There exists a relationship between criminological theory and statistical data. More specifically, statistical data can be used to support criminological theory in various ways. First, criminological theory relies on empirical evidence derived from statistical data to anticipate the happenings in the future. It is worth noting that criminological theory is causal and as such, it relies on variables responsible for the occurrence of crime. Statistical data thus supports criminological theory since outcome variables in crime- related research studies have multiple applications. In addition, statistical data related to distinct aspects of crime allows for the testing of criminological theory. While there are several cons in the relationship between statistical data and criminological theory, the pros demonstrate the significance of the relationship.
The multiple pros of the relationship between statistical data and criminological theory give credence to the contention that statistical data backs criminological theory. Statistical data helps support criminological theory, in that, statistical data on criminal conduct allows for hypothesis testing, as well as statistical analysis. Statistical analysis ultimately allows researchers to draw inferences on varying aspects of crime in society. A notable significance of the relationship between statistical data and criminological knowledge is the utilization of crime statistics in predictive policing. Predictive policing denotes the utilization of different statistical models and representations to anticipate heightened crime risks, in addition to interventions aimed at preventing the anticipated crimes. In the modern context, big data plays an invaluable role in bolstering criminological theory. Chan and Moses (2016) assert that statistical research related to big data involves the employment of computer algorithms as predictive instruments for crime prevention, as well as risk analysis. In addition, predictive policing outspans crime mapping, hotspot analysis, in addition to problem-oriented policing to utilize analytics, as well as data in order to anticipate the location and time of future crimes (Chan & Moses, 2016). Predictive policing is also pivotal to anticipating individuals who are likely to commit offences or felonies in future, identifying potential crime victims, in addition to creating profiles that fit those of likely criminals in specific crimes (Chan & Moses, 2016). Evidently, statistical data helps support criminological theory from a vast array of perspectives.
Criminological theory primarily espouses statements regarding relationships between key facets of criminals and crime victims. Statistical data helps support criminological theory since it is based on scientific inquiry and as a result, it is crucial to proving the stipulations of criminological theory. The credibility and validity of inferences derived from statistical data also allows for accurate support of criminological theory. Nolan (2004) asserts that statistical data may also be utilized to demonstrate the link between the size of a given population and extant crime rate. From a theoretical perspective, extant crime rates in different jurisdictions are directly related to the size of the population such that a large population translates into high crime rates and vice versa. In most cases, statistical data reveals that the size of a given population is directly proportional to the extant crime rates and as such, it is pretty clear that statistical data supports the postulations of criminological theory. Nolan (2004) posits that comprehending the correlation between different variables present in statistical data is pivotal to crime data analysis and subsequent inferencing. In essence, statistical data related to criminal activities helps support criminological theory since it allows for the creation different of data categories. The categories may include the race, gender and age of offenders, as well as crime victims. It is thus pretty apparent that statistical data is pivotal to supporting criminological theory.
There are also a host of cons that may be linked to the relationship between statistical data and criminological theory. With respect to predictive policing, the absence of a criminological theory which justifies the supposition of crime continuity and/or proof of its credibility, big data that relies on historical statistical data may not be utilized to anticipate future crimes actively (Chan & Moses, 2016). The cons of the relationship are also manifest when statistical data refutes criminological theory. Case in point, in the event that the size of the population is not directly proportional to extant crime rates, law enforcement bodies are forced to apply imputation, as well as outlier detection in statistical data analysis (Nolan, 2004).
In conclusion, it is evident that while there are several cons in the relationship between statistical data and criminological theory, the pros demonstrate the significance of the relationship. A significant pro that is manifest in the above relationship is the use of statistical data for predictive policing. Cons associated with the aforementioned relationship are primarily related to the inability to use statistical data to accurately anticipate key aspects of future crimes.
- Chan, J., & Bennett Moses, L. (2016). Is Big Data Challenging Criminology?. Theoretical criminology, 20(1), 21-39.
- Nolan, J. J. (2004). Establishing the statistical relationship between population size and UCR crime rate: Its impact and implications. Journal of Criminal Justice, 32(6), 547-555.