On Tuesday, the Pew Internet and American Life Project released a new, must-read report on Teens and Mobile Phones. The project was a collaboration between Pew and the University of Michigan’s Communication Studies department, and it involves some of the top researchers of teens and technology (Amanda Lenhart, Richard Ling, Scott Campbell and Kristen Purcell).
In addition to releasing the great report, Pew did something new by simultaneously releasing the data sets used in the report (if I’m not mistaken, they’re usually embargoed a few months). As someone who pays very close attention to Pew’s research, I was very pleased to see this – if I have questions or want to explore something further, I could go right to the data.
One of the questions in the Pew report was a modification of the General Social Survey’s (GSS) “discussion networks” question. Questions of this sort ask individuals to list how many people with which they can discuss personal matters, which seems to be a good proxy for one’s close, supportive network. Using the GSS data, Peter Marsden found in 1987 that Americans, on average, have three discussants. Replicating the analysis in 2006, McPherson and colleagues found that discussion networks had shrunk to an average of two. There’s been plenty of criticism of the measure (my favorite being Peter Bearman’s Headless frogs.. paper, see also Fischer, 2009). Most recently, Hampton and colleagues explored the effect of technology on discussion networks in a great Pew report entitled Social Isolation and New Technology.
One of the great promises of “social technologies” is that they connect us to important others. By participating in a social network site, for example, we’re able to keep in touch with a broader range of diverse contacts. Critics are quick to point out that all those ties may be meaningless; in research, we draw distinctions between tie strength. Ellison and colleagues have demonstrated that use of Facebook among undergraduates increases a form of bridging (weak-tie) social capital. The “important matters” question, on the other hand, is more reflective of bonding (strong-tie) relations. Therefore we can use Pew’s new data to explore the relationship between use (and intensity of use) of social technologies and a teenager’s strong-tie supportive network.
First, some important notes. From hereon I am going to be talking about novel data analysis. This is a blog post, so I am going to keep the reporting informal. If you wish to explore my analysis, or re-run it, I have included a zip file that contains the questionnaire, data, reasonably commented do-file and output log. Sorry, R fans, Stata wins for survey analysis; these files are compatible with Stata 11. The analysis I’ll be talking about is weighted (individuals as PSU, using PSRAI’s omnibus weight). The dependent variable is an overdispersed (mean=~5, variance=~10) count, the proper regression being negative binomial (confirmed with LR test on the alpha). Finally, the question explored in this analysis is not a direct match to the GSS question, it is actually quite different (GSS is a name generator). Therefore, the results are not directly comparable, but they are likely informative. See the Pew report methodology section for a full description of the sample.
Teenage Discussion Networks
For the Teens and Mobile Technology study, interviewers spoke to 800 teens age 12-17, asking a range of questions about technology use. Included in the questionnaire was the question about discussion networks. In this questions, interviewers asked how many people the individual “feel[s] very close to and with whom you are frequently in contact to discuss various things, including your personal issues and feelings.” The mean response was a little over 5, with a standard deviation of three. The density plot is included at right.
First, I explored if demographic and socio-economic factors were associated with the size of teenage discussion networks. Pew collected data on age, gender, family income, parent’s ethnicity, and total number of kids in the household. These variables could impact the teen’s ability to form discussion networks for a variety of reasons, so it is worthwhile to retain them as control variables. I found only one variable significant: being of “black, non-hispanic” parentage. Compared to teens of “white, non-hispanic” parentage, teens of “black, non-hispanic” parentage have a lower incidence rate of reported discussants (IRR=.8041, p=0.011, Model1.pdf).
Next, I wanted to explore the effects of internet use, social network site use, and mobile phone ownership on the size of teenage discussion network, controlling for demographic factors. I found that use of the internet, use of social network site, and ownership of a mobile phone were all positively and significantly (p<.05) associated with the size of the support network (Model2.pdf). Importantly, ethnicity remained negative and significant, indicating that teens of “black, non-hispanic” parentage do not make up the gap in the support network size due to technology use.
Of course, most teens do not use technology in isolation. In fact, Pew’s report indicates that most teens use the internet, SNS, and mobile phones in combination. Therefore, we should explore the effects of these technologies simultaneously to identify the robust contribution to the size of the discussion network. When we evaluate these simultaneously controlling for demographic factors, we find that internet use and mobile phone use no longer significantly contribute to the size of a teen’s discussion network. Use of social network sites, however, remains significant (IRR=1.142, p=.028, Model3.pdf). It appears that teens who use social network sites are more likely to report larger discussion networks. This is pretty impressive!
Before we get too excited about the promise of social network sites, let’s consider what we know about them. For most teens, the social network site represents an online space for interacting with offline friends. If use of the social network site really adds people to the core discussion network, where are they coming from? Couldn’t an alternate explanation be that individuals who are more social offline are also more social online? Pew also asked about frequency of offline socialization, and we can enter this measure as a control in our model. When we do, we see that none of the technologies remain significant, and offline interaction emerges as a significant predictor (IRR=1.074, p=.010, Model3.pdf). It turns out that teens that are more active with their friends have larger discussion networks, controlling for demographics and social technology use.
Some Discussion
It should be noted that Pew’s report did contain a number of “technology intensity” or “differential technology use” variables (e.g. how often do you…). I included these in my exploratory analysis and none were significant, so I focused on use effects. In the study of “social impact of technology”, there is a long history of attribution error regarding the “effects of technology.” My goals in this analysis were twofold: First, to explore a re-occurring question that is addressable with Pew’s data (is technology use robustly associated with larger discussion networks), and to explore some alternate hypotheses to the findings (a common theme in “discussion networks” research).
What I see in this data is a manifestation of the ubiquity of technology in teenage life. If our technology is used to connect to those around us, the effects of the technology will be constrained within the social setting. What we may be seeing here is that teens that are already outgoing are more likely to use social technologies. That is, the use of the network is built into the everyday processes that would be associated with the growth of a discussion/support network. This finding is mundane, but it begs the question – how might we leverage technologies to enable less outgoing teenagers to expand their support networks?
Finally, please treat this post as a rough draft, a work in progress. The fact I feel it is acceptable to write a blog post like this is evidence I’ve been in grad school too long, so it is time to get back to my dissertation.
Ugh, Citations on a blog!
- Bearman, P. and Parigi, P. (2004). Cloning Headless Frogs and Other Important Matters: Conversation Topics and Network Structure. Social Forces, 83(2), 535–557.
- Ellison, N. B., Steinfield, C., and Lampe, C. (2007). The Benefits of Facebook “Friends:” Social Capital and College Students’ Use of Online Social Network Sites. Journal of Computer Mediated Communications, 12(4).
- Fischer, C. S. (2009). The 2004 GSS Finding of Shrunken Social Networks: An Artifact?. American Sociological Review, 74(4), 657–669.
- Hampton, K., Sessions, L., Her, E. J., and Rainie, L. (November 4, 2009). Social Isolation and New Technology. Pew Internet and American Life Project. Retrieved November 4, 2009 from http://www.pewinternet.org/Reports/2009/18–Social-Isolation-and-New-Technology.aspx.
- Marsden, P. V. (1987). Core Discussion Networks of Americans. American Sociological Review, 52(1), 122-131.
- McPherson, M., Smith-Lovin, L., and Brashears, M. (2006). Social Isolation in America: Changes in Core Discussion Networks over Two Decades. American Sociological Review, 71(3), 353-375.
Hey Fred, I read this *very* quickly but am intrigued. Couldn’t see Model3.pdf – file not found. I think some of your questions can be explored using the notion of latent ties and the ways in which these sites can lower transaction costs for interaction. Teasing out the mechanism behind these relationships will be tricky (and here’s where qual data might be nice); for instance it may be that use of the social tools are enabling offline interactions by lowering the barriers for organizing, or that those who use these tools to learn about others are more comfortable approaching them in offline contexts. Would looking at the interaction term for offline interaction & social media use tell you anything?
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Hey Nicole, I’ve fixed the link, thanks! I re-ran the final model with an interaction term – there’s no significant effect of offline interaction between users and non-users. I agree with your points, the type of interaction discussed here is very much focused on the strongest ties, but it seemed like there is a possibility that social media enhances communication with the strongest ties due to a ease-of-interaction effect. My guess is that for very young users, family-based strong ties are still being kept out of social media. I need to go back to the Hampton paper and see what the effect of social media use across demographics…for older adults, I could see social media as an additive as long-lost, high-value ties are re-activated.