Exploring the impact of measurement error in police recorded crime rates through sensitivity analysis

Abstract

Abstract It is well known that police recorded crime data is susceptible to substantial measurement error. However, despite its limitations, police data is widely used in regression models exploring the causes and effects of crime, which can lead to different types of bias. Here, we introduce a new R package (‘rcme’: Recounting Crime with Measurement Error) that can be used to facilitate sensitivity assessments of the impact of measurement error in analyses using police recorded crime rates across a wide range of settings. To demonstrate the potential of such sensitivity analysis, we explore the robustness of the effect of collective efficacy on criminal damage across Greater London’s neighbourhoods. We show how the crime reduction effect attributed to collective efficacy appears robust, even when most criminal damage incidents are not recorded by the police, and if we accept that under-recording rates are moderately affected by collective efficacy.

Keywords

• Police data
• Crime rates
• Under-reporting
• Bias