You Are Who You Hang Out with: Identification of College Students' Social Networks and Their Association with College Student Drinking

You are who you hang out with: Identification of college students’ social networks and their association with college student drinking. 

 

Background:

Previous research has shown that choosing to engage in some college experiences, like drinking can be influenced by ones social network. From a behavior change perspective, the Theory of Planned Behavior suggests that a person’s social relationships can shape their intentions to act in particular ways through norms.  Therefore, it is critical to investigate the different kinds of social networks that college students develop, and if particular network structures are more likely to be associated with drinking.

 

Methods:

The purpose of this study is to capture the complexities of social network structures in which alcohol use is imbedded among a sample of college students.  This includes exploring their social networks; attitudes, intentions, norms around alcohol us, and actual alcohol use.

RQ 1: What LCA structure (social network profiles) adequately represents party and more cautions social networks among college students in regards to drinking?
RQ 2:  How might the social network profiles be associated with relevant individual characteristics like age, drinking intentions, and drinking behaviors?

In spring/summer/fall semesters 2012, three-hundred college students completed the HUDSoN (Hooking Up Drinking Social Network) questionnaire on three consecutive days during a weekend.  The HUDSoN includes: 1) questions on norms, intentions, and attitudes towards alcohol use and 2) a social network inventory that examines who students are hanging out, characteristics of those individuals within the participants’ network, and the multi-dimensional relationships network members have with each other. 

 

Results:

Latent Class Analysis (LCA) was used to identify four network profiles or latent classes based on seven network indicators; network size, network gender diversity, drinking buddies in the network, meeting place of each network member, level of trust within the network, age of network members, and frequency of drinking.  Three covariates were then used with the LCA model; intentions to drink, amount of alcohol consumed, and age.  All covariates were significant predictors into network class.  Results showed that participants who had higher intentions to drink, who consumed more alcohol, and who were older where more likely to be in a particular network class than other participants. 

 

Conclusions:

This study illuminates the critical interactions between specific social network structures and participants drinking behavior.  These findings could inform interventions that aim to reduce drinking among college students by specifically targeting certain network characteristics.