The reasons why children go missing are complex, and there is a myriad of contributing factors. Running away is often preceded by conflict with parents or carers, frequently caused by family breakdown, problems at school, or experiences of abuse and neglect (Rees, 2011). Although many children leave voluntarily, often they feel they have no alternative (Kurtz et al.,1991; Wade, 2002). Some may be seeking to get away from something or someone, for example, bullying, sexual exploitation, or a violent home. Others may "pulled" towards someone who has sexually exploited them, or something, such as drug-taking, or committing a crime (Newiss, 1999).Repeat disappearances are frequent and often indicate underlying vulnerabilities. Children who go missing three or more times are at particularly high risk, as they are more likely to be exposed to alcohol and drugs and are also vulnerable to physical or sexual exploitation. Despite this, there has been limited research to identify and support this specific group.
By drawing on the concepts of Pragmatic Psychology, the study aims to uncover risk factors that increase the probability of a child repeatedly going missing. By using police missing person cases, the study will develop a statistical model which will predict the likelihood of a child going missing again. Just as accurate forecasts of crime hotspots are useful when an officer is deciding where to patrol, a reliable forecast for a missing child could assist a police officer in determining what action to take to ensure they do not go missing again.There are three key objectives:
Examine the prevalence of repeat missing episodes by children.
Identify the risk factors that contribute to a child repeatedly running away.
Develop a statistical model of risk factors that can be used in practice by police, to predict the likelihood of a child repeatedly going missing
The study will focus on children reported missing in Dorset in 2019. By analysis of a large data set of pre-existing missing person cases, logistic regression will be used to identify predictive variables which increase the chance of a child going missing, and then combine them into a predictive model which will predict the odds of a child going missing repeatedly.