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Preventing crime online: Identifying determinants of online preventive behaviors using structural equation modeling and canonical correlation analysis

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Crime Prevention and Community Safety Aims and scope

Abstract

The purpose of the present research was to identify determinants of online crime prevention behaviors by examining the relationships between online victimization, online exposure, online communication behaviors and online prevention within an opportunity framework. Utilizing a large national sample of residents of Canada from the General Social Survey, structural equation modeling and canonical correlation analysis were used to assess the effects of opportunity-based routines upon preventive behaviors as well as the effects of individual elements of each construct on the relationship between the constructs. Results from the structural equation models indicate that there is a positive and significant relationship between online victimization and the adoption of online preventative routines, while the canonical correlation analyses suggest that these relationships are complex and that specific types of victimization are related to particular prevention efforts. The present research provides early evidence that online lifestyles influence the theorized cycle of online victimization and preventative efforts. We find that indicators of online exposure and communications routines were positive predictors of online victimization, and that online victimization is positively associated with taking precautionary measures.

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Notes

  1. For example, if a relationship between the construct’s ‘exposure’ and ‘victimization’ were established, each individual item used to build ‘exposure’ may have a unique impact on the higher order relationship between ‘exposure’ and ‘victimization.’ The resulting information may help guide future research by identifying particularly impactful behaviors.

  2. The number of email users in the sample was so great that little variability existed that would benefit CCA.

  3. ‘Canonical correlation analysis can accommodate any metric variable without the strict assumption of normality. However, normality is desirable because it allows for the highest correlation among the variables. Indeed, canonical correlation analysis can accommodate non-normal variables if the distributional form (e.g., highly skewed) does not decrease the correlation with other variables. This allows for transformed non-metric data (in the form of dummy variables) to be used as well … Thus, although normality is not strictly require, it is highly recommended that all variables be evaluated for normality and transformed if possible’ (Hair et al, 2010, p. 15).

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Acknowledgements

The authors would like to thank Marcus Felson for his insights in the development of this article. This analysis is based on the Statistics Canada General Social Survey, Cycle 23, 2009. All computations, use and interpretation of these data are entirely those of the authors.

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Authors and Affiliations

  1. Department of Criminal Justice, Weber State University, 1299 Edvalson Street, Department 1206, Ogden, UT 84408-1206, USA

    Bradford W Reyns

  2. Department of Criminology and Criminal Justice, Sam Houston State University, George J. Beto Criminal Justice Center, Huntsville, TX 77341, USA

    Ryan Randa

  3. Department of Criminal Justice, Shippensburg University, 1871 Old Main, Drive, Shippensburg, PA 17257, USA

    Billy Henson

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  1. Bradford W Reyns
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Correspondence to Bradford W Reyns.

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Reyns, B., Randa, R. & Henson, B. Preventing crime online: Identifying determinants of online preventive behaviors using structural equation modeling and canonical correlation analysis. Crime Prev Community Saf 18, 38–59 (2016). https://doi.org/10.1057/cpcs.2015.21

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