Over the past few years, the importance of statistics in criminal justice leadership has been at the forefront of national debates. Stakeholders have embarked on finding out the significance of using statistical data in the scope of criminal justice and how to better the system with the help of such data. Recently, stakeholders in the field of law and criminal justice have settled on making use of statistics to inform on their decision particularly regarding human behavior and nature of the crime. Data from various fields such as but not limited to victimization rates, identify trends, crime rates, as well as success rates, has proven to be helpful. Hence its fundamentality cannot be overstated In the field of criminal justice, statistical methods such as a measure of central tendency (including mean, mode, and median) are common among the human population and largely used to fathom the nature and characteristics of human beings including their cause of actions. This essay focuses on how as well as why statistical data has remained significant in criminal justice leadership. The essay will, therefore, pay attention to criminal justice research in support of community policing, community engagement, crime reduction and policy making to arrive at its key objective which is to elucidate on the importance of using statistical data from criminal justice and leadership point of view.
Statistical support to criminal justice policy making
Data-driven decision making by policy makers as well as correctional managers has created high reliance on statistical methods. The need for statistical data has been pressurized by decision makers and system administrators to ensure accountability, reliance, and validity of decisions thus improving the functioning of criminal justice system. In order to arrive at credible decisions and policies, stakeholders in the field of criminal justice have emphasized on data driven decision-making framework to impact on positive outcomes. This has been arrived at by ensuring collection of information such as crime rates, nature of crimes and crime trends and statistically analyzing it to enable them to make valid inferences (Birkland, 2014). Methods conducting statistical analysis such as inferential statistics and descriptive statistics are some of the procedures mainly used when researching a sample population. Making use of results from statistical analysis produces is more meaningful as well as accurate inferences than the traditional methods, hence criminal justice leaders and policy makers can rely on it to better in formulating policies needed to fight crime.
Criminal justice research in support of community engagement
In the verge of collecting raw data to be used in statistical analysis from the larger population, communication increases between community members and research agents/ officers. One on one interview with the society members increases community’s willingness to share information with the police department with the hope of eradicating crime. This consequently builds on police-community relationship thus impacting positively in community engagement and involvement in efforts of reducing crime (Mazerolle, Antrobus, Bennett & Tyler, 2013). Statistical research that measures fatality of police and positive contacts between society members and the police recognizes the need to improve police-community relations. Research data such police brutality and how often does police officers kill civilians who are unarmed helps keep track of officer’s profile which makes it easy to monitor those that go against the law. To ensure effective formulation of policies, monitoring the use of illegal force by police officers, and address matters related to law enforcement, we must develop concise information regarding the nature and scope of the problem. This can only be achieved by use of statistical methods that provide detailed data on deaths caused by officers. This data entails details about location, time of death, whether the victim was armed or not and officer’s past profile regarding the use of force among others. This information will lay a solid basis for understanding crime and guide law enforcement practices. These activities put the community member’s well-being before that of the police thus changes the way members perceive and interact with police officers (Wolfe & Nix, 2016).
Criminal justice research in support of community policing
Community policing systematically builds on proactive problem solving through decentralization and community involvement and consultation. As opposed to crime response in the event of its occurrence, community policing embraces a more proactive strategy to finding solutions to the problems affecting the larger society by encouraging the development of solutions to the underlying issues that affect public safety (Jonathan-Zamir, Mastrofski, & Moyal, 2015). Police departments are encouraged to view police response from a broader scope and understand police arrests as only one among many other ways of responding to crime. Community policing focuses on quality rather than quantity arrests. It looks at ways to which police departments can impact intelligence gathering of information to acquire actionable data regarding prevention and clearing of deterrence. Crime responses should, therefore, be based on close collaboration with the community as this increases community satisfaction and enhancement of police legitimacy which is achieved through the use of community surveys.
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Birkland, T. A. (2014). An introduction to the policy process: Theories, concepts, and models of public policy making. London, Routledge.
Jonathan-Zamir, T., Mastrofski, S. D., & Moyal, S. (2015). Measuring procedural justice in police-citizen encounters. Justice Quarterly, 32(5), 845-871.
Mazerolle, L., Antrobus, E., Bennett, S., & Tyler, T. R. (2013). Shaping citizen perceptions of police legitimacy: A randomized field trial of procedural justice. Criminology, 51(1), 33-63.
Wolfe, S. E., & Nix, J. (2016). The alleged “Ferguson Effect” and police willingness to engage in a community partnership. Law and human behavior, 40(1), 1.