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The influence of social networking sites on high school students' social and academic development

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The influence of social networking sites on high school students' social and academic development

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Content THE INFLUENCE OF SOCIAL NETWORKING SITES ON HIGH SCHOOL
STUDENTS’ SOCIAL AND ACADEMIC DEVELOPMENT

by

June Ahn

A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(EDUCATION)

August 2010

Copyright 2010 June Ahn
 
 
ii 
Acknowledgements

This dissertation could not have been possible without the generosity of numerous
individuals. I am forever indebted and grateful for all the support I’ve received
throughout this past year. I particularly want to thank:

My wife Emy, for your unconditional love and support.

My advisor Dr. Dominic J. Brewer, for your steady guidance in all things.

My friend Paul Pasaba, whose software and technical assistance made this dissertation a
reality.

My committee Dr. Guilbert Hentschke, Dr. Janet Fulk, Dr. Richard Clark, and Dr. David
Dwyer for your invaluable mentorship.

Andrew McEachin, Dr. Richard Brown, and Dr. Kathy Stowe for offering a timely
helping hand at various stages of this ambitious project.

And finally I thank all of the superintendants, high school principals, and teachers who
welcomed me into their classrooms and participated in this research project.

 
 
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Table of Contents

Acknowledgements ii
List of Tables iv
Abstract v
Chapter 1: Introduction 1
Chapter 2: The Effect of Social Network Sites on Adolescents’ Social
and Academic Development – Contemporary Issues and Research
7
Figure 2.1: Average Number of One’s Active Network

35
Chapter 3: Digital Divide and Social Network Sites – Which Students
Participate in Social Media?

60
Chapter 4: The Effect of Social Network Sites on High School Students
– A Cluster-Randomized Trial

85
Chapter 5: Future Directions for Research on Social Network Sites

121
Bibliography

127
Appendix A 139

 
 
iv 
List of Tables

Table 2.1: Digital Divides in Year 2000 22

Table 2.2: Overview of Empirical Studies on SNS

52
Table 3.1: Descriptive Statistics of Key Variables

71
Table 3.2: Cross Tabulations of Youth SNS Usage

74
Table 3.3: Results of Binary Logistic Model on Probability of
having a SNS Profile

76
Table 3.4: Predicted Probability of Having a SNS Profile

79
Table 3.5: Internet Ownership and Internet Access for SNS Users 80

Table 4.1: Power Calculations when Classrooms are the
Randomized Unit

98
Table 4.2: Required Classroom Clusters by ICC x MDE

99
Table 4.3: Characteristics of Classrooms in SNS and Control
Groups

101
Table 4.4: Descriptive Statistics of Dependent Variables and
Covariates

107
Table 4.5: Percentage of Missing Data on Dependent Variables

109
Table 4.6: Results of Random Effects Models for each Dependent
Variable
113

 
 
v 
Abstract

This dissertation examines the effects of social network sites on youth social and
academic development. First, I provide a critical analysis of the extant research literature
surrounding social network sites and youth. I merge scholarly thought in the areas of
Internet studies, digital divides, social capital theory, psychological well-being, identity
development, academic engagement, and educational technology to understand how
researchers might examine new social technologies and youth. Second, I examine the
question of digital divide, or whether particular teenage populations do not have access to
online social networks. Using a nationally representative dataset from the Pew Internet &
American Life study, I explore whether there are disparities in teenage access to social
network sites. Third, I report a cluster-randomized trial that was designed to explore
whether social network sites have a beneficial impact when used in high school
classrooms. A total of 50 classrooms, and nearly 1,400 students were randomly assigned
to use an experimental social network site. The results highlight the challenges and
potential of this technology when applied to school contexts.

 
 
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Chapter 1: Introduction

As the world moves into the second decade of the 21
st
century, one of the major
markers of this era is the rise and use of online communities. In particular, a paradigm
called Web 2.0 describes recent technologies that focus on networking mass numbers of
individuals into distinct communities over the Internet (O’Reilly, 2007). Social
networking sites (SNS) are online communities designed to connect individuals to wider
networks of relationships, and are one major example of Web 2.0 applications. Sites such
as Facebook have exploded in membership. In a short period of 2007 – 2010, Facebook
estimates that its membership has grown from 50 million to over 400 million users
(Facebook, n.d.). Online social networks are now an integrated part of daily life and
compel questions of how these media platforms affect human development, relationships,
and interaction.
Teenagers are among the most avid users of technology in general and social
network sites in particular (Lenhart, Madden, Macgill, & Smith, 2007b). Recent reports
find that youth spend nearly 10 hours per day using some form of technology, with
socially networked media playing a large role in their daily lives (Rideout, Foehr, &
Roberts, 2010). New technologies are deeply intertwined with adult perceptions about
teenage life. Mimi Ito and colleagues observe that, “Although today’s questions about
‘kids these days’ have a familiar ring to them, the contemporary version is somewhat
unusual in how strongly it equates generational identity with technology identity” (Ito.
 
 
2 
Baumer, Bittanti, boyd, & Cody et al., n.d.). The clear finding is that today’s youth are
increasingly connected to the world through socially networked media.
While teenagers are engaged with technology, they are ever more disengaged
from another major component of their lives – school. National analyses find that nearly
30% of high school students do not obtain their diploma on time (Cataldi, Laird,
KewalRamani, 2009). High school completion rates are difficult to measure, but various
independent studies also suggest that nearly one-third of students ultimately drop out of
school (Barton, 2005). When one compares these competing aspects of teenage life –
technology versus education – a simple strategy clearly emerges. Perhaps if educators
begin to integrate social technologies into learning, they will increase student engagement
and achievement in school. Heeding the call of scholars (i.e. Jenkins, 2006; Ito et al. n.d.)
recent policy and research efforts are now racing to develop new social media platforms
and technologies for learning. For example, the Federal Department of Education and
organizations such as the MacArthur Foundation have invested millions of dollars to
build social media platforms, video games, and other digital tools for learning
(Whitehouse, n.d.).
Despite the optimism that social media tools might improve student engagement
and learning, the stark reality is that these new technologies often conflict with the
practices of K-12 schools. Surveys find that the vast majority of school district leaders
believe social technology can improve student learning. However, these same district
administrators typically block student access to online resources like social network sites
(Lemke & Coughlin, 2009). The decision to ban students from accessing social network
 
 
3 
sites underscores a major conundrum for educators. Online social networks widen a
students’ access to resources and social support and may have beneficial effects on their
development. Conversely, as student access to the world widens they are inevitably
exposed to potentially negative material and interactions.
The simplest strategy to limit liability and safeguard school districts is to ban
access to these new digital tools. However, such policies neglect the potentially large
benefits of using social media in the classroom. To alleviate this dilemma, educators and
policymakers need a deeper understanding of social media and youth. Several questions
are critical in the area of youth learning with social technologies, including:
• Which youth are using particular social technologies?
• How do they use these technologies to communicate, develop relationships,
socialize, and learn?
• What are the effects of these technologies on youth development?
• What are the effects of these technologies when applied in educational contexts
such as the classroom?
In this dissertation, I explore these questions by examining a particular technology: the
social network site. Communities such as Facebook and MySpace mediate teenage life,
affecting how youth communicate and learn from one another. In addition, social
networks are intertwined into just about every major online community today
(Livingstone, 2008). These factors make SNS a particularly salient focus for evaluation.
Throughout the following chapters I examine different questions surrounding the
phenomena of social network sites and teenage youth. In Chapter 2, I review the extant
 
 
4 
research literature that examines SNS. I consider several controversies around SNS and
youth: (a) What kinds of youth are using social networking sites? (b) Does student
participation in these online communities affect their privacy and social relationships? (c)
Do student activities in SNS influence their personal development in terms of self-esteem
and psychological well-being? (d) Does SNS use affect student grades and learning? The
review highlights how research in this field is only just emerging. The few studies that
examine social network sites are mainly exploratory. However, media researchers have a
rich history of scholarship from which to draw new insights. I integrate previous thought
on Digital Divides, Psychological Well-being, Social Capital Theory, and Cognitive and
Social Learning theories to guide SNS researchers in future studies.
In Chapter 3, I present an empirical analysis using a national dataset of teenagers
from the Pew Internet & American Life Project (Lenhart et al., 2007b; Pew Internet &
American Life Project, n.d.). In this study, I ask whether demographic variables such as
education, socioeconomic status, and access to the Internet are significantly related to
whether teenagers participate in social network sites. This line of analysis is typical of
digital divide studies that examine whether particular populations have less access to new
technologies. If new technologies do have positive benefits for individuals, but under-
represented populations do not have access to such tools, there are tremendous issues of
equity and access yet to be addressed (Jenkins, 2006). Most studies of digital divide and
SNS examine adult and college-age populations. I present an analysis of teenage
populations to examine their usage patterns. The results of this paper highlight how the
association between demographic indicators and social media use are weaker in 2007
 
 
5 
than seen in earlier studies. Teenage youth of all backgrounds increasingly find ways to
connect with others using social network sites.
In Chapter 4, I consider a question of particular importance to teachers and
education leaders. Through a large-scale experiment, I examine whether using social
network sites in urban classrooms has any causal effect on students’ social capital,
engagement with school, or academic achievement. I build an experimental social
network site that approximates the functionality seen in sites such as Facebook and
MySpace. The key difference in this experimental condition is that the site is private to
two urban, school districts and explicitly for use to exchange educational information.
Working with 50 classrooms and nearly 1,400 students, I utilize a cluster-randomized
trial, where class periods are randomly assigned to use the experimental site. Employing
this randomized trial design, I find that an academic social network site does not
necessarily improve student engagement with their peers, their classes, or increase
student achievement. However, I find exploratory evidence that existing social network
sites such as Facebook and MySpace improve students’ feelings of connection with their
school community. The study offers evidence for one compelling idea: Perhaps schools
should attempt to leverage students existing social networks, rather than block access to
them or impose their own.
In Chapter 5, I outline what is needed in future research about social network
sites, and new technologies in general, to better inform the policies and practices of
schools, educators, parents, and those interested in youth development. In particular,
previous scholarly thought has focused on either a technologically deterministic or social
 
 
6 
agency perspective. Technological determinism suggests that a media tool itself affects
social outcomes such as learning, but a long history of research underscores the fallacy of
this philosophy. Scholars who focus instead on social agency, explore how individuals
use new technologies in cultural and social contexts. However, this stream of research
neglects rigorous evaluation of how new media affect youth. Both perspectives in
isolation offer incomplete analyses of how new media, such as SNS, impact youth. I
argue that future researchers must develop and test finer hypotheses that simultaneously
consider the technological affordances of social network sites, the social and cultural
institutions within which SNS are used, and the actual interactions between individuals
that occur in these online communities.
The chapters in this dissertation examine the phenomena of social network sites
and youth through different but complementary lenses: theoretical, descriptive, and
experimental. The summative contribution of these analyses is a deeper picture of how
teenage youth use SNS and its effects on their academic and social development. The
studies show that youth of all backgrounds are increasingly connected via online social
networks. The empirical analyses also show that social network sites are no silver bullet
for improving learning in high school classrooms. The technology itself does not improve
learning, but social media might help students become more connected and engaged with
their school communities. The implications for educators and schools are numerous.
Problems such as student disengagement with education are profoundly significant
issues, and additional research is needed to better understand how online networks
influence youth development and learning.
 
 
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Chapter 2: The Effect of Social Network Sites on Adolescents’ Social and Academic
Development – Contemporary Issues and Research

The current tools of teenage communication go by a peculiar set of names. Wall
Posts, Status Updates, Activity Feeds, Thumbs Ups, Facebook Quizzes, and Profiles are
some of the ways that youth today communicate with one another. These tools are
features of social network sites (SNS), such as Facebook and Myspace. SNS are part of a
suite of recent web applications, also called social media, which utilize Web 2.0
principles. The term Web 2.0 defines websites that are designed to: (a) rely on the
participation of mass groups of users rather than centrally controlled content providers,
(b) aggregate and remix content from multiple sources, and (c) more intensely network
users and content together (O’Reilly, 2007). People use these web applications to interact
in hyper-aware ways and the scale of this mass communication phenomena is significant.
As of May 2009, Facebook ranked as the 4
th
most trafficked website in the world and
Myspace ranked 11
th
highest (Alexa, n.d.). That high school youth are connected to these
global online communities is both a frightening prospect for parents and educators and an
intriguing area for social science research.
Educators and parents in the United States face difficult quandaries concerning
students and SNS. No one denies that youth use these technologies to communicate with
the world, and they do so with high frequency and intensity (Lenhart et al., 2007b). Many
scholars suggest that students learn in new ways using social media and that educators
should embrace these new platforms (Ito et al., n.d.; Jenkins, 2006). In a recent national
 
 
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survey, the vast majority of school district leaders report that they view social media as a
positive development for education (Lemke & Coughlin, 2009). Nevertheless, 70% of
districts also report that they banned all access to SNS in their schools. Despite the clear
understanding that social media can be vital to student learning and digital literacy,
educators currently struggle with how to comply with regulations like the Children’s
Internet Protection Act (CIPA), as well as overcome general fears about student
interactions in social network sites. To inform both the policy concerns of district leaders
and the local practices of teachers and parents, research is needed to understand how
youth use SNS and what effects it has on their social and academic development.
In this chapter, I consider several key controversies around youth usage of SNS,
and review relevant research that begins to inform these debates. I first define the media
effects framework and outline how this research tradition attempts to understand the
effects of new technologies on social outcomes. Second, I define social network sites and
describe studies that capture how youth use these technologies to develop relationships,
hang out with friends, and learn new skills. Third, the chapter reviews relevant research
that informs several controversies concerning SNS and adolescents. I also connect these
contemporary debates with previous scholarly thought about students’ out-of-school time
(OST) and traditional concerns about the effect of technology on learning. The specific
controversies reviewed are:
• What kinds of youth are using social networking sites?
• Does student participation in these online communities affect their privacy
and social relationships?
 
 
9 
• Do student activities in SNS influence their personal development in terms of
self-esteem and psychological well-being?
• Does SNS use affect student grades and learning?
Finally, I outline the overall condition of research on SNS and youth. The current state of
the literature is suggestive of the effects on adolescent social and academic development,
and primarily consists of ethnographic and cross-sectional data. I outline the future
questions that will be critical for the field and suggest relevant methodological directions
to move this emergent research stream forward.
What Can We Learn from a Media Effects Framework?
Many of the controversial questions concerning social network sites ask what
kinds of effects these technologies have on youth development. Given this focus, I work
primarily from a media effects tradition of research. Media effects scholars examine the
outcomes that arise when people use new technologies. Talking about effects engenders
important theoretical discussions that must be laid clear when examining studies. Most
significantly, the term implies a focus on causality. Studies in this framework imply that a
media form, or the features of the technology, causally influences some outcome
(Eveland, 2003). The structure of questions from this perspective is usually in the form
of: Does media affect learning? Does television influence student achievement? Or do
social network sites affect the psychological well-being of adolescents? Media effects
scholars in a variety of fields have quickly come to realize that the answers to these
 
 
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questions are more complex. Very rarely, if ever, is there a direct causal relationship
between a technology and a social outcome such as learning (Clark, 1983; Clark, 1991;
Schmidt & Vandewater, 2008).
Early media questions often used a technological framework or object-centered
approach (Fulk & DeSanctis, 1999; Nass & Mason, 1990). Such a perspective assumes
and tests whether a technology itself causally affects a social outcome. For example, in
Education a major question of technology research is whether media affects learning.
Education researchers now firmly conclude that media does not affect student learning
(Clark, Yates, Early, & Moulton, In Press). Numerous studies show that the media tool
neither improves nor negatively impacts learning when compared to the same teaching
strategy in the classroom (Bernard, Abrami, Lou, Borokhovski, Wade, Wozney et al.,
2004; Clark, 1983; Clark, 1991). What matters is not the computer, but the learning
behaviors that occur within the software or educational program.
The findings of non-significant media effects on student learning do not mean that
technology has no influence. For example, Richard Mayer (2001) shows through a series
of experiments that the design of a multimedia presentation affects student learning of a
topic. Putting words and pictures closer together on the screen, when they are relevant to
each other, helps students retain more knowledge than when the elements are placed
further apart on the screen. These results do not validate a technological orientation,
where one expects that the computers themselves improve learning. Rather, the
pedagogical strategy of placing relevant words and images together in a presentation
affects cognition. Media researchers understand that the features of a technology afford
 
 
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certain possibilities for activity. A multimedia video on the computer allows one to
design words and images on the screen, while a computer simulation might guide a
learner using models of real-world cases. A media tool allows for different possible
learning behaviors (Kozma, 1991).
This subtle difference in theoretical orientation is what scholars call an emergent
perspective (Fulk & DeSanctis, 1999) or a variable-based approach (Nass & Mason,
1990). Scholars using an emergent or variable-based approach view technology as a
structuring factor. Features of a technology, not the technology itself, enable and
constrain how one uses that tool. Conversely, social forces such as cultural norms and
behavioral practices influence how one ultimately uses a technology. William Eveland
(2003) offers five characteristics of media effects research that help define how studies
take into account both technological and social variables. Media effects studies have: (1)
A focus on an audience, (2) Some expectation of influence, (3) A belief that the influence
is due to the form or content of the media or technology, (4) An understanding of the
variables that may explain the causality, and (5) The creation of empirically testable
hypotheses.
A focus on audience compels researchers to understand the characteristics of the
youth who use SNS. Knowing who uses, or does not use, social network sites is an
important sociological question for scholars of digital divide. In addition, Hornik (1981)
notes the possible differential effects for disparate populations, “If communication
researchers have learned anything during the previous three decades, it is that
communication effects vary with members of the audience” (p. 197). Current media
 
 
12 
studies also focus on the form or content of a technology, and move away from making
black-box comparisons between technologies. Questions that ask whether Facebook is
related to lower grades, or if MySpace is unsafe for children, are broad and uninformative
directions for future media effects studies. Instead, the pivotal questions explore how the
features of SNS enable or constrain behavior. Future media studies about SNS and youth
should not frame questions using a technologically deterministic perspective where one
expects the technology to cause an outcome. Instead, media scholars identify how youth
interaction, communication, and information sharing are the critical variables in
understanding SNS effects on social and academic outcomes. This understanding of
media effects research helps define finer-grained hypotheses of why a tool like SNS
might affect student development, under what uses, for whom, and when.
What are Social Network Sites and How Do Youth Use Them?
When a teenager joins a site like Facebook they first create a personal profile.
These profiles display information such as your name, relationship status, occupation,
photos, videos, religion, ethnicity, and personal interests. What differentiates SNS from
previous media like a personal homepage is the display of one’s friends (boyd & Ellison,
2007). In addition to exhibiting your network of friends, other users can then click on
their profiles and traverse ever widening social networks. These three features – profiles,
friends, traversing friend lists – represent the core, defining characteristics of social
networking sites.
One will notice that SNS also include other media tools such as video and photo
uploading and many websites now employ social networking features. For example,
 
 
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YouTube is primarily a video sharing service, but users can add others as their friends or
subscribe to a member’s collection of videos. Using boyd & Ellison’s (2007) definition,
YouTube can be included as a type of social network site. As researchers examine the
effects of SNS on social behaviors, they will undoubtedly come across these blurring of
technologies. Sonia Livingstone (2008) notes that SNS invite “convergence among the
hitherto separate activities of email, messaging, website creation, diaries, photo albums
and music or video uploading and downloading” (p. 394). This convergence of
technologies may complicate what one means by the term social network site.
Amidst the sea of what websites can be termed SNS, the technical definition of
social network sites still provides a shared conceptual foundation. Comparing across
common features – i.e. profiles and friend networks – researchers can begin to understand
how various communities co-opt these characteristics to create entirely new cultural and
social uses of the technology. Patricia Lange’s (2007) ethnographic study of YouTube
shows that users deal with issues concerning public and private sharing of video. Some
YouTube users post videos intended for wide audiences, but share very little about their
own identities. Their motivations might be to achieve Internet fame and gather viewers.
Other members upload videos intended for a small network of friends and may restrict
the privacy settings to only allow access to those individuals. The concepts of friend and
social network for these users are entirely distinct.
Dodgeball, an early and now defunct mobile-SNS, is another social network site
that has been studied. In Dodgeball, a user broadcasts their location via cell-phone to
their network of friends:
 
 
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For example, when users get to a bar or cafe, they can "check in" by sending a
text message to Dodgeball such as "@ Irish Pub." Dodgeball then broadcasts their
location via text message to people in their Dodgeball network. Users can also be
alerted when friends of friends who have checked in to Dodgeball are within a 10-
block radius (Humphries, 2007, para. 5).

The case of Dodgeball highlights how the social network is utilized differently with this
technology. In YouTube, individuals interact with their social network in ways that are
influenced by the video-sharing focus of the site. In Dodgeball, the SNS technology is
used to mobilize and meet up with friends in real-world spaces. The cases of YouTube
and Dodgeball show how friends, friend networks, and profiles are used in very diverse
ways depending on the individual’s motivations and the cultural norms of a particular
online community.
The early research on what is commonly understood as SNS (i.e. Facebook and
MySpace) also explores how teenagers utilize profiles, friends, and friend-networks. The
process of creating profiles has been a major focus of theoretical and empirical
discussion. The common features of profiles include personal information such as one’s
name, location, school affiliation, occupation, and personal interests such as favorite
movies or music. Other vital components of the profile are pictures, videos, and the
comments one’s peers leave on the page. Profiles can be updated at any time and some
sites like MySpace allow individuals control as to how their profile looks. Using
programming techniques, youth frequently apply “skins” to their MySpace profiles that
completely alter the visual design or interface of their pages (boyd, 2008).
Researchers have understood profiles as both an internal and external process of
identity development. Some studies focus on how individuals display and experiment
 
 
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with their identities through their profiles (boyd, 2008; Liu, 2007; Livingstone, 2008;
Manago, Graham, Greenfield & Salimkhan, 2008; Schmitt, Dayanim, & Matthias, 2008).
Other studies focus on how one’s network of contacts interpret and assess the profile
(boyd, 2008; Walther, Van Der Heide, Kim, Westerman, & Tong, 2008; Walther, Van
Der Heide, Hamel, & Shulman, 2009). The emerging picture is that youth make explicit
decisions to disclose information about themselves on their profiles, and their networks
provide social feedback to those profile displays. Most interactions in social network sites
follow this cycle of self-disclosure and feedback.
The process of displaying elements of one’s identity to the public in SNS is
similar to how humans interact off-line. Donath (2007) observes that, “Whether face-to-
face or online, much of what people want to know is not directly observable” (para. 10).
She contends that much of human interaction consists of signals that communicate the
status and characteristics of an individual. Donath utilizes signaling theory to examine
how one’s self-presentation in SNS develops identity and trust with others. For example,
when a user displays a contact as a “friend” he or she is – in an indirect way – vetting that
that person is in fact who they claim to be. Thus, members who indiscriminately add any
and all friend requests (including fake profiles or people they do not know) in an effort to
seem popular may instead damage their credibility and trustworthiness to others. Among
teenagers, boyd (2008) finds that “it is cool to have Friends on MySpace but if you have
too many Friends, you are seen as a MySpace whore” (p. 129).
In face-to-face settings, identity signals come in forms such as speech, body
movements, facial expressions, and taste statements like fashion. On SNS, these signals
 
 
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are written textually and visually on the personal profiles of members (boyd, 2008).
Qualitative studies of adolescents and their media use suggest that (1) identity
development is a major element of teenage life, and (2) youth use media such as profiles
and personal pages to express themselves (Livingstone, 2008; Schmitt et al., 2008).
Participation in social network sites also allows youth to participate in different social
groups, and experiment with their identity displays (Manago et al., 2008). In their study
of college students, Manago and her colleagues also note the possible negative aspects of
identity experimentation. They find that the males and females in their sample often
portray themselves in stereotypical, gendered ways. In addition, social comparison plays
a large role in SNS. When profile and friend information is readily available, students
frequently check in on their peers to see how they stack up.
In addition to deciding what to present on a profile, friendship practices play a
particularly large role in adolescent life. Teenagers need to navigate a variety of decisions
including whom to accept as their SNS friend, and setting privacy controls to allow
certain members to view their profile (boyd, n.d.). Studies suggest that the technical
design and cultural norms of a respective SNS influences friendship behaviors. In
YouTube, the motivations for sharing video influence the privacy controls a member sets
on their media (Lange, 2007). Tools such as Dodgeball, that explicitly intends to facilitate
off-line meeting up, have members whose real world networks more closely match their
online contacts (Humphries, 2007). When MySpace introduced its Top 8 function, where
users designated their top friends on their profile, it set off a firestorm of social drama
amongst teens. boyd (2006) noted, “There are tremendous politics behind the Top 8, not
 
 
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unlike the drama over best and bestest friends in middle school” (para. 32). These
examples highlight how the structure, function, and mission of a respective SNS
influences networking behavior.
Peers exert influence on a youth’s social and academic development. Friends
share information and influence one to behave within group-accepted norms (Ryan,
2000). The characteristics of one’s friends also signal much information about a person.
Donath & boyd (2004) observe some of the ways that individuals’ relationships reflect
their social identity:
In the physical world, people display their connections in many ways. They have
parties in which they introduce friends who they think would like – or impress –
each other. They drop the names of high status acquaintances casually in their
conversation. They decorate their refrigerator with photos. Simply appearing in
public with one’s acquaintances is a display of connection (p. 72).

One question is whether our public, friend networks affect how others’ view us. Just as in
off-line contexts, are we known by the company we keep in our online settings?
Emerging laboratory experiments find that human beings do in fact judge SNS
profiles, and these judgments are quite strong. In an experiment by Walther et al. (2008),
researchers created fake Facebook profiles of a hypothetical college student. The stimuli
profiles differed in several ways: (1) the pictures of the fake person’s friends were either
attractive or unattractive, and (2) the text of the wall posts (comments made by friends)
were either positive or negative towards the profile owner. In total, 8 stimuli were
presented to form a 2 (attractive/unattractive friends) x 2 (positive or negative wall posts)
x 2 (gender of profile owner) design.
 
 
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Actual college students were recruited and randomly assigned to view one of the
8 stimuli and assess the social and physical attractiveness of the fake, Facebook student.
The researchers found that participants rated the Facebook student as more physically and
socially attractive when his/her friends were more attractive. Positive and negative
comments left by friends also affected how participants rated the Facebook student.
Finally, Walther et al. (2008) found interesting interactions between profile impressions
and gender. Female profiles were rated as more attractive when the wall posts were
positive. However, male profiles were rated more attractive when the wall posts were
negative. In this study, an example negative wall post was “WOW were you ever trashed
last night!” (p. 39). The study suggests that gender stereotypes also emerge quite clearly
in SNS interactions. Females who drink heavily (as implied by friends’ wall posts) were
not seen as attractive, while the same behavior signaled positive attractiveness for males.
In a similar experiment, Walther et al. (2009) manipulated whether identity cues
suggesting extraversion and physical attractiveness were more effective if they came
from the profile owner or from others. Signaling theory and warranting theory suggests
that people would assess other-generated statements as more credible. This hypothesis is
especially likely in social network sites because profile owners can manipulate what
information is presented on their page. Thus, statements from others might be seen as
more credible than statements from the individual. As theory suggested, participants rated
the fake, Facebook students as more extraverted or attractive when others (through wall
posts) suggested as such compared to when the individual (through self-statements on the
profile) asserted this identity.
 
 
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The two studies offer compelling experimental evidence that what one puts on
their SNS profile is assessed by others and the characteristics of friends are strongly
related to how one is viewed. In addition, the feedback provided by one’s network in a
SNS is influential in the development of social identity. Adolescents use social network
sites in a variety of ways. They disclose personal information about their identities and
tastes on their profiles (Livingstone, 2008). Teenagers must also add or reject friend
requests from their peers, navigating the complicated web of friendship practices (boyd,
n.d.). Finally, the interactions and feedback that one’s network provides in SNS, through
wall posts and comments, show how complex social identity and peer influence processes
occur in these online communities (Walther et al., 2008; Walther et al., 2009).
The majority of current research on social network sites attempts to understand
the phenomena itself. Scholars have been interested in how youth use these technologies,
what cultural practices emerge in these online contexts, and what theoretical implications
SNS have on personal identity and social relationships. The early descriptive and
ethnographic research on youth, Internet, and social media offer rich evidence that (a) the
features of different technologies, for example the MySpace Top 8 case, influence the
social practices of youth within those online communities, (b) SNS are important places
for youth to develop their personal identity, and (c) youth are using technologies like
SNS to mediate their relationships with friends, romantic partners, and broader groups of
peers (Ito et al., n.d.). The questions that parents, educators, and researchers now grapple
with concern the effects SNS have on adolescent outcomes.

 
 
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Effects of Social Network Sites on Adolescent and Student Development
Discussions about adolescents today differ considerably from the past through the
central role that technology plays in youth lives. Many scholars agree that the:
… values and norms surrounding education, literacy, and public participation are
being challenged by a shifting landscape of media and communications where
youth are central actors. Although today’s questions about “kids these days” have
a familiar ring to them, the contemporary version is somewhat unusual in how
strongly it equates generational identity with technology identity (Ito et al., n.d.,
Introduction).

Similarly, Prensky (2001) coined the popular term “digital natives” to describe the
current generation of youth. He asserts that youth today have grown up entirely
surrounded by technology and their use of it shapes all aspects of their lives.
The technologies that youth utilize today are most definitely new and how
teenagers use them to communicate with each other are clearly novel. Nevertheless, the
technologically mediated activities that youth participate in are similar to past
generations:
Just as they have done in parking lots and shopping malls, teens gather in
networked public spaces for a variety of purposes, including to negotiate identity,
gossip, support one another, jockey for status, collaborate, share information, flirt,
joke, and goof around. In other words, they go there to “hang out” (boyd, n.d.,
para. 2).

Not surprisingly, the apprehensions of parents and educators about SNS are also
comparable to past questions about how youth spend their time.
Social network sites represent a new environment within which to examine
adolescent development and learning. Within this context, I focus on several areas of
concern including: youth characteristics, privacy and safety, psychological well-being,
and learning. Many of these questions have been asked before of other technologies. For
 
 
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example, Emmers-Sommer & Allen (1999) state that, “The effect of television,
particularly on children’s viewing, was the predominant mass media force in the 1970s”
(p. 479). The authors also note that concern about television and student academic
achievement became a major topic in the 1980’s. In the following sections, I survey the
nascent research about SNS within a media effects framework. I also highlight how
related questions from other media studies (i.e. television) and out-of-school time (OST)
research inform how one can examine SNS effects on students.
What Kinds of Youth are Using Social Network Sites?
Scholars assert that social media represent new skills and ways of participating in
the world. If students are not allowed to use new technologies and contribute to online
communities like SNS, they will not be able to develop the necessary skills and technical
literacy that will be vital in the future (Jenkins, 2006). Stemming from this belief,
researchers continue to wonder whether certain groups of students are systematically
hindered from using new technologies. For example, Seiter (2008) observes that “Young
people famously use digital communications – instant messaging, cell phone texting, and
social networking Web sites – to maintain their social capital, at least with those peers
who can afford to keep up with the costly requirements of these technologies” (p. 39).
The statement succinctly outlines the critical concern of digital divide scholars: (1) there
is an understanding that many people are using technology, (2) the use has some positive
outcome, i.e. developing social capital, and (3) questions remain as to the systemic and
unequal access to the technology.
 
 
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Concerns about the digital divide arose because early researchers found startling
differences in which individuals used technology. In 1996 scholars generally agreed that
Internet users were “largely politically conservative white men, often single, English-
speaking, residing in North America, and professionals, managers, or students”
(Wellman, Salaff, Dimitrova, Garton, Gulia et al., 1996, p. 215-216). Similarly, digital
divide studies identified gender differences. For example, boys were more likely to own a
computer and use it in their leisure time compared to girls (Volman & van Eck, 2001).
An early U.S. Department of Commerce (2000) publication, Falling through the Net,
highlighted disparities in technology use by race, income, and education level. Table 2.1
reports several of the digital divides found in the report.
Table 2.1: Digital Divides in Year 2000
Demographic Indicator Percent with Access to Computer or Internet
Race
White 55.7%
Asian/Pacific Islander 65.6%
Hispanic 33.7%
Black 32.6%
Income
Over 75k 86.3%
Under 15k 19.2%
Education
BA Degree 64.0%
High School Degree 29.9%
Less H.S. 11.7%

Technology adoption in the year 2000 exhibited stark inequalities. For example, 55.7% of
White households and 65.6% of Asian and Pacific Islander households owned a personal
computer. Only 33.7% of Hispanic and 32.6% of Black households owned a computer.
Such initial reports framed the discussion of digital divides as inequities along major
demographic and social variables: race, income, education, and age.
 
 
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Nearly a decade later, the picture surrounding access to technology is appreciably
better in the United States. In a recent national survey of families by the Pew Internet &
American Life Project, researchers find that 94% of families (married adults with
children) currently own a computer (Kennedy, Smith, Wells, & Wellman, 2008).
Ownership of computers and Internet access appears widespread, but individuals’ online
behaviors and activities differ considerably. For example, in the United States nearly
every teenager is online, but disparities exist concerning what teenagers do online. Some
write blogs, others create personal web pages, and others create videos (Lenhart, Arafeh,
Smith, & Macgill, 2008; Lenhart et al., 2007b). In terms of participation in social
network sites, Lenhart et al. (2007b) find that 55% of online teens currently have a profile
on a SNS. These current trends in Internet access and participation suggest a critical shift
in how scholars explore digital divides. The term as defined as access to computers and
the Internet may not be as useful as conceptualizing the divide along differences in
participation and skills (Cheong, 2008; Hargittai, 2004).
In a study of the characteristics of college students’ who use of SNS, Eszter
Hargittai (2007) finds few, significant demographic differences between users and non-
users. Gender still appeared as a significant predictor, with females being 1.6 times more
likely to use a SNS than males. In addition, having Internet access through friends or
family also significantly predicted whether a college student used social network sites.
Other traditional indicators such as race and parent’s education had no significant
correlation to the use of SNS. Hargittai’s study underscores the developing trend of mass
 
 
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adoption of social network sites. Among the college students in her sample, there
appeared to be few systemic inequalities in their access to SNS.
Hargittai (2007) also disaggregated her results based on different social network
sites – Facebook, MySpace, Xanga, and Friendster. She found interesting and significant
correlations between race and particular SNS communities. For example, Hispanic
students were more likely to use MySpace, but less likely to use Facebook compared to
Caucasian students. Asian students were significantly more likely to use Xanga and
Friendster. Such patterns problematize some of the theoretical benefits of social
networks. For example, Wellman et al. (1996) theorized that, “People can greatly extend
the number and diversity of their social contacts when they become members of
computerized conferences or broadcast information to other CSSN [computer supported
social network] members” (p. 225). However, Hargittai notes that if particular groups of
people gravitate to respective communities, offline inequalities may persist online.
Researchers have several opportunities in future digital divide studies. First,
scholars are moving away from questions of access to examine participation and
communication patterns. What matters will not be whether a student has a SNS profile,
but instead what he or she is doing in the social network. Second, one limitation of
Hargittai’s analysis is the lack of a nationally representative sample. Her study examines
a smaller sample of college students. Future studies are needed to examine whether
systematic differences exist in nationally representative data sets of adolescent
populations. Although recent ethnographic studies suggest youth of all backgrounds find
ways to participate in SNS even when they do not own technologies at home (Ito et al.,
 
 
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n.d.), empirical studies are still imperative. Finally, most digital divide studies offer
cross-sectional, one-time snapshots of user demographics. This data is helpful to
understand the social context of SNS at a particular time. However, user demographics
change rapidly in SNS as millions of new members join every day. Studies are needed to
track the longitudinal trends of these online communities.
Examining the user characteristics of SNS communities is not only helpful to map
trends in inequality, but also to consider finer tuned hypotheses about media effects for
whom. Survey data suggests that female and male youth might use social network sites in
different ways (Lenhart et al., 2007b). Several of the studies reviewed below also suggest
that SNS use has differential effects for individuals with high/low levels of self-esteem or
extraversion (i.e. Steinfield, Ellison, & Lampe, 2008; Zywica & Danowski, 2008).
Research is needed to identify how the cultural, psychological, and cognitive
characteristics of individuals influence their online behaviors, and vice versa. Both
anthropologists and psychologists assert that identity and social development is a prime
component of adolescent life (i.e. Ito et al., n.d.; Livingstone, 2008; Schmitt et al., 2008).
The rapidly changing developmental context of youth offers a promising area for future
empirical work.
Concerns about Privacy and Social Relationships in SNS
A major controversy surrounding social network sites is youth safety and privacy.
Approximately 70% of school districts block access to SNS, and the main reason for this
trend centers on fears about student safety (Lemke & Coughlin, 2009). Legislation such
as CIPA requires school districts to enact policies that safeguard students from
 
 
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inappropriate content and educate them about safe online behavior (Federal
Communications Commission, n.d.). Given these concerns from educators and parents,
two key questions emerge for SNS researchers. First, researchers have examined whether
students understand the privacy implications of social network sites and also if youth
actively safeguard their privacy online. The second critical question is to examine
whether adolescent relationships with others in SNS offer positive benefits in the form of
social capital.
The early picture concerning youth and online privacy is mainly positive. Nearly
every major social network site offers privacy controls. In fact, “These privacy measures
have given adolescent users a great deal of control over who views their profiles, who
views the content that they upload, and with whom they interact on these online forums”
(Subrahmanyam & Greenfield, 2008, p. 123). Initial research suggests that teenagers
disclose a variety of personal information on their profiles, but they also proactively use
privacy features to manage who can view their content (Hinduja & Patchin, 2008;
Lenhart & Madden, 2007a). Lenhart & Madden report that from a nationally
representative sample of youth, 66% of teenagers limit their profile to particular people in
their network. A cross-sectional study of a college student sample also finds that privacy
concerns did not hinder users’ desire to share personal information on their profiles.
Rather, students used privacy features to control and limit who could view their
information (Tufecki, 2008).
The vast majority of youth, 91% of those who use SNS, report that they utilize the
sites to communicate with already known friends (Lenhart & Madden, 2007a).
 
 
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Qualitative studies also converge with this finding that U.S. youth mostly use SNS to
interact with friends and not to meet strangers (i.e. boyd, 2008). Descriptive data offer a
different picture of British youth. A survey of approximately 2600 youth in England
found that 54% received friend invitations from strangers either occasionally or
frequently, and that 51% accepted those friend requests (Sharples, Graber, Harrison, &
Logan, 2009). The researchers recommend caution on the part of educators in using
social media, and suggest more education for children on proper online behavior. The
limitation of cross-sectional survey data and descriptive studies is that the results tell us
little about the individual behaviors of youth. The data provide general trends that are
snapshots of one point in time. These trends are prone to change drastically with the
rapidly evolving demographics and use of SNS at any given time.
Additional studies are needed to identify those youth who might be prone to risky
online behavior and why they participate in such activities. While youth generally seem
to understand how to navigate their online privacy, clear variations in individual behavior
exist. Many teenagers are very adept at managing their online information and using safe
practices. A lesser percentage of teenagers may seek out relationships with strangers and
engage in risky behavior. Little research has been conducted on the negative interactions
that might happen in social network sites. However, research on other Internet
applications and youth safety offer lessons for SNS researchers.
In a survey of 412 Dutch teenagers, Peter, Valkenburg, & Schouten (2006)
explored several factors that are related to how much teenagers spoke with strangers
online. They found that younger adolescents were more likely to talk with strangers. In
 
 
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addition, teens that used the Internet to explicitly meet new friends or to overcome their
own shyness (social compensation) also communicated with strangers more often. The
Taste, Ties, and Time study used a longitudinal dataset of college students in the class of
2009 (Lewis, Kaufman, & Christakis, 2008). The researchers found that female students
tended to have more Facebook friends, and were more likely to have private profiles, than
males. However, the most consistent finding was that students were more likely to have a
private profile if their friends or roommates also used the privacy settings. These findings
validate some important, common sense ideas of youth and online safety. Younger
adolescents or teens that use the Internet for social compensation may be more prone to
strangers online. In addition, those who have fewer peers that understand the privacy
features of SNS may be more likely to have open, public profiles.
The features of the technology tool may also influence the likelihood of
contacting strangers on the Internet. Peter et al. (2006) find that youth who spend more
time in chat rooms talk with more strangers. Ybarra & Mitchell (2008) also find that
“Youth are less likely to be targeted for unwanted sexual solicitation in social networking
sites than they are through IM [instant message] and in chat rooms… and are less likely
to be a target of harassment on social networking sites than they are through IM” (p.
355). A comparison of the technological environments helps explain these trends.
Instant messaging is a tool that is predominantly used by known friends to chat
with each other. Ybarra & Mitchell (2008) note that most cases of bullying and sexual
solicitation happen with others that the teenager already knows. Chat rooms are often
public and un-monitored spaces where multiple people talk synchronously. The
 
 
29 
frequency of risky behavior and unwanted interactions is higher in these online forums.
Finally, early research notes variations within different SNS themselves. Dwyer, Hiltz, &
Passerini (2007) find that MySpace users utilize the site to meet new people more often
than Facebook members. Such patterns might be related to the norms of each site.
Facebook originally began as a college-campus based SNS, and thus established
boundaries around one’s social networks (boyd & Ellison, 2007). MySpace began as a
broader and open network. As Facebook has slowly opened its network to high school
students, then to any individual, these dynamics may change. The key point is that
technical and social elements of a respective SNS community may facilitate or inhibit
behavior, and this question requires further examination.
In general, scholars find that youth behavior in social network sites is not as
dangerous as popular fears would suggest (Wolak, Finkelhor, Mitchell, & Ybarra, 2008).
Most youth post much information about themselves in their profiles, but this activity by
itself does not appear to expose adolescents to online predators. Instead, youth who
engage in risky behaviors such as interacting frequently with strangers may more likely
encounter negative outcomes. Teenagers are generally cognizant of the privacy features
of SNS, and utilize them to limit who can interact with them online (Lenhart & Madden,
2007a). While legislation such as CIPA is necessary to guide schools, studies also
suggest that the regulation does little to actually influence students’ knowledge about
Internet safety or behavior (Yan, 2009). Students frequently use SNS outside of school
and learn a great deal about managing their privacy through experience and peers. This
 
 
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fact does not rule out the need for educators and adults to be involved. The research
reviewed here underscores how vital it is for schools, parents, and teachers to educate
youth about the positive uses of SNS.
Studies about adolescent privacy and safety focus on the potential negative
relationships – with strangers and predators – that can be formed online. However,
scholars also posit that the Internet widens our social networks and provides positive
benefits in the form of social capital (Wellman et al., 1996). Various theorists focus on
disparate elements of social capital theory, which often leads to confusion on the part of
research studies that use the framework (Portes, 1998). Pierre Bourdieu (1986) focuses
his definition on people’s membership to social groups that have cultural and financial
wealth. If one is a member of a group with many resources, he or she can accrue benefits
– financial, cultural, or social – from having that access. James Coleman (1990) defines
social capital in terms of relationship and group norms. Groups that exhibit a high level
of trust have more social capital because they are more likely to help each other. Putnam
(2000) also popularized the term in his book Bowling Alone and SNS researchers have
utilized his ideas of bridging and bonding capital in recent studies. Putnam observed that
diverse social groups provided bridges to new information and ideas, while homogenous
groups most often offered bonding relationships based on social support.
Researchers have linked Putnam’s (2000) concepts to the earlier work of Mark
Granovetter (1983) who used the terms strong and weak ties to describe the quality of
relationships between two people. Granovetter defines weak ties as those who are
acquaintances. These relationships often provide access to information and diverse ideas.
 
 
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Strong ties are with close friends and family, and like bonding capital, provide social and
emotional support. The diverse perspectives on social capital are worth noting because
SNS scholars often evoke one or more of these definitions; always under the banner of
social capital theory. Portes (1998) offers a more general definition that highlights the
explicit conceptual link between SNS and the theory: “Despite these differences [in
definitions], the consensus is growing in the literature that social capital stands for the
ability of actors to secure benefits by virtue of membership in social networks” (p. 6).
Hypothetically, SNS have the potential to widen a person’s social networks and
provide access to valuable resources, information, and social support (Wellman et al.,
1996). Donath and boyd (2004) observe, “Social networks – our connections with other
people – have many important functions. They are sources of emotional and financial
support, and of information about jobs, other people, and the world at large… Today we
are seeing the advent of social networks formed in cyberspace” (p. 71). Of course, early
Internet theorists (i.e. Wellman et al., 1996) already recognized that social networks were
being formed on the Internet with tools like message boards and forums. Nevertheless,
Donath and boyd highlight the particular potential of a new technology, the social
network site. They also forward a popular hypothesis in Communication research that
online social networks facilitate the creation of weak ties, but not strong ties. The
common theory is that Internet use can widen one’s social networks, but is less effective
in helping individuals develop close relationships.
A series of studies with college students and Facebook test these particular social
capital hypotheses. Ellison, Steinfield, & Lampe (2007) surveyed 286 undergraduate
 
 
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students to examine whether the use of Facebook was correlated to their levels of
bridging and bonding capital. Using regression analysis, researchers found that higher
Facebook use was positively correlated to bridging social capital. Facebook use was also
positively correlated with bonding capital, combined with other factors such as whether
students lived on campus. This finding highlights the fact that bonding capital, close
relationships, would likely also require face-to-face contact, not just Facebook contact.
Most interestingly, the authors found interactions between Facebook use and measures of
self-esteem (SE) and life satisfaction (LS). For example, among infrequent Facebook
users, high SE students had more bridging capital. Among frequent Facebook users the
comparison reversed. Low SE students had more bridging capital than their high SE
peers, indicating an interaction. College students who have low self-esteem or life
satisfaction appear to benefit more from Facebook usage.
The first study (Ellison et al., 2007) used a cross-sectional dataset. Steinfield,
Ellison, & Lampe (2008) extended the discussion of Facebook and social capital in
another study using a longitudinal data-set. The researchers used a cross-lagged
correlation strategy to simultaneously test competing hypotheses: (a) That Facebook use
at time 1 predicted bridging capital in time 2, and (b) Or the converse hypothesis that
bridging capital in time 1 predicted Facebook use in time 2. They found the correlation
for relationship A to be 0.48 and for relationship B at 0.14, and the differences in the two
correlations were statistically significant. Steinfield et al. conclude that Facebook use was
a better predictor of future bridging capital. The researchers also ran the cross-lagged
 
 
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analyses on subsamples split by high or low self-esteem. They find that Facebook use had
a higher, positive impact for low self-esteem users.
A study by Beaudoin (2008) used structural equation modeling to examine how
one’s motivation for Internet use was related to the development of interpersonal trust.
Beaudoin, citing Putnam (2000), describes trustful relationships as a component of social
capital. However, taking Coleman’s (1990) perspective on social capital, one could
plausibly define trust itself as positive social capital. Beaudoin finds that Internet users
who are motivated to develop their social relationships online (i.e. a social motivation):
(a) use the Internet more, and (b) have less perceived information overload from the
Internet. These two mechanisms, in turn, significantly predict the level of interpersonal
trust that the participant felt in their relationships.
These three studies suggest opportunities for future research using a social capital
framework. The study by Steinfield et al. (2008) is an important step towards longitudinal
analyses that examine SNS use over time. In addition, they highlight the benefit of testing
competing hypotheses to address the mutual correlation issue of cross-sectional analyses.
Does SNS use develop more social capital? Or do individuals with higher initial social
capital use SNS more often? These are critical questions for non-experimental field
studies. Future correlational, survey research that utilizes longitudinal designs will make
the most impact in the SNS literature.
At a conceptual level, cross-sectional research helps to identify key theoretical
mechanisms. For example, Facebook activity appears to widen one’s social networks
(Ellison et al., 2007). Recent data from Facebook also highlights how SNS increase the
 
 
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number of people one can interact with regularly (Sandberg, 2009). Figure 2.1 shows the
number of people an average Facebook user interacts with regularly. Reciprocal
communications are conversations between two individuals, and direct communication is
when one sends a message to another user but does not expect or receive a reply. Finally,
Facebook introduced a feature called the “stream” which is a constantly updated listing of
all recent activity by your network. The stream appears on one’s homepage and the
company has found that the feature has increased the number of people an average user
now communicates with (called stream communication in Figure 2.1).
As one can see from Figure 2.1, users approximately double the number of people
they actively communicate with using social network features of the site. For education
scholars the implications are clear. Membership into wider, more diverse, and resource
rich social networks (i.e. social capital) has been associated with a variety of student
outcomes including high school graduation and college going (Dika & Singh, 2002). An
important direction for future research will be to examine whether social capital mediates
the relationship between SNS use and these outcomes.

 
 
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Figure 2.1: Average Number of One’s Active Network

Source: http://blog.facebook.com/blog.php?post=72975227130
A final insight from cross-sectional analyses is the identification of moderating
conditions. Facebook users who were low in self-esteem and life satisfaction seem to gain
more bridging social capital from using the site (Ellison et al., 2007). In addition,
individuals who are motivated to use the Internet to develop their social contacts, appear
to develop more trusting relationships (Beaudoin, 2008). Perhaps SNS allow students
who may otherwise be less social in school, to find avenues of participation in social life.
Similarly, Education scholars suggest that participation in multiple, diverse social groups
are vital for student success – particularly for traditionally under-represented,
disadvantaged youth (Dika & Singh, 2002; Stanton-Salazar, 1997; Stanton-Salazar &
Spina, 2000). Researchers have a current opportunity to examine how social network
sites facilitate social capital development for youth; particularly its effects on education
outcomes. As of this review, no studies of the educational uses of SNS exist that use the
social capital framework, and the area is ripe for new theoretical and practical impact.
 
 
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Do Student Activities in Social Network Sites Affect Their Personal Development?
Self-esteem and psychological well-being are the two most common outcomes of
interest in prior Internet and SNS studies. Researchers typically measure self-esteem
using established scales such as the Rosenberg Self-Esteem Scale (used in Ellison et al.,
2007). Psychological well-being often refers to various measures that capture an
individual’s satisfaction with life. Scholars use a variety of scales that include measures
of loneliness, depression and overall life satisfaction (i.e. Kraut, Patterson, Lundmark,
Kiesler, & Mukopadhyay et al., 1998). A key debate among researchers considers
whether higher use of the Internet affects one’s self-esteem and psychological well-being
(Kraut et al., 1998; Valkenburg & Peter, 2009a). Such Internet research informs how
SNS researchers examine psychological well-being.
The often-cited HomeNet study by Kraut et al. (1998) recorded the number of
hours individuals spent on the Internet (using tracking software on the participant’s
computers) and it’s relationship to future measures of social involvement and
psychological well-being. The researchers used path-analysis on their longitudinal dataset
and found that longer use of the Internet was related to increased depression, loneliness,
and smaller social circles. The results suggest that Internet use isolates individuals from
their friends and family, and has a negative impact on one’s psychological well-being.
This effect is known as the reduction hypothesis (Valkenburg & Peter, 2009a).
After the HomeNet project, Internet studies exhibited a wide variety of findings
concerning psychological well-being. Most studies reported no significant relationship
between Internet use and well-being (i.e. Gross, Juvonen, & Gable, 2002). A longitudinal
 
 
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follow-up to the original HomeNet study also found no long-term effects of Internet use
on loneliness or depression (Kraut, Kiesler, Boneva, Cummings, & Helgeson et al.,
2002). A few studies reported evidence supporting the reduction hypothesis. A study by
van den Eijnden et al. (2008) used longitudinal cross-lagged analysis to examine whether
adolescent use of various technologies was related to future compulsive Internet use,
loneliness, and depression. The researchers found mostly inconclusive relationships, but
did find a significant relationship between instant messenger use and depression. Youth
who used IM more frequently reported higher levels of depression afterwards.
The authors (van den Eijnden et al., 2008) offer various hypotheses for this
finding. Perhaps youth that spend more time on IM developing weak ties, spend less time
face-to-face developing strong ties. Alternatively, socially isolated youth who use IM
may not find the social support they seek in these online environments, thus increasing
their feelings of depression (a poor get poorer hypothesis). These hypotheses remain open
for future research in SNS contexts, and van den Eijnden et al. note that, “Future research
should address the conditions under which adolescents low in social resources either
benefit from or are harmed by online communication with people met online” (p. 664).
The key question for media researchers is to identify those conditions – both
technological and social – that help explain Internet effects.
Valkenburg, Peter, & Schouten (2006) note a major shortcoming of previous
Internet research. Many of the studies treated Internet use as a one-dimensional activity.
In reality, individuals use the Internet for many goals such as information gathering
versus social interaction. In addition, prior studies often do not specify what activities
 
 
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might affect self-esteem and well-being, and why those specific activities might plausibly
affect these outcomes. Binary specifications of whether a teenager uses a particular
technology or not, will likely prove to be an inconclusive predictor of self-esteem and
well-being. Instead media scholars are now moving towards finer definitions of the
technological environment, activities within that environment, and theoretical
specifications about why those interactions would affect social and psychological
outcomes.
Current media effects studies that examine online interactions instead of broad
Internet use, generally find positive outcomes for youth. Valkenburg et al. (2006)
surveyed over 800 Dutch adolescents to examine relationships between social network
site use, self-esteem, and psychological well-being. The researchers used an overall
satisfaction with life scale to indicate well-being. Using structural equation modeling,
they found an indirect relationship between SNS use to self-esteem and psychological
well-being. Adolescents who frequently used SNS had more friends on the site and also
more reactions on their profile (i.e. friends posted more comments and wall posts). In
addition, the researchers measured the valence of this feedback. Having more positive
reactions on one’s SNS profile was correlated with higher self-esteem, and higher self-
esteem was significantly correlated with satisfaction with life. The results highlight the
emerging sense that use of SNS itself does not cause feelings of well-being. Rather, the
positive or negative reactions that youth experience within the site are the key mechanism
for their social development.
 
 
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A study by Zywica & Danowski (2008) also illuminates how SNS in particular
might facilitate positive interactions for youth. The researchers surveyed a sample of
college students to explore how their levels of self-esteem and extroversion (sociability)
were related to their ideas of popularity on the site Facebook. They found that high self-
esteem and sociable individuals tended to be more popular both offline and online. Low
self-esteem and sociable individuals had lower levels of popularity. Nevertheless, low SE
users shared more information online, expressed different facets of themselves more
often, and admitted to having done something to appear more popular on Facebook.
The results of this study (Zywica & Danowski, 2008) offer insight into how
behavior, specifically in SNS, might influence self-esteem and well-being. Youth bring
offline experiences – existing friend networks and levels of popularity – to the online
context. Additionally, teenagers who may be lower in self-esteem can use SNS sites to
express themselves and engage in positive interactions. Positive relations accumulate to
portray positive self-images. The study suggests that social network sites give youth
opportunities to find a social niche, express themselves in positive ways, portray their
best selves, and allow different conceptions of popularity. These kinds of interactions
may facilitate positive outcomes in well-being.
Why would earlier Internet studies report negative psychological outcomes, while
recent studies find positive personal development? Valkenburg & Peter (2009a) observe
two changes in Internet behavior that help explain recent, positive results of SNS. First,
the authors contend that when prior studies occurred, “… it was hard to maintain one’s
existing social network on the Internet because the great part of this network was not yet
 
 
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online” (p. 1). In the late 1990’s, one had less family members and friends online with
which to communicate. Past Internet applications such as chat rooms and forums were
designed to facilitate conversation between strangers. The situation now is starkly
different as teenagers and parents, youth and teachers, all find themselves connected in
social network sites. Adolescents typically do not join Facebook to meet strangers.
Instead, they join because their friends are already members and have invited them to
participate. The Internet is no longer isolating, but connecting people.
The fact that youth frequently encounter known friends and family online
underscores the second change in the Internet (Valkenburg & Peter, 2009a). Web 2.0 or
social media applications are designed to facilitate interaction and communication
through networks. Prior uses of the Internet primarily focused on an individualistic
process of presenting or finding information. Information exchange still plays a
prominent role in online communication. However, current tools make one’s social
network an explicit and visible resource from which to get that information. Social
network sites, through the use of profiles and friend networks, enhance the ways in which
people share information about themselves, their friends, and their lives. Again, the focus
of Web 2.0 applications has been to connect persons rather than information.
Self-disclosure also plays a large role in SNS effects on well-being. Specifically,
researchers posit that when youth disclose and express more information about
themselves the quality of their relationships improves. These positive interactions lead to
improved self-esteem and psychological well-being (Valkenburg & Peter, 2009a;
Valkenburg & Peter, 2009b). This theoretical direction is directly related to scholarly
 
 
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thought in other frameworks including signaling theory (Donath, 2007) and warranting
theory (Walther et al., 2009). Future studies of social network sites and youth must
consider more detailed measurement of behaviors within the online community. These
interactions – positive, negative, informative, or social – may then better predict
outcomes of youth well-being.
I also note that higher self-disclosure was previously viewed as potentially
negative for youth privacy and safety. Youth, particularly those lower in self-esteem or
sociability, who disclose more about themselves online may develop positive outcomes
of well-being. Conversely, these same youth may be prone to risky behaviors and
negative relationships online. Future studies that consider both outcomes, and further
identify the conditions that interact to lead to positive and negative outcomes, will be
particularly important contributions. The practical impact of such studies is significant.
Parents, educators, and other stakeholders are particularly concerned about if, how, and
why youth may benefit from being online.
Does Social Network Site Use Affect Student Grades and Learning?
Research on social networking sites and learning is particularly slight when
compared to the studies of privacy, safety, and psychological well-being. Of the few
research projects on SNS and learning, some are as of yet unpublished studies (see
Blanding, 2009; Karpinski, 2009a). Most studies of social media and youth education
define learning from a literacy studies perspective (Ito et al., n.d.; Jenkins, 2006). The
literacy perspective focuses on educational practices, such as creating media, rather than
traditional measures of learning such as grades or standardized assessments. This
 
 
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direction is particularly fruitful to consider how youth’s everyday practices with
technology constitute learning in and of itself, and how these activities are in stark
contrast to practices within school. However, research on traditional measures of learning
– grades and assessments – is even more scant.
To date, two studies exemplify the debate surrounding social network sites, youth,
and learning. A conference paper by Karpinski (2009a) received much media attention
when the researcher found that college Facebook users had lower GPA’s than students
who were not users of the site. The author surveyed approximately 220 undergraduate
and graduate students of one Midwestern University. Using MANOVA analysis,
Karpinski finds that students who were Facebook users typically had a GPA in the range
of 3.0-3.5, while non-users had a GPA in the 3.5-4.0 ranges. The author also finds that
Facebook users were more likely to participate in extra-curricular activities and also
come from science, technology, engineering, and math (STEM) fields. From these
results, the author offers several conjectures and hypotheses. For example, perhaps
Facebook users spend too much time online and less time studying. However, the study
did not rigorously examine counter hypotheses and remains a rather exploratory, basic
attempt to understand the effect of SNS on learning.
Pasek, more, & Hargittai (2009a) quickly published a study to counter the
Karpinski (2009a) paper. The authors note several clear limitations of the Karpinski
study. First, the sample of students is clearly limited. Second, the first Facebook study
utilized few control variables in the analysis. And finally, Pasek et al. took issue with the
liberal conclusions of Karpinski, namely that the original study offered strong evidence
 
 
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for a negative relationship between Facebook use and grades. Pasek et al. present three
additional analyses that use a larger sample of undergraduate students, a nationally
representative sample of 14-22 year olds, and a longitudinal dataset. The authors utilize
more control variables including race, socioeconomic status, and previous academic
achievement variables. From this analysis, the researchers show that Facebook usage has
no significant relationship to GPA in any of their datasets.
In subsequent responses by Karpinski (2009b) and Pasek et al. (2009b) the
researchers debate the merits of their statistical analyses, including their specifications of
GPA and their participant samples. I refer readers to the particular articles for this
discussion, and instead focus here on more general limitations of this strand of research.
All of the researchers in the debate suggest that the Facebook-GPA relationship is an
interesting avenue for future studies. However, aside from the fact that many youth use
Facebook, there appear to be no substantive theoretical reasons why Facebook use might
influence GPA. Future researchers of social network sites would do well to learn from
previous educational technology and media effects research.
In numerous meta-analyses of media and learning, scholars have firmly concluded
that media itself does not influence learning performance (Clark, 1991; Clark et al., In
press). A particularly salient meta-analysis examined research concerning the effect of
online education on learning (Bernard et al., 2004). The authors firmly conclude that
there is no significant relationship – positive or negative – between online courses and
learning when compared to classroom instruction. Framing the potential learning effects
of SNS as a Facebook-GPA relationship is no more interesting than exploring the
 
 
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MySpace-GPA, Xanga-GPA, or any new SNS-GPA relationship. In less facetious terms,
using correlational studies to make black-box comparisons between media types will
likely produce an ultimate finding of no significant effects. Some studies may find
positive correlations while others may find negative correlations. This type of discussion
may produce many articles and counter-articles, but the ultimate finding will expectedly
leave media scholars and education practitioners wanting more.
Specifying the SNS to Learning Relationship: Lessons from Out-of-School Time and
Media Learning Research
Fortunately, researchers do not need to re-invent the wheel when developing
frameworks to explore the SNS and learning relationship. Insights from research on OST
learning help to create a theoretical link between SNS and learning. One strategy is to
redefine what one means by the term learning. In their review of OST literacy research,
Hull & Schultz (2001) note that one major contribution of literacy scholars is to
understand the concept of practices. Children’s activities in school – i.e. listening to a
teacher’s lecture, practicing problems on worksheets, taking tests to assess their learning
– can be seen as specialized literacy practices. Formal schooling is designed to teach
students to perform well in those behaviors. However, literacy practices outside of school
may serve very disparate functions than expected in the classroom. In the context of new
technologies, youth today communicate and learn very different practices outside of
school. Creating a YouTube video or engaging in social networking interactions are
different literacy practices than successfully completing a multiple-choice test.
 
 
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Much of the ethnographic research and conceptual thought on new media and
learning, explicitly or implicitly, take this literacy approach to describe learning (i.e. Ito
et al., n.d.). While these studies are significant as rich descriptions of youth practices,
cultural and literacy perspectives are also vital for media effects scholars. A focus on
practices helps to better specify the theories of why SNS interactions might lead to
traditional learning outcomes. Much of what youth do in SNS deal with personal profile
creation and a virtual hanging out with friends. These behaviors naturally relate to
outcomes such as personal identity, relationship development, and overall well-being.
Subsequently scholars should not be surprised that research on SNS and social outcomes
is more frequent, compared to studies linking social network sites to traditional learning
measures. Researchers interested in traditional academic outcomes such as high school
completion, academic engagement, grades, and test scores must specify what practices
would theoretically improve these outcomes.
The research on SNS, social capital, and psychological well-being offer an
additional link to student learning through the mechanism of academic engagement. The
concept of engagement can be defined in behavioral, emotional, and cognitive terms
(Fredericks, Blumenfeld, & Paris, 2004). Behavioral engagement refers to participation in
academic, social, or extracurricular activities. Emotional engagement describes the
positive and negative feelings students may have towards teachers, peers, and the broader
school community. Cognitive engagement depicts the idea that that a student is willing to
expend the energy to comprehend difficult concepts and learn new skills. As noted in this
review, much of the research on SNS suggests that as students more frequently disclose
 
 
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information about themselves and interact with their network, they develop higher quality
relationships with others. Education researchers who examine the social context of
learning in areas such as out-of-school time, extracurricular activity, and classroom
climate also find a link between high quality relationships, students’ academic
engagement, and achievement (Eccles & Templeton, 2002; Feldman & Matjasko, 2005;
Martin & Dowson, 2009).
A major hypothesis amongst education scholars is that youth participation in
extracurricular and school activities increases their social connectedness with teachers
and peers (Eccles & Templeton, 2002; Feldman & Matjasko, 2005). This connectedness
is related to increased engagement with school and academics. Numerous studies find a
positive correlation between extracurricular activities with grades and student
achievement on standardized assessments. Engagement has also been related to a lesser
likelihood to drop out of school (Fredericks et al., 2004). These hypotheses are still major
questions for education research and reform efforts. Social network sites offer a new
context within which to observe how relationships influence school engagement, grades,
and student achievement.
Researchers of social network sites have the ability to directly observe how online
relationship networks may facilitate this social learning process. What interactions in
SNS might a researcher expect to affect student engagement? Martin & Dowson (2009)
offer some hypotheses culled from a variety of social learning theories such as
Expectancy Theory, Goal Theory, Self-Determination Theory, and Self-Efficacy.
Expectancy theory and goal theory suggests that one’s peers communicate which
 
 
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behaviors and goals are of value. For example, a student will value achieving good grades
and set this as a goal, if his or her friends also strive for high achievement. Similarly,
Eccles & Templeton (2002) also suggest that peer groups transmit a social identity that
affects student behaviors. Self-Determination theory proposes that if a student’s
psychological need to belong is met, he or she is much more likely to take academic
risks, explore more ideas, and persist when presented with difficult work. Self-efficacy, a
major part of Bandura’s (2002) social cognitive theory, describes how capable one feels
about accomplishing a task. When teachers, parents, and friends model the kinds of
behavior that lead to academic success (i.e. study habits or information seeking), a
student subsequently feels more capable about achieving success.
Martin & Dowson (2009) observe that high quality relationships with adults,
teachers, and peers impact these social learning mechanisms. These theories also
highlight the educational impact of social network sites. Quality relationships might
allow students to feel more connected to school and thus take academic risks. Other peers
might communicate what goals and behaviors are valued, through their status messages
and wall posts. Finally, students might model positive academic behaviors by posting
their behaviors or sharing information in SNS. These types of interactions begin to
specify how relationship development in SNS may contribute to increased engagement
and learning. Perhaps teachers can utilize social network sites to engage their students,
develop closer relationships, and model positive learning behaviors over time. Such
educational hypotheses have yet to be attempted or tested in formal studies.
 
 
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Social mechanisms such as psychological well-being and engagement offer a
natural way to link SNS to learning outcomes. In addition, academic content and
information sharing in out-of-school activities predict learning effects. Lauer et al (2006)
conducted a meta-analysis of 35 studies that examined the effect of out-of-school
programs for the reading and math achievement of at-risk student populations. They only
included studies that used experimental or quasi-experimental methods, and find that
OST programs improve students’ reading and math achievement. Supplemental academic
programs appear to improve student achievement in math and reading and social network
sites present an intriguing new setting to deliver new academic content.
Scholarly thought in media learning also suggests that attention and engagement
are important variables for students’ academic learning. Previous media researchers find
that students’ mental effort influences how well they learn from media (Kozma, 1991).
Salomon (1984) found that 6
th
grade students rated books as a much more difficult
medium to process than television. Students then viewed comparable stories in one of the
two media forms, and those in the book condition performed better in assessments of
their knowledge. Such converging results from disparate research traditions offer a
compelling hypothesis for SNS scholars. The fact that social networking sites are fun and
part of a teenager’s daily life suggests that educators might leverage these tools to
increase engagement with school activities. Interventions that take advantage of this
natural engagement and combine it with challenging academic content may be the
mechanism through which students’ might achieve higher levels of learning through this
medium.
 
 
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The theoretical discussions from previous OST and media research suggest the
need for finer specifications of behaviors and practices that may lead to learning
outcomes. So what is it about social network sites that might make it a cognitively
beneficial tool? In discussing the benefit of computer supported social networks,
Wellman et al. (1996) state that, “The nature of the medium supports a focus on
information exchanges, as people can easily post a question or comment and receive
information in return” (p. 219). Social network sites potentially allow individuals to
marshal their contacts to solve a problem or find information.
Internet scholars have long understood that “The Net makes possible new types of
decentralised collaborations, enabling large numbers of people to work together on
shared tasks…” (Resnick, 2004, p. 117). The logic is simple. Thousands of individuals
can collaborate to create an online encyclopedia, Wikipedia, because someone in the
world will know and write about a given topic. A user with a large Twitter network can
post a question and expect someone in his or her online circle of contacts to reply with an
answer. Online social networks structure information sharing so that large groups can
solve problems and share knowledge. Resnick suggests that to realize the learning
potential of online networks, researchers must be willing to work with networked styles
of thinking.
While the Internet can leverage mass numbers of users to find a correct answer,
formal education remains an individual process. Our school systems, policies, and
assessments are designed to evaluate whether a particular student possesses some
knowledge. Thus, a critical theoretical question for education researchers is how to
 
 
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leverage the power of online social networks to benefit the internal learning of the
individual student. Insights from cognitive and educational psychology offer some
recommendations for structuring individual learning from SNS. Kirschner, Sweller, &
Clark (2006) observe that human cognition results from an interplay of long-term and
working memory. Experts in a particular area are proficient because they possess large
amounts of information, mental models in long-term memory, about that situated
knowledge domain. Conversely, one’s working memory is limited in how much new
information a person can process.
The implication of human cognitive architecture on learning is that novice
students need guidance. Kirschner et al. (2006) report that, “Controlled experiments
almost uniformly indicate that when dealing with novel information, learners should be
explicitly shown what to do and how to do it” (p. 79). Online social networks increase the
number of others that one can learn from (Wellman et al., 1996). The larger one’s social
network, the higher probability that someone, somewhere will be proficient in a given
topic. However, the size of a person’s social network is not the only significant variable.
Learners need guidance on what to do and how to do it. This precise definition of the
specific type of information that guides learners helps researchers observe and look for
particular types of learning interactions in SNS. These interactions, not a uni-dimensional
concept of technology use, provide theoretical links to learning.
Finally, SNS researchers can learn much from past studies in television and
adolescent learning. For example, Karpinski (2009a) offers a possible hypothesis that
Facebook users might spend less time studying, thus explaining their lower GPA. This
 
 
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idea is called the displacement hypothesis, and has been examined by early television
researchers who posited that television took away students’ study time (Hornik, 1981).
Studies of students’ extracurricular activities instead suggest that new media, such as
Facebook, replace or enhance other leisure activities, but do not take away time from
youth (Roberts & Foehr, 2008). The critical question for future studies is not whether
youth use one technology or another, but what kinds of interactions and content they
experience in these virtual settings.
Chapter 2 Conclusions
This paper offers a narrative review of the emerging research surrounding social
network sites and youth. SNS are an intriguing new environment to study because the
technology is such an integral part of teenage life. Given its popularity, parents and
educators have considerable concerns about the effects of SNS on their children and
students. In Table 2.2, I summarize the empirical studies that examine social network
sites. The overview highlights several characteristics of the research base at this current
time. The scholarly literature is clearly just emerging about social network sites.

 
 
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Table 2.2: Overview of Empirical Studies on SNS
Broader Debate Citation Methodology General Findings
boyd (2006) Qualitative
boyd (2008) Qualitative
boyd (forthcoming) Qualitative
Hinduja & Patchin
(2008)
Content Analysis
Humphries (2007) Qualitative
Ito et al., (forthcoming) Qualitative
Lange (2007) Qualitative
Lemke & Coughlin
(2009)
Cross Sectional
Lenhart, Madden,
Macgill, & Smith
(2007b)
Descriptive Survey
Liu (2007) Content Analysis
Livingstone (2008) Qualitative
Manago, Graham,
Greenfield, &
Salimkhan (2008)
Qualitative

Walther et al. (2008) Randomized Control
Trial
What are SNS and How
Do Youth use Them?
Walther et al. (2009) Randomized Control
Trial

Social Network Sites
organize information
through profiles and
friend networks

Youth use them to
express their individual
identity, personality,
and tastes

Teenagers “hang out”
with their friends in
SNS

Controlled experiments
find that users actively
judge others’ profiles
and can form clear
impressions of a person
through their profile
and friend network

What kinds of Youth
Use SNS?
Hargittai (2007) Cross Sectional Demographic variables
do not predict overall
SNS use

Lenhart & Madden
(2007a)
Descriptive Survey
Lewis, Kaufman, &
Christakis (2008)
Longitudinal
Sharples, Graber,
Harrison, & Logan
(2009)
Descriptive Survey
Tufecki (2008) Cross Sectional
SNS and Teen
Safety/Privacy
Ybarra & Mitchell
(2008)
Descriptive Survey
Youth mostly use SNS
to interact with known
friends

Youth are largely
aware of the dangers of
SNS, but certain youth
may be prone to risky
behavior

Beaudoin (2008) Cross Sectional
Ellison, Steinfield, &
Lampe (2007)
Cross Sectional
Effects of SNS on Social
Relationships Steinfield, Ellison, &
Lampe (2008)

Longitudinal
Use of SNS appears to
increase one’s social
capital

Valkenburg, Peter, &
Schouten (2006)
Cross Sectional
SNS Effects on
Psychological Well-
Being
Zywica & Danowski
(2008)

Cross Sectional
Use of SNS is
positively correlated to
well-being
Ito et al. (forthcoming) Qualitative
Karpinski (2009a) Cross Sectional
SNS Effects on
Learning
Pasek, more, &
Hargittai (2009a)
Cross Sectional &
Longitudinal
Youth learn different
digital skills outside of
school.

Mixed findings on the
correlation between
Facebook and GPA

 
 
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Most of the research focuses on the phenomenon itself and reveals much about how SNS
is defined and how people use these online communities. Scholars now understand that
social network sites are comprised of particular technical features – profiles, friends, and
friend networks. Youth use these Web applications to express themselves, and “hang out”
with their friends.
I also outline several of the major controversies that surround youth and social
network sites. Parents and educators have tremendous concern about youth privacy,
safety, psychological well-being, social development, and academic performance. While
there is much theoretical discussion about the effects of SNS on youth, the empirical
research that informs these popular debates is currently in a nascent stage. This current
state of the scholarly literature affords researchers a unique opportunity to quickly
contribute new empirical studies about SNS and adolescent development. In addition,
research about social network sites extends previously established research areas. SNS
researchers have currently framed their studies from a variety of perspectives:
Psychological well-being, Social Capital Theory, Signaling Theory, Warranting Theory,
as well as Cultural and Literacy frameworks.
Subrahmanyam & Greenfield (2008) observe that the lines between virtual and
real-world is increasingly blurred for youth today: “… for today’s youth, media
technologies are an important social variable and … physical and virtual worlds are
psychologically connected; consequently, the virtual world serves as a playing ground for
developmental issues from the physical world” (p. 124). The key questions for the field
of youth development and SNS focus on what the emotional, social, and cognitive effects
 
 
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of using the technology are for adolescents. Empirical studies that examine SNS effects
are few, but fortunately researchers have the opportunity to incorporate insights from a
variety of previous research traditions beyond the theoretical perspectives outlined in the
current literature. For Education researchers, theories of out-of-school time, student
engagement, and cognition inform particular hypotheses about why SNS interactions may
lead to improved academic outcomes.
Methodological Issues in SNS Research
The empirical research on social network sites is largely descriptive and
correlational (see Table 2.2). Future studies in the field will need to develop stronger
evidence of causation, and studying the effects of SNS on youth necessitates multiple
perspectives and methodologies. I take a particular viewpoint in this paper, the Media
Effects tradition, with a stated leaning towards identifying causal mechanisms.
Nevertheless, this review highlights how different methodologies are needed to mutually
inform future studies. Debates about quantitative versus qualitative methodology are well
played out by past scholars (Berliner, 2002; Burkhardt & Schoenfeld, 2003; Feuer,
Towne, & Shavelson, 2002; Slavin, 2002). Instead, the key question for future SNS
researchers is not about methodology, but how each research design contributes to the
questions people have about social network sites.
Qualitative studies about youth and new media will always be vital to the field.
Evaluated as stand-alone empirical studies, ethnographic and other qualitative strategies
offer rich descriptions of culture, practice, and phenomena at a given point in time.
Understood in the context of other research paradigms, the early work of SNS researchers
 
 
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(i.e. boyd, n.d.; boyd, 2008; Ito et al., n.d., Humphries, 2007; Lange, 2007) sets the stage
for both popular discussion and future empirical work. Berliner (2002) also notes a
common challenge for social science that, “Solid scientific findings in one decade end up
of little use in another decade because of changes in the social environment that
invalidate the research or render it irrelevant” (p. 20). Technology is one area where time
plays a significant role because technologies, and our cultural uses of technology, evolve
rapidly. For this reason alone, descriptive accounts and alternative theoretical frameworks
will always remain vital for media research.
The questions and intent of qualitative approaches are distinctly different than
quantitative and experimental strategies. The critical questions for SNS researchers will
be: How are youth using the technology? In what ways are they interacting with
technology? And what are the critical contexts – cultural, social, and economic – that
shape the use of technology? Future studies in this area will be most helpful when they
particularly highlight trends and changes in youth culture and technology across time,
communities, gender, and a variety of other factors. Studies that further identify the
theories behind youth development, new media, and social outcomes will always be
needed to inform policy, practice, and further research.
The majority of studies in the SNS literature use non-experimental survey
methods. The analysis techniques are varied, but most of the studies utilize cross-
sectional data. A few studies use longitudinal designs (i.e. Lewis et al., 2008; Steinfield et
al., 2008). Strategies such as longitudinal cross-lagged models help to address the issue of
mutual correlation, and push researchers to identify which competing hypothesis is
 
 
56 
stronger. For example, do highly sociable youth use SNS more, or does frequent use of
SNS develop higher sociability? Such questions are critical and cannot be answered with
cross-sectional data. Future SNS studies that evaluate trends in use and developmental
outcomes over time offer particularly promising ways to address the mutual causation
issue.
A second challenge of observational data is the presence of selection bias (Slavin
2007). Selection bias occurs when subjects actively choose to participate in a program. In
the case of social network sites, preliminary evidence suggests that youth do decide to
join particular communities (Hargittai, 2007) such as MySpace versus Facebook.
Researchers cannot make strong causal claims about media effects if these selection
patterns are not accounted for in the analysis. For example, Karpinski (2009a) found that
Facebook users in the study sample were more likely to come from STEM fields. If
students in STEM fields typically received lower average GPA’s than their peers in the
humanities, this self-selection bias could explain why Facebook membership was
correlated to lower GPA.
Scholars utilize several strategies with observational data with the aim of reducing
selection bias. Research designs such as regression discontinuity, propensity score
matching, or longitudinal models with fixed effects (Schneider, Carnoy, Kilpatrick,
Schmidt, & Shavelson, 2007) offer ways to control for the influence of individuals and
their self-selection into social networking communities. These strategies for non-
experimental datasets offer several benefits for SNS researchers. It is often difficult to
randomly assign youth in already existing social network communities. In addition, SNS
 
 
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are built from networked, inter-related individuals. It may be unnatural to randomly
assign one student to a new networked-learning program, but not assign her SNS friend.
The main benefit of approximating random-assignment using observational data is the
ability for the researcher to examine naturally occurring phenomena, but pinpoint causal
relationships using statistical controls.
Quasi-experimental studies match subjects based on similar background
characteristics and may be viable for SNS studies. While the researcher attempts to
approximate randomization on a number of background characteristics, one cannot be
totally sure that the groups are random (Slavin, 2007). Nevertheless, this strategy may
offer scholars the best way to compare socially networked groups of youth. For instance,
a researcher could find two high schools whose students are similar to each other in
socioeconomic status and academic achievement. One school might receive an SNS-
based intervention designed to increase student engagement. This treatment would be
natural because the students are likely SNS friends with others in the school. The other
school, the control group, would also be a natural context because these students will
likely not be SNS friends with those in the treatment school. Thus, the researcher could
make better causal claims about the social network effect between these entire
communities.
Finally, randomized control trials (RCT) offer the strongest strategy to test causal
hypotheses. By randomly assigning subjects to a treatment or condition, each group has a
high probability of being similar on all background characteristics. Thus, any difference
can be attributed to the treatment (Slavin, 2007). RCT for social-network hypotheses are
 
 
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particularly challenging to consider. The main issue is that social network hypotheses
inherently rely on individuals’ inter-related relationships with their network. The network
is not randomly chosen, individuals actively choose who their SNS friends are. For
example, one might attribute learning gains to the amount of information one’s friends
shared over the SNS. Any random assignment, with a large enough sample, should
equalize pre-existing network characteristics in the treatment and control groups.
However, SNS researchers should (a) pay special attention to these network
characteristics, (b) control for these variables in analyses, and (c) monitor the fidelity of
their treatment.
In a hypothetical example, consider a study where high school students in one
school are randomly assigned to a Facebook-based treatment. The researcher
hypothesizes that more frequent SNS interactions with friends will lead to higher
academic engagement. The random assignments will necessarily cut off many students’
natural social networks. Although random assignment should even out network
characteristics in large samples, the researcher should measure several independent
variables. Individuals in a given group (control or experimental) will have friends who
are also in their group, and friends who were assigned to the opposite condition. If
disparities exist prior to the experiment, ANCOVA or multiple regression analyses that
control for network size will be required. The final measures of engagement in this
experiment may be related to the number of Facebook friends that were also invited to
the treatment. Conversely, scholars should consider that a student in the SNS treatment
might have friends who were not included in the treatment. Perhaps these students would
 
 
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still share information or affect each other’s academic engagement regardless of the SNS
intervention. Social network hypotheses require that researchers monitor the fidelity of
the intervention and control for the challenges of self-selected relationships.
Future Directions
Researchers of social network sites have a unique opportunity to build a new area
of study, extend previous Internet research, and apply a variety of new theoretical
perspectives that have not yet been explored. Irrespective of the theory SNS scholars
decide to utilize, this chapter also offers four general recommendations for future
research in the media effects framework:
1. Identify the moderating effects of users’ individual characteristics and the
technological affordances of a given social network site.
2. Specify and measure the exact kinds of interactions that occur in the SNS. Move
beyond uni-dimensional specifications of use into more descriptive and
theoretically relevant measures of interaction.
3. Examine how a given construct – i.e. Social Capital or Well-Being – mediates the
relationship between SNS use and students’ education outcomes.
4. Utilize methodologies that make stronger causal claims about the media effect of
SNS.

 
 
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Chapter 3: Digital Divide and Social Network Sites – Which Students Participate in
Social Media?

Since the release of the first social network site (SNS) in 1997 the growth of these
online communities has skyrocketed. Familiar examples of SNS include Facebook and
MySpace, but there are hundreds of services that cater to a variety of populations (boyd
& Ellison, 2007). These online communities are tremendously popular. For example, as
of December 2009 Facebook reports over 350 million active users (Facebook, n.d.). In
the United States, teenagers who range from ages 12-17 represent a significant and
growing population of SNS users. In a 2007 survey from the Pew Internet & American
Life project, researchers find that approximately 55% of online teens have created a
personal profile compared to 20% of adult Internet users (Lenhart et al., 2007b). Social
network sites now mediate a vast array of communication between students. Research on
SNS offers a ripe arena to explore how youth work, play, and learn in these online
environments. The scholarly literature in this area is nascent, but swiftly accumulating
with descriptive evidence of innovative learning and communication among youth (i.e.
Ito et al., n.d.).
The emerging picture of youth and social network sites suggests that these online
communities mediate a wide variety of peer social practices and learning (Ito et al., 2009;
Jenkins, 2006). Teenage users of social network sites appear to be adept in a variety of
other technologies such as blogging and multimedia production (Lenhart et al., 2007b).
Youth who use SNS appear to be much more comfortable networking with others,
 
 
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creating their own multimedia, and learning new media literacy skills that are essential in
an increasingly technology-mediated world (see Jenkins, 2006). A great deal of learning
happens between youth in these online communities. Teens are not merely messaging and
flirting in social network sites. They negotiate identity, learn social skills, and become
subject matter experts through peer teaching in topics that pique their interests (boyd,
2006; boyd, 2008; boyd, n.d.; Horst, Herr-Stephenson, & Robinson, 2009). Initial studies
of Facebook usage among college students suggest that individuals also develop more
social capital in these online communities (Ellison et al., 2007). Youth participation in
social network communities may signal the development of important technical skills and
social development.
Not surprisingly, education scholars are now turning their attention to the learning
implications of social network sites (Greenhow & Robelia, 2009; Greenhow, Robelia, &
Hughes, 2009). As researchers begin to explore how youth use social media to learn in a
networked environment, questions about access become ever more critical. The term
digital divide describes the concern about unequal access and participation in new
technologies (Norris, 2001). Youth that are systematically excluded from social network
sites may also lose out on opportunities to develop technical skills, social interactions,
and relationship networks. For example, Seiter (2008) states that teens on SNS can
leverage their social networks for their benefit; well “at least with those peers who can
afford to keep up with the costly requirements of these technologies” (p. 39). Thus,
researchers of media and education must still consider the “fundamental inequalities in
young people’s access to new media technologies and the opportunities for participation
 
 
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they represent” (Jenkins, 2006, p. 12). Such theoretical discussion gives rise to empirical
questions such as: (1) who is using these technologies? And (2), is there unequal access
to these technologies or new digital divides of participation? Thus, I contribute to the
emerging discussion of social network sites and teenage youth, by revisiting questions of
inequality and digital divide.
In this chapter, I use a nationally representative survey of teenagers in the United
States to examine SNS participation (Pew Internet & American Life Project, n.d.). In the
following sections, I first outline how the term digital divide has been used in the
research literature of the past decade. The discussion highlights how scholars have moved
from questions of mere access to computers to current questions of social participation in
technologies like SNS. I summarize the demographic, socioeconomic, and social
variables that researchers utilize to explore the digital divide. Second, I test whether
traditional predictors of the divide (such as race, education, technology literacy etc.) are
also factors of students’ involvement in social network sites. I utilize logistic regression
to examine whether demographic, socioeconomic, and student-level variables are related
to students’ use of SNS. The results offer surprising insight into the social factors that
relate to youth use of SNS and question some long standing digital divide discussions
surrounding race, education, and access.
Applying Digital Divide Research to Youth and SNS
Scholars loosely use the term digital divide to describe the gaps in access to
technology and technology-related activities among a population. One way to clarify the
term is to clearly define the dimensions along which one describes the divide. For
 
 
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example, Pippa Norris (2001) uses a three-pronged framework to describe digital divides
as global, social, and democratic:
“The global divide refers to the divergence of Internet access between
industrialized and developing societies. The social divide concerns the gap
between rich and poor in each nation. And finally within the online community,
the democratic divide signifies the difference between those who do, and do not,
use the panoply of digital resources to engage, mobilize, and participate in public
life” (Norris, 2001, p. 4).

As Norris’ definition illuminates, researchers can use the term digital divide to describe
various levels of analysis from global comparisons to smaller communities. In addition,
scholars also consider the unequal access to both the technology itself (i.e. a computer)
and opportunities to participate in media.
Researchers typically examine digital divide based on factors such as gender,
race, or socioeconomic status. For example, an early U.S. Department of Commerce
(2000) publication, Falling through the Net, found disparities in computer access across a
variety of variables. In the year 2000, 55.7% of White households and 65.6% of Asian
and Pacific Islander households owned a personal computer. Only 33.7% of Hispanic and
32.6% of Black households owned a computer. The disparities of computer access based
on income level were even more striking nearly a decade ago. Approximately 86.3% of
households with incomes of seventy-five thousand dollars or more reported having access
to computers while only 19.2% of households making less than fifteen thousand had
access to computers. Finally, Falling through the Net highlighted the inequalities based
 
 
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on educational level. 64% of households where a member held a bachelor’s degree
subscribed to Internet services, compared to 29.9% of high school graduates and 11.7%
of those who did not complete high school.
Such initial reports such as Falling Through the Net (U.S. Department of
Commerce, 2000) established the understanding that access to technology was highly
unequal. Ethnic minority groups were less likely to use technology. Those from lower
socioeconomic backgrounds also had fewer opportunities to use media tools. Early
studies of computer usage also found that males were more likely than females to use
technology (Volman & van Eck, 2001). As technology becomes a more integral part of
society, the question of whether patterns of inequality continue to exist remains a critical
question.
Current studies suggest that access to technology is becoming quite ubiquitous,
but participation with different media varies considerably. For example, in a recent
national survey of families, researchers find that 94% of families currently own a
computer (Kennedy et al., 2008). While ownership of computers and Internet access
appears widespread, individuals use technology for very diverse purposes and needs
(Kennedy et al., 2008). Teenage populations also exhibit highly differential rates of
media participation. Some youth write blogs, others create personal web pages, and
others create videos (Lenhart et al, 2007a). In terms of participation in social network
sites, Lenhart et al. find that 55% of online teens currently have a profile on a SNS.
These current trends in Internet access and participation suggest a critical shift in
how scholars explore digital divides. The term as defined as access to computers and the
 
 
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Internet may not be as useful as conceptualizing the divide along differences in
participation and skills (Cheong, 2008; Hargittai, 2004). Recent digital divide scholarship
has explored other factors such as technical knowledge and expertise, continued concerns
about gender, and the role of schools in facilitating access (Aslanidou & Menexis, 2008;
Hohlfeld, Ritzhaupt, Barron, & Kemker, 2008; Livingstone & Helsper, 2007; Tien & Fu,
2008).
What Factors Might Relate to Teenagers’ Use of Social Network Sites?
Previous discussions of the digital divide highlight disparities in technology
access and participation along key demographic variables. Education level, racial and
ethnic grouping, and age were significant lines through which to view the digital divide.
Thus, analyses of new technologies and media usage should control for these variables.
However, computer ownership and Internet access have spread rapidly in the last decade
(Kennedy et al, 2008; Lenhart et al, 2007b). With such wide coverage, there are questions
to whether access divides along demographic variables remain significant indicators.
Emerging evidence from researchers of social network sites suggest that demographic
background is not a significant predictor of access to these online communities.
In her ethnographic research on social network sites, dana boyd (2008) states that,
“Poor urban black teens appear to be just as likely to join the site as white teens from
wealthier backgrounds…” (p. 121). Eszter Hargittai (2007) used logistic regression to
examine a sample of college students and found that age, race, and parent’s education
level did not have a significant relationship to whether they used a SNS. However, both
boyd and Hargittai assert that gender remains a significant variable with which to see
 
 
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access divides in SNS. Teenagers also exhibit different behaviors online depending on
age. While no studies focus the influence of age on youth social network site
participation, numerous studies highlight how young people behave differently online.
For example, Peter, Valkenburg, & Schouten (2006) find that younger, less experienced
adolescents are more prone to talk to strangers online.
Social network sites, and most forms of social technology, are designed to
promote interaction between individuals. Recent surveys find that girls are significantly
more likely to participate in these social communities (Lenhart et al., 2007b). In addition,
older youth are more likely to participate in social network sites. This pattern could be the
result of many factors. Perhaps younger youth have more restrictions and parental
supervision concerning their online activity. Older youth may have more freedom, and
technical literacy, to explore new media tools. The history of digital divide research, and
most recently studies on participation in SNS, offer the foundation for a set of hypotheses
concerning demographic variables:
H1: Given teenagers’ widespread access to computers and the Internet,
demographic variables such as race and parental education level will no longer
have a significant relationship to usage of SNS.
H2: However, gender and age will still have a significant relationship with social
network site use.
The hypotheses underscore the relative trend of increased computer ownership and
Internet access in the United States. If computer ownership and Internet access is
widespread, demographic variables should not be significantly correlated to online
 
 
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activities. However, teenage life is characterized by rapid changes in personal
development and social contexts. Factors such as gender and age may still be
significantly correlated to participation in different media such as SNS.
Beyond demographic indicators, subtle social and cultural contexts may play a
larger role in youth media practices. For instance, socialization in families may be
significant factors in children’s access to computers and comfort with using technology.
Recent surveys find that technology is becoming a vital part of family life (Kennedy et al,
2008). Family members who actively utilize technology in daily life may also influence
increased participation in digital media by teens. In addition, type of Internet access may
also be subtle factors for student use of social network sites. In 2008, two-thirds of
households reported having high-speed, broadband Internet access. Thus, a significant
portion of households still uses dial-up connections or no connection. Whether students
access the Internet from home, school, or libraries may affect their participation in online
communities such as SNS. Programs such as E-rate offer discounts to public schools for
network infrastructure and Internet connections (Federal Communications Commission,
n.d.). These programs drastically increased Internet access in schools. In 1994 3% of
classrooms had Internet access, while in 2005 94% of public school classrooms were
connected (National Center for Education Statistics, 2006).
Some students may primarily access the Internet at school rather than home.
Schools often have policy restrictions on Internet usage, block websites, or restrict access
time for non-instructional purposes. Such situations might limit students’ participation in
social network sites. For example, dana boyd (2008) observes that,
 
 
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Those who only access their accounts in schools use it primarily as an
asynchronous communication tool, while those with continuous nighttime access
at home spend more time surfing the network, modifying their profile, collecting
friends and talking to strangers. When it comes to social network sites, there
appears to be a far greater participatory divide than an access divide (p. 121).

Where students primarily access the Internet could impact their participation in social
network sites. These discussions outline a set of hypotheses concerning types of access:
H3: Parental use of the Internet is positively related to teenagers’ use of social
network sites.
H4: Teenagers with broadband Internet access at home (versus dial-up or no
connections) are more likely to be SNS users.
H5: Teenagers who access the Internet primarily from home (versus any other
location) will be more likely to use social network sites.
These factors then contribute to the evolution of digital divide debates by examining
subtle variations in access and participation.
In this study, I also consider a set of individual level factors. As noted earlier,
teens that are social network users also tend to be super communicators. They are apt to
use a variety of other technologies to communicate frequently with their friends. Thus,
one would expect a significant relationship between a teenager’s intensity of Internet use
and whether they also participate in online social networking.
H6: As teenagers use the Internet and related technologies more frequently, they
are more likely to be users of social network sites.
Initial research on youth and digital media suggest that teenagers use social network sites
mainly to keep in touch with their friends (boyd, 2008; Ito et al., 2009; Lenhart et al.,
 
 
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2007b). Thus, one might also expect that those teenagers who communicate with their
friends more intensely would also be social network users. Their offline relationships and
activities will continue online as well. These conceptual discussions motivate hypotheses
about the individual factors that teens bring to their SNS membership.
H7: Teens who communicate more frequently with friends and family using
technology, will be more likely to also use social network sites.
These sets of hypotheses are posited by scholars in a variety of fields such as Education,
Communication, and Media Studies. Taken together, they move one’s conceptualization
of the digital divide away from simplistic questions about access to hardware or software.
Instead, they are finer grained reflections on the participation divide. This study
contributes to the evolving discussion of digital divides by (1) considering these new
theories about youth digital divide, (2) applying them to a new online phenomena of
social network sites, and (3) extending initial research in the same vein that has examined
college students (see Hargittai, 2007), to now also consider teenage youth.
Method
Sample and Data
This study utilizes a survey conducted by the Pew Internet & American Life
(PIAL) Project (n.d.). The PIAL conducts regular surveys of various topics focusing on
trends of Internet and technology usage in the country. The data for this study comes
from the Teens and Writing survey that was conducted in 2007. The intent of this
particular survey was to gather evidence about teenagers’ writing habits in relation to
their use of various Internet and social media. From September to November 2007, phone
 
 
70 
interviews were conducted from a nationally representative sample of 700 teenagers and
their parents. The survey covered an array of demographic and access questions, but also
gathered detailed data about how teens write in their daily lives and what technologies
they utilize. One subset of questions considered how teens use social network sites, and is
particularly salient for this analysis.
Descriptive statistics of the sample are provided in Table 3.1 below. In all
analyses, this study makes use of the sampling weights provided by the PIAL (see
Methodology in Lenhart, Arafeh, Smith, & Macgill, 2008). The sample weights correct
for oversampling of particular segments of the population and adjust the frequency tables
to better match the population sample of the U.S. Census. After applying the weight, the
total sample for this study is 4,855 (n = 4,855).

 
 
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Table 3.1: Descriptive Statistics of Key Variables
Mean Frequency Percentage Range
Teenager's Gender - - - 0 – 1
Female - 2365 48.7% -
Male - 2490 51.3% -
Race/Ethnicity - - - -
White or White-Hispanic - 3223 66.4% -
Black or Black-Hispanic - 581 12.0% 0 – 1
Hispanic - 784 16.1% 0 – 1
Asian, Other, and Other-Hispanic - 262 5.4% 0 – 1
Missing - 5 0.1% -
Parent's Education - - - -
Less than High School - 570 11.8% -
High School - 1650 34.0% 0 – 1
Some College - 1160 23.9% 0 – 1
College or Over - 1470 30.3% 0 – 1
Missing - 5 0.1% -
Parent Uses Internet - - - 0 – 1
No - 649 13.4% -
Yes - 4206 86.6% -
Teenager's Age 14.52
(1.703)
- - 12 – 17
Home Internet Connection - - - -
Dialup - 1060 21.8% -
Broadband - 3227 66.5% 0 – 1
No Computer or Internet Connection - 496 10.2% 0 – 1
Missing - 72 1.5% -
Primary Internet Access - - - -
Home - 3318 68.3% -
School - 631 13.0% 0 – 1
Other - 274 5.7% 0 – 1
Missing - 632 13.0% -
Number Technologies Teen Uses 1.68
(0.99)
- - 0 – 4
Teen Communicates Everyday with Various
Technologies
1.90
(1.59)
- - 0 – 6
Standard deviations for means in parentheses

 
 
72 
Analysis
In this study I utilize a binary logistic model to determine the probability that a
teenager has a profile on a social networking site. Social network usage is determined by:
a vector of demographic variables (x’A) that includes Race, Gender, Age, and Parental
Education; Access variables (y’B) that include whether the parent is an Internet user, the
type of Internet access available at home, and where the teenager primarily accesses the
Internet; and finally Communication variables (z’C) that include whether students use a
variety of other technologies (i.e. cell phones, blogs, personal websites etc), and whether
students communicate everyday with a variety of technologies (i.e. cell phones, instant
messaging, telephone etc.). The resulting logistic regression model is:
ln(p) = α + x’A + y’B + z’C + ε
Many of the variables in the dataset are categorical and were dummy coded with
particular reference groups. I note these variables to aid in the subsequent interpretation
and Table 1 illustrates the categorical variables. The reference groups for the following
variables is as follows: Gender reference is female, Parent’s Education is less than high
school, Parental Internet User reference is no, Home modem is dial-up, and Primary
Internet Access is home. The race variable is also noted. The Teens and Writing Survey
structured the race question to capture the diversity inherent in the Hispanic population.
Thus, race is coded into categories such as White or White-Hispanic, Black or Black-
Hispanic, Hispanic, and Other-Hispanic which encompasses the Asian-Pacific Islander,
multi-racial, and other categories. White or White-Hispanic is the reference category.

 
 
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Findings
In Table 3.2, I present some descriptive cross-tabulations of the digital divide
indicators and the percentage of youth who use SNS. The results offer some intriguing
evidence that the digital divide, at least when it comes to youth participation in social
network sites, is beginning to even out. The term – beginning – is key because while
some demographic indicators exhibit equal access, others still describe gaps. For
example, 60% of both White and Black youth use SNS. However, the rates for Hispanic,
Asian, and other ethnic minorities remain lower in this dataset. Female teenagers are
more likely SNS users than their male peers. Older teens are SNS users at higher rates
than younger teens. Most interesting is the relationship between type of Internet access
and SNS participation. Youth with dialup and broadband access use SNS at equal rates
(approximately 60%), but nearly 68% of youth with no computer or Internet access use
social network sites. These descriptive results fly against what one would expect from a
digital divide perspective.
The descriptive cross-tabulations alone cannot discern the relationship between
digital divide indicators and SNS access. To better examine the relationship between any
one factor and SNS access, one must also control for other important and related
variables. I utilize binary logistic regression to control for the major digital divide
indicators used in this analysis. The results of the regression analyses are outlined in
Table 3.3 below. The variables were entered using a hierarchical strategy. In model 1, I
enter the demographic variables (x’A). The access variables (y’B) are added in model 2.
Finally, I add the communication variables (z’C) to examine the full model. The columns
 
 
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of Table 3.3 outline the results of each model, and I utilize the full model (model 3) for
interpretation of the data.

Table 3.2: Cross Tabulations of Youth SNS Usage
Percent of Youth with a SNS Profile
White/White-Hispanic 60.0%
Black/Black-Hispanic 60.4%
Hispanic 53.6%
Asian & Other 38.9%

Female 66.5%
Male 49.8%

Ages 12-14 38.3%
Ages 15-17 77.3%

Parent Education
Less Than High School 60.8%
High School 54.2%
Some College 59.9%
College or Above 59.5%

Internet
Dialup 59.2%
Broadband 59.8%
No computer or Internet 67.7%

Primary Internet Access
Home 63.5%
School 51.0%
Someplace Else 60.9%

Hypothesis 1 suggested that traditional digital divide indicators such as race and
parental education would no longer be significant predictors of SNS participation. The
data offer mixed support of this hypothesis. Parental education was largely insignificant
in its relationship to whether a teenager used SNS. Remember that the reference group is
parents with less than a high school degree. Thus, college educated parents did not have a
significantly different influence on their child’s access to and use of social network sites.
However, parents with a high school degree had a positive relationship with the
 
 
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probability that their child used SNS. Such mixed results lend support to the notion that
social and socioeconomic indicators, such as parent education level, may not be major
predictors of participation divides among youth.
While parent education did not largely define any gaps in SNS use, race was still
a significant predictor for teen participation in social network sites. However, the results
at times go against the common thought around race and digital divides. For example,
Black and Black-Hispanic teens were more likely than White and White-Hispanic teens
(the reference group) to be social network site users. Looking at the odds ratio for this
variable, one sees that the odds that Black/Black-Hispanic teenagers are members of
social network sites are 1.423 times higher than their White/White-Hispanic peers.
boyd’s (2008) assertion that black teens are just as likely as white teens to use social
networking sites holds true in this dataset. While Black/Black-Hispanic teens were more
likely to be SNS users, Hispanic and youth of other ethnic backgrounds were
significantly less likely to be on social network sites. The mixed findings relating
demographic patterns to SNS participation suggest that some digital divides may still
exist, but the relationships are less clear than found in Internet studies of the past.
Differences exist across race and socioeconomic status, but these variations are not
stereotypical.

 
 
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Table 3.3: Results of Binary Logistic Model on Probability of having a SNS Profile
Model 1 Model 2 Model 3 Odds Ratios for
Model 3
Constant

-6.822**
(.345)
-7.330**
(.385)
-7.264**
(.409)
0.001
Race-
Black or Black-Hispanic
.370**
(.117)

.460**
(.122)
.353**
(.130)
1.423
Race-
Hispanic
-.150
(.109)

-.242*
(.113)
-.272*
(.118)
0.762
Race-
Other
-.657**
(.155)

-.752**
(.160)
-.880**
(.169)
0.415
Gender -.855**
(.073)

-.857**
(.075)
-.555**
(.080)
0.574
Age .522**
(.023)

.567**
(.024)
.454**
(.025)
1.575
Parent Education-
High School
.348**
(.129)

.158
(.137)
.328*
(.146)
1.388
Parent Education-
Some College
.304*
(.133)

.043
(.144)
.011
(.153)
1.011
Parent Education
College or Graduate
Degree
.234
(.130)

-.090
(.143)
.120
(.153)
1.127
Parent is Internet User - .052
(.141)

-.024
(.152)
0.976
Youth has Broadband
Internet
- .216*
(.091)

.024
(.098)
1.024
Youth has no Internet - -1.235**
(.191)

-1.199**
(.206)
0.301
Primary Internet Access
is at School
- -.404**
(.112)

-.119
(.119)
0.888
Primary Internet Access
is not School or Home
- .703**
(.175)

.824**
(.189)
2.280
Youth uses other
technologies
- - .481**
(.044)

1.618
Youth uses other
technologies to
communicate with peers
- - .359**
(.028)
1.432

Standard errors in parentheses
* p < .05, ** p < .01

 
 
77 
Hypothesis 2 stated that gender and age would have a significant relationship to
SNS usage amongst youth. The findings clearly support this hypothesis. Male teenagers
were approximately 43% less likely to use social network sites than females. Previous
research often found that male youth were more likely than females to use digital media
(Volman & van Eck, 2001). The relationship with gender appears to shift when it comes
to participation in social network communities. In addition to gender, older teens were
more likely to use SNS than their younger peers. Each year of age increased the
probability of using social network sites by 57.5%. Such patterns might be explained by
parental influences on younger teens. Perhaps older teenagers have less regulations or
rules concerning participation in social network sites.
Parental use of the Internet was not significantly related to youth use of SNS
(H3). In addition, having broadband versus dialup Internet at home did not significantly
influence whether a teenager used SNS (H4). An unsurprising, but still noteworthy
finding is that those teens that do not own a computer or are not connected to the Internet
were nearly 70% less likely to participate in online social networks. Finally, there appears
to be no difference in SNS usage for teens that primarily access the Internet from home or
from school (H5). Surprisingly, teens that report having primary access in other locations
(perhaps libraries, friends’ homes, or most plausibly their mobile phones) were
significantly more likely to be SNS users. Perhaps youth are more likely to use social
network sites away from parental or adult supervision. Another distinct possibility is that
with the rising use of mobile technology such as smart phones and iPods, youth access
their social networks with these devices over their school or home computers.
 
 
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The results of hypotheses 3-5 suggest that parental influence and Internet access
points are statistically insignificant predictors of the participation divide in social network
sites. It seems that a minimal level of Internet access is sufficient for teens to access
social network sites. The findings support qualitative evidence that youth find a way to
participate in digital media activities, even when they might not own or have access to the
latest technologies (Ito et al., 2009).
Hypotheses 6 and 7 stated that youth who use SNS are also very literate in other
technologies (Lenhart et al., 2007b). The findings support these hypotheses. Teenagers
who used other technologies (i.e. cell phone, computer, blogs etc.) were over 61% more
likely to also use social network sites than those who did not. In addition, youth who
were comfortable using other technologies to communicate everyday with their peers
were 43% more likely to use SNS (see Odds Ratios in Table 3.3). These “super
communicator” teens connect with friends over a variety of mediums, including social
network sites.
Table 3.4 below further illustrates the relationship between technical literacy
practices on the probability of SNS use. The table outlines the predicted probability that a
teen has a SNS profile, given their use of other technologies to communicate everyday.
All other variables are held at their means. The qualifier everyday is important in the
interpretation. A “yes” answer means that a technology is a daily part of one’s life, and
indicates that one has a strong level of comfort with the tool. Table 3.4 shows that as
teens report higher use of different technologies, they are also more likely to be social
network users. Teens who report using one more technology on a daily basis, such as a
 
 
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cell phone or email, increase their probability that they will also use social network sites
by nearly 3-9%. Youth who are use 6 different technologies are nearly 40% more likely
to also participate in social network sites, compared to those who use no technologies to
communicate with their friends. The analysis underscores the idea that youth, who are
active in online social networks, are also show high levels of literacy with other digital
tools.

Table 3.4: Predicted Probability of Having a SNS Profile
Predicted Probability
Number of
Technologies used to
Communicate
"Everyday" Social Network Site User Not a User Discrete Change
0 0.5217 0.4783
1 0.6097 0.3903 0.0880
2 0.6910 0.3090 0.0813
3 0.7620 0.2380 0.0710
4 0.8209 0.1791 0.0589
5 0.8678 0.1322 0.0469
6 0.9038 0.0962 0.0360

Finally, the results presented here highlight the extremely complex social contexts
that youth reside in. For example, the descriptive cross-tabs in table 3.2 show that nearly
68% of youth without a home computer or Internet access SNS, compared to
approximately 60% of youth with home technology access. However, the regression
analyses suggest that having no home Internet access has a significant, negative
relationship to participating in SNS (controlling for other factors). The reality is that
youth perhaps find different ways to find access to social media. Table 3.5 displays a
 
 
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cross-tabulation of only those youth who use social network sites. They are categorized
by both their Internet ownership and where they primarily access the Internet.

Table 3.5: Internet Ownership and Internet Access for SNS Users
Where Youth Primarily Accesses the Internet
Home School Someplace Else Total
Type of Modem at Home
Dialup 473
(77.7%)

112
(18.4%)
24
(3.9%)
609
(100%)
Broadband 1,606
(87.3%)
160
(8.7%)
74
(4.0%)
1,840
(100%)

None Computer
and/or Internet
8
(6.2%)
51
(39.5%)
70
(54.3%)
129
(100%)

The results of Table 3.5 suggest that the overwhelming majority of youth who
have dialup or broadband Internet at home, access the web from their residence. The
majority of youth (93.8%) who either do not own a computer, or do not have Internet at
home, find access at school or in other venues. These youth appear to find their way
online, despite obstacles such as a lack of technology access at home. In addition, using
the Internet in places other than home or school, often the most regulated places, was
positively associated with using social network sites. These combined results offer
several compelling hypotheses for digital divide researchers. First, variables such as
technology ownership may not significantly describe digital divides as in past studies.
Home ownership of computers, in an evolving world where mobile devices and phones
are increasingly connected to the Internet, may not be huge obstacles for youth any
longer. Second, when it comes to social media, youth may participate in these online
communities more often when they access them in less regulated places (i.e. away from
 
 
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the watchful eye of parents or the schools that ban access to these sites). The patterns of
how youth access social technologies are more complex than seen in previous time
periods, and traditional digital divide indicators are fading in terms of their association
with youth participation in online communities.
Chapter 3 Conclusions
This chapter contributes to past discussion of the digital divide by examining the
current phenomenon of teens and social network sites. I utilize past thought on digital
divides to conceptualize both questions of access and participation. Then I test those
theories to examine the differential participation rates of teenagers in online social
network communities. The results pose new questions to traditional digital divide
conversations. For example, race remained a significant predictor of SNS usage, but in
non-obvious ways. Black and Black-Hispanic students were more likely to participate in
social network sites than their White/White-Hispanic peers. Parental education level,
level of Internet access, and place of Internet access are also insignificant factors in this
analysis. Such findings are in accord with emerging descriptive studies that find that
teenagers are consistently immersed in technology, communicating with their friends
using digital media, and learning from these interactions. Teenagers are getting connected
online and traditional conceptions of digital divide in terms of access may not be
significant in this context. In terms of participating in online social network communities,
youth find a way to get connected.
This study also began to examine social and participation divides. Namely,
teenagers’ overall technical literacy was a significant predictor of SNS membership. Such
 
 
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findings highlight the importance of allowing youth to explore, use, and gain comfort in
using a variety of technologies. This technical literacy may help these teenagers evolve
and participate in new forms of media. In addition, factors such as gender and age are
significantly related to youth participation in online networking communities. These
findings differ from those of adult populations (Hargittai, 2007), and suggest that youth
are particularly unique sub-populations to examine in future research. Studies that
examine the role of age, gender, and cultural contexts in the ways that teenagers use new
social media platforms promise to be fruitful directions for future research.
The limitations of this study also illuminate future research needs. This analysis
utilizes a nationally representative dataset of youth. However, I only consider a binary
outcome of whether a teenager had a social network profile or not. Recent studies of adult
populations find that divides exist in different kinds of platforms (Hargittai, 2007). For
example, Hargittai finds that white college students are more likely to use Facebook
while Hispanic students were more likely to use MySpace. Future studies that examine
youth populations along different communities may uncover similar divides in SNS
participation. Researchers might also consider more detailed indicators of participation.
For example, teenagers undertake a variety of activities in social network sites. They
write on each other’s walls, send messages, post pictures, comment on each other’s
postings, and interact in numerous ways. Future analyses might consider these degrees of
activity – from minimal to diverse – to uncover a better understanding of the participation
divide.
 
 
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This chapter also continues the tradition of digital divide scholarship that
examines the relationship between demographic indicators and technology access.
However, as technology ownership and access becomes ever more widespread, scholars
must consider other factors that contribute to why a particular population will utilize a
new media tool. Social and cultural indicators may better predict why youth use
particular online communities, beyond indicators such as race or socioeconomic status.
Already, emerging studies suggest that there is a relationship between SNS use and
factors such as self-esteem, popularity, and trust (Beaudoin, 2008; Ellison et al., 2007;
Zywica & Danowski, 2008). These psychological and cultural factors may ultimately
provide more informative when examining why individuals use or do not use a particular
technology.
This chapter illuminates the need for more detailed data concerning teenagers and
their use of social network sites. Scholars must keep one eye on continual questions about
digital divides. As new media emerge, questions of who is accessing and using new
technologies will remain foundational concerns. Digital divide research considers issues
of equality and opportunity for using new media. However, understanding the user
characteristics of new technologies is also imperative for other research endeavors.
Studies that take into account selection effects and patterns of participation, promise to
offer finer insights into the social and educational effects of social media on youth.
Finally, continued research is needed because trends in media use change quite rapidly.
This study only considers a cross-sectional dataset, which was collected in November of
2007. Patterns of media usage undoubtedly changes quite rapidly each year. For example,
 
 
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Facebook saw an increase in membership from 100 million members in 2008 to over 350
million in 2009. Such widespread adoption of social network technologies suggest that
traditional digital divide indicators will be less meaningful in the near future. Instead,
finer understanding of the social and cultural trends amongst youth and technology are
needed.
 
 
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Chapter 4: Effect of Social Network Sites on High School Students – A Cluster-
Randomized Trial

In a world in which knowledge production is collective and
communication occurs across an array of different media, the capacity to
network emerges as a core social skill and cultural competency (Jenkins,
2006, p. 49).

At the present time, social networking software is under fire from adult
authorities, and federal law makes it more difficult to access and deploy
these tools in the classroom (Jenkins, 2006, p. 51).

The two quotes from media scholar Henry Jenkins succinctly convey a set of
conflicting developments in K-12 education today. Current information technologies,
what O’Reilly (2007) terms Web 2.0, use the network as its main organizing paradigm.
Social network sites (SNS) such as Facebook and MySpace allow individuals to post
personal profiles and connect with their friends. Twitter encourages users to post short
updates of their lives for their network to see. These examples of social media have
permeated every aspect of society, revolutionized the way companies do business, and
altered how individuals communicate with their friends and colleagues (O’Reilly, 2007).
Scholars also assert that social technologies offer tremendous potential to change how we
educate students in this media landscape (Jenkins, 2006; Ito et al., 2009). No longer do
youth only learn by sitting in classrooms and listening to teacher lectures. They learn
from their peers and the larger community as they text, twitter, network, search for and
create their own knowledge through the Internet. Media scholars observe that youth learn
 
 
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in these contexts and are also deeply engaged with learning with these technologies
(Greenhow, Robelia, & Hughes, 2009).
Not surprisingly schools are under increasing pressure to utilize new technologies
to engage students and increase achievement. Recent polls of school district leaders find
that nearly 75% view social technology as having a positive impact on students’
communication skills, relationships between students and teachers, and quality of
schoolwork (Lemke & Coughlin, 2009). These K-12 leaders also hope that social media
can help keep students engaged with school. Despite such optimism, 70% of school
districts enact policies that ban student access to social network sites. Districts would
rather manage and control technologies in-house, than allow students to use the sites they
frequent such as Facebook and MySpace. The issues surrounding web 2.0 tools in
schools center on questions of student safety and academic use. District administrators
are deeply concerned about poor student behavior such as bullying, access to sexually
explicit material, or social media being a distraction from schoolwork. Therein lies the
conundrum for educators today. Social technologies capture the attention of students, but
questions remain concerning whether these new tools have a beneficial impact on student
outcomes.
In this paper, I address these concerns by examining a popular technology – the
social network site. Examples of these online communities include Facebook and
MySpace, which are among the most visited sites in the world (Alexa, n.d.). First, I
outline how SNS function and summarize several common theories-of-action that may
link to student outcomes. For example, SNS are commonly assumed to help individuals
 
 
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develop relationships with peers and feel connected to their communities (Beaudoin,
2008; Ellison et al., 2007). Would using such technologies in a school context improve
student relationships with others and motivation for school? Second, I report an
experiment that was conducted in two urban school districts. A social network site was
built for private use in high school classrooms. Using a cluster-randomized design, a total
of 50 classrooms were randomly assigned to use the SNS for a 6-week period. Students
were assessed on measures of social capital, school engagement, and course performance.
Third, I report the results of the experiment. The school-based social network site was
poorly received and had little impact on student outcomes. However, students’ use of
existing social networks on Facebook and MySpace had positive and significant
relationships with social capital. Finally, the paper offers recommendations and future
directions for research that examines the effect of social technologies in education
contexts.
Theoretical Framework
Technology is an integral part of teenage life. A recent report from the Kaiser
Family Foundation (Rideout, Foehr, & Roberts, 2010) finds that teenagers spend over 10
hours per day using some form of media – including television, music, mobile devices,
and computers. Youth also increasingly access their media through networked, online
platforms such as social network sites. As noted earlier, boyd & Ellison (2007) define
SNS as having three underlying features. First, users create profiles that describe
themselves to others. Second, profiles display the friends of that individual. Finally, users
can click on profiles and friends to traverse ever-wider social networks. This simple set
 
 
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of features facilitates frequent communication between large numbers of individuals.
Most research on SNS show that social interaction in these online communities works
much the same as offline relationships. Individuals learn about each other through their
profiles, develop judgments on others’ personalities and social traits, and interact with
their networks (Walther, Van Der Heide, Kim, Westerman & Tong, 2008; Walther, Van
Der Heide, Hamel & Shulman, 2009). The online medium intensifies and broadens the
scope of these interactions.
One can find elements of social network sites in just about every major Internet-
based community or technology. For example, YouTube is a SNS that distributes online
video. Facebook is a SNS where users keep in touch with their friends. MySpace has a
niche as a community of musicians and their fans. All of these online communities
revolve around a social network where users connect with one another and share media
from text messages to photos and videos (Livingstone, 2008). From a simple foundation
of profiles, friends, and networks are Internet technologies that encourage individuals to
interact, share, and communicate in a rapid and constantly connected way.
Media scholars theorize that this networked sharing and interaction represents
new learning opportunities for youth (Ito et al., 2009; Jenkins, 2006). For example, youth
write their own blogs, create their own videos and media, and share what they know with
their peers. From this scholarly base, recent initiatives such as the federal government’s
Educate to Innovate fund and the MacArthur Foundation’s Digital Media and Learning
grants have invested millions of dollars to develop social technologies for learning
(Whitehouse, n.d.). The hope is that by engaging students with social technology, and by
 
 
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encouraging a learning process of information sharing and networking, we might improve
student engagement and learning.
Despite such optimism for social media and learning, little empirical evidence
exists around the effects of these tools on student outcomes. Research in
communications, sociology, and developmental psychology suggest that social network
sites help teenage youth create social capital and develop their personal identities (i.e.
Ellison et al., 2007; Livingstone, 2008; Manago, Graham, Greenfield, & Salimkhan,
2008; Schmitt, Dayanim, & Matthias, 2008). Education researchers are only now
beginning to turn their attention to the role of social technologies in education
(Greenhow, Robelia, & Hughes, 2009). Most research of SNS to date often (a) consider
college student populations, with few studies that examine K-12 youth and (b) offer
limited theoretical understanding for how social technologies affect education-related
outcomes. For K-12 policymakers, teachers, and school leaders interested in how SNS
affect their students, there is a slight research base to inform education decisions. For
education researchers, one must look to studies in other disciplinary and theoretical
foundations to extrapolate the potential of social network sites on educational outcomes.
The Case for SNS Effects on Students’ Social Capital
The clearest potential benefit of social network sites is the ability to develop
relationships with friends, family, and colleagues. Similarly, scholars have posited that
participation in SNS might affect a person’s social capital. Different theorists focus on
particular aspects of social capital theory (see Bourdieu, 1986; Coleman, 1990; Lin,
1999; Putnam, 2000). However, Portes (1998) notes that, “Despite these differences [in
 
 
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definitions], the consensus is growing in the literature that social capital stands for the
ability of actors to secure benefits by virtue of membership in social networks” (p. 6).
Social network sites are a major place for teenagers to hang out, communicate with
others, and develop relationships (Ito et al., 2009). The intuitive theory is that as youth
develop better relationships, social capital, they may also accrue other benefits associated
with these networks.
The benefits of having positive relationships with others are numerous. In the
education literature, social capital is positively related to important education outcomes.
For example, students with higher levels of social capital are less likely to drop out of
school, achieve higher scores on standardized assessments, and expend more effort on
homework (Dika & Singh, 2002). Early studies of social network site use among college
students find a positive correlation with social capital development (Ellison, Steinfield, &
Lampe, 2007; Steinfield, Ellison, & Lampe, 2008). Students who use Facebook
frequently develop more acquaintances over time that they might turn to for information
or advice. Researchers also find suggestive evidence that SNS benefit students with lower
self-esteem and life satisfaction at higher rates than those already high in these traits.
Finally, recent studies of Internet usage suggest that individuals who go online to interact
with others tend to develop more trusting relationships (Beaudoin, 2008).
From a social capital framework, using SNS in school contexts may offer positive
benefits to students. It is plausible that utilizing social network sites in schools may
encourage students to build relationships with peers, teachers, educators, and other
school-based stakeholders. Another potential effect of SNS is that individuals develop
 
 
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more relationships with others online, versus in their offline contexts (Williams, 2006).
Perhaps networking sites have less effects on relationships one already has, but aids in
widening one’s social network in online communities. The first two hypotheses in this
study consider the effect of SNS on youth social capital in both their offline, school
community and in their online relationships. The first hypothesis asks whether using SNS
leads students to develop stronger relationships with others in their school community.
H1: Students who use social network sites will have higher social capital in their
school relationships than those who do not.
Similarly, students who use SNS may also develop relationships with others online. Thus,
the second hypothesis states:
H2: Students who use social network sites will have higher levels of social capital
in their online relationships than those who do not.
The Case for SNS Effects on Students’ School Engagement
Keeping students engaged with school is cited as the most prevalent hope for
social technologies (Lemke & Coughlin, 2009). However, no studies to date empirically
evaluate whether using technologies like SNS positively impacts K-12 student
engagement. Emerging ethnographic studies suggest that students are quite engaged with
technology (Ito et al., 2009). In addition, youth are very engaged with the topics and
skills they encounter while online. The natural extension of these findings is to suggest
that using social media in schools will increase student engagement with academic
classes. However, learning a hobby on one’s own time creates a very different
motivational and contextual situation than learning in a formal classroom. Questions
 
 
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remain as to how a technology like SNS will impact students’ engagement when applied
in a classroom context.
Academics are only one of the ways that students feel engaged with their school
communities. Fredericks, Blumenfeld, & Paris (2004) identify three general types of
engagement: cognitive, behavioral, and emotional. Students may be interested in the
subjects they learn, feel connected with friends at school, with teachers with whom they
have supportive relationships, or may feel satisfied or happy with their school experience
for a variety of factors. In these psychosocial areas, previous research on the Internet and
social technology offers circumstantial evidence that these tools might improve student
perceptions of their school. As noted earlier, social network sites might help students
create positive relationships or social capital with peers and teachers. Such positive
connections might also be related to being more engaged with the school community.
Internet researchers also find that interacting with others online is related to
higher levels of self-esteem and psychological well-being. The literature on
psychological well-being often refers to levels of life satisfaction or happiness.
Valkenburg, Peter, & Schouten (2006) surveyed over 800 Dutch adolescents and find that
those who used SNS had higher numbers of friends online. These teenagers also had
higher frequency of interaction with friends in the social network, which was related to
higher levels of self-esteem and life satisfaction. Furthermore, in a study of college
students, researchers found that students who were low in self-esteem used SNS to
express themselves and engage in positive relationships with their peers (Zywica &
Danowski, 2008). These studies suggest that social network sites allow students to find a
 
 
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social niche, express themselves, and develop positive relationships with their peers.
These behaviors lead to higher levels of satisfaction or self-esteem.
SNS research on self-esteem and psychological well-being proposes that these
online communities help youth develop positive relationships with others. The concepts
of self-esteem and well-being provide a link to the idea of emotional engagement
proposed by Fredericks et al. (2004). If students use SNS to develop better relationships
with their peers and teachers, perhaps they will also exhibit positive perceptions of their
school community as well. The third hypothesis in this study is:
H3: Students who use SNS will exhibit higher levels of emotional engagement
with their school community than those who do not.
The Case for SNS Effects on Students’ Academic Achievement
The ultimate question for educators and school district leaders is whether using
new technologies like SNS improves student learning. The few studies in this domain
offer scant evidence or theoretical understanding for how social network technologies
might impact student achievement. The first study to consider the relationship between
SNS and student achievement appeared in a conference paper by Karpinksi (2009a). The
researcher surveyed approximately 220 undergraduate and graduate students and found
that college Facebook users had a lower GPA than non-users. She concludes that perhaps
students who use SNS spend more time socializing, less time studying, and thus exhibit
lower academic performance. Pasek, more, & Hargittai (2009a) published a response
using a nationally representative dataset of 14-22 year olds. The authors utilized more
control variables that are correlated to achievement including race, socioeconomic status,
 
 
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and previous academic achievement. The authors find that Facebook usage does not have
a significant relationship to achievement.
All of the authors in this particular debate acknowledge that their correlational
analyses are limited in several ways (Karpinksi, 2009b; Pasek, more, & Hargittai, 2009b).
The studies do not account for self-selection into particular online communities. For
example, studies of who uses different social network sites find distinct demographic
differences in users. Hargittai (2007) finds that in a college population, Caucasian and
higher SES students were more likely to use Facebook. Minority and lower SES students
were more likely to use MySpace. Youth will also likely separate into online
communities along the lines of race, SES, or previous academic achievement. For
example, Karpinksi (2009a) finds that college students who use Facebook had lower
average GPA. However, the Facebook users in this sample were highly correlated to
being in science and engineering majors where GPAs are typically lower than those in the
humanities.
The second issue in these early achievement studies is the misspecification of
SNS usage. Individuals use SNS and the Internet for varying reasons (Valkenburg &
Peter, 2009), but interactions are mostly social rather than academically related. It is not
entirely clear why using Facebook or any SNS would relate to academic achievement.
The previous discussion of social capital and engagement offer tangential influences on
achievement. Perhaps students who are better connected to their school communities will
also exhibit higher achievement measures such as GPA. However, the early SNS studies
on student learning do not control or measure for specific types of usage. One possibility
 
 
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is that if students network for academic related goals, one might observe increases in
achievement levels.
Social networks impact relationships and social environments, but also are a
source for information exchange. As the Internet began to be used by the mainstream
population, Wellman et al. (1996) observed that, “The nature of the medium supports a
focus on information exchanges, as people can easily post a question or comment and
receive information in return” (p. 219). Similarly, social network sites may provide a
platform for students to share information and assist each other during the learning
process. If a student does not understand a topic or a homework question, they can post it
to their online networks with the hope that their peers may help them. Such academic
uses of SNS may provide the link to student achievement. To consider this possibility, the
fourth hypothesis suggests that:
H4: Students who use an academic focused social network site will exhibit higher
course grades than those who do not.
School Based versus Organic Social Network Sites
While there are many potential benefits of SNS for adolescent youth, the majority
of school districts ban access to these technologies. Furthermore, district leaders would
rather build and manage their own tools than allow students to use popular versions such
as Facebook and MySpace (Lemke & Coughlin, 2009). There remain some questions as
to whether this strategy, school-based versus already existing networks, will have any
effects on students. Media scholar danah boyd (2009) theorizes that introducing tools like
Facebook and MySpace to classrooms might be detrimental for students. She argues that
 
 
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these tools serve social purposes outside of school that would dramatically alter when
used within school. Examples of these conflicts emerge consistently in media reports of
SNS use. For example, a Florida student was suspended from school in 2007 for creating
a Facebook page where she and other students criticized a particular teacher (Phillips,
2010). These conflicts occur because schools, students, teachers, and parents do not yet
understand how to meld the potential of social media in school.
The response from education administrators has been to block access to these sites
and attempt to create their own versions for academic use. Little is known about what the
effect of this strategy may be. For example, an academically focused SNS might
encourage students to share information and help each other with class work. There is
little evidence as to whether students already do this on their existing networks in
Facebook or MySpace. Perhaps a school-based network site will have a more direct and
positive impact on student achievement than more socially oriented sites. Conversely,
students might view the school-based network as uninteresting or less engaging,
preferring instead to utilize the networks they have already created organically. In this
case, schools might be better served to leverage students’ already existing networks to
encourage academic engagement and achievement. Opposite of boyd’s (2009) assertion,
it might be better for schools to figure out how to safely utilize Facebook or MySpace to
reach students in their already present communities. Beyond personal declarations and
anecdotal accounts, no studies examine these possibilities. Thus, this study also considers
an exploratory question:
 
 
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R1: Would a school-based social network site offer positive benefits to students
compared to use of already existing social networks in Facebook and MySpace?
Methodology
I conducted a cluster-randomized controlled trial (CRCT) in 2 urban school
districts. A private social network site was built for academic use in the 2 districts.
Classrooms were then randomly assigned to treatment (using the SNS) or control
conditions. A cluster-randomized design within an actual K-12 field setting was chosen
for several reasons. The CRCT design balances the need for experimental evidence and
maintains a level of external validity. Randomly assigned subjects ensure that treatment
and control groups are equal in important background characteristics. Thus, the effects of
an intervention are better attributed to the experiment over extraneous factors.
While randomized control increases the internal validity of a study, there are
particular challenges to utilizing the design in social network sites or in education
contexts. It is difficult to randomly assign youth to a social networking condition such as
Facebook or MySpace because many students are already members of these
communities. However, the school districts in this study ban access to these SNS and
were interested in implementing a within-district solution. This context created a natural
situation where a new tool could be implemented and evaluated using a randomized
control trial. In addition, many education interventions cannot randomly assign individual
students because they are already clustered into classrooms and schools (Hedges &
Hedburg, 2007). This situation requires the researcher to consider clusters, in this case
individual classrooms, as the unit of randomization.
 
 
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Power Analysis
Cluster randomized trials require a significant number of clusters to ensure
enough statistical power to detect effects. The number of clusters is the main contributor
to the effective sample size and statistical power results (Donner & Klar, 2000).
Similarly, attrition of clusters dramatically impacts the ability to detect effects in CRCT
designs. I conducted initial power analyses to determine required sample and minimal
detectable effect (MDE) sizes using the multi-level framework presented by Schochet
(2005; 2008). The MDE units are in terms of standard deviations. For the estimates I
utilize the following assumptions: an intra-class correlation (ICC) of 0.15, alpha of 0.05,
an average class size of 27, and power at 80%. The required sample sizes and MDE are
presented in Table 4.1 below. The use of regression models with baseline covariates also
improves the statistical power of cluster-randomized designs (Donner & Klar, 2000;
Schochet, 2008). Thus, I also include MDE and sample size calculations assuming the
use of baseline covariates, under the assumption that the R
2
of the regression is 0.20 or
0.50 (Schochet, 2008).

Table 4.1: Power Calculations when Classrooms are the Randomized Unit

Required Number of
Classrooms (n)
Minimal Detectable Effect Size
(MDE)
With Baseline Covariates
(R
2
= 0.20)
With Baseline
Covariates
(R
2
= 0.50)

35 0.41 0.37 0.29
50 0.35 0.31 0.25
100 0.25 0.22 0.17
270 0.15 0.13 0.11
600 0.10 0.09 0.07

 
 
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Many social science domains consider an effect size of 0.10 - 0.40 standard
deviations to be a minor effect. However, major educational interventions such as class
size reduction report effect sizes as small as 0.15 (Finn & Achilles, 1999). The definition
of “small” in conventional social science contexts can be significant in education settings.
Given these parameters, Table 4.1 underscores the challenge of conducting cluster-
randomized trials. To achieve an effect size of 0.15, one would require 270 classrooms.
Sample attrition also significantly impacts the statistical power available in the data
analysis.
I chose a target of 100 classrooms (MDE of 0.25) for this study, but as I note
below only 50 classrooms could be recruited. The MDE of 50 classrooms is 0.35 and
approximately 0.31 or smaller depending on the explanatory strength of covariates. I also
ran post-hoc power calculations using new intra-class correlations for each dependent
variable (DV). One DV yielded an ICC of 0.15 (as in Table 4.1). Three dependent
variables produced an ICC between 0.01 or 0.02, which indicates that most of the
variation occurs between individuals and not clusters. Table 4.2 shows the required
sample sizes at these ICC levels by minimal detectable effect (holding all other
assumptions constant).

Table 4.2: Required Classroom Clusters by ICC x MDE
Intra-Class Correlation
Minimal Detectable Effect 0.01 0.02
0.10 150 180
0.20 40 45
0.30 17 20
0.40 10 12

 
 
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Clearly, when clustering is minimal one needs fewer classrooms to detect smaller
effects. As Table 4.2 indicates, a sample of 50 classrooms yields sufficient power to
detect effects as small as 0.20 standard deviations when clustering is minimal. When
cluster-effects are present (i.e. an ICC = 0.15 in Table 4.1) a sample of 50 classrooms
may detect a MDE of 0.25-0.35 standard deviation changes in the best case.
Sample and Randomization
With the assistance of the 2 districts, teachers were identified that were
particularly known for their motivation and interest in using technology in their
classrooms. In education reform, a common obstacle is local implementation
(McLaughlin, 1990). K-12 technology researchers frequently observe that although
classrooms receive much investment in computer technology, teachers infrequently or
almost never adopt the tools in practice (Cuban, 2001). With little or no use, one cannot
expect any effects of a new technology on educational outcomes. Thus, implementation
looms large in any new initiative or intervention. I attempted to control for this major
factor by recruiting teachers who were most likely to utilize a new technology. District
leaders and high school principles nominated teachers who fit this profile and they were
sent an email asking for their participation. With the help of the district technology
coordinators, I also visited the high schools to speak in-person with the nominated
teachers.
A total of 10 high school teachers agreed to participate in the study: 5 Social
Studies, 4 English, and 1 Science. The teachers taught an average of 5 class periods,
which resulted in a sample of 50 separate class periods. Periods had an average class size
 
 
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of 27 students with a total sample of 1,349. To control for any effects of teacher or
subject matter, class periods were randomized within each teacher. For example, each of
the 10 teachers had class periods in both the treatment and control groups. After
randomization, 22 class periods and 528 students were assigned to the experimental SNS
site. In addition, 28 class periods and 821 students were assigned to the control group.
Descriptive statistics of the classrooms are provided in Table 4.3 below.

Table 4.3: Characteristics of Classrooms in SNS and Control Groups

Social Network Site
(n = 22)
Control Group
(n = 28)

% Male 44% 51%
% Female 56% 49%

% White 42% 34%
% Hispanic 30% 37%
% Black 4% 3%
% Asian 23% 26%
% Other < 1% < 1%

% Free Lunch 21% 17%

Prior GPA 3.0 2.9
ELA CST Score 374 368

ELA CST = English Language Arts, California Standards Test

Classrooms in the treatment and control groups did not differ significantly along
demographic indicators. The only statistically significant difference occurred with
gender. The control group had a higher proportion of male students compared to the SNS
group. The classrooms did not differ in measures of prior achievement including grade
point average (GPA) and standardized test scores. The results suggest that the
randomization procedure worked fairly well for this sample of 50 class periods.
 
 
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One major challenge of this study is the possible cross-contamination across
treatment and control groups. Many randomized studies of classrooms or students face
this potential obstacle because students in the same school may likely know who has been
placed in either group. The potential exists for students to speak to one another or behave
in ways that reduce the effectiveness of the intervention. In this study, class periods were
the unit of randomization. Thus, it is possible for students to be placed in both the
experimental or control groups if they were members of specific class periods. In the total
sample of 1,349 observations there were 111 students who are duplicated or were
members of multiple classrooms. Of 111 students, 45 received both the SNS intervention
and control based on their classroom membership. This minimal level of cross-
contamination does not pose a large threat to the study design. I collected various
measures of how the SNS was used, also known as implementation fidelity (detailed
below). The overall level of student and teacher use of the SNS was extremely low, and
the threat of treatment and control spillover is quite minimal.
Social Network Site Intervention
A private social network site was built for use in the 2 districts. The network site
approximated all of the functionality seen on popular SNS. Students and teachers could
create profiles, add friends to their social networks, and post a variety of data including
public text messages (often called wall posts), comments, personal messages, pictures,
video, and other files. The site also allowed for blogging and wiki capabilities. Blogs are
 
 
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personal journals that are written online and can be viewed by other users. Wikis are
collaborative pages that multiple users can edit together to create shared information
resources.
At the beginning of the 2009-2010 school year I met individually with each
teacher. During this meeting, teachers were trained on how to utilize the website and all
research procedures including timeline and survey distribution. In addition, I worked with
each teacher to brainstorm ways to utilize the SNS for their particular courses and topic
areas. For example, two English teachers were teaching a module on the Scarlett Letter
and planned to have their students upload their writing samples to the SNS. They planned
to encourage their students to comment and edit their peers’ assignments over the time
span. The science teacher planned to use the SNS for students to collaboratively create
Wiki pages based on their Earth Science and Physics concepts. His hope was for students
to create study guides together that reviewed the topics they covered. The goal of the
individual teacher meetings was to plan how each teacher might implement the SNS as it
made sense for their particular curriculum and practice. Immediately after the teacher
meetings and orientation, the SNS classrooms used the site for a period of 6 weeks.
Data Collection
At the end of the evaluation period, teachers implemented the posttest survey. The
survey (Appendix A) included questions that assessed students’ level of school
engagement, offline social capital and online social capital (discussed in detail below).
Students were asked if they were Facebook or MySpace users. Students and teachers
were also given open-ended response questions that asked how often they used the SNS
 
 
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and reasons for their use or non-use of the intervention. The posttest response yielded
complete data for approximately 870-930 students (depending on the various outcome
measures). In addition to survey data, I collected information on website usage. The
social network site is built with an underlying database that records user activity. The
database provides information such as the number log-ins and interactions that occurred
on the website during the 6 week period. This quantitative data combined with the
qualitative survey responses supply evidence to the fidelity of implementation, or how
much the teachers and students actually used the tool.
Finally, the participating school districts provided administrative data on all
students who participated in the study. I obtained demographic information including
age, gender, ethnicity, and free lunch status. In addition, the districts provided two
measures of prior academic achievement: the students’ cumulative GPA prior to the
beginning of the school year and their English test scores on the California Standards
Test CST). I merged this administrative data with the survey responses to form the
complete dataset.
Outcome Measures and Covariates
Hypotheses 1-4 consider the effects of social network sites on students’ social
capital, school engagement, and academic achievement. I collected 4 outcome measures
to examine these confirmatory questions.
School Social Capital (SSC): To assess students’ level of school social capital, I
utilized the Internet Social Capital Scales (ISCS) from Williams (2006). The scale was
modified from questions that measure an individual’s offline social capital. Four
 
 
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questions create the summated scale and assess the quality of students’ relationships in
their school community (see Appendix A, Question 8a-d). Students were asked to what
degree they agreed with various statements concerning social support in their school
(range from 1-4). In terms of internal reliability, Cronbach’s α for this scale was .715.
Online Social Capital (OSC): The Internet Social Capital Scales also consist of
items to measure individuals’ level of social capital in online relationships (Williams,
2006). Five items were used to ask students how connected they felt to others based on
their online interactions (Appendix, A, Question 8f-j). Like the SSC scale, students
responded with levels of agreement to each question (range from 1-4). The Cronbach’s α
for this scale was .909.
School Engagement (SE): In this study, I utilize a modified measure of
emotional engagement with school developed by Christophel (1990). The scale consists
of 10 questions that ask students how they feel about their school (Appendix A, Question
6). The original scale asked students about a particular class, but was modified in this
study to ask students about their school community in general. Students indicate from a
1-5 scale their feelings about high school; for example how excited or unexcited they feel
about their school. The Cronbach’s α for the school engagement scale was .903.
Course Performance (GPA): The final outcome measure was an indicator of
students’ academic performance. Teachers provided estimates of student course grades at
the end of the experiment, which were converted into a 0-4 GPA scale.
Covariates: As noted earlier, the power calculations show increased statistical
power when one utilizes regression models with baseline covariates. To maximize
 
 
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statistical power given the sample of 50 classrooms, I utilize two control variables in the
data analysis. The models include students prior cumulative GPA and gender. I include a
measure of prior academic achievement because it is a strong predictor of future
achievement and also correlated to social capital and engagement (Dika & Singh, 2002;
Fredericks et al., 2004). I include gender because the experimental and control groups
still differed in gender makeup after randomization. The reference group for gender is
female.
In addition to the control variables, I also include three variables that directly
relate to the exploratory question in this study. Three dummy variables describe which
social network sites a student is a member of: only Facebook, only MySpace, or whether
the student uses both. The reference group is students that do not use any SNS. The
exploratory research question asks whether student use of already existing networks has
any relationship to social capital, school engagement, or achievement. These variables
allow an analysis of the relationship in comparison to the effects of the experimental
network site.
Fidelity of Intervention Measures: To assess the fidelity of treatment I utilize
two data sources. First, open-ended survey responses from teachers and students offer
evidence of how well the classrooms utilized the social network site. The qualitative data
also sheds some light on the challenges and potential of SNS in educational contexts. In
addition, I coded the website database, which includes a record of all users and their
 
 
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activity during the experiment. The data includes how many teachers and students logged
into the site, the number of times they updated their profile, and the number of times they
interacted with others on the SNS.

Table 4.4: Descriptive Statistics of Dependent Variables and Covariates

Mean Standard Deviation Min Max
School Social Capital 12.29 2.42 4 16
Online Social Capital 13.07 4.03 5 20
School Engagement 34.47 8.13 10 50
Course Grade 2.68 1.33 0 4

Prior GPA 2.96 0.87 0 4
Gender 0.49 0.49 0 1
Facebook 0.21 0.41 0 1
MySpace 0.26 0.44 0 1
Both SNS 0.35 0.48 0 1

Data Analysis
I utilize a random effects (RE) model that accounts for the classroom-clustered
data in the research design. The model is specified as:

Y
ij
= β
0j
+ β
1
treatment
j
+ β
2
facebook
ij
+ β
3
myspace
ij
+ β
4
bothsns
ij
+
β
5
prior_gpa
ij
+ β
6
gender
ij
+ u
j
+ r
ij

In the model, Y
ij
refers to the dependent variable for student i in classroom j. The
intercept β
0j
is the population average of classrooms and the term u
j
represents the
between classroom variance. The coefficient β
1
is the effect of the experimental treatment
and is the primary focus of hypotheses 1-4. The coefficients β
2
- β
4
are the relationships
between students’ existing social networks and the outcome. These variables – facebook,
myspace, and bothsns – directly address the exploratory research question of whether
 
 
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existing social networks are related to students’ outcomes. The model includes control
variables for prior achievement, gender, and finally the term r
ij
represents the within
classroom variance. Note that the RE regression is one case of a multi-level or
hierarchical linear model (HLM), where the intercept or classrooms (β
0j
+ u
j
) are modeled
as random effects.
Missing Data
Missing data on the outcome variables was a significant challenge. The majority
of district data such as student demographics and prior achievement had nearly no
missing data. However, outcome variables had significant missing values. Table 4.5
summarizes the frequencies of subjects with complete and missing data based on the
dependent variables in this analysis. For every model, approximately 30-35% of data
consisted of missing data. The key question is whether the data is missing at random
(MAR), as then one might utilize imputation methods for the missing values (Royston,
2004). Examining the correlations between missing-ness on each outcome and student
background variables showed small relationships. Most correlations ranged from 0.00 to
0.15 and suggest that data were not missing completely at random (MCAR).
Nevertheless, the very small correlations between missing-ness and covariates are
evidence for MAR.

 
 
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Table 4.5: Percentage of Missing Data on Dependent Variables
Dependent Variable Complete (%) Missing (%) Total

Course Grade 936 (69%) 413 (31%) 1,349

School Social Capital 869 (64%) 480 (36%) 1,349

Online Social Capital 873 (65%) 476 (35%) 1,349

School Engagement 880 (65%) 469 (35%) 1,349

With nearly 35% of missing outcome data, the effective cluster sample size
ranges from 35-41 classrooms. This reduction severely impacts statistical power and
since missing-ness was not MCAR, might bias beta estimates and standard errors. To
alleviate these issues, under the assumption that data was MAR, I utilized multiple
imputation using the ice program available in STATA (Royston, 2004). The algorithm
creates 5 datasets of imputed values based on existing covariates. Regression analysis is
conducted on each individual dataset, and the final beta and standard error estimates are
derived from the average of these separate analyses. I compared results using both list-
wise deletion (no missing data handling) and the imputed datasets. The general trend with
the imputed data shows no significant differences in beta estimates. The standard errors
were slightly higher in the imputed data leading to somewhat more conservative tests of
significance. However, these differences did not change any interpretations or
conclusions from the data.
Findings
Fidelity of Intervention
The overall implementation of the social network site faced numerous obstacles.
Only 203 of the 528 students in the treatment group (38%) used the experimental SNS.
 
 
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Their activity also ranged dramatically. On average, the participating students logged in 4
times in the 6-week period. They updated their profiles an average of 2 times. They
interacted in the website – created content, wrote messages, or shared files – an average
of 42 times. This activity level pales in comparison to the hours youth spend on their
already existing networks on Facebook and MySpace. Logins, profile updates, and
interactions would plausibly reach thousands in a similar 6-week period.
While all efforts were made to recruit teachers that were comfortable with
technology and motivated to participate in the study, the general level of teacher adoption
was low. Only 5 of the 10 teachers logged into the website during the 6-week period. Of
these individuals, 3 teachers logged in more than once to create activities for their
students. In follow-up responses, the teachers identified numerous reasons for the
difficulty in using the SNS. Most all of the teachers cited lack of available technology in
their schools and student motivation as the main factors in non-use. For example, an
English teacher stated, “The greatest challenge was computer availability. Some students
also struggled with the log in process. What also made the assignment process difficult
was that only about half the kids were doing it per period so it was difficult to grade.”
Several teachers noted that it is difficult to motivate students to participate in
something new, when overall levels of motivation were quite low for academics. One
Social Studies teacher stated, “my first period class – most do nothing so I knew they
would not get into this…” and another English teacher observed that, “The potential is
there; it could be a very useful tool, but I did not get my students to buy in, yet.” The
most pervasive comment by teachers spoke to the difficulty of adding new curriculum or
 
 
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projects to their existing plans. One teacher’s observations communicated this general
sentiment:
The biggest problem was that the technology was tacked on to an established
curriculum and routine. The students saw that I was testing the waters (I was
quite up-front about this) and that failure to participate would not have more than
a minor effect on their grades. For many students, the grade is god; if I don't
lower a grade for failure to complete work, they will not do the work. I imagine
that if I set up my profile and got a few groups, links, blog posts, and other "stuff"
on the site before the first day of school, the tool would be more useful.

As with many interventions and reforms, the organizational and cultural structure of
public schools provided significant obstacles for teachers to implement a new technology.
Not surprisingly, student response to the SNS was indifferent at best. Many
students logged in, created profiles, and chatted with others on the site. A few students
uploaded assignments or examples of their work, but academic activity was very
minimal. The majority of students indicated that the SNS was not particularly useful for
them because it did not relate to their final course grades.
However, many students felt that social network sites could be beneficial for
school-based goals. Several students suggested that they would definitely use SNS for
clubs, activities, and to keep in touch with everything happening on campus. Some
students noted that, “People feel comfortable with expressing themselves as well” and
viewed SNS as an outlet for student communication. Many youth felt that students might
use SNS to help each other to complete homework and assignments. Finally, many
participants noted that their peers already use other social network sites. When asked if
SNS should be used in school, one teenager stated, “They already are, just not directly…
 
 
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just to know about homework or a test.” Another student also raises the possibility for
schools to leverage their already existing networks on sites such as Facebook and
MySpace:
I think the problem is that there are already too many social networking sites. This
kind of idea needs to be incorporated into existing sites as students are not likely
to switch.

The qualitative responses from teachers and students suggest two considerations. First,
implementing a new technology in school settings requires overcoming many obstacles
including teacher adoption, student motivation, and the structure of curriculum and
assessment in K-12 education. With minimal adoption, particularly in this study, it is
unlikely that technology evaluators will find significant effects on students’ educational
outcomes due to social media tools. Second, as the students noted in their survey
responses, schools might be able to leverage already existing tools and social networks to
engage students. I evaluate these possibilities in the next sections.
Hypothesis 1: Did the Experimental SNS Improve Students’ School Social Capital?
The first hypothesis considers whether the experimental SNS had any effect on
students’ sense of social capital in their school relationships. The variable – Treatment –
in Table 4.6 below shows that the school-based SNS had no statistically significant effect
on school social capital. The coefficient is also negative, which indicates that the
experimental SNS slightly lowered students’ feelings of connection with their school
community. The findings, when coupled with the implementation evidence, suggest the
possibility that social network sites could have potentially negative effects on school
communities when implemented poorly. A SNS is fundamentally dependent on user
 
 
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interactions and if no one is using the site, the online community can be a lonely place.
Perhaps the minimal adoption of the experimental SNS actually left students feeling less
connected to their peers. One cannot assess whether a successfully implemented SNS
would improve school-based relationships, but the results of this study offer evidence that
how educators implement social technologies in classrooms can potentially have negative
effects if done poorly.

Table 4.6: Result of Random Effects Models for each Dependent Variable

School Social
Capital
Online Social
Capital
School
Engagement
Course Grade

Intercept 11.21
(0.46)
10.06
(0.60)
29.42
(1.85)
-0.49
(0.20)
Treatment -0.25
(0.19)
-0.22
(0.35)
0.11
(0.72)
-0.26
(0.14)
Facebook -0.07
(0.34)
2.27**
(0.41)
-0.003
(0.84)
-0.02
(0.09)
MySpace 0.09
(0.36)
1.51**
(0.37)
0.53
(0.81)
0.02
(0.12)
Both SNS 0.47
(0.27)
3.05**
(0.33)
0.61
(0.65)
0.04
(0.07)
Prior
Cumulative
GPA
0.37**
(0.09)
0.35**
(0.15)
1.65**
(0.44)
1.14**
(0.04)
Gender -0.16
(0.25)
0.24
(0.31)
-0.47
(0.64)
-0.06
(0.07)

Standard errors in parentheses, * p < 0.10, ** p < 0.05

Hypothesis 2: Did the Experimental SNS Improve Students’ Online Social Capital?
The second hypothesis examines whether using the experimental social network
site improves students’ social capital in their online relationships. The Treatment variable
(Table 4.6) did not have a statistically significant effect on students’ Online Social
Capital. Similar to the case of School Social Capital, the negligible use of the intervention
probably offered few opportunities for youth to develop relationships in the online
 
 
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community. When 70% of your peers are not using the site you are on, the online
community may feel like an isolating experience.
Hypothesis 3: Did the Experimental SNS Improve Students’ Emotional Engagement with
School?
The third hypothesis considered whether using the school SNS improved
students’ emotional engagement with school. The Treatment variable (Table 4.6) did not
have a statistically significant effect on how engaged students felt with school. Similar to
hypotheses 1 and 2, the evidence shows that a school-based SNS site will have no effect
on students’ emotional engagement with school when the technology is not received well
by teachers and students. The finding appears to contradict the hope of the majority of
education administrators, who believe social media might help improve student
engagement. However, this study only shows that such gains will not occur from poorly
implemented technology alone. The question, “What would happen if we successfully
connected students via social technologies in school?” still remains for investigation.
Perhaps a vibrant and active online school community may yet increase student
engagement.
Hypothesis 4: Did the Experimental SNS Improve Student Achievement?
Finally, the experimental SNS had no significant effect on students’ course grades
(Table 4.6, Treatment variable). The single greatest predictor of a students’ performance
in a given class was their cumulative GPA prior to the start of the school year. Not
surprisingly, a student’s prior academic history largely determined how well they
performed in any given class or subject. Again, the minimal use of the social network site
 
 
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prevents any definitive conclusions from this analysis. The results offer practical
implications however. Merely introducing social technologies into formal classrooms do
not hold much promise to improve students’ academic performance. As many educational
media scholars have noted in past research, the pedagogies that teachers employ with the
social media tool may offer the most plausible connections to improved student learning.
Exploratory Results: School SNS vs. Facebook and MySpace
The exploratory research question asks whether a school-based network site has
differential effects on students compared to their already existing networks in Facebook
and MySpace. The variables Facebook, MySpace, and Both SNS (Table 4.6) directly
allow for a comparison of different online communities with the experimental treatment.
For School Social Capital, none of the social network sites appear to affect students’ level
of connection to their school community. However, those students who used both
Facebook and MySpace had a 0.47 increase in their social capital score compared to
students who do not use any SNS (α < 0.104). This beta estimate yields an effect size of
approximately 0.19 standard deviations which would reach significance had this study
been conducted with a larger sample of classrooms (i.e. 100). The results suggest, albeit
exploratory, that those students who are particularly adept at developing relationships in
social network sites could be better connected in their school communities.
The relationship between SNS use and Online Social Capital is even more
striking. The school-based SNS had no significant effect. However, students who use
Facebook had substantially higher online social capital than their peers who do not use
any SNS (see Table 4.6). Facebook users scored approximately 0.56 standard deviations
 
 
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higher in Online Social Capital. MySpace users also had significantly higher social
capital, at roughly 0.37 standard deviations. Finally, students who are members of both
Facebook and MySpace scored about 0.75 standard deviations higher in social capital
than peers who do not use any. These teenagers are what scholars might call super-
communicators, who use social technology and media tools to connect with wider
networks (Lenhart, Madden, Macgill, & Smith, 2007b). It appears that high school youth
develop positive relationships and social capital in their online activities.
These exploratory findings provide evidence for a critical question facing K-12
administrators and technology directors: Should schools ban access to tools like
Facebook and MySpace, and instead use district-controlled technology? The majority of
school districts utilize this strategy because of concerns about student safety in these
online communities. However, this study demonstrates that a school-based social network
site produces no significant benefits, and at times slightly negative effects on students. A
district-imposed SNS that is poorly used and frankly “un-cool” for students may not
produce the benefits of strengthening student relationships and engagement. Instead, as
the students noted in open-ended survey responses, youth are already members of
existing social networks sites like Facebook and MySpace. These teenagers are also
developing relationships and bonds, social capital, in these communities. If the goal is to
strengthen student connections to their school, the best strategy may be to meet students
where they already are, instead of imposing unnecessary constraints.

 
 
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Chapter 4 Conclusions
Technology is here to stay. K-12 educational institutions may be the last major
sector to fully utilize new digital tools (Cuban, 2001). However, students are increasingly
wired and connected via computers, mobile devices, and social media tools like SNS. The
pressure for schools to use social media to engage students is increasing as policymakers,
researchers, and adults begin to comprehend how integrated these tools are in youth life
(Jenkins, 2006). This study offers an evaluation of a popular tool – the social network site
– and its effect on student outcomes. The results of this study suggest that a school-
imposed social network, which is poorly received by students, will have no significant
effect and perhaps negative effects on student-school relationships, engagement, and
GPA. However, students’ existing social networks on sites such as Facebook and
MySpace may provide ways to improve student relationships with peers, teachers, and
the school community at large. Perhaps K-12 educators might consider leveraging these
existing social networks, rather than banning access to these technologies as many
districts currently do.
The cursory cost-benefit implications of these findings are substantial. Tools such
as Facebook and MySpace are free and widely available. The main costs for school
districts arise from the need to develop policies that allow student access to these sites,
while avoiding issues of youth safety and inappropriate access to online material. These
costs seem quite minimal when compared to the benefits of increasing digital literacy
among youth and improving student-school relationships. Just as corporations and
businesses utilize social networking tools to connect with consumers and build brand
 
 
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loyalty, schools might leverage existing social networks to strengthen relationship
between students and the larger community. The upfront costs are minimal, but the
benefits could be significant.
The limitations of this study also illuminate the challenges of implementing new
technology in K-12 schools, and the subsequent obstacles to realizing positive student
outcomes. The experimental SNS had no significant effect on student outcomes, with
slightly negative relationships to social capital and student achievement. However, much
of these findings can be attributed to the lack of implementation and usage of the site.
Students use Facebook and MySpace with enthusiasm. They did not use the school-based
SNS and felt it was not very hip or useful. When critiquing this study on grounds of
research design, one would note this behavior as lack of implementation fidelity. In
actual K-12 schools, these trends are real challenges to overcome in order to realize any
potential effects of social media. Future studies should critically consider
implementation: the training of teachers, extensive planning and integration of the new
technology into existing curriculum, and resources such as coaches to ensure that
teachers in fact implement the intervention. Similarly, school districts might think twice
about building and utilizing their own in-house social network tools. A poorly
implemented site will have no benefits. However, students are already invested in
existing sites such as Facebook and MySpace. The more effective option may be to
leverage this energy in positive ways, instead of imposing obstacles.
Another limitation of this study concerns the sample size of 50 classrooms. Future
studies will need to consider larger samples, nearing 100 classrooms or more, to have
 
 
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sufficient power to detect smaller (but substantial) effects. The issue of randomized
control also plays a contradictory role when evaluating social media. Randomized control
allows the researcher to more effectively attribute cause to the intervention. However,
when randomizing students or classrooms to use a social network, one necessarily
delimits the social network.
In this study, students in the treatment classrooms could have had friends in the
control classrooms. Since some friends were not included in the network site, treatment
students might not be as compelled to use the site since their friends were not using the
site. Social technology relies on connection and interaction with networks. Randomized
control relies on separation into distinct comparison groups. Future studies may have to
consider entire schools as the unit of randomization, to improve implementation and
realize any network effects. These designs will require substantially more financial and
human resources.
The most promising future studies might consider utilizing Facebook, MySpace,
or other social media tools. Researchers might create school-based applications (i.e. apps)
or group pages on these sites. One cannot randomly assign students to Facebook or
MySpace, but participants might be randomly invited to use the app or join the network
group. These types of interventions might ensure higher implementation fidelity, while
offering better external validity as to how these technologies affect students, schools, and
education communities. This study is one of the first to (a) examine the media effects of
social technology on youth, (b) consider outcomes that relate to education concerns, and
 
 
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(c) utilize a randomized control design. Future studies in this vein will be imperative as
technology becomes ever more present in the way educators deliver education, the way
youth learn, and the way society communicates in the future.
 
 
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Chapter 5: Future Directions for Research on Social Network Sites

The chapters in this dissertation contribute several insights into the use of social
network sites amongst teenage youth. A review of the extant literature (chapter 2)
suggests that SNS research is currently in a very early, exploratory stage of development.
The majority of studies focus on defining social network sites themselves, or examining
the cultural and social practices of particular online communities such as Facebook,
MySpace, YouTube, or Dodgeball (boyd, 2006; boyd & Ellison, 2007; Humphries, 2007;
Walther et al., 2008; Walther et al., 2009). Researchers are also interested in usage
patterns, or whether there are gaps in access to these new social media tools (Hargittai,
2007; Lenhart et al., 2007b). Even less evidence exists surrounding the use and effect of
social technologies on youth, their development or their academic success (Ito et al.,
2009; Karpinski, 2009a).
The two empirical studies presented here offer additional contributions to this
nascent field of research. Chapter 3 examined digital divides among teenage populations
and their access to SNS, and was a direct extension of past digital divide research in adult
populations (i.e. Hargittai, 2007). The findings suggest that teenage youth are
increasingly connected to social network sites and that these online communities may
play an increasing role in their daily interactions and communication. Chapter 4
presented one of the first attempts to experimentally discern any social and learning
effects of SNS on teenage youth. The cluster-randomized trial findings teach us that
social network sites, as a technological tool, do not significantly affect youth social
 
 
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capital, psychological well-being, or their academic achievement. However, I find
exploratory evidence that teenagers’ existing social networks on Facebook and MySpace
were significantly, and positively related to their levels of social capital. The studies offer
two major implications for future research in this domain: (a) the need to develop a
coherent research stream amidst a sea of new social media tools, and (b) the importance
of taking into account social context when studying online communities.
Making Sense of Social Network Sites as They Evolve
Studies of social network sites will likely become a major area of focus in the
coming years. New SNS communities surface everyday. For example, early sites such as
Friendster and MySpace gave way to Facebook, and only recently has Twitter become a
highly utilized social network site. Current research typically focuses on particular online
communities. Some sites, such as Facebook, have succeeded and others, such as
Dodgeball, have disappeared. A multitude of new social network sites emerge and fade
away each day. One critical challenge for future SNS researchers will be to make sense of
the different communities over time and create a coherent picture of socially networked
communication. Studies of particular communities are liable to become quickly obsolete
as technology evolves rapidly.
However, SNS researchers have a unique opportunity to create coherent findings
because social network sites have quickly become the over-riding paradigm of Internet
technologies. As Livingstone (2008) observes, SNS appear in just about every website
available today. For example, YouTube is a video sharing site but is structured around
profiles and friend networks. One can now visit CNN.com to read the daily news, but
 
 
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also share articles by linking their Facebook accounts. Even traditional Internet services
such as email integrate social network site features, such as the Gmail service from
Google. The definition of SNS – profiles, friends, and networks – remains stable, but its
implementation across new Internet technologies is varied. This situation affords future
researchers the ability to work from a similar foundation, but also critically examine how
social network sites are used in different contexts.
There will always be renewed interest in whichever new technology emerges in a
given time period. Research that closely examines and documents new media tools, the
populations that use them, and the cultural conditions that arise from them, will always be
vital. However, scholars will make the most impact if they can begin to relate new social
technologies to past media tools. How does a given technology’s features and design
differ, or align, with other major media platforms? What are the populations that use a
given technology, and how are they similar or different than users of other social network
sites? What are the cultural and social norms that govern interaction and communication
in different social network sites? How are these communication patterns related to the
design and function of the online community? These are the type of questions that can
simultaneously describe new developments in technology, but also link to past research
to create a coherent field of study.
The Importance of the Social Context Surrounding SNS
Another implication of this dissertation is the need for future research to consider
the social context that surrounds a given technology. The randomized trial presented in
Chapter 4 highlights how a strict media effects framework ultimately offers limited
 
 
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insight into the impact of social network sites on youth. The treatment in this experiment
was a social network site that was introduced into a high school classroom context.
However, the lack of use of the technology is likely the main contributor to the non-
significant effects found for the students. The treatment, or lack of use of the SNS,
resulted in no significant effects on high school youth on any of the outcomes: social,
psychological, or academic. As previous media researchers have found, a technology
itself has no direct effect on social outcomes because it is confounded with the use of the
tool (Clark, 1983; Clark, 1991; Kling, 2007).
Scholars from a variety of fields agree that new technologies interact with social
factors to ultimately determine how a tool is used, and what effects they have on
organizations and individuals (Fulk & DeSanctis, 1999; DeSanctis & Poole, 1994; Kling,
2007). A given technology can be utilized in a variety of ways depending on the user,
their goals, and their social context. Social network sites are also utilized in diverse ways.
Youth use them to socialize with friends. Corporations utilize social media to advertise
their products and build customer loyalty. Professionals use them to keep abreast of new
information. Political campaigns utilize social media to mobilize voters. Schools might
use them to network students and enhance academic activities.
Given the disparate uses of the same technology, it is difficult to attribute any
causal effects to a social network site. A SNS alone does not cause youth to learn more or
less effectively. However, teachers might use a social network technology in such a way
that leads to highly effective student learning. The onus for future education technology
researchers will be to discern: (a) how new social media tools change the teaching and
 
 
125 
learning process, (b) what educational practices lead to improved student learning, (c) for
which populations these practices are most effective, and (d) how the cultural and
institutional setting of formal and informal education affects the use and subsequent
effects of a technology. The technology, setting, audience, and utilization are all factors
that future researchers must control for when examining the media effects of social
network sites.
Chapter 5 Conclusions
Teenagers spend a considerable amount of time in social network sites. These
online communities represent a significant setting – perhaps just as noteworthy as home
and school – within which youth socialize, learn, and develop. The current moment in
history affords scholars a unique opportunity to examine SNS, with the adoption of the
technology skyrocketing in just the past few years. The studies in this dissertation
contribute to the nascent discussion surrounding social network sites and teenagers, but
much work remains in this field. As new social technologies emerge and more youth
become connected to online networks, questions of who accesses these communities and
what effects they have on youth become vital considerations for parents, educators, and
policymakers.
Sharples et al. (2009) observe that, “At present, schools are caught between the
rock of parental fears about Internet abuse and the hard place of helping children to
develop responsible and creative use of Web 2.0 for learning” (p. 82). Researchers
interested of SNS have a unique opportunity to inform these concerns. Continued
research in this area is needed to inform critical education policy concerns. Should
 
 
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schools block access to SNS and other social media? How should teaching and learning
occur with social tools like SNS? Finally, Zhao & Lei (2009) note that, “today’s children
are spending more and more time in the digital world… Future research needs to explore
the psychological and cognitive impacts of technology use on children, to understand
how time spent on the Internet affect children developmentally” (p. 688). Technology
will never go away, and social media will continue to shape our children’s life
experiences in ever-deeper ways. The question of how new technologies impact our next
generation of youth, both socially and intellectually, becomes one of the most pivotal
questions for both scholars and society as a whole.

 
 
127 
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Appendix A
1. What is your District/Student ID Number: __________________________________

2. What is your teacher and class period: _____________________________________

3. Do you use MySpace? (Yes/No) __________________________________________

4. Do you use Facebook? (Yes/No) _________________________________________

5. We’d like to know a little about your school experience. Please indicate how true
each statement below describes you (check one on each line).

Not True At
All
Not Very
True
Sort of
True
Very
True
The most important thing for me
right now is improving my grade
point average.

I ask my teachers to clarify things I
don't understand well.

When I can't understand something
in my classes, I ask another student
for help.

Getting a good grade in my classes
is the most satisfying thing for me
right now.

I often come to class unprepared.

The most satisfying thing for me is
to understand what I learn in
school.

In my classes, I prefer material that
sparks my curiosity, even if it is
difficult to learn.

 
 
140 
Even if I have trouble
understanding something in class, I
try to do the work on my own,
without the help of anyone.

When studying for an exam, I
usually set aside time to study with
a group of classmates or my
friends.

When studying for my classes, I
often try to explain the material to
a classmate or friend.

I prefer classes that really
challenge me so I can learn new
things.

I work very hard in school.

I want to do well in my classes
because it is important to show my
ability to my family, friends, or
others.

I try to work with other students to
complete my school assignments.

6. On a scale from 1 to 5, how do you feel about your high school? (check one on each
line)

1 2 3 4 5
Important (1) or Not Important (5)

Involved (1) or Uninvolved (5)

Useful (1) or Not Useful (5)

Enthusiastic (1) or Not Enthusiastic
(5)

Stimulated (1) or Not Stimulated (5)
 
 
141 

Looking forward to it (1) or Dreading
it (5)

Invigorated (1) or Not Invigorated (5)

Excited (1) or Not Excited (5)

Helpful (1) or Not Helpful (5)

Interested (1) or Uninterested (5)

7. How many teams, clubs, school groups, or extracurricular activities are you a part of?
(check one)

One Two Three Four More than Four

8. How strongly do you agree with these statements?

Strongly
Agree
Agree Disagree Strongly
Disagree
A. There are people in my high school I
trust to help solve my problems.

B. There is someone in my high school I
can turn to for advice about making very
important decisions.

C. There is no one in my school that I feel
comfortable talking to about personal
problems.

D. When I feel lonely, there are several
people in my school I can talk to.

E. If I needed an emergency loan of $500,
I know someone in school who I can turn
to.

F. Interacting with people online makes
me interested in things that happen outside

 
 
142 
of my town.

G. Interacting with people online makes
me want to try new things.

H. Talking with people online makes me
curious about other places in the world.

I. Talking with people online makes me
feel like part of a larger community.

J. Interacting with people online makes me
feel connected to the bigger picture.

* Only answer the following questions if you were in a class period that used the social
networking site.

9. Did you use the site often or rarely? _______________________________________

10. Did you find the social networking site useful in your classroom?

11. What do you think could be done better next time to motivate students like yourself to
use a social networking site in your classes? (answer in the space below)

Do you think social networking sites should be used in school? What kinds of school
activities would you use a networking site for? (answer in the space below)

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Asset Metadata

Creator Ahn, June (author)

Core Title The influence of social networking sites on high school students' social and academic development

School Rossier School of Education

Degree Doctor of Philosophy

Degree Program Education

Publication Date 07/13/2010

Defense Date 06/01/2010

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag education,High School,Learning and Instruction,OAI-PMH Harvest,social capital,social media,social networking sites,Technology

Place Name USA (countries)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Brewer, Dominic J. (committee chair), Fulk, Janet (committee member), Hentschke, Guilbert C.. (committee member)

Creator Email ahnjune@gmail.com,joonbug182@hotmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-m3188

Unique identifier UC1236292

Identifier etd-Ahn-3857 (filename),usctheses-m40 (legacy collection record id),usctheses-c127-352705 (legacy record id),usctheses-m3188 (legacy record id)

Legacy Identifier etd-Ahn-3857.pdf

Dmrecord 352705

Document Type Dissertation

Rights Ahn, June

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Repository Name Libraries, University of Southern California

Repository Location Los Angeles, California

Repository Email cisadmin@lib.usc.edu