Doctor of Philosophy (Ph.D.)
Degree Granting Department
Lyndsay N. Boggess, Ph.D.
Lorie A. Fridell, Ph.D.
Michael J. Leiber, Ph.D.
Ojmarrh Mitchell, Ph.D.
Fear of Crime, Multilevel modeling, Reciprocal effects
In the 1960s, the government formed the President's Commission on Law Enforcement and Administration of Justice to looked at the problem of crime and fear of crime in modern American society. In addition to looking at these issues, the Commission also looked at ways to potentially reduce both crime and fear of crime. One of the primary outcomes of the Commission's report was that policing agencies in the United States needed to fundamentally alter the way they served their communities, notably by transitioning to community-oriented policing (COP). Starting in the 1970s, law enforcement agencies around the nation began to embrace the COP philosophy in the hopes that it would effectively reduce crime. A plethora of research suggests that the crime reduction benefits of COP are dubious at best; however, COP shows great promise in reducing fear of crime in neighborhoods. However, scholars remain uncertain as to why COP can effectively reduce fear. The uncertainty surrounding the efficacy of COP lies in the incomplete theoretical understanding of fear of crime.
Three largely divergent fear of crime models have been developed. The first, the social integration model, posits that fear is influenced by the degree to which a person is integrated into their community. The thought being that the more socially integrated a person is, the stronger the sense of informal social and thus the lower the fear of crime. Research generally--although not always--supports this notion. Other scholars developed the disorder model, which posits that disorderly conditions or other signs of incivility can lead residents to feel as though informal social control has broken down, and thus elevate levels of fear. Again, this notion is well supported in the research. The final model suggests fear of crime is a result of sociodemographic differences (e.g., gender and age) that make a person feel more vulnerable to victimization, and thus those feeling most vulnerable exhibit the highest levels of fear. The findings from this so-called vulnerabilities model receive inconsistent support in the research.
The problem with the extant fear of crime research is that it largely relies on singular explanations of fear. In other words, it operates from the premise that one of the models described above is responsible for residents' levels of fear. Recently, scholars have begun developing multimodel explanations in an effort to improve criminologists' ability to explain fear of crime. However, this multimodel approach is not a complete theoretical model of fear because it fails to account for the likely existence of a reciprocal effect between fear of crime and social integration. Further, it fails to account for the effects of social context may exert on fear and the way in which neighborhood differences may condition the individual-level fear of crime relationships.
This dissertation, using two data sources, attempts to predict fear of crime using a more complete fear of crime model than those used in much of the prior research. The first source of data used is the 2004 Hillsborough County Sheriff's Office community survey (N=1898), which was distributed to a random sample of households in unincorporated Hillsborough County. Additionally, to create measures of social context, this dissertation utilizes data from the 2000 United States Census for census designated places in unincorporated Hillsborough County--which serve as the proxy for neighborhoods (N=30). Based on theory and prior research, it was hypothesized that the best fear of crime model would contain elements from all three theoretical models developed in prior research. Additionally, it was hypothesized that there would be a significant and negative reciprocal effect from fear of crime to social integration. Finally, it was hypothesized that social context would condition the relationships between individual-level fear of crime predictors.
As predicted by the hypothesis, the empirically strongest fear of crime model did contain elements from all three explanatory fear of crime models. Additionally as hypothesized, there was a significant reciprocal relationship between fear of crime and social integration. However, contrary to expectations the relationship was positive. In other words, fear of crime motivated residents to become more socially integrated in their neighborhoods. Finally, as hypothesized social context did condition the effects of the individual-level variables. However, contrary to the hypotheses proffered, social context augmented the size of the effect between the individual-level variables.
The findings from this dissertation offer some interesting insights for scholars and posivy makers alike. The findings suggest that it is imperative to use a more complete (e.g., multimodel) approach when explaining fear of crime. Additionally, it is necessary to account for the reciprocal relationship between fear of crime and social integration; otherwise research will yield deceptive parameter estimates for social integration on fear of crime. Lastly, social context matters and needs to be considered in further research. However, the theoretical model in this dissertation--while a step forward--does not represent the theoretical model to explain fear of crime. The results suggest that the model may be even more complex than the model presented here. The results of this dissertation for policy makers suggest that community oriented policing strategies are likely an effective mechanism for reducing residents fear of crime for two reasons; 1) the strengthening of social integration programs in neighborhoods and 2) focusing on reducing disorder problems in neighborhoods. Study strengths and limitations, as well as directions for future research are discussed.
Scholar Commons Citation
Maskaly, Jonathan, "Predicting Fear of Crime using a Multilevel and Multi-Model Approach: A Study in Hillsborough County" (2014). Graduate Theses and Dissertations.