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Dating Extension - Chrome Web Store
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For each level, differentiated prices can be set, depending on the time period. The membership plans feature can be enable or disabled by the administrator. Smarty-based Templating System can be enabled or disabled by the administrator. We control for the intervention program as it may be associated with later romantic involvement and substance use behaviors.
Network Transitivity Ratio and Network Centrality are network structure measures that capture the degree of local clustering in the network and whether the network is hierarchically organized with central actors having many ties and peripheral actors having few Proportion Dating captures the percentage of respondents in a school who reported currently dating at the 9 th grade survey. Our primary hypotheses center on differences in romantic partner and friendship patterns across drinking and smoking behaviors.
These correlations provide preliminary evidence of partner selection processes across the three substance use behaviors. We hypothesize that, prior to dating, partners will be more similar for smoking than the drinking behaviors. We also examine school-level differences in between-friends smoking and drinking correlations at 9 th grade, with friendship defined as the union of sent and received friendship nominations.
This provides evidence of network segregation for the behaviors in question. For example, a high behavioral correlation between friends suggests that the behavior is highly clustered in the peer network, rather than randomly distributed among friends. Our final set of analyses consist of actor-partner interdependence models APIMs 22 of the three substance use outcomes in the sample of heterosexual couples.
These analyses approximate the models of Kreager and Haynie 3 with a more recent sample and the addition of a smoking outcome. Two reviewers suggested that we use actor-based continuous time methodologies e. However, such methods have not yet been developed for addressing our research question, which centers on indirect peer influences from one friendship network friendships by way of a second network dating dyads. The estimated APIMs are multilevel models with partners at level one and couples at level two, which allow for the estimation of individual-level effects while accounting for between-partner dependence.
Following our initial models, we explore potential gender differences by repeating the analyses with interactions between gender and our primary independent variables. See Kreager and Haynie 3 for additional modeling information. To examine partner selection, behavioral stability, and network segregation for different substances, Table 2 lists 1 substance use correlations between dating partners at 8 th grade, 2 the correlation between 8 th grade and 9 th grade substance use for each dating individual, and 3 the school mean differences in substance use friendship segregation at 9 th grade.
This pattern suggests that partners are much more likely to share smoking than drinking behaviors prior to dating. There is also evidence that smoking is a more stable behavior over time.
Finally, school differences appear in 9 th grade substance use correlations between friends. Together, these results provide evidence that partner selection, stability, and friendship segregation are higher for smoking than drinking behaviors. Looking across all of the models, we see several control variables with generally consistent associations with the three dependent variables.
Partner religiosity, parental monitoring, and grades tend to have significant negative associations with substance use. It is also interesting that friendship network size introduced in Model 2 is positively associated with substance use, significant for drinking and smoking, suggesting a positive correlation between substance use and network popularity. Coefficients for the primary independent variables, however, appear quite different across the drinking and smoking outcomes.
Model 2 adds friend and friend-of-partner behavior to the substance use equations. Consistent with Kreager and Haynie 3 , friend-of-partner behavior shows strong and significant associations with the drinking outcomes, net of friend and partner behavior estimates. In contrast to the prior study, however, the friend-of-partner coefficients are not larger than the friend coefficients. Also consistent with Kreager and Haynie 3 , the introduction of prior drunkenness and drinking behaviors in Model 3 attenuate more of the friend than the friend-of-partner coefficients. These results suggest that prior drinking behavior is more likely to directly connect daters with drinking friends i.
In contrast to the drinking outcomes, friend-of-partner behavior has a nonsignificant association with smoking, net of other covariates. Indeed, with the introduction of the lagged dependent variable Model 3 , the association between friend-of-partner and respondent smoking becomes negative. We also examined possible gender moderation by including interaction terms between gender and our primary independent variables.
Table 4 lists coefficients and standard errors for the interaction terms, by substance use outcome. Overall, and consistent with Kreager and Haynie 3 , friend-of-partner behavior tends to be a weaker correlate of substance use for girls than boys, and this pattern is reversed for friend behavior. However, none of the interactions is significant, so we make no strong conclusions from these patterns. This study replicated the work of Kreager and Haynie 3 by examining peer behavior and substance use in a sample of adolescent dating couples.
Kreager and Haynie 3 argued that dating is likely to create a network bridge whereby daters are exposed to the behaviors of new peers through romantic partners. Central aims of the current study were to test this bridging hypothesis in a recent adolescent sample and for drinking and smoking behaviors. For the latter, we hypothesized that greater partner selection, behavioral stability, and network segregation for smoking than drinking may alter behavioral diffusion processes from romantic relationships. Correlational analyses suggested strong differences between smoking and drinking social processes.
Romantic partners were more similar in their prior smoking than drinking, suggesting greater partner selection for the former; daters were more stable in their smoking than drinking between waves; and school friendship networks were more highly clustered by smoking than drinking behaviors. Together, these results suggest that smokers are more likely than drinkers to date one another, and that there is lower likelihood that dating will connect smokers, relative to drinkers, to dissimilar friends.
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In other words, dating and friendship networks appear more homophilous for smoking than drinking behaviors. It is therefore less likely that dating serves as a network bridge to new friends with differential smoking behaviors or opportunities than for drinking. The APIMs of drinking and smoking test the bridging hypothesis for the two substances. It should be noted, however, that the influence from friends-of-partner drinking appears smaller in the current analyses than in Kreager and Haynie 3 , and not significantly larger than the coefficients for direct friends.
A possible explanation for this difference is the shorter time span between the network measures and the outcomes in the PROSPER study 12 months than Add Health 18 months. The closer temporal relation of the friendship measures to the outcomes increases the likelihood that partners will share friends and reduce bridging processes.
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Further research should explore the dynamic nature of dating and overlapping friendship networks. The models of smoking showed no significant association between friend-of-partner smoking and dater smoking, failing to support the bridging hypothesis for this behavior. This is consistent with the idea that smoking homophily leaves little room for influence from indirect peer contacts. First, the sample consists of adolescents living in rural Iowa and Pennsylvania communities, limiting our ability to generalize findings to the national adolescent population.
Unfortunately, little is known about how the experience and interpretation of romantic relationships differs for youth growing up in a rural compared to suburban or urban communities. On the one hand, there is likely to be a smaller pool of potential romantic partners in rural locations. On the other hand, dating and romantic involvement may be of greater importance in rural locations as there is less competition from other activities compared to more urban settings where opportunities to pursue a multitude of interests is possible.
Second, the sample consists of dating couples where both partners are within the same school and grade cohort. Again, we are heartened by the similarity of the current results with those of Kreager and Haynie 3 , which relied on a sample with greater age variation and covered all middle and high school grades.
In both cases, however, out-of-school partners are omitted and these may be of most interest for access to substance use opportunities and other high-risk behaviors. As can be seen, our sample of in-grade daters is generally more conventional and advantaged than is the group of unsampled daters. We are therefore cautious in generalizing our results to other dating populations. Future research should test our hypotheses among other dating couples, including out-of-school partnerships and age-asymmetrical couples. The segregation of smokers in peer networks suggests intervention strategies aimed specifically at smoking groups and couples.
In addition, the strong stability in smoking behaviors highlights the importance of preventing adolescent smoking initiation. Smoking partners and peers appear ideal intervention contexts. As drinking is more evenly distributed in peer networks than smoking, drinking interventions aimed at the broader peer structure may be more effective than targeting specific drinking groups. Such interventions could also profit by reducing the correlation between drinking and peer status, as has been demonstrated in evaluations of the PROSPER intervention study The authors thank Mark Fienberg and three anonymous reviewers for their comments on an earlier draft.
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