Weiss et al.  2010 ; Chopko et al.  2015 ; Geronazzo-Alman et al.  2017 ). The instrument used for this study is the Cumulative Career Traumatic Stress Survey or CCTS. Marshall ( 2006 ) developed the CCTS based on both her experience as a law enforcement officer and as a trauma therapist. Like assertions made by Weiss et al. ( 2010 ) and Van Hasselt et al. ( 2008 ), Marshall ( 2006 ) noted that symptoms of CCTS were like PTSD with the exception that PTSD typically resulted from a single or sudden traumatic event. The impact of the event can result in the slow and subtle deterioration of the officer’s emotional and psychological stability that is more trauma than stress based. This slow deterioration can manifest in the form of intrusive thoughts, flashbacks or nightmares, anxiety, hyperarousal, sleeping and/or eating problems, disconnection from family and/or friends, emotional numbing, and moodiness. They can negatively impact the officer both personally and professionally in the form of impaired job performance, diminished physical health, or marital/family problems (Geronazzo-Alman et al.  2017 ; Marshall  2006 ; Van Hasselt et al.  2008 ). These align with the findings of previous research that focused on symptomology. An additional finding of interest made by Marshall ( 2006 ) was the change in worldview experienced since becoming a law enforcement officer. This included a change in the perception of others, a lack of trust of others, being prejudice towards others, and experiencing a change in faith/beliefs.

Tucker ( 2015 ) noted there appeared to be a gap in the literature that explores the value of organizational support, specifically in stress intervention services. She examined the likelihood of law enforcement officers voluntarily utilizing stress intervention services and found that if officers perceived their organization to be in support of these services, they would be more likely to utilize them. Conversely, if there was the perception of an officer being stigmatized using these services, there was a significant decline in their willingness to do so.

Changing an Ingrained Culture

The federal government has taken steps to ensure the mental health crisis within law enforcement organizations is addressed via the Law Enforcement Mental Health and Wellness Act of 2017 ( 2017 ). The philosophy underlying the policy is to assess whether law enforcement agencies have implemented mental health and wellness programming and determine what impact the implementation of those services has had on their officers and organizations. It incorporates a holistic approach to police officers, staff, and their family’s mental health and wellness after research indicated a direct correlation between the occupation and higher rates of chronic physical illnesses, domestic incidents, substance abuse, and mental health disorders including depression, anxiety, and PTSD. The final report was presented to Congress in early 2019 by Spence et al. ( 2019 ) and included information related to services and resources available to military veterans and law enforcement officers.

That same year, Copple et al. ( 2019 ) submitted a report to the DOJ that provided an overview of several successful and promising law enforcement mental health and wellness strategies that had been implemented in a diverse group of police agencies across the country including agencies creating a sense of ownership and support for the programs from the top police administrative officials down. In addition, the report included a summary of the peer crisis response hotline (Cop2Cop) including the design elements of a hotline and the necessary follow-up care and support provided by well-trained officers communicating with other officers. The report acknowledged that many police agencies have, in place, employee assistance programs (EAP) that are sponsored by the local government where they are located.

Based on the literature, there is a critical need to develop more comprehensive services for police officers exposed to traumatic events and police stress and implement early prevention assessment along with mental health and wellness strategies that are proactive rather than reactive. The current study examines the relationship between years of service, traumatic events, and deleterious outcomes of post-traumatic symptoms and change in perception of world and others. The authors also explored whether these outcomes are different among demographic factors.

Research Questions and Hypotheses

Research questions related to the independent variable = tenure/years of service.

• RQ # 1: Is officer tenure (years of service) associated with reported traumatic events?
• HR # 1: Officer tenure (years of service) is associated with reported traumatic events.
• RQ # 2: Is officer tenure (years of service) associated with reported post-traumatic symptom (PTS) outcomes?
• HR # 2: Officer tenure (years of service) is associated with reported post-traumatic symptom (PTS) outcomes.
• RQ # 3: Is officer tenure (years of service) associated with reported changes in perception of world and others?
• HR # 3: Officer tenure (years of service) is associated with reported changes in perception of world and others.

Research Questions Related to the Independent Variable = Traumatic Events

• RQ # 4: Are traumatic events associated with reported post-traumatic symptoms (PTS) outcomes?
• HR # 4: Traumatic events are associated with reported post-traumatic symptoms (PTS) outcomes.
• RQ # 5 Are traumatic events associated with reported changes in perception of world and others?
• HR # 5: Traumatic events are associated with reported changes in perception of world and others.

Research Questions Related to Independent Variable = Race/Gender

• RQ # 6: Is there a difference in reported traumatic events or psychological/behavioral outcomes on demographic characteristics of gender and race?
• HR # 6: There is a difference in reported traumatic events or psychological/behavioral outcomes on demographic characteristics of gender and race?

An IRB approved, cross-sectional, descriptive study was conducted utilizing a convenience sample of law enforcement officers yielding a total of 408 respondents ( n  = 408). To address the research questions, the Cumulative Career Traumatic Stress Questionnaire-CCTS-R (Marshall  2006 ) was administered (Fig.  1 ). The CCTS-R is a 60-item instrument and includes items measuring their opinion of others; their trust of others; their prejudice towards others; and whether they had experienced a change in faith or religious beliefs, job-related traumatic events, PTSD-like symptoms or experiencing personal/behavioral changes, and whether there was a history of anxiety/depression prior to becoming a sworn law enforcement officer. The instrument is broken down into 4 sections; part I asks respondents to self-report on demographic information (race, gender, rank, years of service, etc.); part II represents 20 items related to traumatic events as respondents to self-report with Yes/No whether they have experienced any of the items since being on the job. Some of the items include Have you confronted a person with a gun? Have you been involved in a shooting? Has a co-worker been shot or killed while on-duty? Have you responded to an incident involving the death of a child? Respondent’s scores can range from 20 to 40 (40 representing the highest reporting of traumatic events).

Study conceptual model

Part III represents 15 items related to post-traumatic stress symptoms, and part IV represents 11 items specific to change in perception of the world and others and contains Likert type responses. Examples of the items include Since being on the job …. I have experienced nightmares as a result of an incident, I have experienced flashbacks of an incident, I experience recurring memories of an event after being reminded by another event, I re-experience physical reactions of an event after being reminded by another event. The lowest possible self-reported post-traumatic stress symptom being a score of 15 and the highest score being 60.

Lastly, the world view and perception of others section represents 11 items that asks respondents to self-report on whether their perception of the world or others has changed since being on the job. Some examples include My opinion of other people has changed, I no longer trust others. Stress from the job has affected my relationship with family members. These scores range from 11 to 44 with 44 indicating the highest change in perception of the world and others.

Descriptive Statistics

The sample population of 408 respondents represented 83% male and 17% female. Over 81% ( n  = 332) identified as being Caucasian; about 5% ( n  = 23) identified as being African American; and 6% ( n  = 26) identified as being Hispanic American. There were significantly lower numbers of those who identified as Asian American (2%) and Native American less than 1%. Close to 3% reported “Other”.

In terms of education and rank, 53% of the sample indicated they held either an associate or baccalaureate degree, and nearly 30% indicated having some college. About 7% of the sample reported “no college” at all. For the variable “Rank”, 35% identified their rank as “Police Officer or Police officer /Trooper 1st Class”, while the rank of “Sergeant and Corporal” accounted for about 29% of the sample. Over 26% reported their rank as lieutenant or above and 10% identified their rank as “other”. While the average number of years reported was 4 years, close to 28% reported having 26 + years.

For the items representing traumatic events , the findings indicated that most officers in the sample had experienced a major traumatic event in their career with close to 92% of the sample reporting that they had confronted a person possessing a gun. Ninety eight percent of the sample indicated that they had confronted a person possessing a weapon other than a firearm and having to use force other than deadly force. The most disturbing call that officers identified was a child abuse/neglect or death of a child complaint. Eighty six percent reported a child abuse/neglect complaint and 73% involving the death of a child. One of the items that is most interesting and consistent with the literature is that 35% of the sample reported that they had a co-worker who committed suicide and 7% of the sample reported that they “sometimes think of suicide” (Hackett and Violante  2003 ; O'Hara et al.  2013 ).

One finding that is inconsistent with the literature is that 42% of the sample reported never using alcohol to relax and less than 10% report never using alcohol. According to the Substance Abuse & Mental Health Data Archive (Substance Abuse and Mental Health Services Administration  2019 ), among the 139.7 million current alcohol users aged 12 or older in 2019 in the USA, 65.8 million people (47.1%) were past month binge drinkers. The literature on police alcohol use indicates a much higher prevalence than reported in this sample (Ménard and Arter  2013 ; Chopko et al.  2013 ).

Among the Change in WorldView and Perception of Others items, the findings showed that close to 40% of the sample no longer trusts others and feels that the world is an unsafe place. Consistent with the literature on police and their family relationships, 36% of the sample reported that the stress from the job has affected their relationship with family members (Karaffa et al.  2015 ). Interestingly 43% of the sample report never having lost faith in religious beliefs. This finding indicates that future research on faith as a coping strategy is worth investigating.

The post-traumatic stress items show that close to 40% of the sample have experienced nightmares as a result of an incident or incidents, had flashbacks of an incident, or experienced recurring memories. A small minority of the sample (14%) reported having no trouble sleeping. Close to 44% of the sample reported that they have difficulty concentrating and experience jumpiness or restlessness. All of these descriptive findings are consistent with the literature on stress symptoms in police (Marshall  2006 ).

Scales Internal Consistency

A reliability analysis was run on the three subscales of the cumulative career traumatic stress measurement. Part II of the instrument represented “Traumatic Events” items, part II I represented “Post Traumatic Symptoms” items, and part IIV represented “Perception of World/Others” items. For part II traumatic events, the findings indicate a Cronbach’s alpha coefficient of α = 0.71 (Table ​ (Table1). 1 ). The Cronbach’s alpha coefficient for post-traumatic symptoms was reported as α = 0.91 (Table ​ (Table2) 2 ) and worldview/perception of others α = 0.85 (Table ​ (Table3 3 ).

Traumatic events items reliability

Post-traumatic stress items reliability

Worldview/perception of others reliability

Inferential Statistics

Tenure/years of service and traumatic event, pts, and world view/perception of others.

To test hypotheses HR # 1, a Spearman’s correlation was run to assess whether there was a relationship between years of service in law enforcement and traumatic events, post-traumatic stress symptoms, and worldview and perception of others. Table ​ Table1 1 illustrates that years of service was significantly correlated with traumatic events (0.471) supporting hypothesis # 1. HR # 2 and HR # 3 were not supported by the analysis (Table ​ (Table4 4 ).

**Correlation is significant at the 0.01 level (2-tailed)

Traumatic Events and PTS Symptoms/WorldView Perception of Others

To test hypotheses HR #4 and 5, a Spearman’s correlation was run to assess whether there was a relationship between Traumatic Events and PTS symptoms. Table ​ Table5 5 indicates a significant association at 0.383. Traumatic events and worldview/perception of others were significantly correlated at 0.272 (Table ​ (Table6). 6 ). HR # 6 was not supported.

TE/worldview

Traumatic Event, PTS, WorldView, and Race/Gender

• HR # 6: There is a difference for traumatic events, PTS symptoms, and worldview on the demographic variables of gender and race.

An independent t -test and ANOVA were run to determine whether there were differences among gender and race on the independent variables. These hypotheses were not supported showing no significant differences.

The findings from this study support the literature that perpetual long-term exposure to critical incidents and traumatic events, within the scope of the duties of a law enforcement officer, has negative implication that can impact both their physical and mental well-being. These symptoms become exacerbated when the officer perceives that receiving any type of service to address these issues would not be supported by law enforcement hierarchy and could, in fact, lead to the officer being declared unfit for duty. The symptoms may be in the form of increased physical ailments, including increased instances of injuries sustained on the job and increased stress-related diagnoses including gastrointestinal ulcers or hypertension. It could also manifest in the form of a significant personality or behavioral change including an officer being quicker to get into a physical altercation with a citizen, increased instances of domestic altercations, increased instances of alcohol and/or prescription drug abuse, noted depression, or suicide. Of interest was that this study did not indicate a correlation between years of service and increased alcohol abuse or between years of service, and a loss of faith and/or religious beliefs yet did indicate an overall loss of faith in the goodness and trustworthiness of people. These specific variables warrant further research to determine if the lack of reported alcohol abuse was simply underreported by this sample or if it indicates something altogether different, for example, the beginning of a change in police culture that no longer stigmatizes officers who seek help after experiencing a traumatic event. Additionally, what role does an individual’s faith or religious beliefs play in their ability to cope with the stressors of the job of being a law enforcement officer? Did they have faith to begin with? If so, was that faith the catalyst that allowed them to persevere during their darkest times on the job? Cox et al. ( 2019 ) indicated that law enforcement officers may, as a survival mechanism, become more cynical, yet the results from this study indicated no significant correlation existed. Additionally, this research did not include an exploration of size and location of the department or type of traumatic event. These variables are worthy of examination for future research as they may either modify or confound the outcome variable.

Implications

Dawson ( 2019 ) notes, “The ‘bulletproof cop’ does not exist. The officers who protect us must also be protected against incapacitating physical, mental and emotional health problems, as well as against the hazards of their jobs” (n.p.). This work contributes to the existing literature by providing empirical evidence to support the need for law enforcement administrators and systems to take a more proactive approach in addressing the mental health and well-being of law enforcement officers. The 2017 Law Enforcement Mental Health and Wellness Act and other subsequent studies provide evidence demonstrating that police agencies who have implemented holistic proactive approaches to officer’s mental health report a decline in the negative impacts of job-related stressors. In an attempt to encourage more of these cultural changes and reduce stigmas associated with police mental health, there are federal grant opportunities for law enforcement agencies to take advantage of to help fund programming. These grants can offer police agencies an opportunity to implement critical infrastructure changes as well as important and evidenced based mental health programming. Investing in our police and their mental and physical well-being may help reduce instances of alcohol abuse, relationship conflict, suicidal ideation, negative perception of others, abuse of authority and excessive force complaints, and officer turnover.

Law enforcement agencies with proactive approaches to mental health report that these efforts lead to recruiting stronger candidates. A mental health paradigm shift from stigma to support can help officers learn strategies to cope with police stressors and traumatic events in a healthy way. The importance of all stakeholders, politicians, police administrators, police unions, and the rank-in file officers themselves, need to “buy-in” to this paradigm shift as a collaborative effort between law enforcement and mental health professionals to help heal our police officers is crucial.

As with any new relationship, and especially in the police culture where suspicion and doubt have become second nature, there needs to be adequate time for trust and a bond to form so that officers feel they can openly and honestly discuss without fear of administrative reprisals. This relationship should ideally begin during the cadet phase and be encouraged to continue throughout the officer’s career. The results of this current study demonstrate that the longer the officer is on the job, the more likely they are to experience traumatic events, thus leading to deleterious mental health outcomes. Therefore, the open lines of trust and communication must be present throughout the officer’s entire tenure with the department.

Critical incidents and job stressors are an unfortunate part of being a law enforcement officer. The key to success, however, is openly acknowledging them, removing the long-standing stigma, having open and honest discussions, and learning strategies of coping with them. Further studies on police stress and mental health outcomes are necessary to be explored in large and small departments as well as rural and urban police agencies. Law enforcement agencies spend millions of dollars each year in training and equipment for officers to ensure their protection and safety. Implementing holistic proactive approaches to that same officer’s mental health and well-being should be treated as an additional piece of equipment they carry with them that allows them to be more effective in their respective roles of protecting and serving the public.

Recommendations for Intervention Strategies

As mentioned earlier, this population is unique in the willingness to seek psychological assistance and support in the first place. Officer perceptions of stigma can be considered an enormous barrier to treatment (Milot  2019 ). Police departments can provide stronger support for their officers by seeking to employ evidenced based and trauma informed assistance. The impact that policing has on an officer’s “worldview” and the ability to navigate the bombardment of traumatic exposure is the challenge for law enforcement executives. Officers are immersed in the experience of interacting with people when they are at their worst. These officers have a front row seat to human suffering. This trauma informed compassion fatigue, burnout, cynicism, and other deleterious psychological outcomes should be a wake-up call to policymakers. Early intervention methods, counseling and psychological services, and peer support are among the common intervention strategies. Stigma and fear of job security are among the reasons that officers are not engaging in these strategies. An officer who is experiencing the impact that traumatic events have on psychological well-being and overall mental health reluctant to come to the supervisor with an honest appraisal of their psychological well-being for fear of having their guns taken away from them and being placed on desk duty. Police executives and policymakers need to investigate intervention strategies that align with the officer’s comfort zone.

There are evidence-based officer mental health and wellness policies and programs that afford officers the proper treatment needed for depression, anxiety, alcohol and prescription medication abuse, and post-traumatic stress syndrome. At the policymaking level, police departments could implement a broad continuum of officer mental health and wellness policies and programs. These strategies include providing officers with access to information on mental health resources, annual mental health wellness checks, in-service stress management awareness training, peer support initiatives, and psychological services (McManus and Argueta 2019 ). Until police executives, administrators, policymakers, legislators, and others in authority are willing to spend the resources, personnel, time, and energy in improving mental health services for police officers, we will continue to see the deleterious impact that trauma and critical event exposure have on these men and women in blue.

Availability of Data and Material

Declarations.

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent was obtained from all individual participants included in the study.

The authors declare no competing interests.

Publisher's Note

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

Contributor Information

Tina B. Craddock, Email: ude.usce@kcoddarcbt .

Grace Telesco, Email: ude.avon@342tg .

• Balaban S, Doubek J (2019) After husbands' suicides, 'best widow friends' want police officers to reach for help. NPR.org. https://www.npr.org/2019/06/09/724283309/after-husbands-suicides-best-widow-friends-want-police-officers-to-reach-for-hel . Accessed 30 Jul 2020
• Barron S. Police officer suicide within the New South Wales police force from 1999 to 2008. Police Pract Res. 2010; 11 (4):371–382. doi: 10.1080/15614263.2010.496568. [ CrossRef ] [ Google Scholar ]
• Blue H.E.L.P (n.d.)  https://www.bluehelp.org . Accessed 30 Jul 2020
• Bonifacio P. The psychological effect of police work. Plenum Press; 1991. [ Google Scholar ]
• Brown JM, Wormald J, Campbell EA (1994) Stress and policing: Sources and strategies. John Wiley & Son
• Chopko B, Palmieri P, Adams R. Associations between police stress and alcohol use: Implications for practice. J Loss Trauma. 2013; 18 (5):482–497. doi: 10.1080/15325024.2012.719340. [ CrossRef ] [ Google Scholar ]
• Cerel J, Jones B, Brown M, Weisenhorn D, Patel K. Suicide exposure in law enforcement officers. Suicide Life Threat Behav. 2019; 49 (5):1281–1289. doi: 10.1111/sltb.12516. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Chopko B, Palmieri P, Adams R. Critical incident history questionnaire replication: frequency and severity of trauma exposure among officers from small and midsize police agencies. J Trauma Stress. 2015; 28 (2):157–161. doi: 10.1002/jts.21996. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Chopko B, Palmieri P, Adams R. Relationships among traumatic experiences, PTSD, and posttraumatic growth for police officers: a path analysis. Psychol Trauma Theory Res Pract Policy. 2018; 10 (2):183–189. doi: 10.1037/tra0000261. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Copple C, Copple J, Drake J, Joyce N, Robinson M, Smoot S, Stephens D, Villaseñor R (2019) Law enforcement mental health and wellness programs: eleven case studies (371). Department of Justice. https://cops.usdoj.gov/RIC/Publications/cops-p370-pub.pdf . Accessed 30 Jul 2020
• Cox S, Massey D, Koski C, Fitch B (2019) Introduction to Policing. 4th ed., Sage Publications. Washington, D. C
• Cross C, Ashley L. Police trauma and addiction: coping with the dangers of the job. PsycEXTRA Dataset. 2004 doi: 10.1037/e311382005-006. [ CrossRef ] [ Google Scholar ]
• Dawson J (2019) Fighting stress in the law enforcement community. Natl Inst Justice J. https://nij.ojp.gov/topics/articles/fighting-stress-law-enforcement-community . Accessed 30 Jul 2020
• Geronazzo-Alman L, Eisenberg R, Shen S, Duarte C, Musa G, Wicks J, Fan B, Doan T, Guffanti G, Bresnahan M, Hoven C. Cumulative exposure to work-related traumatic events and current post-traumatic stress disorder in New York city’s first responders. Compr Psychiatry. 2017; 74 :134–143. doi: 10.1016/j.comppsych.2016.12.003. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hackett D, Violante J (2003) Police suicide: tactics for prevention. Springfield, IL: C. C. Thomas
• Hillard J (2019) New study shows police at highest risk for suicide than any profession. Addiction Center. https://www.addictioncenter.com/news/2019/09/police-at-highest-risk-for-suicide-than-any-profession/ . Accessed 30 Jul 2020
• Karaffa K, Openshaw L, Koch J, Clark H, Harr C, Stewart C. Perceived impact of police work on marital relationships. Fam J. 2015; 23 (2):120–131. doi: 10.1177/1066480714564381. [ CrossRef ] [ Google Scholar ]
• Law Enforcement Mental Health and Wellness Act. U.S.C. S.867 (2017) https://www.congress.gov/bill/115th-congress/senate-bill/867 . Accessed 30 Jul 2020
• Lowell A, Suarez-Jimenez B, Helpman L, Zhu X, Durosky A, Hilburn A, Schneier F, Gross R, Neria Y. 9/11-related PTSD among highly exposed populations: a systematic review 15 years after the attack. Psychol Med. 2017; 48 (4):537–553. doi: 10.1017/s0033291717002033. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Marshall E. Cumulative career traumatic stress (CCTS): a pilot study of traumatic stress in law enforcement. J Police Crim Psychol. 2006; 21 (1):62–71. doi: 10.1007/bf02849503. [ CrossRef ] [ Google Scholar ]
• McManus HD, Argueta J Jr (2019) Officer wellness programs: research evidence and a call to action. Police Chief 86(10):16. Retrieved from https://ezproxylocal.library.nova.edu/login?url=https://www.proquest.com/trade-journals/officer-wellness-programs-research-evidence-call/docview/2307719208/se-2?accountid=6579 . Accessed 22 Oct 2021
• Ménard K, Arter M. Police officer alcohol use and trauma symptoms: associations with critical incidents, coping, and social stressors. Int J Stress Manag. 2013; 20 (1):37–56. doi: 10.1037/a0031434. [ CrossRef ] [ Google Scholar ]
• Milot M (2019) Stigma as a barrier to the use of employee assistance programs. A Workreach Solutions research report
• Moore T, Celona L (2019) Off-duty veteran NYPD cop commits suicide, ninth this year. New York Post. https://nypost.com/2019/08/14/off-duty-veteran-nypd-cop-shoots-himself-in-the-head-in-queens/ . Accessed 30 Jul 2020
• Morash M, Haarr R, Kwat D. Multilevel influences on police stress. J Contemp Crim Justice. 2006; 22 (1):26–43. doi: 10.1177/1043986205285055. [ CrossRef ] [ Google Scholar ]
• Mumford E, Liu W, Taylor B (2021) Profiles of U.S. law enforcement officers’ physical, psychological, and behavioral health: results from a nationally representative survey of officers. Police Q. 10.1177/1098611121991111
• O’Hara A, Violanti J, Levenson R, Clark R (2013) National police suicide estimates: web surveillance study III. Int J Emerg Ment Health and Human Resilience 15(1):31–38 [ PubMed ]
• Pai A, Suris A, North C. Posttraumatic stress disorder in the DSM-5: controversy, change, and conceptual considerations. Behav Sci. 2017; 7 (4):7. doi: 10.3390/bs7010007. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pinellas County Sheriff's office (2017) 17–238 off-duty sergeant dies of self-inflicted gunshot wound in Largo -. Pinellas County Sheriff's Office. https://pcsoweb.com/17-238-off-duty-sergeant-dies-of-self-inflicted-gunshot-wound-in-largo . Accessed 30 Jul 2020
• Price M. Psychiatric disability in law enforcement. Behav Sci Law. 2017; 35 :113–123. doi: 10.1002/bsl.2278. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Shane J. Organizational stressors and police performance. J Crim Just. 2010; 38 :807–818. doi: 10.1016/j.jcrimjus.2010.05.008. [ CrossRef ] [ Google Scholar ]
• Sheehan DD, Van Hasselt VB (2003) Identifying law enforcement stress reactions early. PsycEXTRA Dataset, 12-17.10.1037/e314622004-002
• Soomro S, Yanos P. Predictors of mental health stigma among police officers: the role of trauma and PTSD. J Police Crim Psychol. 2018; 34 :175–183. doi: 10.1007/s11896-018-9285-x. [ CrossRef ] [ Google Scholar ]
• Spence DL, Fox M, Moore GC, Estill S, Comrie N (2019) Law enforcement mental health & wellness act: report to congress. Department of Justice. https://cops.usdoj.gov/RIC/Publications/cops-p370-pub.pdf . Accessed 30 Jul 2020
• Spielberger CD, Westberry LG, Grier KS, Greenvield G (1981a) Police stress survey: sources of stress in law enforcement. https://www.ncjrs.gov/pdffiles1/Digitization/80993NCJRS.pdf . Accessed 30 Jul 2020
• Spielberger CD, Westberry LG, Grier KS, Greenvield G (1981b) Police stress survey (PSS). Statistics Solutions. https://www.statisticssolutions.com/police-stress-survey-pss/ . Accessed 30 Jul 2020
• Stephens C, Long N. Posttraumatic stress disorder in the New Zealand police: the moderating role of social support following traumatic stress. Anxiety Stress Coping. 1999; 13 (3):247–265. doi: 10.1080/10615809908250477. [ CrossRef ] [ Google Scholar ]
• Stinchcomb JB. Searching for stress in all the wrong places: combating chronic organizational stressors in policing. Police Pract Res. 2004; 5 (3):259–277. doi: 10.1080/156142604200227594. [ CrossRef ] [ Google Scholar ]
• Substance Abuse and Mental Health Data Archive (2018) Disaster technical assistance center supplemental research bulletin: first responders: behavioral health concerns, Emergency response, and trama. https://www.samhsa.gov/sites/default/files/dtac/supplementalresearchbulletin-firstresponders-may2018.pdf . Accessed 30 Jul 2020
• Substance Abuse and Mental Health Data Archive (2019) National Survey of Drug Use and Health 2019. https://www.samhsa.gov/data/release/2019-national-survey-drug-use-and-health-nsduh-releases . Accessed 30 Jul 2020
• Telesco GA (2019) Never forget, sometimes forgotten, often haunted. Journal of the American Academy of Experts in Traumatic Stress, Fall Issue
• Tucker JM (2015) Police officer willingness to use stress intervention services: the role of perceived organizational support (POS), confidentiality and stigma. OMICS 17(1):304–314
• Van Hasselt VB, Sheehan DC, Malcolm AS, Sellers AH, Baker MT, Couwels J. The law enforcement officer stress survey (LEOSS) Behav Modif. 2008; 32 (1):133–151. doi: 10.1177/0145445507308571. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Violanti JM. Police organizational stress: the impact of negative discipline. Int J Emerg Ment Health. 2011; 13 (1):31–36. [ PubMed ] [ Google Scholar ]
• Wagoner S, White N, Fyfe T, Matthews L, Randall C, Regehr C, White M, Alden L, Buys N, Carey M, Corneil W, Fraess- Phillips A, Krutop E, Fleishmann M. Systematic review of post traumatic stress disorder in police officers following routine work-related critical incident exposure. Am J Ind Med. 2020; 63 :600–615. doi: 10.1002/ajim.23120. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Weiss D, Brunet A, Best S, Metzler T, Liberman A, Pole N, Fagan J, Marmar C. Frequency and severity approaches to indexing exposure to trauma: the critical incident history questionnaire for police officers. J Trauma Stress. 2010; 23 (6):734–743. doi: 10.1002/jts.20576. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Witt H (2005) Jim Ramsey: tragedy haunts the heroes. Chicago Tribune. https://www.chicagotribune.com/business/careers/chi-0504180178apr18-story.html . Accessed 30 Jul 2020

• Skip to main content
• Skip to primary sidebar

Criminal Justice

IResearchNet

Academic Writing Services

Quantitative criminology.

The foundation of a sound quantitative criminology is a solid base of descriptive information. Descriptive inference in criminology turns out to be quite challenging. Criminal offending is covert activity, and exclusive reliance on official records leads to highly deficient inferences. Despite important challenges in descriptive analysis, researchers and policymakers still strive to reach a better understanding of the effects of interventions, policies, and life experiences on criminal behavior. (adsbygoogle = window.adsbygoogle || []).push({});

1. Introduction

2. quantitative data sources, 3. logical and inferential issues, 3.1. time horizon, 3.2. unit of analysis, 3.3. sampling, 3.4. target population, 3.5. concepts and variables, 3.6. descriptive and causal inference, 3.7. validity, 3.8. reliability, 3.9. relationship between reliability and validity, 3.10. estimates and estimators, 3.11. estimator properties: bias, efficiency, and consistency, 4. assessing evidence, 5. methods for descriptive inference, 5.1. measures of central tendency, 5.2. measures of dispersion, 5.3. criminal careers, 5.4. recidivism rates, 5.5. trajectories and developmental pathways, 6. analytic methods for causal inference, 6.1. independent variables and outcomes, 6.2. contingency tables, 6.3. measures of association, 6.4. chi-square, t tests, and analysis of variance, 6.5. linear regression, 6.6. regression for qualitative and counted outcomes, 6.7. structural equation models, 6.8. interrupted time series analysis, 6.9. models for hierarchical and panel data, 6.10. counterfactual reasoning and treatment effects, 6.11. randomized experiments, 6.12. natural experiments and instrumental variable estimators, 6.13. matching, 7. conclusion.

Since its inception as a field of scientific inquiry, criminology and criminal justice (CCJ) researchers have used quantitative data to describe and explain criminal behavior and social responses to criminal behavior. Although other types of data have been used to make important contributions to criminological thought, the analysis of quantitative data has always played an important role in the development of knowledge about crime. This research paper discusses the various types of quantitative data typically encountered by CCJ researchers. Then, some of the logical and inferential issues that arise when researchers work with quantitative data are described. Next, the research paper considers different analytic frameworks for evaluating evidence, testing hypotheses, and answering research questions. Finally, a discussion of the range of methodological approaches used by contemporary CCJ researchers is provided.

CCJ researchers commonly work with data collected for official recordkeeping by government or quasi-government agencies. Such data often include records of criminal events, offender and victim characteristics, and information about how cases are handled or disposed. Detailed information about crimes known to the police and crimes cleared by arrest are available in the UniformCrime Reports (UCR) and the National Incident Based Reporting System (NIBRS). In addition, for purposes of specific research projects, criminal justice agencies often make their administrative records available to criminologists—provided that appropriate steps are taken to protect individual identities. For example, the Bureau of Justice Statistics has conducted two major studies of recidivism rates for prisoners returning to the community in multiple states. Such projects require coordinated use of state correctional databases and access to criminal records, including arrests, convictions, and reincarceration.

More recently, researchers have also relied on information collected through direct interviews and surveys with various populations. In these surveys, respondents are asked about their involvement in offending activities, victimization experiences, background characteristics, perceptions, and life circumstances. Analyses from data collected through the National Crime Victimization Survey; the Arrestee Drug Abuse Monitoring program; the RAND inmate survey; the National Youth Survey; the National Longitudinal Survey of Youth; the Adolescent Health Study; Monitoring the Future (MTF); Research on Pathways to Desistance, and the Office of Juvenile Justice and Delinquency Prevention’s longitudinal youth studies in Rochester, New York, Pittsburgh, Pennsylvania, and Denver, Colorado, have all made important contributions to criminological thought and public policy.

Researchers have also attempted, in some studies, to collect detailed quantitative databases composed of information from both administrative and direct surveys on the same individuals. Among other findings, this research has consistently shown that most crime victimizations are not reported to the police and that most offending activities do not result in an arrest.

The analysis of quantitative crime-related data, like any other type of analysis, depends primarily on the question one is asking and the capabilities of the data available. This section briefly discusses some of the most prominent issues that crime researchers consider when analyzing quantitative data.

Regardless of the data source, research projects using quantitative data can generally be characterized as crosssectional or longitudinal. Cross-sectional studies examine individuals or populations at a single point in time, whereas longitudinal studies follow the same individuals or populations over a period of time. Among longitudinal studies, an important consideration is whether the data will be collected prospectively or retrospectively. In prospective studies, individuals are enrolled in the study and then followed to see what happens to them. In retrospective studies, individuals are enrolled in the study, and researchers then examine historical information about them. Some studies include both prospective and retrospective elements. For example, the Research on Pathways to Desistance study enrolled adolescent offenders in Phoenix, Arizona, and Philadelphia to see how these offenders adapt to the transition from adolescence to adulthood. In that sense, the study is prospective; however, historical information about the individuals included in the study is available and has been collected retrospectively as well.

In most studies, it is clear whether the project is crosssectional or longitudinal, but there are exceptions. For example, the MTF study repeatedly surveys nationally representative samples of high school seniors. This study can be viewed as cross-sectional because it does not survey the same individuals repeatedly, but it can also be viewed as longitudinal because the same methodology for drawing the sample and analyzing the data is repeated over time. Similar issues arise with UCR and NIBRS data. Often, specific studies using a repeated cross-sectional data source, such as MTF, UCR, or NIBRS, will tend to emphasize either crosssectional or longitudinal features of the data.

It is also useful to think about research projects in terms of the basic source of variation to be studied. For example, some studies focus on variation in crime between communities, whereas other studies examine variation in criminality between individual persons. Still other studies attempt to describe and explain variation in behavior over time for the same community or individual. In some studies, the unit of analysis is unambiguous, whereas in other instances, there may be multiple logical analysis units (e.g., multiple observations on the same person and multiple persons per community). These studies are generally referred to as hierarchical or multilevel analyses. An important issue arising in these analyses is lack of independence among observations belonging to a logical higher-order group. For example, individuals who live in the same community or who attend the same school are not likely to be truly independent of each other.

The list of all cases that are eligible to be included in a study is called the sampling frame. The sample included in the study will either be identical to the sampling frame or it will be a subset of the sampling frame. In some instances, the sampling frame is explicitly defined; at other times, the sampling frame is vague. Researchers generally describe the manner in which the sample was selected from the sampling frame in terms of probability or nonprobability sampling. In probability sampling, each case in the sampling frame has a known, non-zero probability of being selected for the sample. Samples selected in any other way are called nonprobability samples. The most basic form of probability sampling is simple random sampling, when each member of the sampling frame has an equal probability of being selected for the sample. More complicated forms of probability sampling, such as stratified random sampling, cluster sampling, and stratified multistage cluster sampling, are all commonly used in CCJ research.

The use of probability sampling allows researchers to make clear statements about the generalizability of their results. Although this is a desirable feature of probability samples, much CCJ research is based on nonprobability samples. The 1945 and 1958 Philadelphia birth cohort studies conducted by Marvin Wolfgang and his colleagues (Wolfgang, Figlio, & Sellin, 1972) focused on an entire population of individuals rather than a sample. Still, one can view the choice of the years 1945 and 1958 as a means of sampling. In fact, when populations are studied, there is almost always a way to conceive of them as nonprobability samples. In other studies, a researcher may survey all children in attendance at a school on a particular day. The resulting sample would be called a convenience or availability sample. Still other research projects rely on the purposive selection of certain numbers of people meeting particular criteria to ensure representation of people from different groups (i.e., males, females, blacks, whites, etc.). These samples are usually called quota samples.A key feature of nonprobability samples is that one is not able to make explicit probabilistic statements about quantities in the population based on what one observes in the sample. Nevertheless, nonprobability samples are quite useful and necessary for addressing many interesting research and policy questions that arise in CCJ research.

A key aspect of any scientific work is the identification of empirical regularities that transcend specific individuals, places, or times. Thus, the population to which the results of a study generalize is of considerable importance. In general, researchers tend to prefer studies that identify the target population and discuss how well the results are likely to generalize to that population. But the target population is sometimes ambiguous. If one studies all individuals in attendance at a particular school on a given day, one could argue that the sample is synonymous with the target population. The research community, however, is not likely to be interested in what is occurring at that individual school unless it somehow relates to what is occurring at other schools in other locations and at other times. This ambiguity means that one cannot make precise statements about the generalizability of the results to other settings. Thus, clear statements about the composition and boundaries of the target population are often the exception rather than the rule.

Scientific theories describe relationships between concepts. In this sense, concepts represent the key elements of a well-developed theory. Concepts are verbal cues or symbols that sometimes refer to simple or complicated sources of variation. Sex (male vs. female), for example, refers to a simple, objective source of variation, whereas the meaning of concepts such as delinquency or socioeconomic status is potentially quite complicated. Still, reference to concepts for purposes of theory and hypothesis development can be sufficient. For purposes of conducting empirical tests of theories and hypotheses, however, more rigor and specificity are required.

Variables are the language of actual empirical work. A researcher’s description of a variable explicitly defines how the concept in question is to be measured for purposes of an actual research project. An operational description or definition of a variable attends to how the variable was measured and what values the variable can take on. Variables such as sex and race are categorical, whereas variables such as age and income are quantitative. Categorical variables can be nominal (unordered categories) or ordinal (ordered categories, but the distance between categories is not well-defined). Quantitative variables can be interval (equal distance between categories) or ratio (existence of a true zero). Still another type of variable, of particular interest to criminologists, is a count of events. Event-count variables represent the number of times an event occurs within some period of time. One way to think of an event-count variable is to consider a two-category variable: Either an event occurs or does not occur within some small time interval. If one adds up the number of times an event occurs over many of these small time intervals, one gets a total count of events.

Some concepts are too broad to be measured effectively with a single variable. Socioeconomic status, for example, is often linked to a combination of at least three subordinate concepts: (1) educational attainment, (2) income, and (3) occupational prestige. Often, variables associated with closely related subordinate concepts can be combined into a scale or index that measures the conceptual variation of interest. There are different ways to form scales and indexes. Some are driven by mathematical decision rules based on correlations between the items comprising the scale or index, and others are based on conceptual considerations.

Still another important feature of any quantitative study is whether it emphasizes description or the identification of cause–effect relationships. Descriptive inference is a characterization or summary of important features of a population. For example, the main objective of the 1993 Bureau of Justice Statistics recidivism study was to estimate the percentage of offenders released from prison in 1993 who experienced subsequent involvement with the criminal justice system within 3 years of their release. No effort was made to explain variation in the recidivism rate; instead, the goal was pure description.

Causal inference is the process of distinguishing between a correlation or statistical association between two or more variables and a cause–effect relationship between those variables. In order for a variable x to be considered a cause of variable y, three criteria must be satisfied: (1) x precedes y in time, (2) x and y are statistically associated, and (3) the statistical association between x and y is not spurious (i.e., there is no other variable that can account for or explain the statistical association between x and y). It turns out that establishing the first two criteria is reasonably straightforward. Convincingly demonstrating nonspuriousness, however, is much more difficult. This issue is discussed in more detail in the “Analytic Methods for Causal Inference” section.

The word validity is often used in two broad contexts in CCJ research. It may be used to indicate whether (or to what extent) a specific measure is an accurate characterization of the concept being studied. For example, one might ask whether an IQ test is a valid measure of intelligence. The word validity is also used as a way of characterizing a study or particular methodological approach. In this case, the concern is whether the study or method is likely to faithfully present the world as it really operates or whether it will distort the phenomena under study in some important way. As an example of this usage, one might consider whether a study with a pretest outcome measurement followed by an intervention and then a posttest outcome measurement but no control group (a group that does not experience the intervention) is a valid study.

A number of different types of validity appear in the CCJ literature. A few common types are discussed here. Assessments of face validity are subjective judgments about whether a measurement or methodology is likely to yield accurate results. If a measure successfully predicts variation in a logically linked outcome, one can say that it rates high on criterion or predictive validity. For example, if one has a parole risk assessment instrument that is designed to predict likelihood of recidivism and the instrument, in fact, does do a good job of recidivism prediction, then one can say that it exhibits criterion validity. Measures with good construct validity are correlated with wellestablished indicators of the phenomenon in question. Such measures should also be independent of indicators that are not relevant to the phenomenon in question.

Studies with high internal validity take convincing steps to ensure that the logic of the study as applied to the individuals actually being studied is sound. External validity, on the other hand, refers to the generalizability of the study’s results to individuals other than those actually included in the study. Internal validity tends to be maximized when the researcher is able to exert a great deal of control over the study and the environment in which the study is conducted (i.e., a laboratory setting). Unfortunately, when the researcher exerts great control, the conditions of the study sometimes become more artificial and less realistic. This raises questions about how well the study results will generalize to other cases. To the extent that the researcher attempts to allow for more realistic study environments (and greater external validity), this will often lead to less control over the study, which produces threats to internal validity. Researchers desire studies that maximize both internal and external validity, but this is often difficult to achieve.

Reliability refers to the consistency, stability, or repeatability of results when a particular measurement procedure or instrument is used. Researchers aspire to the use of instruments and procedures that will produce consistent results (provided that the phenomena under study have not changed). There are different ways of assessing and quantifying reliability. One approach is to take a measurement at a particular point in time and then repeat that same measurement at a later point in time. The correlation between the two measurements is called test–retest reliability. Another approach is to conduct multiple measurements with some variation in the precise measurement method; for example, multiple questionnaires with variations in the wording of various items can be administered to the same individuals. The correlation between the various instruments is called parallel forms reliability.

In some instances, researchers need to code various pieces of information into quantitative research data. A concern often arises about whether the coding rules are written in such a way that multiple properly trained coders will reach the same coding decisions. Interrater reliability is considered to be high when there is a high correlation between the decisions of multiple coders who have reviewed the same information.

Reliability can also be assessed by examining correlations between multiple indicators of the same underlying concept. Assume, for example, that a researcher believes that a key influence on criminal behavior is an individual’s level of self-control. Because there is no single definitive measure of self-control, the researcher might measure many indicators and characteristics of individuals that he believes to be manifestations of one’s level of self-control (i.e., time spent on homework each day, grades in school, time spent watching television, etc.). One way of assessing the reliability of a scale or index that combines this information is to calculate the correlations between all of the indicators, which can then be used to calculate internal-consistency reliability. High levels of internal-consistency reliability imply that the various characteristics and indicators being studied are closely related to each other.

Measures or procedures for capturing measurements can be highly reliable but also invalid. It is possible, for example, to obtain consistent but wrong or misleading measurements. Measures or procedures can also be both unreliable and invalid. In general, however, if a measure is valid it must also, by definition, be reliable.

An estimate is a person’s guess about the value of some interesting quantity or parameter for a target population. Researchers obtain an estimate by applying a formula or estimator to observed data that can be used to develop inferences about the target population. The most straightforward case is when one studies observed data from a simple random sample drawn from a well-defined target population. The goal is to infer the value of a parameter or quantity in the population on the basis of what one observes in the sample. A researcher plugs the observed data into an estimator and then uses the estimator, or formula, to calculate an estimate of the quantity of interest in the population.

In the case of a probability sample drawn from a welldefined population, there is a true population parameter or quantity that researchers seek to estimate on the basis of what they see in the sample.An important issue is whether the estimator applied to the sample will—over the course of drawing many, many probability samples—on average lead to the correct inference about the population parameter. If the average of the parameter estimates is different from the true population parameter, one says that the estimator is biased.

Sometimes there are different unbiased estimators or formulas that could be used to estimate a population quantity. An important question is how to choose one estimator over another. Generally speaking, in this situation researchers would prefer the unbiased estimator that exhibits the least amount of variation in the estimates generated over many samples drawn from the same population. The estimator that exhibits the minimum amount of sample-to-sample variation in the estimates is the most efficient estimator. For example, the sample mean, the sample median, and the sample mode (see “Measures of Central Tendency” section) are both valid estimators for the population mean of a normally distributed variable. The sample mean, however, is a more efficient estimator than the sample median, which is itself more efficient than the sample mode.

In some circumstances, an unbiased estimator is not available. When this happens, researchers typically try to use a consistent estimator. A consistent estimator is biased in small samples, but the bias decreases as the size of the sample increases. Many commonly used estimators in the social sciences, such as logistic regression (discussed later in this research paper), are consistent rather than unbiased.

A statistical model is a description of a process that explains (or fails to explain) the distribution of the observed data. A problem that arises in quantitative CCJ research is how to consider the extent to which a particular statistical model is consistent with the observed data. This section describes several common frameworks for thinking about this correspondence.

4.1. Relative Frequency

In quantitative crime research, decisions about whether to reject or fail to reject a particular hypothesis are often of central importance. For example, a hypothesis may assert that there is no statistical association between two variables in the target population. A test of this hypothesis amounts to asking the following question: What is the probability of observing a statistical association at least as large (either in absolute value or in a single direction) as the one observed in this sample if the true statistical association in the target population is equal to zero? Put another way, assume that there is a target population in which the statistical association is truly equal to zero. If a researcher drew many simple random samples from that population and calculated the statistical association in each of those samples, he or she she would have a sampling distribution of the statistical association parameter estimates. This theoretical sampling distribution could be used to indicate what percentage of the time the statistical association would be at least as large as the association the researcher observed in the original random sample.

Generally speaking, if the percentage is sufficiently low (often, less than 5%), one would reject the hypothesis of no statistical association in the target population. A concern that arises in these kinds of tests is that the hypothesis to be tested is usually very specific (i.e., the statistical association in the target population is equal to zero). With a very large sample size it becomes quite likely that the so-called test statistic will lead a person to reject the hypothesis even if it is only slightly wrong. With a very small sample size, the test statistic is less likely to lead one to reject the hypothesis even if it is very wrong. With this in mind, it is important for researchers to remember that hypothesis tests based on the relative frequency approach are not tests of whether the statistical association in question is large or substantively meaningful. It is also important to keep in mind that the interpretation of statistical tests outside of the framework of well-defined target populations and probability samples is much more ambiguous and controversial.

4.2. Bayesian Methods

Researchers often find the relative frequency framework to be technically easy to use but conceptually difficult to interpret. In fact, researchers and policymakers are not necessarily so concerned with the truth or falsehood of a specific hypothesis (e.g., that a population parameter is equal to zero) as they are with the probability distribution of that parameter. For example, it might be of more interest to estimate the probability that a parameter is greater than zero rather than the probability that a sample test statistic could be as least as large as it is if the population parameter is equal to zero. Analysis conducted in the Bayesian tradition (named after the Rev. Thomas Bayes, who developed the well-known conditional probability theorem) places most of its emphasis on the estimation of the full probability distribution of the parameter(s) of interest. In general, Bayesian methods tend not to be as widely used as relative frequency (or frequentist) methods in CCJ research. This is probably due to the training received by most criminologists, which tends to underemphasize Bayesian analysis. Because Bayesian analyses can often be presented in terms that are easier for policy and lay audiences to understand, it is likely that Bayesian methods will become more prominent in the years ahead.

4.3. Parameter Estimation and Model Selection

CCJ researchers typically rely on quantitative criteria to estimate parameters and select statistical models. Common criteria for parameter estimation include least squares (LS) and maximum likelihood (ML). LS estimators minimize the sum of the squared deviations between the predicted and actual values of the outcome variable. ML estimators produce estimates that maximize the probability of the data looking the way they do. Provided the necessary assumptions are met, LS estimators are unbiased and exhibit minimum sampling variation (efficiency). ML estimators, on the other hand, are typically consistent, and they become efficient as the sample size grows (asymptotic efficiency).

Model selection involves the choice of one model from a comparison of two or more models (i.e., a model space). The most prominent model selection tools include F tests (selection based on explained variation) and likelihoodratio tests (selection based on likelihood comparisons). An important issue with these tests is that they typically require that one model be a special case of the other models in the model space. For these approaches, tests are therefore limited to comparisons of models that are closely related to each other. Increasingly, model selection problems require researchers to make comparisons between models that are not special cases of each other. In recent years, two more general model selection criteria have become more widely used: (1) the Akaike information criterion (AIC) and (2) the Bayesian information criterion (BIC). These criteria can be used to compare both nested and non-nested models provided the outcome data being used for the comparison are the same. Like F tests and likelihood-ratio tests, AIC and BIC penalize for the number of parameters being estimated. The logic for penalizing is that, all other things equal, we expect a model with more parameters to be more consistent with the observed data. In addition to penalizing for parameters, the BIC also penalizes for increasing sample size. This provides a counterweight to tests of statistical significance, such as the F test and the likelihood-ratio test, which are more likely to select more complicated models when the sample size is large. As modeling choices continue to proliferate, it seems likely that use of AIC and BIC will continue to increase.

This section briefly considers some descriptive parameters often studied in CCJ research. The first two subsections deal with parameters that are usually of interest to all social scientists. The final three subsections emphasize issues of particular importance for CCJ research.

Central tendency measures provide researchers with information about what is typical for the cases involved in a study for a particular variable. The mean or arithmetic average (i.e., the sum of the variable scores divided by the number of scores) is a common measure of central tendency for quantitative variables. The mean has an advantage in that each case’s numerical value has a direct effect on the estimate; thus, the mean uses all of the information in the scores to describe the “typical” case. A problem with the mean is that cases with extreme scores can cause the mean to be much higher or much lower than what is typical for the cases in the study. In situations where the mean is affected by extreme scores, researchers often prefer to use the median as a measure of central tendency. The median is the middle score of the distribution; half of the cases have scores above the median, and the other half have scores below the median. The median can also be viewed as the 50th percentile of the distribution. Unlike the mean, the median does not use all of the information in the data, but it is also not susceptible to the influence of extreme scores. For categorical variables, the mode (i.e., the most frequently occurring category) is often used as a measure of central tendency. For dichotomous or two-category variables, the most commonly used measure of central tendency is the proportion of cases in one of the categories.

In addition to summarizing what is typical for the cases in a study, researchers usually consider the amount of variation as well. Several common summaries of variation, or dispersion, are commonly reported in the literature. The most common measure of dispersion for quantitative variables is the variance and/or its square root, the standard deviation. Many interesting social science variables are either normally or approximately normally distributed (i.e., the distribution looks like a bell-shaped curve). In these types of distributions, approximately two thirds of the cases fall within 1 standard deviation of the mean, and about 95% of the cases fall within 2 standard deviations of the mean. Thus, for variables with a bell-shaped distribution, the standard deviation has a very clear interpretation. This is particularly important because sampling distributions are often assumed to have normal distributions. Thus, the standard error calculation that appears in much quantitative CCJ research is actually an estimate of the standard deviation of the sampling distribution. It can be used to form confidence intervals and other measures of uncertainty for parameter estimates in the relative frequency framework.

For qualitative or categorical variables, a common measure of dispersion is the diversity index, which measures the probability that cases come from different categories. Some CCJ researchers have used the diversity index to study offending specialization and ethnic–racial heterogeneity in communities and neighborhoods. A generalized version of the diversity index that adjusts for the number of categories is the index of qualitative variation, which indicates the extent to which individuals are clustered within the same category or distributed across multiple categories.

Over the past three to four decades, criminologists have developed the concept of the criminal career. According to researchers who study criminal career issues, within any given time period the population can be divided into two groups: (1) active offenders and (2) everyone else. The percentage of the population in the active offender category is the crime participation rate. Within that same time period, active offenders vary in several respects: (a) the number of offenses committed, (b) the seriousness of the offenses committed, and (c) the length of time the offender is actively involved in criminal activity. A key idea within the criminal career framework is that the causes of participation may not be the same as the causes of offense frequency, seriousness, or the length of time the offender is active.

There is an extensive body of research devoted to estimating these parameters for general and higher-risk populations, and more recent research has treated these criminal career dimensions as outcomes in their own right. For example, a large amount of research has been devoted to the study of offense frequency distributions. This literature shows that in both general and high-risk populations offense frequency distributions tend to be highly skewed, with most individuals exhibiting low frequencies and a relatively small number of individuals exhibiting high frequencies. Among the most prominent findings in the field came from Wolfgang et al.’s (1972) study of the 1945 Philadelphia male birth cohort, which showed that about 6% of the boys in the cohort were responsible for over 50% of the police contacts for the entire cohort.

A particularly important parameter for criminal justice policy is the rate at which individuals who have offended in the past commit new crimes in the future (the recidivism or reoffending rate). Recidivism rates are based on three key pieces of information: (1) the size of the population of prior offenders at risk to recidivate in the future, (2) the number of individuals who actually do reoffend by whatever measure is used (i.e., self-report of new criminal activity, rearrest, reconviction, return to prison), and (3) a known follow-up period or length of time that individuals will be followed. Recidivism is also sometimes studied in terms of the length of time that lapses between one’s entry into the population of offenders at risk to recidivate and the timing of one’s first recidivism incident.

With the advent of a large number of longitudinal studies of criminal and precriminal antisocial and aggressive behaviors, researchers have become increasingly interested in the developmental course of criminality as people age. To aid in the discovery of developmental trends and patterns, criminologists have turned to several types of statistical models that provide helpful lenses through which to view behavior change. The most prominent of these models are growth curve models, semiparametric trajectory models, and growth curve mixture models. These all assume that there is important variation in longitudinal patterns of offending. Some individuals begin offending early and continue at a sustained high rate of offending throughout their lives, whereas others who begin offending early seem to stop offending during adolescence and early adulthood. Some individuals avoid offending at all, whereas others offend in fairly unsystematic ways over time. Growth and trajectory models provide ways of summarizing and describing variation in the development of criminal behavior as individuals move through the life span.

The foundation of a sound quantitative criminology is a solid base of descriptive information. Descriptive inference in criminology turns out to be quite challenging. Criminal offending is covert activity, and exclusive reliance on official records leads to highly deficient inferences. Despite important challenges in descriptive analysis, researchers and policymakers still strive to reach a better understanding of the effects of interventions, policies, and life experiences on criminal behavior. Much of the CCJ literature is therefore focused on efforts to develop valid causal inferences. This section discusses some of the most prominent analytic methods used for studying cause and effect in CCJ research.

CCJ researchers typically distinguish between independent variables and dependent or outcome variables. In general, researchers conceive of dependent or outcome variables as variation that depends on the independent or predictor variables. Thus, independent variables explain variation in dependent or outcome variables. Sometimes researchers use stronger language, suggesting that independent variables cause variation in dependent variables. The burden of proof for use of the word cause is very high, however, and many researchers are careful to qualify their results if they do not think this burden of proof has been met.

Contingency tables are a useful way of presenting frequency distributions for two or three categorical variables at the same time. For example, if a person wanted to create a measure of offending participation (either someone offends in a particular time period or he or she does not) and then compare the distribution of that variable for individuals who are employed and those who are not employed, a contingency table could be constructed to display this information. Several measures of the strength of the statistical association (analogous to a correlation coefficient) have been designed for contingency tables. Although contingency tables are not often used for studying cause–effect relationships (except in randomized experiments), they are quite useful for exploratory data analysis and foundational work for more elaborate statistical models.

Researchers often want to summarize the strength of the statistical association between two variables. Correlation coefficients and other measures of association are used for this purpose. In general, measures of association are arrayed on a scale of – 1 to 1 or 0 to 1, where 0 usually represents no association at all and – 1 or 1 represents a perfect negative or positive association. Measures of association have been developed for categorical and quantitative variables. Some measures of association, such as the relative risk ratio and the odds ratio, are calibrated so that 1 implies no statistical association, whereas numbers close to zero and large positive numbers indicate strong association. Researchers often conduct tests of statistical significance to test the hypothesis of “no association” in the population.

CCJ researchers are able to draw on a wide variety of tools for conducting tests of statistical significance. In a contingency table setting, researchers often are interested in testing the hypothesis that two categorical variables are statistically independent. The chi-square test of independence is frequently used for this purpose. Sometimes, a researcher will want to test the hypothesis that the mean of a continuous variable is the same for two populations. The independent samples t test is most often used to conduct this test. In addition, researchers may need to test the hypothesis that the mean of a continuous variable remains the same at two time points. In this setting, the paired samples t test will most likely be used. Finally, if a researcher wants to test the hypothesis that a continuous variable has the same mean in three or more populations, then analysis of variance will be used. There are many statistical tests for many types of problems. Although these are among the most common applications, many others are available for more complicated situations.

Linear regression models are a class of statistical models summarizing the relationship between a quantitative or continuous outcome variable and one or more independent variables. Careful use of these models requires attention to a number of assumptions about the distribution of the outcome variable, the correctness of the model’s specification, and the independence of the observations in the analysis. If the assumptions underlying the model are valid, then the parameter estimates can provide useful information about the relationship between the independent variable or variables and the outcome variable.

Many outcome variables in CCJ are not continuous or do not meet some of the distributional assumptions required for linear regression. Statistical models for these variables, therefore, do not fit well into the linear regression framework. Examples of this problem include dichotomous and event-count outcomes. For dichotomous outcomes, researchers often estimate logistic or probit regression models; for counted outcomes, specialized models for event counts are usually estimated (i.e., binomial, Poisson, negative binomial).

CCJ researchers sometimes have well-developed ideas about the relationships between a complex system of independent and dependent variables. These ideas are usually based on theories or findings from previous empirical research. Structural equation models can be used to investigate whether the relationships between the variables in the system are in accord with the researcher’s predictions.

A time series analysis is based on the study of a particular cross-sectional unit (e.g., a community or city) over a sustained period of time. Over that period of time, the study takes repeated measurements of the phenomenon of interest (e.g., the number of gun homicides each month). Sometimes, an intervention occurs (e.g., the introduction of a new law restricting access to handguns) and the researcher has access to both the preintervention time series and the postintervention time series. These time series can be combined into a single interrupted time series analysis to study the effect of the intervention on the series. Researchers conducting interrupted time series analysis usually include both a series in which the intervention occurs and a series in which there is no intervention (a control series). If there is an apparent effect of the intervention in the interrupted time series analysis and the effect reflects a genuine causal effect, then there should be no corresponding change in the control series.

As discussed earlier (see the “Unit ofAnalysis” section), some data sets have more than one logical unit of analysis. For example, the National Longitudinal Survey of Youth follows the same individuals repeatedly over a sustained period of time (panel data). Other studies, such as the MTF study, sample schools and then sample multiple individuals within each school. A variety of modeling tools (i.e., fixed effect, random effect, hierarchical, and multilevel models) exist for working these kinds of data. An important feature of all of these tools is that they attend specifically to dependence within higher order units of analysis.

Increasingly, CCJ researchers are thinking about cause and effect in terms of counterfactual reasoning. Ultimately, this is an exercise in observing what actually occurs under a specific set of circumstances and then asking how things might have occurred differently if the circumstances had been different. The hypothetical aspect of the problem is a counterfactual, because it involves speculation about what might have occurred but actually did not occur. Counterfactual reasoning is particularly applicable to the problem of estimating treatment effects. For example, a researcher considers a group of people who received a particular treatment and observes their outcomes. What he would like to know (but cannot know for sure) is what outcomes these same people would have experienced if they had not received the treatment. The difference between the actual, observed outcome and the hypothetical outcome is the treatment effect. CCJ researchers usually look to the experience of a control group to estimate the hypothetical outcome.An important problemin CCJ research is the identification of appropriate control groups.

A randomized experiment is a study in which individuals are randomly assigned to treatment or control groups prior to treatment. They provide a useful framework for estimating valid counterfactuals because random assignment to treatment and control conditions ensures that the groups are statistically comparable to each other prior to treatment. Thus, the experience of the control group provides a very convincing answer to the question of what would happen to the treatment group if the treatment group did not receive treatment.

For a variety of reasons, randomized experiments are not possible in many instances, but sometimes conditions that closely approximate an experiment occur because of a key event or policy change. When researchers recognize these conditions, a natural experiment is possible—even when more conventional studies fail. Consider the problem of estimating the effect of police strength on crime rates. Estimating correlations and conventional regression models cannot help much with this problem. The critical ambiguity is that street crime almost certainly has an effect on police strength and that police strength almost certainly has some effect on street crime. Natural experiments can provide more convincing evidence.A recent study conducted in Washington, D.C., is illustrative (Klick & Tabarrok, 2005). It was based on the insight that changes in terror alert levels lead to meaningful changes in the presence of police on the street. The researchers examined what happened to crime rates when street-level police presence increased and decreased as terror alert levels changed. Researchers sometimes refer to natural experimentally based treatments as instrumental variable estimators, and they can provide a powerful method for estimating treatment effects when randomized experiments cannot be conducted.

Another approach to developing valid counterfactuals is to identify a group of cases that receive treatment and then identify another group of cases—the control group—that are similar to the treatment cases but do not receive treatment. To ensure that the treatment and control groups are similar, researchers match the groups on characteristics that are thought to be important. The direct matching approach guarantees that the treatment and control groups look alike on the matched characteristics.A problem is that the groups may look different from each other on characteristics that were not matched. Thus, in general, counterfactuals produced by the matching approach will not be as convincing as those produced by a randomized or natural experiment. However, in instances where experiments are not possible, direct matching designs can still provide convincing evidence about treatment effects. A generalization of the matching design involves matching on indexes based on combinations of variables. Propensity scores, which increasingly appear in the CCJ literature, are one such index. It can be shown that matching on a properly created index can lead to treatment and control groups that look like each other on many characteristics. It is likely that CCJ researchers will rely more and more heavily on matching designs and propensity scores to study treatment effects, in particular when randomized experiments are not possible.

Some aspects of quantitative CCJ research have remained relatively constant throughout the field’s history. Some CCJ research problems are very much like problems studied in other fields, and some are quite different, yet there has always been a major emphasis on description and learning about how much crime is occurring and what populations are at highest risk of criminal involvement and victimization. Other aspects, such as repeatedly and systematically following the same individuals over time and rigorously measuring the effects of changing policies, are more recent developments. CCJ is an interdisciplinary field that relies on insights from sociology, psychology, economics, political science, and statistics as well as its own rapidly emerging traditions. One thing is certain: Analytic methods in the field will continue to evolve. It is critical that quantitativeCCJ researchers monitor developments in their own field and stay well connected with developments in other allied fields to strengthen their efforts at descriptive and causal inference.

References:

• Bachman, R., & Paternoster, R. (1997). Statistical methods for criminology and criminal justice. New York: McGraw-Hill.
• Berk, R. A. (1991). Drug use, prostitution and the prevalence of AIDS: An analysis using census tracts. Journal of Sex Research, 27, 607–621.
• Blumstein, A., & Cohen, J. (1987, August 28). Characterizing criminal careers. Science, 237, 985–991.
• Blumstein, A., Cohen, J., Roth, J., & Visher, C. A. (Eds.). (1986). Criminal careers and “career criminals.”Washington, DC: National Academy Press.
• Bushway, S. D., Thornberry, T. P., & Krohn, M. D. (2003). Desistance as a developmental process: A comparison of static and dynamic approaches. Journal of Quantitative Criminology, 19, 129–153.
• Campbell, A., Berk, R. A., & Fyfe, J. J. (1998). Deployment of violence: The Los Angeles Police Department’s use of dogs. Evaluation Review, 22, 535–565.
• Eliott, D. S., Huizinga, D., & Menard, S. (1989). Multiple problem youth: Delinquency, substance use, and mental health problems. New York: Springer-Verlag.
• Ezell, M. E., & Cohen, L. E. (2005). Desisting from crime: Continuity and change in long-term patterns of serious chronic offenders. Oxford, UK: Oxford University Press.
• Fox, J. A. (Ed.). (1981). Models in quantitative criminology. New York: Academic Press.
• Greenberg, D. F. (1979). Mathematical criminology. New Brunswick, NJ: Rutgers University Press.
• Haviland, A. M., & Nagin, D. S. (2005). Causal inferences with group based trajectory models. Psychometrika, 70, 1–22.
• Johnston, L. D., O’Malley, P. M., Bachman, J. G., & Schulenberg, J. E. (2007). Monitoring the Future national survey results on drug use, 1975–2006. Bethesda, MD: National Institute on Drug Abuse.
• Klick, J., & Tabarrok, A. (2005). Using terror alert levels to estimate the effect of police on crime. Journal of Law and Economics, 48, 267–279.
• Land, K. C. (1992). Models of criminal careers: Some suggestions for moving beyond the current debate. Criminology, 30, 149–155.
• Langan, P. A., & Levin, D. J. (2002). Recidivism of prisoners released in 1994. Washington, DC: Bureau of Justice Statistics.
• Laub, J. H., & Sampson, R. J. (2003). Shared beginnings, divergent lives: Delinquent boys to age 70. Cambridge, MA: Harvard University Press.
• Loftin, C., McDowall, D., Wiersema, B., & Cottey, T. J. (1991). Effects of restrictive licensing of handguns on homicide and suicide in the District of Columbia. New England Journal of Medicine, 325, 1615–1620.
• Maltz, M. D. (1984). Recidivism. New York: Academic Press.
• Mulvey, E. P., Steinberg, L. D., Fagan, J., Cauffman, E., Piquero, A. R., Chassin, L., et al. (2004). Theory and research on desistance from antisocial activity among serious adolescent offenders. Youth Violence and Juvenile Justice, 2, 213–236.
• Nagin, D. S. (2005). Group-based modeling of development. Cambridge, MA: Harvard University Press.
• Osgood, D. W. (2000). Poisson-based regression analysis of aggregate crime rates. Journal of Quantitative Criminology, 16, 21–43.
• Osgood, D.W., & Rowe, D. C. (1994). Bridging criminal careers, theory, and policy through latent variable models of individual offending. Criminology, 32, 517–554.
• Piquero, A. R., Farrington, D. P., & Blumstein, A. (2007). Key issues in criminal career research. New York: Cambridge University Press.
• Rubin, D. B. (2006). Matched sampling for causal effects. New York: Cambridge University Press.
• Sampson, R. J., & Laub, J. H. (1993). Crime in the making: Pathways and turning points through life. Cambridge, MA: Harvard University Press.
• Schmidt, P., & Witte, A. D. (1984). An economic analysis of crime and justice. Orlando, FL: Academic Press.
• Thornberry, T. P., & Krohn, M. D. (Eds.). (2003). Taking stock of delinquency: An overview of findings from contemporary longitudinal studies. New York: Plenum Press.
• Tracy, P., & Kempf-Leonard, K. (1996). Continuity and discontinuity in criminal careers. New York: Plenum Press.
• Widom, C. S. (1989, April 14). The cycle of violence. Science, 244, 160–166.
• Wolfgang, M. E., Figlio, R. M., & Sellin, T. (1972). Delinquency in a birth cohort. Chicago: University of Chicago Press.

• Liberty University
• Jerry Falwell Library
• Special Collections

Home > School of Education > Faculty Publications > 237

Faculty Publications and Presentations

Overcoming recruitment and retention challenges in law enforcement: a systematic review.

Richard Odin Segovia , Liberty University Follow

Publication Date

Document type, disciplines.

Law Enforcement and Corrections

Purpose: This systematic review explores the recruitment and retention challenges in law enforcement, focusing on their impact on operational effectiveness and community safety. The goal is to synthesize existing literature to identify research gaps and suggest directions for future studies. By examining qualitative and quantitative research, this review aims to provide practical strategies to improve recruitment and retention in law enforcement. Methods: Searches were conducted using Google Scholar, JSTOR, and ProQuest to capture a broad range of law enforcement recruitment and retention studies. The selection process involved a systematic search that yielded 135 records. After removing duplicates, 42 studies were screened based on title and abstract, leading to 34 full-text articles assessed for eligibility. Twenty-five studies met the inclusion criteria and were included in the qualitative and quantitative synthesis, and five additional sources were used for background and contextual information. This review adhered to PRISMA guidelines. Results: The review highlights key factors influencing recruitment and retention, including public perceptions, competing labor markets, and organizational culture. Enhanced recruitment efforts, such as digital campaigns and targeted outreach, significantly increase applications and improve the quality of applicants. Supportive workplace environments and wellness programs substantially reduce turnover rates and improve job satisfaction. Effective recruitment and retention strategies also enhance community trust and workforce diversity. Conclusions: The review underscores the need for well-structured research to substantiate effective recruitment and retention strategies. It recommends areas for in-depth exploration in future studies, especially longitudinal research on the long-term impacts of innovative recruitment and retention strategies. Application to Law Enforcement: Integrating digital recruitment, community engagement, and wellness programs can enhance workforce stability and effectiveness for law enforcement leaders. These strategies improve officer recruitment and retention, reduce turnover, and build stronger relationships with the community, leading to more effective policing outcomes.

Recommended Citation

Segovia, Richard Odin, "Overcoming Recruitment and Retention Challenges in Law Enforcement: A Systematic Review" (2024). Faculty Publications and Presentations . 237. https://digitalcommons.liberty.edu/educ_fac_pubs/237

Since June 21, 2024

Included in

Law Enforcement and Corrections Commons

• Collections
• Faculty Expert Gallery
• Theses and Dissertations
• Conferences and Events
• Open Educational Resources (OER)
• Explore Disciplines

Advanced Search

• Notify me via email or RSS .

Faculty Authors

• Submit Research
• Expert Gallery Login

Student Authors

• Undergraduate Submissions
• Graduate Submissions
• Honors Submissions
• SOE Website

Home | About | FAQ | My Account | Accessibility Statement

Privacy Copyright

An official website of the United States government, Department of Justice.

Here's how you know

Official websites use .gov A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS A lock ( Lock A locked padlock ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

The IMPACTT of a Patrol Officer: Evaluating Productivity Metrics

Patrol officers perform a large number of diverse community services that both police researchers and police agencies have struggled to validly quantify, implement, and evaluate. [1] Although numerous studies have admirably described the duties and responsibilities of patrol officers, [2] far fewer studies have attempted to quantify and evaluate these activities using metrics beyond raw outputs, such as arrests or citations.

There are many reasons for the lack of sophisticated metrics of patrol officer productivity. Lack of data has traditionally stymied researchers, but so has the increasing complexity of a patrol officer’s job — as well as differences in communities’ geography and public safety priorities. Yet despite these methodological challenges and differences across jurisdictions, citizens expect patrol officers to use tax dollars and resources efficiently and productively, and they rightfully expect law enforcement agencies to evaluate the performance of patrol officers.

This is not merely an academic question but a sobering quandary for police agencies and communities. A perceived misuse of time and resources may negatively impact public perceptions of law enforcement, and studies suggest that if citizens perceive police officers as incompetent or unproductive, their trust in and willingness to cooperate with officers may suffer considerably. [3] A lack of valid, standardized productivity metrics may also cause expectations to vary among line-level officers and police supervisors, potentially leading to unexpected criticism and denied pay increases during annual evaluations.

This article advances the discussion on patrol officer productivity by discussing traditional methods for evaluating productivity, identifying recurrent issues concerning productivity metrics, examining innovative methods for evaluating patrol officers, and introducing new guidelines for those who create, use, and analyze patrol officer productivity metrics.

Traditional Patrol Officer Productivity Metrics

Productivity scholars have operationalized productivity in several ways, but the basic concept usually includes two dimensions: efficiency and effectiveness. [4] Efficiency denotes how a person or organization generates an output using the least possible resources, whereas effectiveness measures the quality of a person’s or organization’s outputs. Historically, research analyzing patrol officer productivity has focused on efficiency measures, mainly because agencies generally track and measure activity through raw outputs, such the number of arrests or citations. [5]

However, there is considerable variation in the police literature in how police agencies and researchers measure officer efficiency, and even more broadly, what constitutes productivity. Psychological studies have analyzed police productivity using supervisor evaluations [6] as well as the number of reprimands and citizen complaints received. [7] Other police researchers have approached patrol officer productivity by examining arrest rates, [8] investigative inquiries and quarterly performance evaluations, [9] clearance rates and crime reduction, [10] and traffic citation issuing rates. [11] Although studies have identified and correlated many different predictors of productivity, there is much less focus on whether these measures validly capture the diverse range of patrol officer activities.

There are two main reasons for the substantial variation in how law enforcement agencies and researchers have analyzed patrol officer productivity. First, in-depth law enforcement data about specific officers (beyond their arrests and other readily retrieved outputs) are often not widely available. [12] Second, if these data are available, they may contain raw outputs for a limited number of police activities, such as arrests or citations, rather than detailed information about a wide array of activities, such as directed patrols, community presentations or meetings, and assisting other officers on calls for service. In short, researchers have historically been limited by the lack of availability and depth of data on patrol officer activity as well the subjectivity and difficulty in measuring and analyzing these data. [13]

However, as agencies have improved their methods for capturing a wider variety of patrol officer activity and as fruitful partnerships between researchers and law enforcement agencies have flourished, there has been a renewed interest in developing and improving productivity metrics for patrol officers. Yet despite advances in technology, methodology, and collaborative research between scholars and police agencies, several threats routinely undermine the validity of any measure of patrol officer productivity.

Recurrent Threats to the Construct Validity of Productivity Measures

One of the most significant threats to productivity metrics involves Goodhart’s Law, often phrased as: “When a measure becomes a target, it ceases to be a good measure.” [14] Goodhart’s Law may be especially problematic for the law enforcement profession because productivity is frequently measured in raw outputs, such as citations or arrests. [15] For example, if officers believe their productivity is largely measured in arrests, their patrol activity might solely consist of making arrests, potentially even in situations where arrest is not supported by probable cause, policy, or proper use of discretion. The declining quality of police service is one possible consequence of defining productivity with a small number of measures that can easily become targets.

Another threat involves divergence between the priorities of line-level officers and command staff. If the priorities of command staff do not mirror those of line-level officers and are not clearly communicated to them, then patrol officers may engage in activities unrelated to productivity metrics. For instance, if traffic enforcement is a high priority for command staff but line-level officers view narcotics activity or robberies as more pressing issues, then officers may engage in focused deterrence and other strategies instead of writing traffic tickets. In this situation, patrol officers may not be rated as productive, even though their activities may nonetheless positively impact the community far beyond their performance rating.

A third threat involves the correlation of outputs to community outcomes. A patrol officer’s activities should be correlated with positive community outcomes, such as reductions in violent and property crime and increases in overall quality of life. Productivity metrics that have little or no relationship with crime, disorder, or quality of life likely have little or no validity for agencies and communities, even if officers score exceptionally high on such metrics.

However, the biggest threat to validity may be tracking and measuring tasks that do not fall within the traditional realm of law enforcement activities. Police officers are asked to do more and display a greater array of skills than in prior decades. They must effectively engage with a wide variety of community members and groups, use data and science to advance department and community goals, and connect citizens with resources for any number of issues (e.g., homelessness, mental health, and domestic violence). Police researchers must find ways to validly and reliably measure these types of activities, especially if police departments prioritize them.

Improved Measures of Patrol Officer Productivity

Researchers have attempted to improve traditional measures of patrol officer productivity in several ways. One way involves using a larger number of diverse productivity indicators. Including multidimensional indicators reduces the chances of one measure becoming a target (i.e., it defies Goodhart’s Law), necessitates discussion between line-level and command staff members about what outputs should be included, and allows researchers and agencies to correlate a wide variety of outputs with community outcomes. For instance, instead of only measuring arrests, some researchers have measured police performance using self-initiated stops, warnings, citations, administrative complaints, sick hours, and on-duty injuries as well as z-score summaries to more easily interpret an officer’s performance. [16]

Van Meter’s zero-based approach is another innovative attempt to quantify and evaluate patrol officer performance. [17] His system views police officers as productive before evaluation and assigns each officer a zero, the best possible score. The system analyzes nonscheduled absenteeism, cost of preventable error, and productive use of time to evaluate police officers, and the zero indicates that a police officer has no uncorrected performance issues. However, some have criticized Van Meter’s system for preventing police officers from prioritizing their daily activities, [18] suggesting the potential for a disconnect between command staff and line-level officer priorities.

Borrowing from a baseball statistic called Value Over Replacement Player, researchers have constructed Value Over Replacement Cop (VORC), a metric that accounts for the diverse activities of patrol officers, weights different outputs, evaluates officers in terms of productive time and prosecution rates, and offers police agencies the flexibility to prioritize and weight patrol officer activities ( see Exhibit 1 ). [19] VORC’s major strength is also its weakness — namely, that it allows agencies to prioritize and weight outputs, which leads to criticisms about the appropriate prioritization and weighting of outputs.

A close relative of VORC is Z-Score per Productive Time (Z-PRO), a more sophisticated measure that estimates a patrol officer’s performance in terms of productive time using a combination of z-scores for directed patrols, traffic warnings, traffic citations, DUIs, misdemeanor arrests, felony arrests, and warrant arrests. [20] Exhibit 2 displays Z-PRO’s wide variety of outputs as well as other important measures, such as the number and types of completed reports, minutes spent on follow-up investigations, calls for service minutes, and minutes spent assisting other officers — a major advantage over traditional, more simplistic measures.

One key weakness of these metrics, as well as other innovative metrics, is that their relationship to community outcomes is unknown. Although researchers have examined the correlation between potential components of a productivity metric and community outcomes (e.g., traffic citations and motor vehicle collisions), much less is known about the correlation between broader productivity indices and community outcomes (e.g., how Z-PRO correlates with crime and disorder). However, although I recognize the importance of such outcome measures, instead of narrowing the point of focus to outcomes only, I urge researchers and police agencies to consider the following guidelines when developing, implementing, and analyzing patrol officer productivity metrics.

IMPACTT Guidelines

I designed the IMPACTT guidelines to help police researchers and practitioners evaluate the validity of patrol officer productivity metrics. IMPACTT is an acronym for the following recommendations: The outputs of any productivity metric must be  I dentified and prioritized, be  M easured both quantitatively and qualitatively, be evaluated in terms of  P roductive time, account for a diverse  A rray of duties, be  C orrelated with community outcomes, and be  T racked and  T ested over a prolonged period.

First, agencies must  Identify  and prioritize the outputs of a metric and communicate these priorities to line-level officers. Patrol officers should have a clear understanding of which activities are valued most by their department and community. I also recommend that agencies either weight outputs or use a z-score index to more easily distinguish between low- and high-performing officers.

Second, the outputs of productivity metrics should be  Measured  both quantitatively and qualitatively. Before implementing a metric, agencies must have the technology to record and measure the targeted outputs, as well as the ability to create and maintain searchable databases so the outputs can be analyzed and evaluated. In terms of qualitatively evaluating outcomes, the rate of prosecution for citations and arrests may be one quality control measure. If an officer makes a large number of arrests that fail to result in charges because of shoddy investigations or follow-up, then the officer’s performance metric should reflect this deficiency.

Third, performance metrics must evaluate patrol officers in terms of  Productive  time. Too often, researchers and agencies analyze totals for arrests, patrols, or citations without accounting for how many calls for service an officer handled or how many minutes an officer spent assisting other officers, writing reports, or conducting follow-up investigations. The validity of any productivity metric is vastly improved when it accounts for an officer’s available minutes for self-initiated activities, e.g., traffic or warrant enforcement.

Fourth, productivity metrics must include an  Array  of activities, especially in communities where police agencies are generalist departments. Generalist patrol officers not only respond to calls for service and make arrests but also may conduct traffic enforcement, warrant searches, follow-up investigations, community presentations, directed patrols in high-crime areas, and many other activities. Productivity metrics must be multidimensional to quantitatively capture the diverse array of a patrol officer’s activities.

Fifth, productivity metrics should be  Correlated  with community outcomes. Agencies should be able to demonstrate that patrol officer outputs (e.g., citations, arrests, performance evaluations) are related to property or violent crime rates, quality of life outcomes, public perceptions of and trust in the police, or public perceptions of crime and disorder. Moreover, agencies should be able to demonstrate that a productivity index — and not just its individual components — is also correlated with community outcomes.

Finally, patrol officer productivity measures should be  Tracked  and  Tested  over prolonged periods. This practice allows researchers and agencies to correlate outputs with community outcomes, reprioritize outputs if necessary, and guard against a limited number of measures becoming targets.

For many decades, research on methodologies for measuring patrol officer productivity has failed to advance due to a lack of data, insufficient technology to track patrol activities, and a narrow focus on a few types of outputs. Although more sophisticated metrics have been developed, researchers and law enforcement agencies must remain cautious of threats to the validity of these metrics, including the potential for outputs to become targets, a disconnect between the priorities of command staff and line-level officers, and low correlation between metrics and community outcomes. To improve the validity of productivity metrics and guard against recurrent threats, I put forth a series of suggestions called the IMPACTT guidelines. These guidelines recommend that the outputs of productivity metrics should be identified and prioritized, be measured both quantitatively and qualitatively, be evaluated in terms of productive time, account for a diverse array of duties, be correlated with community outcomes, and be tracked and tested over a prolonged period. I believe that researchers and law enforcement agencies can use these guidelines to develop, refine, and assess new methods for evaluating patrol officer productivity.

Disclaimer: Opinions or points of view expressed in this document are those of the authors and do not reflect the official position of the U.S. Department of Justice. Findings and conclusions of the research reported here are those of the authors and do not reflect the official position or policies of the U.S. Department of Justice.

[note 1] Egon Bittner, “Legality and Workmanship,” Introduction in Maurice Punch, ed., Control in the Police Organization (Cambridge, MA: MIT Press, 1983), 1-10.

[note 2] Robin S. Engel, “Patrol Officer Supervision in the Community Policing Era,” Journal of Criminal Justice 30 no. 1 (2002): 51-64; Christine N. Famega, James Frank, and Lorraine Mazerolle, “Managing Police Patrol Time: The Role of Supervisor Directives,” Justice Quarterly 22 no. 4 (2005): 540-559; John Liederbach, “Addressing the ‘Elephant in the Living Room’: An Observational Study of the Work of Suburban Police,” Policing: An International Journal of Police Strategies & Management 28 no. 3 (2005): 415-434; and Brad W. Smith et al., “Explaining Police Officer Discretionary Activity,” Criminal Justice Review 30 no. 3 (2005): 325-346.

[note 3] Jason Sunshine and Tom R. Tyler, “The Role of Procedural Justice and Legitimacy in Shaping Public Support for Policing,” Law & Society Review 37 no. 3 (2003): 513-548.

[note 4] Harry P. Hatry, “Approaches to Productivity Measurement and Program Evaluation,” Public Productivity Review 1 no. 3 (1976): 21-28.

[note 5] John P. Crank, “The Influence of Environmental and Organizational Factors on Police Style in Urban and Rural Environments,” Journal of Research in Crime and Delinquency 27 (1990): 166-189.

[note 6] Owusu-Ansah Agyapong, “The Effect of Professionalism on Police Job Performance: An Empirical Assessment,” unpublished doctoral dissertation, Florida State University, 1988.

[note 7] Larry E. Beutler et al., “Parameters in the Prediction of Police Officer Performance,” Professional Psychology: Research and Practice 16 no. 2 (1985): 324-335; Steven G. Brandl, Meghan S. Stroshine, and James Frank, “Who are the Complaint Prone Officers? An Examination of the Relationship Between Police Officers’ Attributes, Arrest Activity, Assignment, and Citizens’ Complaints About Excessive Force,” Journal of Criminal Justice 29 no. 6 (2001): 521-529; and Kim Michelle Lersch, “ Are Citizen Complaints Just Another Measure of Officer Productivity? An Analysis of Citizen Complaints and Officer Activity Measures ,” Police Practice and Research 3 no. 2 (2002): 135-147.

[note 8] Crank, “The Influence of Environmental and Organizational Factors”; and Jeffrey S. Slovak, Styles of Urban Policing: Organization, Environment, and Police Styles in Selected American Cities (New York: New York University Press, 1986).

[note 9] Samuel Nunn and Kenna Quinet, “Evaluating the Effects of Information Technology on Problem-Oriented Policing,” Evaluation Review 26 no. 1 (2002): 81-108.

[note 10] Luis Garicano and Paul Heaton, “Information Technology, Organization, and Productivity in the Public Sector: Evidence from Police Departments,” Journal of Labor Economics 28 no. 1 (2010): 167-201.

[note 11] Richard Johnson, “Officer Attitudes and Management Influences on Police Work Productivity,” American Journal of Criminal Justice 36 (2011): 293-306.

[note 12] Allison T. Chappell, John M. MacDonald, and Patrick W. Manz, “The Organizational Determinants of Police Arrest Decisions,” Crime & Delinquency 52 no. 2 (2006): 287-306.

[note 13] D.M. Gorby, “ The Failure of Traditional Measures of Police Performance and the Rise of Broader Measures of Performance ,” Policing: A Journal of Policy and Practice 7 no. 4 (2013): 392-400.

[note 14] Charles Goodhart, “Problems of Monetary Management: The U.K. Experience,” in Anthony S. Courakis, ed., Inflation, Depression, and Economic Policy in the West (London: Alexandrine Publishing, 1981), 111-146; and Marilyn Strathern, “‘Improving Ratings’: Audit in the British University System,” European Review 5 no. 3 (July 1997): 305-321.

[note 15] Crank, “The Influence of Environmental and Organizational Factors”; Chappell, MacDonald, and Manz, “The Organizational Determinants”; Johnson, “Officer Attitudes and Management Influences”; and Richard Johnson, “Explaining Patrol Officer Drug Arrest Activity Through Expectancy Theory,” Policing: An International Journal of Police Strategies & Management 32 no. 1 (2009): 6-20.

[note 16] Jon M. Shane, “Organizational Stressors and Police Performance,” Journal of Criminal Justice 38 no. 4 (2010): 807-818; and Jon M. Shane, “Daily Work Experiences and Police Performance,” Police Practice and Research 13 no. 3 (2011): 1-19.

[note 17] D.J. Van Meter, Evaluating Dysfunctional Police Performance: A Zero-Based Approach (Springfield, IL: Charles C Thomas, 2001).

[note 18] Kyle Jahner, “Union, Chief Dolan Trade Major Blows Over New Police Evaluation System,” Raleigh News & Observer, July 5, 2012.

[note 19] See figure 1 in Luke Bonkiewicz, “Bobbies and Baseball Players: Evaluating Patrol Officer Productivity Using Sabermetrics,” Police Quarterly 18 no. 1 (2015): 55-78.

[note 20] Luke Bonkiewicz, “Shooting Stars: Estimating the Career Productivity Trajectories of Patrol Officers,” Police Quarterly 20 no. 2 (2017): 164-188.

About the author

Luke Bonkiewicz is a police officer with 13 years of experience in not only law enforcement, but also quantitative research methods and data analysis. He has analyzed data on racial disparities in traffic stops, driver’s license suspension programs, assault-on-officer incidents, and use-of-control incidents. He has also published peer-reviewed research on patrol officer productivity, police response to mental health calls, and the role of police in disasters and evacuations. Bonkiewicz coordinates the Lincoln (NE) Police Department’s Study of Police Experience for the Advancement of Research and Service Center, whose goal is to develop and evaluate evidence-based policing practices.

Cite this Article

Read more about:.