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Misconduct and deviance amongst police officers are substantial issues in policing around the world. This study aims to propose a prediction model for serious police misconduct by variation of the theory of planned behaviour.

Using two data sets, one quantitative and one qualitative, provided by an Australian policing agency, a random forest analysis and a qualitative content analysis was performed. Results were used to inform and extend the framework of the theory of planned behaviour. The traditional and extended theory of planned behaviour models were then tested for predictive utility.

Each model demonstrated noteworthy predictive power, however, the extended model performed particularly well. Prior instances of minor misconduct amongst officers appeared important in this rate of prediction, suggesting that remediation of problematic behaviour was a substantial issue amongst misconduct prone officers.

It is an important implication for policing agencies that prior misconduct was predictive of further misconduct. A robust complaint investigation and remediation process are pivotal to anticipating, remediating and limiting police misconduct, however, early intervention models should not be viewed as the panacea for police misconduct.

This research constitutes the first behavioural model for police misconduct produced in Australia. This research seeks to contribute to the field of behavioural prediction amongst deviant police officers, and offer an alternative methodology for understanding these behaviours.

There is a paucity of data available relating to the misconduct of police officers in larger policing agencies, typically resulting in case study approaches and limited insight into the factors associated with serious misconduct. This paper seeks to contribute to the emerging knowledge base on police misconduct through analysis of 28,429 complaints among 3,830 officers in the New York Police Department, between 2000 and 2019.

This study utilized a data set consisting of officer and complainant demographics, and officer complaint records. Machine learning analytics were employed, specifically random forest, to consider which variables were most associated with serious misconduct among officers that committed misconduct. Partial dependence plots were employed among variables identified as important to consider the points at which misconduct was most, and least likely to occur.

Prior instances of serious misconduct were particularly associated with further instances of serious misconduct, while remedial action did not appear to have an impact in preventing further misconduct. Inexperience, both in rank and age, was associated with misconduct. Specific prior complaints, such as minor use of force, did not appear to be particularly associated with instances of serious misconduct. The characteristics of the complainant held more importance than the characteristics of the officer.

The ability to analyze a data set of this size is unusual and important to progressing the knowledge area regarding police misconduct. This study contributes to the growing use of machine learning in understanding the police misconduct environment, and more accurately tailoring misconduct prevention policy and practice.

The purpose of this paper is to detect and examine any trends in the self-reported causation of misconduct among officers being considered for dismissal.

The data utilised in this study consisted of show cause notice (SCN) responses. In the process of being considered for dismissal, officers’ may offer the causes of or mitigating factors to their misconduct as a means of avoiding dismissal. This study utilised these responses as a data source. Data collection occurred between January 2013 and October 2016, resulting in a cohort of n=100 responses comprised of between 1 and ~1,000 pages of free text. Qualitative methods were preferred, a conventional content analysis was performed with coding categories derived from SCN response text.

The results of this study indicate noteworthy levels of post-traumatic stress disorder (PTSD) and related mental health issues amongst this cohort, as well as pervasive financial and workplace stressors, which officers frequently link to the causation of their misconduct. Of particular note is the consistent co-occurrence of work-related stressors and health issues, most commonly through formally diagnosed PTSD.

This study indicates an opportunity for support services to impact positively on mental health and stress, and subsequently misconduct among police officers.

There is still very little understanding of the causation of misconduct among police officers. This is the first time that this data have been utilised in any form of the research, it provides valuable insight into a potential alternative method of addressing misconduct to reactionary investigative action.