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. 2008 Mar;40(2):576-85.
doi: 10.1016/j.aap.2007.08.013. Epub 2007 Sep 19.

Identifying social mechanisms for the prevention of adolescent drinking and driving

Meng-Jinn Chen  1 , Joel W Grube, Peter Nygaard, Brenda A Miller
Affiliations

Identifying social mechanisms for the prevention of adolescent drinking and driving

Meng-Jinn Chen et al. Accid Anal Prev. 2008 Mar.

Abstract

This study identifies social mechanisms that might help prevent youth from being involved in driving under the influence of alcohol (DUI) and riding with drinking drivers (RWDD). Data collected through telephone surveys with 1534 adolescents and young adults aged 15-20 years (mean=17.6, S.D.=1.6) in California, USA, were analyzed. Structural equation modeling analyses showed that DUI and RWDD were strongly related to drinking in unstructured situations, modeling of DUI by peers and parents, and perceived peer approval or disapproval of DUI. DUI outcome expectancies were indirectly related to DUI and RWDD through situational drinking. Parental monitoring and DUI law enforcement were also indirectly related to DUI and RWDD through DUI expectancies and other mechanisms. The findings, overall, suggest that parental influence remains important even through late adolescence. Parental monitoring, in particular, might help to reduce unstructured socializing with peers, drinking, and affiliation with peers who engage in DUI. Parental monitoring may also foster beliefs about the risks of DUI. Conversely, parents' own DUI behavior may normalize drinking and DUI behaviors, thus countering monitoring efforts.

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Figures

Figure 1
Figure 1. Hypothesized relationships among the main constructs in the DUI and RWDD models
Figure 2
Figure 2. Latent variables and factor loadings from the measurement models
Note: All numbers are standardized coefficients. The double-arrow lines indicate the error covariances that were added to improve the model fit.
Figure 3
Figure 3. DUI model
Variables in circles are latent variables and variables in squares are measured variables. Significant direct associations between control variables and these main constructs are presented in Table 2 Model fit: CFI=.93, RMSEA=.036 (90% CI = .034-.039). *p < .05, †p < .01, ‡p < .001.
Figure 4
Figure 4. RWDD model
Variables in circles are latent variables and variables in squares are measured variables. Significant direct associations between control variables and these main constructs are presented in Table 2. Model fit: CFI=.94, RMSEA=.034 (90% CI = .032–.035). *p < .05, †p < .01, ‡p < .001.
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