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Risky Lifestyles, Low Self-control, and Violent Victimization Across Gendered Pathways to Crime

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Abstract

Objectives

The present study addresses whether unique or general processes lead to victimization across gendered pathways to crime. Specifically, the effects of low self-control and risky lifestyles—specified as various forms of offending and substance abuse—on violent victimization across developmental typologies for both men and women are examined.

Methods

Using data from three waves of the National Longitudinal Study of Adolescent Health, a two-stage cluster analysis is used to identify taxonomic groups for males and females that represent different pathways to crime. Multivariate negative binomial regression models are estimated to assess whether both self-control and risky lifestyles (e.g., criminal offending) are significant predictors of general forms of violent victimization across each identified cluster.

Results

Low self-control and risky lifestyles significantly predict violent victimization across each of the taxonomic groups identified in the data, suggesting that these causal processes are universal rather than unique to any particular gendered pathway.

Conclusions

Although inferences cannot be made for types of victimization beyond those observed in the study (e.g., intimate partner violence and sexual assault), the findings lend credence to the notion that self-control and risky lifestyles are critical to the study of violent victimization among men and women following different gendered pathways.

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Notes

  1. Opportunity theories generally link crime and victimization to broader ecological processes, which in turn alter lifestyle patterns and increase exposure to criminals. Such perspectives discount the role of offender motivation (e.g., Cohen and Felson 1979). In contrast, motivation (or propensity) theories posit that enduring individual differences cause people to self-select into lifestyles conducive to crime and victimization (Gottfredson and Hirschi 1990). The apparent relationship between lifestyle patterns and crime/victimization is therefore spurious.

  2. This was accomplished using the mi suite for multiple imputation with chained equations available in Stata 12.

  3. To determine the robustness of the findings, supplemental analyses were conducted using listwise deletion to handle missing data. In terms of sign and significance, the results closely mirrored those observed using multiple imputation.

  4. These scale items originate from the novelty-seeking dimension of Cloninger’s Tridimensional Personality Questionnaire (Cloninger 1987). Prior studies using the Add Health data have also used these items to operationalize low self-control (e.g., Boisvert et al. 2012; Hu et al. 2006; Holtfreter et al. 2010b).

  5. The alpha coefficient for this scale is below that standard threshold, which may make it more difficult to observe significant effects (Carmines and Zellner 1979). The parameter estimates for violent offending should therefore be interpreted with caution.

  6. For our purposes, a more precise substance use scale would better reflect risky behaviors that place individuals in the proximity of motivated offenders, perhaps by taking into account the settings in which respondents’ use and purchase substances (e.g., on the streets).

  7. Although the alpha for the financial hardship scale is slightly below the .70 cutoff, its inclusion in the analysis is necessary as an important correlate of both offending and victimization (e.g., Farrington 1995; Lauritsen and Heimer 2010).

  8. For the negative binomial models presented here, the likelihood ratio χ2 tests indicate that the null hypothesis that the alpha parameters are equal to zero can be rejected (e.g., LR χ2 = 151.69, p < .01 for Model 1 in Table 2), thus indicating overdispersed outcome distributions. The model F-test for each model, which is used in place of a model χ2 in Stata 12 when using multiple imputation to replace missing case values, indicates that the null hypothesis that all coefficients are equal to zero can be rejected. Diagnostic procedures did not detect the presence of harmful collinearity (e.g., VIF < 1.50 and mean VIF = 1.11 for Model 2 in Table 2). However, the Breusch-Pagan/Cook-Weisberg tests confirmed that heteroskedasticity is potentially problematic (e.g., χ2 = 4,917.76, p < .01 for Model 2 in Table 2). Accordingly, robust standard errors were estimated.

  9. Comparisons were conducted using the test statistic recommended by Brame et al. (1998: 258) for maximum-likelihood regression coefficients.

  10. Nearly all of the items used in the cluster analysis were gleaned from the wave 1 and 2 surveys. The one exception was the child abuse item, which was reported retrospectively at wave 3. It is possible that retrospective interpretations of child abuse might result in over- or under-reporting maltreatment (Brewin et al. 1993) since respondents might “forget” or redefine their experiences in light of later life circumstances and their current situation (Widom 1989; Widom and Shepard 1996).

  11. Variables were automatically standardized by the algorithm used to identify clusters in the data. Accordingly, the cluster solutions reported here are robust to scale differences among the various variables included in the cluster analysis.

  12. While the number of clusters is not perfectly aligned with those specified by Loeber et al. (1993) and Daly (1994), the purpose of the present study was to derive clusters from the data rather to force clusters upon the data. In any event, the empirically defined groups capture significant between- and within-gender variability in the sample.

  13. Pathway-specific descriptive statistics for the variables used in Table 5 are provided in Appendix 1.

  14. Religiosity is composed of a six-item summated scale that includes the time per week spent engaging in religious activities at home, the time per week spent praying privately, the number of times in the past year religious services were attended, the importance of spiritual life, and the extent to which respondents rated themselves as spiritual people and religious people. Each item was coded to range from 0 to 3, where higher values represent higher levels of religiosity (Cronbach’s alpha = .87). Education and childcare are each scored along a 5-point ordinal scale. Married is a dummy coded variable (1 = yes, 0 = otherwise).

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Authors and Affiliations

  1. School of Criminology and Criminal Justice, Arizona State University, 411 N. Central Ave., Suite 600, Phoenix, AZ, 85004, USA

    Jillian J. Turanovic & Michael D. Reisig

  2. Corrections Institute, University of Cincinnati, 556 Dyer Hall, Cincinnati, OH, 45220, USA

    Travis C. Pratt

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  1. Jillian J. Turanovic
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  2. Michael D. Reisig
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  3. Travis C. Pratt
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Correspondence to Jillian J. Turanovic.

Appendix 1

Appendix 1

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Table 7 Pathway-specific descriptive statistics
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Turanovic, J.J., Reisig, M.D. & Pratt, T.C. Risky Lifestyles, Low Self-control, and Violent Victimization Across Gendered Pathways to Crime. J Quant Criminol 31, 183–206 (2015). https://doi.org/10.1007/s10940-014-9230-9

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