Blog 10 – Recidivism and Psychopathy

Because this is cool… check out recidivism in the NFL

Ok, back to the real business…

First, after reviewing other’s articles over the past few weeks and diving into my own, it seems obvious that students and most tend to relate and group themselves with those that they find similar to themselves (although this can likely also be latent or implicit), I wanted to find an article to read that, perhaps, touched on this phenomenon.

Work by Fortuin, van Geel, and Vedder (2015), which examined adolescent’s tendency to group themselves with those similar to them and how that relates to internalizing or externalizing problems, seemed to focus right at this observation. They sought to better understand the influence of internalizing and externalizing problem behavior upon the selection, socialization, withdrawal, and avoidance of peers. They presented dualing hypotheses regarding the extent to which different types of socialization patterns would present with those of varying degrees of internalizing or externalizing problem behaviors. That is, over the course of a school year they recorded three instances of each student’s (N = 542) friends that they liked and their internalizing and externalizing problems through survey methods. In this case, the students served as nodes with their peer nominations to other students as the edges.

Using longitudinal social network analyses, Fortuin and colleagues (2015) found that adolescences tend to prefer peers with similar externalizing problems, while no significant selection effect was found. Over time groups tended to become more similar in externalizing problems, but not internalized problems. Interestingly, and through the use of some fancy software, they were able to control for obvious correlates of gender and ethnicity. Using SNA allowed them to further examine how relationships transpire over time and what particular attributes, such as externalizing or internalizing behavior, influence or change within such groups of students. In other words, they employed SNA to examine the forming and existing of groups over time, while examining how those groups changed in respect to their internalizing or externalizing problem behaviors.

Next up, and again after reading other’s reviews, I thought it would be interesting to see the use of networks that are derived from experience sampling techniques (more frequent data collection. Work by Weerman, Wilcox, and Sullivan (2018) examined short-term changes in peer relationships, deviant behavior, and routine activities to better understand selection, socialization, and situational peer influence. Using high-school students (N = 155), they collected survey data to collect their peer relationships, activities, and offending behaviors over five time points over no more than a two-week span. This was in an effort to answer four main research questions displayed below, but in general involve: how volatile are peer relations, substance use, and delinquent behavior? How structural network effects influence iterative peer changes? Do those who socialize adopt the delinquent behavior of their peers? and is delinquent behavior a product of changes in socializing and substance use?

Results indicate very “volatile” networks that exhibited changes in delinquent offending that were related to situational changes in unstructured socializing, alcohol use, and marijuana use. In that, they concluded that “long-term peer influence processes like socialization may be less important in the short run, while situational peer effcts might be more salient.” Ultimately, they demonstrated that adolescent changes in peer relations is volatile and changes very quickly and drastically.

Ultimately, the use of social network analysis here was similar to the first article, in that they viewed peer relationships and how another aspect of them, in this case delinquency and substance use, was influential and influenced therein.

Interestingly, both articles presented here used the R package: RSiena. More information about it can be found HERE. In essence, it is a statistical package used for longitudinal network analysis that focus on nodal relation changes over time.


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