Using typologies of victimization worry to create strategies for reducing fear of crime

Abstract

The purpose of this paper is to develop actionable strategies designed for law enforcement agencies seeking to reduce fear of crime among those living within their jurisdictions. A conjunctive analysis of case configurations is conducted on data collected from residents living in southeast Queensland (Australia) (N = 713) in order to identify context-specific typologies of victimization worry. Main effects for each component of the typologies are examined in order to identify the impact each has on reducing negative attitudes towards crime. Current findings suggest that agencies will likely reduce fear of crime among community residents the most by focusing on decreasing concerns related to the consequences of victimization. Results are consistent for both crimes against persons and property offenses.

Keywords:

• Risk perception
• conjunctive analysis of case configurations
• social-psychological process model

Notes

1. The Gold Coast is located in southeast Queensland, Australia. It is the second most populous city in the state, with approximately 600,000 residents. The Gold Coast covers about 160mi².

2. For the personal crime model, the absolute fit index was significant (χ² = 61.37; df = 9; p < .001), suggesting a poor fitting model. However, approximate fit indexes indicated that the model had a somewhat reasonable fit with the data (RMSEA = .09). The model fit was significant and above suggested cut-off ranges for several fit (CFI = .93; NFI = .92; IFI = .93; GFI = .98). For the property crime model, the absolute fit index was significant (χ² = 62.85; df = 9; p < .001) and the approximate fit indexes showed that the model had a somewhat reasonable fit to the data (RMSEA = .09). Moreover, the model fit was adequate and above suggested cut-off ranges (CFI = .93; NFI = .92; IFI = .93; GFI = .98). Descriptions of model fit as ‘sufficient’ and as ‘somewhat reasonable’ is in line with Kline’s recommendations: ‘A healthy perspective on approximate fit indexes is to view them as providing qualitative or descriptive information about model fit’ (2011, p. 205).