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Adverse Neighborhood Conditions and Sanction Risk Perceptions: Using SEM to Examine Direct and Indirect Effects

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Abstract

Objectives

The present study examines how individuals’ sanction risk perceptions are shaped by neighborhood context.

Methods

Using structural equation modeling on data from waves 6 and 7 of the National Youth Survey, we assess the direct and indirect relationships between adverse neighborhood conditions and two dimensions of sanction risk perceptions: the certainty of punishment and perceived shame. In addition, the role of shame as a mediator between neighborhood context and certainty of punishment is also investigated.

Results

The results indicate that adverse neighborhood conditions indirectly affect both forms of sanction risk perceptions, and additional results show that perceived shame fully mediates the effect of neighborhood conditions on perceptions of the certainty of punishment.

Conclusions

The perceptual deterrence/rational choice perspective will need to be revised to accommodate more explicitly the role of neighborhood context in shaping sanction risk perceptions.

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Notes

  1. Regarding our latter research question, we are aware that there may be a reciprocal relationship between formal sanction threats and informal sanction risk. Even so, we restrict our focus on the unidirectional pathway from perceived shame to the certainty of punishment since most of the strong arguments involving the relationship between the two dimensions of sanction risks are more focused on the effect of informal sanctions on formal sanctions (Etzioni 1988; Zimring and Hawkins 1973) and not vice versa.

  2. The personal offending experience data collected in wave 7 with regard to the preceding year (1986) was measured as categorical variables, but the measures on offending experiences between 1984 and 1985 were measured as binary variables. For these reasons, the personal offending behavior index is constructed using binary indicators.

  3. There was one outlying value of 12. This value has been recoded into four since it does not have any meaningful impact on the outcome.

  4. Following the lead of Horney and Marshall (1992) and Anwar and Loughran (2011), we also employed an “arrest to crime ratio” variable. However, none of our exogenous variables successfully predicted this new ratio after it was entered in the model. Therefore, we decided to treat the two variables independently.

  5. Maximum likelihood (ML) estimation is most commonly used in SEM. ML estimation assumes that the observed variables follow a multivariate normal distribution. As noted above, however, the categorical indicators of some of our measures in the current study do not allow us to use ML. Instead, oftentimes WLS (Weighted Least Square) has been relied upon as an alternative since it is known to be appropriate when the data do not follow a multivariate normal distribution (Bollen 1989). Nevertheless, Muthén et al. (1997) reported that WLS was found to be inferior to WLSMV and therefore WLSMV has been designated as a default estimator in Mplus when categorical endogenous variables or categorical indicators are involved (Muthen and Muthen 2010).

  6. This correlation matrix could be obtained using the OUTPUT TECH4 option provided by M-plus. This command is useful in that it actually provides the correlation matrix derived from latent constructs rather than observed variables.

  7. We also dropped the personal arrest experience variable, since the variable is not related to any of the dependent or the independent variables.

  8. We recognize that, as with any study that uses a non-experimental research design, causality cannot be inferred in the present study. We have, however, established statistical associations with proper temporal ordering along with extensive controls to avoid potential spuriousness. We are therefore confident that the relationships we observed are consistent with the propositions made by the theoretical perspectives we are testing.

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

    Byungbae Kim, Travis C. Pratt & Danielle Wallace

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  1. Byungbae Kim
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  2. Travis C. Pratt
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Correspondence to Byungbae Kim.

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Kim, B., Pratt, T.C. & Wallace, D. Adverse Neighborhood Conditions and Sanction Risk Perceptions: Using SEM to Examine Direct and Indirect Effects. J Quant Criminol 30, 505–526 (2014). https://doi.org/10.1007/s10940-013-9212-3

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