Blog 9: SNA, friendships, and community

Article 1:

In this article, Kleit (2001) uses social network analysis to investigate the role that social networks play in job searches, specifically for individuals who live in scattered-site public housing communities.  Based on the theory (see Figure 1) that housing lower-income individuals with more affluent peers open them to more opportunities, Kleit hypothesized that living in more dispersed housing would provide lower-income individuals with access to more jobs.  Thus, to explore this, she sought to answer the following research question: Does living in small clusters of public housing in a non-poor area instead of in a dispersed housing pattern influence the types of social ties poor people use when they look for jobs? Using in-person survey data from 253 women living in either dispersed or clustered housing, Kleit asked participants to report the people who they had talked to or would talk to about jobs, as well as various other questions to account for the persons role in their life.  Kleit then identified which of the individuals listed were neighbors.  Thus, the nodes in this project were the individuals who lived int he neighborhood, and the links represent whether or not two individuals would communicate about jobs. 

She found that although there is greater diversity in dispersed communities, these communities are not as close as clustered communities, which she posits may influence their willingness to reach out for job connections as women in dispersed communities were less likely to reach out to neighbors than those in clustered communities.  In this sense, SNA allows us to view the structural impact of housing policies as they apply to entire communities as opposed to solely individuals. 

Article 2:

In their article, Valente and colleagues (2009) use social network analysis to investigate adolescent friendship choices and weight.  As highlighted in the meme below, and evidenced through decades of scholarship, peer relationships are vastly influential in adolescence.

Based on past research which suggests that weight gain seems to spread through family and friend networks, the researchers hypothesized that obesity may spread similarly through adolescent friendships.  Thus, the goal of the study was to investigate if overweight adolescents were more or less likely to have overweight friends.  Using in-person surveys with 685 students between ages 11 and 15, the researchers asked students to report about their friends in class as well as took measures of their weight.  Thus, the nodes in this project were the 685 students, the links represent those who were nominated as friends, and the students BMI were used as node attributes. 

They found that overweight girls were less likely to be nominated as a friend, even though overweight students nominated more friends. Additionally, their network analyses suggested that friendships were more likely between students of similar rather than different weight.  In this way, SNA allowed the researchers to investigate the various relationships of children through nodes and links as opposed to just singular relationships.  This provides for a more accurate depiction of the adolescent friendship network structure as it relates to weight.

Blog 8: Density and Friendship Development

Aggression is not a common trait that we seek in a friendship.  However, research suggests that aggressive children are still known to have friends.  In their article “Forms and Functions of Aggression in Adolescent Friendship Selection and Influence: A Longitudinal Social Network Analysis,” Sijtsema and colleagues (2010) investigate the impacts of aggression on adolescent friendship.  Through this work they seek to explore:

1. The development of friendship networks in adolescence

2. How friendship selection may be predicted by aggression?

To explore these questions, they collected survey data from 6th – 9th grade students over the course of 3 years.  Students were asked to nominate up to 18 friends in their grade at school as well as provide information on the length of their friendship.  During this data collection, they also collected a self-report survey about the students aggression.  The sample population for this study is a longitudinal sample of adolescents in middle school in a medium-sized urban community in the North East.  The sample was comprised of 65% White students, 17% Black students, 6% Latinx students  and 13% students of other race/ ethnicities.  About half of the sample identified as females and all participants were between the ages of 12 and 14. 

In this analysis, the students were used as the nodes, and the friendships between them were the links, whether one-sided or reciprocated.  The density metric was investigated to understand  the first research question about how friendship networks develop over middle school.  Density was defined as depicted in Figure 1, and as much could increase or decrease over time.  

Figure 1. Defining Density

By and large, Sijtsema and colleagues  found that the density of the network decreased over time, suggesting that students made fewer nominations of friends as they progressed through middle school.  Figure 2 demonstrates how this phenomenon occurred across 7 of the 8 different networks.  From this finding, they concluded that “adolescents were less inclined to just nominate classmates as friends. Instead they favored friendships that were mutual….” This contributed to the decrease in density of the network, but didnt necessarily suggest that adolescents had lost friendships between time point 1 and time point 3.

Figure 2. Density over time

In this way, the density metric provided us with an understanding of how the structure of the network as a whole changed over time.  This goes above and beyond what we could have learned from a nodal metric that only allows us to investigate how individual nodes function in a network.   Instead, structural metrics like this one inform us about the functioning of the friendship network as opposed to a few adolescents in the network.  

Sijtsema, J. J., Ojanen, T., Veenstra, R., Lindenberg, S., Hawley, P. H., & Little, T. D. (2010). Forms and functions of aggression in adolescent friendship selection and influence: A longitudinal social network analysis. Social Development19(3), 515-534.

Blog 6: Social Networks and Student Engagement

By and large we know that students struggle to stay engaged in the classroom and classrooms at any level can end up looking similar to Figure 1.

Whether from our own experience as a student, teacher, or parent; this is an often-experienced phenomenon. In fact, research has confirmed throughout time that many students are not engaged in school (Steinberg, Brown, & Dornbush, 1996; Yazzie-Mintz, 2007). Jennifer Fredericks, a leading scholar in the field of engagement warned of the consequences of disengagement stating that they “are especially severe. These youth are less likely to graduate from high school and face limited employment prospects, increasing their risk for poverty, poorer health, and involvement in the criminal justice system…” (Fredericks, 2011). For this reason, I find it extremely important to explore student engagement in a variety of contexts to not only understand the consequences of disengagement, but also to understand what is associated with higher rates of engagement.

For this reason, I plan to investigate the relationship between how connected a student is in the classroom social network, and their individual reports of engagement. Kindermann and colleagues suggest that students make friends with others who have similar levels of engagement (1993; 1996), and Ryan (2000) reports that peers can support each other’s engagement through information sharing and modeling. Given this evidence, I hypothesize that highly connected students in the social network will report higher levels of engagement. To investigate this idea, I plan to answer the following three research questions:

  1. Is a student’s level of connection to a classroom social network related to their levels of engagement in the course
    • Are highly connected students more or less engaged?
  2. Does a student’s level of connection to a classroom social network differentially impact the 4 different dimensions of engagement?
  3. Are there differences in level of connection by gender?

To investigate these questions, I plan to collect a network data (specifically a directed graph) using an online Google survey in an undergraduate human development course that I teach. I will ask students in my section of the course about their engagement as well as who they frequently work with. Demographic and network survey items will include:

  • Please provide your gender: _______________________
  • Year in school:
    • Freshman
    • Sophomore
    • Junior
    • Senior
    • Graduate student
  • List the people in this class that you have collaborated with. (Collaboration)
  • List the people in this class that you would study with or go to for help. (Dependency)
  • List the people in this class that you frequently talk with. (Neutral)

Additionally, since engagement is often conceptualized as a multi-dimensional construct including cognitive, affective, behavioral, and social aspects (see Figure 2), I will use the following 4 items to investigate students’ engagement.

  • How true are the following statements of you?
    • I try to connect what I am learning in this class to things I have learned before.
    • I put effort into learning in this course.
    • I look forward to this class.
    • I try to understand other people’s ideas in this class.

Given the lack of research I’ve found investigating the role of social networks in classroom engagement, I think that this will be a contribution to the field in understanding what it is that encourages student engagement.

It’s a Small World After All…

“What is the probability that any two people, selected arbitrarily from a large population, such as that of the United States, will know each other?”

This is the question that Travers and Milgram (1969) sought to investigate, and in essence defines the small world theory. The Small World Theory basically is the idea that our unexpected social links connect us to far more individuals than we realize, regardless of location. Kadushin (2011) demonstrates this idea in the first chapter of Understanding Social Networks by discussing how social media sites such as Facebook connect us to networks that make the world seem small. For example, through my 500 friends on Facebook, I am connected to various other individuals by either one or two degrees of separation. In other words, I am connected to my friends, their friends, and further their friends. The expansive nature of this system can be visualized in the image below.

However, these connections don’t solely exist online. This week’s lectures also demonstrated the small world theory, as it exists practically in our policy arena. The lecture mentioned Obama’s decision to change telephone surveillance of terrorists from 3 degrees to only 2 degrees of separation, demonstrating how wide our social networks go and the actual number of individuals who are still under surveillance given the change.

However, this example also highlights one of the very real risks of the small world theory. Kadushin (2011) warns that these weak ties have the danger of exposing us to more people than we desire to be exposed to. This seems to occur for a few reasons:

  1. Kadushin (2011) explains that weak ties are what connect networks that otherwise would be disconnected, connecting us outside of our insular networks.
  2. Granovetter (1973) claims that “the stronger the tie connecting two individuals, the more similar they are” (p.1362), which seems to suggest that weak ties connect us to those whom we are dissimilar from.

For these reasons, weak ties, such as those emphasized in the small world theory connect us to those who we are different from, and otherwise would likely not be connected to. This provides the opportunity for us to 1) be connected to more people than we desire and 2) be connected to individuals we may wish we weren’t connected to.

So, what is the impact of weak ties on the big world?

As previously mentioned, weak ties connect us people we otherwise would not be connected to. Further, Granovetter (1973) explains how weak ties allow us to explore connections between social networks, stating:

“Emphasis on weak ties lends itself to discussion of relations between groups and to analysis of segments of social structure not easily defined in terms of primary groups” (p. 1360).

Thus, the presence of weak ties allows us to consider the social networks of the world as cohesive as opposed to disjointed systems. We therefore can investigate the connection between various networks instead of solely focusing our research within insular networks. Due to this, we can begin to collect data and ask questions about how networks are connected, how we maintain such connections, and the importance of these bridges.

From this perspective, weak ties seem to be just as important as strong ties. In my opinion, they may even rival strong ties. Given that they are the source of newer ideas being introduced into social groups from dissimilar groups, they serve an important role of ensuring that our social networks do not become too isolated. This perhaps is most evident when considering the impact that weak ties likely played in the last presidential election.

So, who is Kevin Bacon, and why is he important in this discussion?

Kevin Bacon, the actor in the photo above, became the face of the small world theory when a few college students designed a game called “The Six Degrees of Kevin Bacon.” The goal of this game is to connect any actor to Kevin Bacon through no more than 6 other actors, thus demonstrating that even the world of Hollywood is subject to the small world theory.

Understanding our Modern Society

Given what we learned last week about the power of weak and strong ties on individuals, it is clear that social networks have the power to influence individuals’ behaviors, emotions, outcomes, etc. In this way, social networks provide us with the opportunity to explore human tendencies, and further understand how human beings exist in and engage with modern society. The social network perspective, according to Keim (2011) is “explains social actions not on the basis of individual attributes but in the context of social relations (Wellman, 1988: 31).” (p. 21). That being said, social network analysis steps beyond understanding modern society as a product of individuals’ traits or circumstances, and considers how the interactions between individuals or the interactions between systems influence such actions.

If however, we are considering interactions between individuals, it is important to consider the perspective from which we are viewing such networks. For example, in egocentric studies only the “network immediately surrounding participants is observed” (Robins, 2015, p.40). This means that our perception of a network is limited to what is immediately surrounding participants, and lacks information about how the network expands beyond these people or how it might be experienced by others. Thus, if we were to shift to the perspectives of a different sample of participants, the network likely would look different. Given this, it is important to consider the bias in SNA just as we would in any other traditional social science design.

So then, how does SNA data differ from traditional social science data?   SNA data differs from traditional social science data in that it necessitates two data sets, namely the nodes and the relations between them. For example, on our practicum, we had one data sheet with the characteristics / attribute of the nodes– similar to what we would see with traditional social science data (sheet A). However, this data sheet was paired with a data set that recorded the connections or relations between the nodes (sheet B). Thus, one of the main differences in SNA and traditional social science is the format of the data itself. Further, the main unit of analysis in SNA is the node or the tie as opposed to an attribute or outcome in traditional data analysis.  In this way, relational data must be treated differently than our average data set.

Though inherently different, relational data still necessitates descriptive analysis so that similarities and differences between nodes and ties can be considered. Through these descriptions researchers are able to consider the similarities and differences in groups within the network, further explaining how humans engage similarly and differently with modern society. However, diverging from traditional data analyses, one key reason that relational data makes predictive analysis challenging is because of causal ambiguity. Yang, Keller, and Zheng (2017) define causal ambiguity as the fact that we don’t know which data point has caused change in the other. Instead, we know that the two are related non some factor.

Overall, there seem to be some similarities between SNA and traditional social science research. However, SNA provides us with a new language and new techniques to further explore human beings’ interactions, emotions, and daily actions in modern society.

A thought-provoking quote:

  • “From a network perspective, one would argue that peers are not influential per se, but can become important if they are engaged in frequent contact, build cohesive networks of high density, transmit information quickly, and produce homogenous evaluations and normative pressures” (Keim, 2011, p. 21).


Blog Post 1: Connected

Never before had I considered the drastic impact that networks have on our lives. As a rather well connected young adult, I have thought extensively about relationships. However, these thoughts have often stopped at the individual relationship level, and never extended to my social networks. In their book Connected, Christakis and Fowler dive deeply into explaining the power of social networks’ influence on our daily lives. Beginning with social networks broadly, they distinguish between the ideas of connection and influence. Connection, which goes with the idea of “six degrees of separation”, indicates that six steps connect us to another person. However, they discuss the idea of three degrees of influence, suggesting that actual influence can travel three separate degrees. Therefore, we affect our friend, our friend’s friend, and our friend’s friend’s friend.

Figure 1. Three degrees of influence


But how do we affect these people? Christakis and Fowler discuss a few specific ways in which our social networks affect us:

  1. They affect our outcomes. Citing the “rich get richer” phenomenon, Christakis and Fowler discuss how social networks tend to reinforce both situational and positional inequality.
  2. They affect our emotions. Referencing examples of mass psychogenic illnesses such as this video below, they discuss how emotions are transmitted like diseases through social networks, infecting those in our sphere of influence. In this way, they discuss how emotions such as happiness and loneliness spread through social networks.

  3. They affect our knowledge. Citing examples of suicide contagions, social activism, and successful presidential campaigns, Christakis and Fowler demonstrate how the spread of information through social networks affects our knowledge basis and as a result impacts our actions such as voting or engaging in activism activities.

Focusing specifically on digital networks and the Internet, Christakis and Fowler address both the pessimistic and optimistic views of how the Internet impacts our social networks. On one hand, people feel that social media hinders face-to-face interaction and communication skills. However, others feel that digital networks and the Internet only extend our opportunities for social interaction and networks. I am inclined to side in the middle. I think that the Internet does provide opportunities to extend our social networks in healthy ways by providing access to wider communities and opportunities to practice social skills. However, I do think we run the risk of being consumed by virtual interactions as opposed to face-to-face interactions, and therefore, like all things, the Internet should be used in moderation for these purposes. Life is about balance.

However, the authors note that regardless of opinion, social networking sites shape social networks in four distinct ways.

  1. Enormity – they increase the scale and reach of our social networks.
  2. Communality – they extend the ways in which we share information and collaborate
  3. Specificity – they further particularize the ties we can make within networks
  4. Virtuality – they allow us to assume separate virtual identities

These are four impacts that I can agree with — and note that I have seen evidence of in my life. Generally, I think that Christakis and Fowler do a good job of explaining and evidencing the ways in which the Internet impacts our social networks, which consequentially impacts our actions, beliefs, and behaviors. At large this book has encouraged me to consider the ways in which I personally am influenced by my social networks. On a more academic level, it has prompted thoughts on how the students I work with are influenced by their social networks, and how these networks impact their decisions to engage in productive afterschool activities.

Two Quotes that struck me from this book:

  1. “Happiness is thus not merely a function of individual experience or choice; it is also a property of groups of people” (p. 66).
  2. “Loneliness can actually shape the social network. People who feel lonely all the time will lose about 8 percent of their friends, on average, over two to four years. Lonely people tend to attract fewer friends, but they also tend to name fewer people as friends as well. What this means is that loneliness is both a cause and a consequence…” (p. 70).