From adults in prison to adolescents using drugs: How SNA helps us understand peer networks

Social network analysis helps researchers and practitioners better understand crime and delinquency networks.  By analyzing the relationships and connections between network members, they have much richer data to help explain complex behaviors, attitudes, and beliefs.  Two articles from different populations demonstrate this point.

Schaefer, Bouchard, Young, and Kreager (2017) used SNA to investigate social structures in an adult prison.  They set relationships between incarcerated persons as the point of analysis instead of individual attributes because they believed it would give a more complete picture of the prison groups.  Specifically, the researchers wanted to determine if the characteristics shown in previous research to influence network structure (race and ethnicity) were the actually the predominant factors in group formation.  The researchers also included other factors past research past research suggested may impact grouping, such as age, religion, gang status, prison tenure, and power.  They collected the data through interviews and department of corrections information.  The incarcerated people were the nodes and the “get along with” relationships were the links.  The “get along with” nomination did not have to be reciprocal to count.  Since there was little existing SNA-based research on prison social structures, the researchers compared the prison network to adolescent friendship networks.

The researchers did find evidence of some race/ethnicity homophily within networks; however, the impact was not as high as prior research suggested (Schaefer, 2017).  In fact, the homophily rates were similar to those in adolescent friendship networks.  The subgroups and cliques showed more racial and religious diversity than segregation.  The participants also had similar numbers of friends as adolescents, although there was less reciprocity in the relationships, making the ties somewhat weaker.

This research advanced the understanding of prison network composition because the analysis showed less racial/ethnic divisions and gang structures than previous research.  This is critical information for people who run programs within prisons and programs targeted toward people recently released from prison. 

Kirke (2004) used SNA to better understand the contributions of peer selection and influence in adolescent substance use.  Researchers have long tried to determine the impact of selection versus influence on adolescent behavior.  The research question for this study was: is there direct evidence of substance abuse similarity in adolescents’ peer networks and is there evidence of influence between similar peers?  Kirke collected data through in-person interviews of 267 teenagers in one district in Ireland.  The teenagers were the nodes and friendship relationships were the links.  She used the name generator technique to construct the links.  

Kirke (2004) found a complex pattern regarding substance use in adolescent peer networks.  Participants chose friends who had both similar and different substance use patterns from them.  Participants were also influenced by peers both within their networks and outside of their networks.

Previous research had focused on influence from within the group and had largely ignored the implications of out-group influence.  Social network analysis allowed the Kirke to see the nuances in the friendship networks and clarify the roles of selection and influence.  This knowledge, in turn, can help professionals create effective prevention and intervention programs.

References

Kirke, D. M. (2004). Chain reactions in adolescents’ cigarette, alcohol, and drug use: Similarity through peer influence or the patterning of ties in peer networks? Social Networks, 26, 3-28. doi: 10.1016/j.socnet.2003.12.001

Schaefer, D. R., Bouchard, M., Young, J. T. N., & Kreager, D. A. (2017). Friends in locked places: An investigation of prison inmate network structure. Social Networks, 51, 88-103. doi: 10.1016/j.socnet.2016.12.006

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