Community (in) Colleges: The Relationship Between Online Network Involvement and Academic Persistence at a Community College*

Community (in) Colleges: The Relationship Between Online Network Involvement and Academic Persistence at a Community College* Eliza D. Evans, Daniel A. McFarland Graduate School of Education, Stanford University, Stanford, CA, USA Cecilia Rios-Aguilar Claremont Graduate University, Claremont, CA, USA Regina Deil-Amen The University of Arizona, Tucson, AZ, USA Keywords community colleges, higher education, social networks, retention *ACKNOWLEDGMENTS *FUNDING This material is based upon data collected through a grant funded by The Bill & Melinda Gates Foundation and work supported by the Stanford Graduate Fellowship Program and the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE-114747. *Corresponding Author: Eliza D. Evans, Stanford Graduate School of Education, 485 Lasuen Mall, Stanford, CA 94305, USA Email: elizaevans@stanford.edu

2 Abstract This study explores the relationship between online social network involvement and credit completion at a community college. Prior theory hypothesizes that as students become socially integrated into a college, they are more likely to persist. This theory has weak empirical backing in community colleges, due primarily to ill-defined or absent communities. Online social networks offer a new and viable forum for social integration on these campuses. Does involvement with online social networks positively influence academic persistence at a community college? This study finds evidence of peer influence in online friendships: the academic performance of a student s online friends is predictive of the student s likelihood of academic persistence. However, contrary to expectations, an increasing number of network friendships reduces the likelihood that the student completes all attempted credits. In light of these findings, we offer new ways of thinking about the contents, costs, and benefits of social networks at community colleges.

3 INTRODUCTION Community colleges are the mass educators of the American higher education system, teaching a wide range of students for a low cost. In fall of 2012, 45 percent of American undergraduates were enrolled in community colleges (American Association of Community Colleges 2013a), and for their education, they were paying a much lower average cost than that paid by students in other sectors of higher education. 1 As a result of the low-cost, open-access education they provide, community colleges have become a primary focus of national programs for increasing access to and completion of postsecondary education (Lewin 2012). Community colleges, however, have a weak history of retention and academic performance (Braxton, Hirschy, and McClendon 2004; Rosenbaum, Deil-Amen, and Person 2006), a history that makes it difficult to fulfill their educational goals. As measured in the fall of 2005, only 21 percent of students at public two-year colleges were completing their associates degrees within three years (Aud et al. 2011). For both students and community colleges, this level of attrition is a problem. Completing a degree or certificate is associated with better earnings for students (Belfield and Bailey 2011; Hout 2012), and colleges, in addition to falling short of their educational goals and missives, lose out on tuition money that keeps them financially solvent. What can community colleges do to stem the flood of students out of their institutions? Tinto (1993) theorizes that most students who voluntarily withdraw from higher education do so because of a lack of social and academic integration within their postsecondary institution. They feel isolated from other members of the institution, and as a result, they leave the community. Based on Tinto s suggestions, fostering better academic or social integration for students could help to alleviate the retention problems at community colleges.

4 However, in a review of empirical studies that have tested Tinto s claims, Braxton et al. (2004) find that Tinto s theories receive varying levels of empirical support across different classes of postsecondary institutions, especially when measuring the role of social integration in student persistence. While empirical studies strongly support a positive relationship between social integration and persistence in residential colleges, social integration appears to have no relationship with persistence in non-residential institutional settings a classification that includes 75 percent of community colleges (American Association of Community Colleges 2013b). Braxton et al. conclude that this differential role of social integration arises because there is little to no social community present in non-residential schools. In-person, on-campus social communities are difficult to foster at non-residential community colleges, where students do not have access to the foundational social networks that develop through dorm residence (Festinger, Schachter, and Back 1950; Schudde 2011) or take classes part-time, which limits their exposure to the college and reduces the opportunities for community formation. Without a community, students at non-residential community colleges have no social space into which they can socially integrate and, as a result, have no access to the positive influence of social integration on persistence that benefits their peers at residential colleges. Some community colleges are trying a new platform for fostering stronger campus communities and social integration: online social networks. Online social networks like Facebook or Twitter provide a well-defined and omnipresent space in which social community can develop for students at community colleges. The characteristics of online networks specifically offset the barriers to community formation present in community colleges. An online platform creates a space in which social contact is unbounded by the physical distances between students homes and their campus. Even if students work part-time or live far from each other,

5 they can contact each other through the online network as long as they have internet access. The two-way, unrestricted, and often public communication offered by online networks allows for information exchange and community building. We argue that online social networks serve as a viable community through which students at non-residential community colleges can socially integrate with their peers. As a new space for social integration, the online networks are unlike the weak or absent communities that Braxton et. al (2004) observe in non-residential colleges. An online network is omnipresent, well-defined, and facilitates two-way communication among students, no matter their physical location or part-time status. As a novel and untested space for social integration on nonresidential campuses, online social networks merit empirical testing of how student activities online are predictive of their persistence, offline. We hypothesize a positive relationship between increasing online network involvement and student persistence at a non-residential community college, as Braxton et al. (2004) saw in the empirical studies of social integration at residential colleges. To test our hypothesis, we estimate a series of logistic regression models using data from the 2011-12 academic year and model the relationships between academic persistence and social network involvement on the Schools App network, a Facebook-based network for college students. We explore the association between Schools App involvement and 100 percent credit completion, testing for heterogeneity in this relationship based on the distance a student lives from the college and three different kinds of network activities: joining the network, making network friendships, and joining interest groups on the network. In subsequent analyses, we investigate different contents and structures within the network itself. We ask if the mean academic performance of a student s network friends influences her likelihood of completing all

6 her attempted credits, and we look for variation in the level of influence, based on the structural characteristics of a student s network structure. Through these analyses, we test multiple theories about the relationships between different modes of online social network participation and academic persistence in a previously unexplored setting of online networks at a community college. We explore a new platform for social integration that is accessible to the students of community colleges and may be positively related to persistence, as we see for their peers on residential campuses (Braxton et al. 2004). Our findings fill a gap in the research literature, suggest new directions for future research, and provide empirical evidence for administrative decision-making about the implementation and influence of online networks at community colleges. LITERATURE REVIEW Social Integration and Academic Persistence According to Tinto (1993), fewer than 15 percent of students leave college because they fail to meet minimum academic standards. Instead, he finds that students leave because they feel a lack of social or academic integration into the community. He argues that, in attending college, students undergo a rite of passage in which they become members of a new society and community at their school. Tinto theorizes that voluntary withdrawal from higher education occurred in response to a failure to incorporate into this new community. Student withdrawal, then, is based on social or academic misalignment, not academic performance. Based on his theory, Tinto suggests that institutions should reach out to make personal contact with students beyond the formal domains of academic life (1993:139) to improve retention. The ways in which this kind of outreach facilitates social and academic integration are

7 subtle and small. Tinto emphasizes the need for repetitive contact (1993:98) if students are to integrate into their colleges, because casual and frequent interactions with faculty, staff, or students are the pathways through which integration takes place. While having deep, strong ties among students or between students and faculty mentors can be important for integration, a student s general sense of alignment with and belonging to the institution (or, at the least, a subset of people within the institution) is the key aspect of both types of integration. Tinto s theories have become paradigmatic in the study of student persistence (Bensimon 2007), but Braxton et al. (2004) contend that Tinto s theories may not have empirical support or, if they do, may not apply evenly to all students across all types of postsecondary institutions. For example, a positive relationship between academic integration and student persistence is not strongly supported by empirical work in any of the types of postsecondary institutions that Braxton et al. review; social integration, on the other hand, has varying empirical support across types of institutions. In the case of social integration, Braxton et al. (2004) find strong empirical support for its positive relationship with student persistence in residential colleges and universities, but that in non-residential colleges, social integration is much less if at all related to persistence. The authors state that the absence of residentiality results in weak or nonexistent social communities at commuter colleges and universities (2004:81) and that this lack of well-defined social communities provides an explanation for the failure of social integration to positively impact subsequent institutional commitment (2004: 19-20) at nonresidential colleges. Social integration, then, is not necessarily unrelated to persistence in nonresidential colleges, but instead, received indeterminate support or [was] not the subject of empirical testing (Braxton et al. 2004:17) on non-residential campuses, since there were few communities and little variation in social integration to analyze.

8 We argue that online social networks provide the well-defined and vibrant social communities that non-residential colleges may lack and are a setting in which we can empirically test social integration and its relationship with student persistence in non-residential colleges. In this study, we perform this analysis, empirically testing Tinto s theory of social integration in an online community at a large, non-residential community college. Before we do so, we must address other theoretical questions that undergird our study. First, are online networks communities in the same sense as the offline networks in which conventional social integration occurs for the students of residential colleges? And, if so, can the mechanisms behind social integration and its positive relationship with persistence occur on an online platform? Below we address these questions in turn. Communities: Online vs. Offline Although different disciplines and scholars conceive of it differently, community can be loosely defined as a social network based upon the social exchange of information or goods. If social exchange is happening, community exists to a greater or lesser extent (Wellman and Hampton 1999). This definition does not require any place-based interaction or face-to-face communication, which allows it to work equally as well for online communities as it does for offline communities. However, some scholars dispute this all-encompassing definition of community. They argue that community requires face-to-face interaction, and online activity harms its formation, rather than acting as an instance of community in and of itself (Fox 1995; Slouka 1995). Nie (2001) was a particular critic of online networks, finding that internet users do not become more social through their internet use, but rather are already more social than an average person, because users tend to be younger, wealthier, and more educated than non-internet users. Nie goes

9 on to argue that, since time is a finite resource, time spent online inevitably lowers face-to-face sociability, decreasing a person s real networks, not enhancing them. Wellman (2001) disagrees. Due to widespread internet access, Wellman argues that computer-mediated communication has become a part of everyday life and social networks; online sociability is no longer (if it ever was) a separate, different network from face-to-face networks. Increasingly, people engage in networked individualism (Wellman 2002), which connects individuals in a person-to-person manner, regardless of spatial or temporal restraints. Haythornthwaite s (2005) findings support Wellman s arguments. In a study of members in an academic research department, Haythornthwaite shows that stronger ties are not either on- or offline, but rather that the stronger the tie, the greater the number of communication media that the two people use to communicate. Online networks, then, have the potential to bolster and extend offline networks, rather than deplete them. This debate is far from limited to only the scholars discussed above. For every study that finds negative or no effects for online networks (e.g., Byrne 2008; Raacke and Bonds-Raacke 2013; Valkenburg and Peter 2007) on the sociability, friendships, or academic well-being of adults and students, alike, there is an answering study with positive findings (e.g., Ellison et al. 2007; Hampton and Wellman 2000, 2001; Howard et al. 2001; Parks and Kory 1995; Trepte et al. 2012). With so many conflicting findings on online networks and their effect on members, what should we conclude? For the purposes of this study, we heed the warnings of the critics of online networks, keeping in mind the limitations of the technology and its inability to capture the presence, quality, or substance of offline interactions. However, we also draw on the positive findings about online networks to argue that they can still allow for communication, information exchange, and friendship formation perhaps better than offline networks can, given the unique

10 institutional characteristics of community colleges. We see online networks as a viable environment in which social integration can occur, as we describe below. Social Integration for Students in an Online Setting In addition to Tinto s work, there is a broad literature exploring the relationship between students social networks and their actions in an academic setting (e.g., Antonio 2004; Bean 1980, 1982, 1983, 1990; Bers and Smith 1991; McFarland and Pals 2005; Rios-Aguilar and Deil- Amen 2012; Thomas 2000). These studies often find that social interactions and integration can influence students behavior, academic identities, and aspirations. However, these studies focus on offline, in-person, self-reported interaction. Do the theories and evidence for the importance of offline social integration for academic persistence and performance translate to their online counterparts? One thing is undeniable: students are going online and communicating with each other often using social network sites. Researchers find that around 90 percent of college undergraduates use Facebook (Dahlstrom et al. 2011; Junco 2012b), and undergraduates report that they spend more than an hour each day on the site (Junco 2012a, 2012b; Ellison et al. 2011). While Junco (2013) finds that students self-reported Facebook usage is greater than their actual usage, the overestimation in the self-reports may speak to a heightened attention to and interest in the social network: subjectively, Facebook is a larger part of students lives than it measurably is. Studies show that online social networks are, indeed, impactful for teens, who are a large demographic in community colleges. Researchers find that the hours spent using online networks help to shape identity, achieve status, and learn norms, all of which are important for social integration (e.g., Bessière et al. 2008; Boyd 2007; Valkenburg and Peter 2007). Additionally, Liu

11 and Larose (2008) find that perceived online social support has direct, positive effects on school life satisfaction in an academic setting. Previous studies of online social network involvement that specify its relationship to social integration and social capital have been based in residential colleges. In a study of students at a four-year, public, residential college, Ellison et al. (2007) find a positive association between Facebook use and the maintenance and generation of social capital. Junco (2012a) finds that, more than the amount of time spent on the social networking site, the specific activities that students pursue on Facebook are of greater predictive value for their sense of engagement with their college and other institutional outcomes like time spent preparing for class and participation in extracurricular activities. Using Facebook for sharing and collecting information was associated with positive academic outcomes, but using it for purely social purposes was negatively associated with academic measures (Junco 2012a, 2012b). These findings, though in residential, four-year institutions, lend weight to our hypothesis that online networks are capable of facilitating social integration for students at non-residential community colleges. However, as a caveat, not all social integration positively affects youth in the classroom. Tinto (1993) notes the potential to become socially integrated into a school, but for the socialization to be heavy on the social. Theories of hedonistic youth culture (Eisenstadt 1956) suggest that if student networks are used only for social activities, they may ultimately detract from academic achievement and persistence, rather than encouraging it. But, on the whole, Tinto argues that social integration into schools is more likely to have positive than negative effects, and findings by Ellison et. al (2011), Junco (2012a, 2012b), and other authors suggest the potential of online social networks to foster social integration and have a positive relationship with persistence.

12 HYPOTHESES Hypotheses 1 and 2: The Role of the Network If online networks facilitate social integration into community colleges, we would expect to see a relationship between increasing online network involvement and increased academic persistence, consistent with Braxton et al. s (2004) reviewed findings for residential colleges. Based on this potential link between online network involvement and retention, mediated through social integration, we test two hypotheses that compare network users to non-users, examining full credit completion as an outcome variable. As we describe in greater detail below, we operationalize retention as a student completing all of the credits that he or she attempts in her first semester of network exposure. The first of our hypotheses focuses on the relationship between network usage and credit completion among all students. The second examines this same relationship, but specifically tests for heterogeneity in the relationship based on the distance that a student lives from the college. Because online networks are not place-based, this may tease out particular benefits of an online network for a commuting student population. Below, we describe each hypothesis in detail. Network Involvement and Academic Persistence Social Support Hypothesis (H 1 ): Online network involvement is associated with better academic persistence. We argue that online social networks provide present and well-defined communities that many non-residential colleges lack (Braxton et al. 2004) and hypothesize that their presence will enable a positive relationship between student involvement on the network and persistence. Unlike prior analyses of social integration (e.g., Bers and Smith 1991; Deil-Amen 2012; Thomas 2000), we look at an online network, rather than students self-reported, offline friendships.

13 Based on Junco s (2012a) findings that different social network activities are variably predictive of student engagement, we test three different forms of social involvement within the network that may foster social integration: 1) joining the network; 2) forming relational friendship ties; and 3) joining interest groups on the network. By differentiating among these three forms of network involvement and analyzing each separately, we gain a finer-grained understanding of the mechanisms of social integration and the relationship between online network involvement and student persistence. The Role of Distance An early touchstone work of network analysis took place in the dormitories of a college campus. Festinger et al. (1950) studies the friendship patterns of students living in married students housing at Massachusetts Institute of Technology (MIT) and finds that students are more likely to befriend others who live physically closer to them in the dorm, net of shared beliefs or interests. Propinquity arguments grew from this work, looking at the importance of physical location and proximity in the development and subsequent influence of social networks. Propinquity arguments complement Braxton et al. s (2004) emphasis on nonresidentiality and its role in the relationship between social integration and student persistence. In light of these theoretical and empirical groundings, our second hypothesis focuses on the distances between students homes and their college campus: Distance Hypothesis (H 2 ): Students who live farther from campus experience a stronger relationship between network involvement and full credit completion. If the online social network can serve as an alternative to on-campus options for social integration and community building, we hypothesize that students who live farther from campus will experience a stronger relationship between network involvement and persistence. While

14 students who live close to the campus may have previously benefitted (and continue to benefit) from on-campus social interactions (limited though they may be), the network provides the first forum for social integration to distant students. As a result, we anticipate a greater relationship between online network involvement and persistence for more distant students. Hypotheses 3 and 4: Intra-Network Dynamics In the first two hypotheses, the relationship between online network involvement and persistence is operationalized in a binary way: students are either involved in the network or not. We then test this binary difference and its association with student persistence. The second two hypotheses take a more refined view of network involvement, seeking to understand if specific arrangements or contents of online social networks might facilitate social integration differently and change the relationship between network involvement and persistence. Rather than arguing that involvement, structured in any way, may have an effect on credit completion, we consider the content and structure of a student s friendships in the network. Network Autocorrelation and Peer Influence To examine the contents of a student s online friendships and their relationship with academic persistence, we consider the role of peer influence. In many social networks, people tend to be friends with others who are similar to themselves, and some researchers have argued that having similar characteristics may be predictive of friendship formation (McPherson, Smith- Lovin, and Cook 2001). Feld and Grofman (2009) disagree and argue that this observed similarity between connected actors is not a cause behind friendships, but instead results from associations as actors change their behaviors and preferences to be more like their friends. Feld and Grofman call this process network autocorrelation, but it can also be thought of as peer influence. Empirical work suggests the importance of attending to peer influence among

15 students. For example, McFarland and Pals (2005) find that among adolescents, network connections are a driving mechanism behind identity development and social conformity among friends. Specifically in the context of college academics, Hasan and Bagde (2013) find evidence of roommates influencing each other s academic performance. The network and student data in this study allow us to look for peer influence within the online network. In particular, we focus on academic influence in the form of credit completion. Mayer and Puller (2008) find that the average GPA of a student s Facebook friends is predictive of the student s own GPA. The time lag in our data allows us to search for influence, rather than just homophily. If friendships in the network have the potential to influence a student s academic behavior, the past credit completion of a student s friends may have a role in predicting a student s likelihood of completing all attempted credits after she joins the network. To examine this, we test a third hypothesis for students in the network: Peer Influence Hypothesis (H 3 ): Within the network, the mean completion percentage of a student s friends is predictive of his or her probability of completing all attempted units, controlling for prior completion percentage. By computing the mean prior completion percentages for all of a student s friends on the network, we can look for evidence of peer influence on students college persistence after joining the network. Tight-Knit Groups and Norms of Behavior The prior hypothesis focuses on the content that might transfer through a student s relationships with her friends. The following hypothesis focuses on the structure of these friendships and how that might influence the flow of behaviors among peers. As an example, Feld (1981) argues that social networks develop around foci, which are common interests or

16 activities that organize people into groups. As groups organize around the foci, certain norms develop within each group, and members of the group adopt these norms. In making this claim, Feld studied the parenting techniques of a group of families, all living in close quarters in graduate student housing. Though propinquity arguments (Festinger et al. 1950) would suggest that one prevailing community norm would guide parenting behaviors, Feld finds that three different norms prevailed, all derived from different families smaller, foci-centered networks. Peer influences on the parenting styles, then, were determined by the networks that were more tightly-knit and bound to a single foci, rather than looser, propinquity-based networks. Thinking about this finding in the context of an online social network at a community college, it may not be that the academic characteristics of a student s friends generally influence his or her academic performance, but rather that the influence of peers varies according to variations in the structure of a student s personal network. The idea that smaller, tightly-knit groups may have a stronger influence on a student s academic behavior leads to a fourth hypothesis to test within the network: Structural Hypothesis (H 4 ): Within the network, closure amplifies the peer influence on a student s probability of completing all attempted units. Within clusters of friends that are increasingly dense in their connections to one another, are students more likely to shape one another s academic behavior? Coleman (1988) examined students academic behavior based on intergenerational closure among students and parents; here, we focus on the closure among peers. We compute a closure score for each student, based on the number of the student s friends that are friends with each other and interact it with these friends past credit completion percentages. From this parameter, we can analyze how the structural characteristics of a student s network might interact with the influence of peers.

17 DATA The Community College (henceforth, CC) provided us with all student-level demographic and academic data for the 2011-12 academic year. CC is a large, accredited, nonresidential community college in the Midwest that enrolled an estimated 30,065 students in fall 2011 (U.S. Department of Education 2012). All of its 11 campuses are located in an urban setting with a population of nearly 400,000 people. There is no on-campus housing, but over 90 percent of students do live within 20 miles of a CC campus. CC offers a variety of majors, workforce training, college credits that transfer to four-year institutions, certificates, and associate s degrees. Sixty-five percent of the students in 2012 were part-time students, carrying an average course load of 8.8 credit hours. CC has a high job placement rate for its graduates: 90 percent of graduates from the career programs find employment, and 83 percent of those employees have jobs in their field of study (CC website 2012). We study CC not because it is a representative example of all community colleges nationwide, but rather because it is a particularly good case for exploring our research questions relating to Tinto s theory of social integration, Braxton et. al s (2004) review findings, and the relationship between online social network integration and academic persistence at two-year colleges. First, CC s retention problems are greater than those of an average community college in the United States. In 2011-12, CC had a retention rate for full-time students of 47 percent, 9 percentage points and 0.89 standard deviations below the mean for all American public two-year schools (U.S Department of Education 2012), and from 2003-06, CC had a combined transfer and graduation rate of only 15.5 percent (CC Website 2012). Additionally, CC is larger than most community colleges and has multiple campuses in a suburban setting, characteristics which increase the obstacles to place-based community formation at the college. Most importantly for

18 our research questions, CC has implemented an online network that is limited to use among its students and faculty in an effort to actively foster social networks and improve retention. In the college s strategic plan for the upcoming years, retention is a primary goal, and the purchase and implementation of an online network can be viewed as an active step towards this goal. Given its retention problems and implementation of the online network, CC is an excellent setting for investigating our research questions about social integration, online networks, and boosting academic persistence in a setting with low previous persistence. We now turn to describing CC s online network in more detail. The online network at CC is Schools App, an application that operates on the Facebook platform and is available for purchase from an engineering firm in San Francisco, California. The application is marketed to colleges as Facebook for College Enrollment and Retention (Uversity, Inc. 2013), and the main webpage is clearly geared towards college administrators: there is a section addressing institutional needs, including references to the Family Educational Rights and Privacy Act (FERPA) restrictions and data security. Schools App presents itself as a tool for improving retention and academic performance but has not yet offered rigorous, empirical evidence that it does so. Schools App is similar in structure to the larger Facebook site, but is limited to the students at a particular college. Students can join the network to see public postings, ask questions, and interact with other students on the app. Schools App also offers users the opportunity to friend other users, either virtually reflecting relationships they have already established offline or providing a method to contact new people. Users may search for friends, or the app will suggest potential friends, all of whom are also students at the college and users of Schools App. Once a student friends another student, that friendship is reflected on both the

19 Schools App platform and the student's Facebook page. Schools App also offers the opportunity to join interest groups, which are a collection of forums based on a wide variety of topics, activities, and identities. If a user joins an interest group, stories linked to that group will appear on the user's home page. The stories are open-ended questions that are based on the interest group's topic and provide users a low-risk way to engage with other students online. Schools App is available to all students at the college, free of charge. Schools App was launched at CC in August 2011. All students and an unspecified number of faculty were invited to join Schools App with an e-mail solicitation. Admitted students and currently enrolled students were both invited, but in this study we only focus on students who had prior academic records at CC; 22,263 students meet this selection criterion. As of May 2012, 2,690 students of the 22,263 students from the 2011-12 year with prior academic records at CC had joined the Schools App network, which is 12.1 percent of the total number of students in the study. Community, as defined by social exchange, has developed at CC on the Schools App platform: students have been actively forming friendships, joining interest groups, and responding to the story questions on their home pages. METHODOLOGY To test the hypotheses that we identify above, we estimate a series of logistic regression models predicting students credit completion as a function of network, demographic, academic, and socioeconomic attributes. In all of the analyses, the dependent variable is a binary value based on the percentage of attempted credits that a student completes in her first semester of network exposure. If a student completes all of her attempted units, she receives a value of 1; if she completes under 100 percent, she receives a value of 0. 2

20 We define persistence as a binary variable based on one semester of credit completion for four reasons. First, completing all attempted course units is the building block of academic retention. Credits must be passed and completed in order to stay in a program, transfer credits to a four-year institution, or complete a degree. Operationalizing persistence as a completion percentage captures the concept at its root. Second, community college students are often not full-time students and take a reduced number of credits. Thinking with an eye toward retention, the emphasis should be on completing what you attempt, which a percentage captures, rather than attempting more, which a raw count of units captures. Community college students may take fewer units or enroll for non-consecutive semesters, giving a third reason to operationalize persistence as we do. For example, there are 8,072 students who are enrolled in CC in the spring with prior records, but do not appear in the fall records, indicating that they enrolled in nonconsecutive semesters. By looking at credit completion in a single semester of network involvement, the analysis captures persistent behavior in its most fine-grained instance and does not misclassify students who take a semester off as dropouts. Fourth, our data come from a single academic year, limiting the ability to examine persistence over a longer time scale. Given these four reasons, full credit completion serves as the operationalization of academic persistence for our study. Figure 1 is a histogram of the dichotomized credit completion percentages for all students at CC during their respective first semesters of network exposure during the 2011-2012 school year. The figure shows that 58 percent of students (N=12,949) completed all of their attempted units during their first semester of network exposure, while 42 percent of students (N = 9,314) did not. 3 [FIGURE 1 ABOUT HERE]

21 Independent Variables In Table 1, we present descriptive statistics for all the students in our sample, and then differentiate between the students who do and do not join the Schools App network at CC. The t- test values listed in the fourth column test for significant differences between the network joiners and the students who do not join the network. Because participation in the network is voluntary, we anticipate and do, in fact, see that the network joiners are not statistically indistinguishable from the non-joiners. Though the differences between the two groups are statistically significant in most categories, the observable differences between the groups are minimal, as we describe below. Many of the descriptive characteristics in Table 1 also serve as control parameters in our logistic regression models. [TABLE 1 ABOUT HERE] Of the 22,263 students who meet the criteria for our study, half are over the age of 30, and 89 percent are 20 or older, reflecting the older student population common among community colleges. Just over half of students are white, 35 percent are black, and the other 14 percent of students self-identify as Asian, Hispanic, Native American, Pacific Islander, or choose not to list a race. Sixty-three percent of students are female. Among the network joiners, black students are underrepresented in the network and women are overrepresented, each by five percentage points as compared to the whole student body and their non-network-joining peers. The other demographic categories based on age and race are within fewer than five percentage points of each other across the different groups. In the analyses, white students and those over the age of 29 are used as reference groups. Students live an average of 10.03 miles from the nearest CC campus. Network joiners live slightly farther away than the non-joiners on average, and the higher standard deviation

22 suggests a wider distribution of distances among these students. The distance variable was calculated as the distance between the center of a student s home zip code and the zip code of his or her primary CC campus. For students who had no specific campus listed (2,925 of 22,263 students), the distance value is measured between the student s home zip code and the nearest campus to obtain the most conservative value of distance. The mean distance that students live from campus is similar for both network joiners and the full student population and is rightskewed for both groups. As a result, we log transform the distance parameter in our models. Interestingly, the area in which the network joiners most differ from the non-networkjoining students is in their rate of applying for federal funding and the amount of federal dollars they receive. Network joiners are 17 percentage points (27.4 percent) more likely to apply for federal funding as compared to the non-joiners. 4 Possibly, this reflects greater technological skill and fluency, since aid applications are often processed online. Students with better computer skills and more technological interest may be both more likely to apply for aid and join the Schools App network. The institutional data provided for this study had no intentional proxy variables for the kind of ineffable qualities that researchers may try to identify behind student success, such as motivation, dedication, or grit. This discrepancy in application rates for federal funding between the network joiners and the larger student body may provide a small window into this kind of information. The network joiners were more likely to seek out assistance, perhaps suggesting a larger tendency to seek out aid through institutional means and a possible impetus for joining the network. A student s prior credit completion is the percentage of units that she completed over all semesters prior to the first semester of network involvement. GPA is calculated over the same

23 time period. The average network joiners are slightly higher achieving academically than the average students who did not join the network, based on these two measures: they complete 3 percent points more of the units they attempt and have a GPA 0.09 percentage points higher. The degree goal variable is binary. Students who listed a goal of completing an associate s degree, obtaining a vocational degree, or transferring to a four-year college are considered degree-seeking students. Those who are pursuing personal interests or taking a single class are not considered degree-seeking. The average student in the network is 5.9 percent more likely to be degree-seeking than the average student who did not join the network. In addition to the academic and demographic variables described in Table 1, we include network variables in our models to capture the relationships between different aspects of network involvement and credit completion. Listed in Table 1, joined is a binary variable for whether or not a student joined the network. Within the models, this term captures network participation of any kind even just joining the network, but not making any friends or joining any interest groups. It captures only the step of signing up for the network the most basic form of network involvement. The more detailed measures of network involvement are listed in Table 2. Number of friends 5 describes the total number of friends a student has on the Schools App network. Friend ties on the Schools App network are undirected, mutual ties between students. This means that if Student A is a friend of Student B in the network, B is also a friend of A. Although one student does initiate the tie, the other student must accept the tie for it to exist on the network. Therefore, there is no directionality for the ties, and all ties are assumed to be mutual between dyads of students. In total, there are 3,158 students who have friend ties in the network. Among these students, there are 7,788 friend ties, and 2,465 of the students have more than one friend tie. Of

24 the total 3,158 network joiners with friends, 1,845 also meet the criteria described above namely, having a prior academic record at CC and enrolling in either fall or spring of the 2011-12 school year. The histogram of the number of friends that students have on Schools App is right skewed as are most graphs of ties in social networks. 6 As mentioned above, students have the opportunity to join interest groups on Schools App. Of all network joiners, 48.2 percent have joined at least one active interest group, which is defined as having at least two members and one post or comment. The interest groups vary in topic from making friends to musical tastes, from a variety of sports to a large group for the class of 2012. The number of memberships students have is also varied (s.d. = 19.79) and has a strong right skew. As a result, we use the natural log of the number of interest group memberships as a parameter in the models to take into account percent changes in the number of memberships, rather than changes in the count of the number of memberships. Closure is measured as a function of the number of friends that each student has in the network. The closure value is calculated as the number of ties present among a student s friends as a proportion of all possible ties among them: Closure = (# of ties between friends) (num. friends) * (num. friends -1) For every tie present between two of a students friends, her closure score increases. If a student is part of a tightly connected clique, more of her friends should be friends with each other on the network, making this term higher and representing a tighter social cluster in the network. If a student has 0 or 1 friends, she is automatically given a closure value of 0. [TABLE 2 ABOUT HERE]

25 RESULTS Social Support Hypothesis (H 1 ): Online network involvement is associated with better academic persistence. To test the association between online network involvement and increased academic persistence, we estimate a series of logistic regression models that describe the relationships between different kinds of Schools App activities and student persistence. In Table 3, we present the results of these models as both odds ratios (e b ) and marginal effects for ease of interpretation. The base model features only network parameters, while subsequent models include additional control variables based on demographic, socioeconomic, and academic characteristics of the students. In the base model, joining the network without making any friends or joining any interest groups is associated with a 13 percent increase in a student s odds of completing all the credits she attempts. This corresponds with a 0.03 increase in the probability of full credit completion. However, as a student becomes involved in the network in different ways, the base model reveals an unexpected decrease in the odds that she completes all attempted units. With each additional network friendship, the odds that a student completes all of her units drops by 5 percent. This corresponds to a 0.01 decline in her probability of completing all attempted units. Increasing interest group memberships are associated with a decline in the odds of full credit completion. In Models 2-4, many of the academic and demographic control parameters are associated with full credit completion in ways that prior research would lead us to expect. A student s prior credit completion is the strongest predictor of 100 percent credit completion during the first term of network exposure, though having a degree goal, surprisingly, is associated with an 11 percent

26 decline in the odds of full credit completion. As compared to the reference groups of students over 30 and white students, younger and non-white students have lower odds of full credit completion, while women have higher odds of completing all their attempted units than their male peers. Receiving federal grants and loans is also negatively associated with the likelihood of 100 percent credit completion, likely showing its role as a proxy for socio-economic standing, which has frequently been shown to correlate with academic performance and persistence. The distance a student lives from the CC campus is a surprisingly strong predictor of 100 percent credit completion, which may show it acting as a proxy for motivation or commitment. [TABLE 3 ABOUT HERE] With the inclusion of the control parameters in Models 2-4, the positive association between joining the network and completing all attempted units loses statistical significance. However, even with the inclusion of these additional variables, the negative association between adding friends on the network and a student s likelihood of completing all attempted units persists. Holding all other parameters constant, with each additional friend a student makes on Schools App, her odds of full credit completion drop by 2 percent (Model 4). This corresponds with a 0.005 decrease in a student s probability of full credit completion for each additional friend. To better understand this relationship, in Figure 2, we graph the mean credit completion percentages for bins of students with increasing numbers of network friends and fit an ordinary least squares model to the points (b = -0.01, p <= 0.05). Bearing in mind that 91 percent of students (N = 20,256) have zero friends on the network, the negative relationship between number of network friendships and the probability of full credit completion is clear among the other 9 percent of students who do have network friends. [FIGURE 2 ABOUT HERE]

27 From this first set of models, we learn that three different types of Schools App involvement joining the network, forming friendships, and joining interest groups are not associated with better academic persistence for the students at CC, when controlling for other academic and demographic characteristics. Though joining the network has a positive association with finishing all attempted units in the base models, this relationship is no longer statistically significant after the inclusion of control variables. The only lasting association between network involvement and academic persistence is negative. After the inclusion of the controls, an increasing number of friendships is associated with a decline in the probability that a student completes all attempted units. Next, we see if there is heterogeneity in this finding, based upon the distance a student lives from the CC campus. [TABLE 4 ABOUT HERE] Distance Hypothesis (H 2 ): Students who live farther from campus experience a stronger relationship between network involvement and full credit completion. In Table 4, we present the results of the logistic models that test heterogeneity in the association between different kinds of network involvement and credit completion based on the distance a student lives from the CC campus. In Model 1, we test for an interaction between distance and joining the network. Though the marginal effects of both distance and joining the network are statistically significant and positive, the interaction term is negative. Figure 3 is a graph of the interaction term from Model 1. Due to the positive main effect of joining the network, network joiners who live a short distance from the CC campus have a higher probability of finishing all their attempted units. However, for network joiners who live greater than 14.5 miles from the school, there is a diminished positive association between distance and

28 the likelihood of completing all attempted credits, as shown by the flatter slope of the dotted line after its intersection with the continuous line. When a student joins the network, she experiences diminished positive returns from the distance she lives from the campus. [FIGURE 3 ABOUT HERE] The interaction between distance and the number of friends a student has on the network (Model 2) is not significant, but the interaction term between distance and interest groups (Model 3) is weakly significant at the p <= 0.10 level and, like the interaction term between joining the network and distance, shows that this type of network involvement reduces the positive returns from distance. Though the interaction term between distance and joining the network is robust to the inclusion of some control variables (Model 4), it loses statistical significance with the inclusion of all the controls in Model 5. The variance explained by this term in the base model is better predicted by the control parameters we include. As a result, we reject our hypothesis and conclude that there is not heterogeneity in the relationship between network involvement and likelihood of full credit completion based on the distance a student lives from the CC campus. Peer Effect Hypothesis and Structural Hypothesis (H 3 ): Within the network, the mean completion percentage of a student s friends is predictive of his or her probability of completing all attempted units, controlling for prior completion percentage. In Table 5, we present three models that examine how a student s network friendships influence her likelihood of completing all attempted credits. We present our results as both odds ratios and marginal effects. For these analyses, we examine only the students within the network who have friendships. In Model 1, the base model, we find that a student s friends prior credit

29 completion is positively associated with the student s likelihood of completing all attempted credits. This association is robust to the inclusion of all control variables, as show in Model 3. In Model 3, for a student with a mean prior credit completion (0.79), having friends who average one standard deviation above the mean in credit completion is associated with an 0.08 increase in the probability of full credit completion, while having friends who average one standard deviation below the mean is related to a 0.08 decline in the probability of full credit completion over a baseline probability of 0.62 for a student with no network friends. Within the network population, race no longer has the significant association with 100 percent credit completion we saw in the full population models in Table 3. However, youth remains significantly, negatively associated with the probability of full credit completion, as does the amount of federal funding a student receives. The influence of gender remains significant, as women are 25 percent more likely than men to complete all of their attempted units, holding other parameters constant. Overall, these analyses confirm our third hypothesis that peer influence exists in the network. A student s friends prior academic records offer predictive power of a student s own likelihood of academic persistence after joining the network. Next, we test for heterogeneity of the influence based on the network structure among the student s friends. [TABLE 5 ABOUT HERE] Structural Hypothesis (H 4 ): Within the network, closure amplifies the peer influence on a student s probability of completing all attempted units. Models 1 and 2 in Table 5 also include a term for closure, and Model 2 tests its interaction with the mean of friends prior completion percentages. In these models, there is not a

30 significant relationship between closure and a student s likelihood of full credit completion, nor does closure lead to heterogeneity in the relationship between friends prior completion percentages and that of a student. Though the main effect of friends completion percentages remains significant in all the models, neither the closure term nor the interaction term has a statistically significant association with a student s credit completion percentage during the first term of network use. These findings fail to support our fourth hypothesis, which suggests that increasing closure among a student s friends would amplify the peer influence on a student s likelihood of full credit completion. DISCUSSION This study is the first to consider online social network integration at a non-residential community college and its relationship with student persistence. Previous empirical studies show that social integration is not related to persistence for students of non-residential colleges (Braxton et al. 2004). However, these findings are based on institutions with weak or ill-defined social communities. In our study of Schools App at CC, the online social network is a welldefined and active network. As a new and present forum for social integration in a nonresidential college setting, online social networks merit empirical testing of the relationship between student online network involvement and persistence. Through such an analysis, we better understand online networks potential to offer commuting students a path toward social integration and, possibly, its positive associations with persistence that we observe among the students of residential colleges (Braxton et al. 2004). From our analyses, we learn that as students integrate into the online network through network friendships, the likelihood that they complete all of their attempted units declines,

31 controlling for a variety of demographic and academic characteristics. This relationship does not vary according to the distance a student lives from the campus. Within the network, we find evidence that a student s friends prior academic persistence is predictive of the student s likelihood of full credit completion after establishing these network friendships, and that this peer influence does not vary according to the network closure among a student s friends. There are two key findings from our analyses that merit further discussion: first, the negative relationship between a student s number of network friendships and academic persistence and, second, the evidence for peer influence on academic persistence. The negative relationship between a student s number of network friendships and her academic persistence ran counter to prior theory and our hypothesis. To resolve this conflict between our expectations and findings, we turn to other theoretical interpretations and some qualitative insights based on observations of and conversations with student network users at CC. Through two site visits and a qualitative content analysis of the interactions on Schools App (CC Site Visit Report 2012, 2013), our team finds that students are primarily using the network for gathering information about academic decisions and institutional navigation. For example, students ask about the quality of particular instructors or the course requirements for a given major. This kind of information gathering, in which students interact socially about academic matters may exemplify an online version of the socio-academic moments that Deil- Amen (2012) finds to be important to the engagement and integration of community college students. In her qualitative interviews with community college students, Deil-Amen (2012) finds that students feel engaged and integrated into their institutions when they can relate both academically and socially with peers. This kind of network activity is also akin to the

32 information seeking and commenting on Facebook that Junco (2012a) finds to have a positive relationship with student engagement. To participate in this socio-academic exchange of information, students must only join the network; network friendships or group memberships are not prerequisites for engaging in this prevalent form of network activity. In Table 3, we see in the base model (Model 1) that joining the network without any network friendships or group memberships is positively associated with persistence. Though this relationship loses statistical significance with the inclusion of the controls, the positive relationship in the base model complements Deil-Amen s (2012) and Junco s (2012a) prior findings. For students who only join the Schools App network, exposure to and participation in this kind of socio-academic exchange is associated with better academic persistence. Since socio-academic information exchange is available to students who just join Schools App, the pursuit of individual friendship ties suggests that students seek something in addition to these socio-academic exchanges. What might the individual friendship ties on Schools App offer to students that joining the network (without friendship ties) does not? It is beyond the scope of this paper to specify what community college students seek through online friendships or what these friendships may, exactly, provide. Though we leave it to future researchers to examine these questions in detail, we suggest some theoretical foundations that may inform future inquiry. In general, sociological researchers perceive social ties as only positive for an individual, and Tinto s (1993) theories certainly steer us in this direction in the context of higher education. However, other researchers have different interpretations of relational ties and how they affect individuals. For example, Jackson (2009) and colleagues (Jackson, Rodriguez-Barraquer, and

33 Tan 2012) describe relationships as costs for an individual, rather than gains. They claim that the maintenance of a friendship is expensive in time and energy and also carries an on-going opportunity for the friend to request a favor or additional time. In this way, a tie acts more as social debt, rather than social capital. Other researchers, though they do not go so far as saying that every tie is costly, discuss the negative content transmitted through certain ties, like suicidal thoughts (Bearman and Moody 2004) or delinquency (Haynie 2001). In both of these theoretical framings, additional ties mean additional costs or additional avenues for detrimental peer influence, and so an increasing number of friends would associate with reduced outcomes. The time costs of attending to an increasing number of online friendships could detract from time spent on academic pursuits and lie behind the negative association between Schools App friendships and academic persistence. Junco s (2012) findings provide empirical support for this possible interpretation: he finds that increased time on Facebook is negatively predictive of GPA for college students at a residential college, after controlling for various kinds of Facebook activities. Though Checking up on friends is one of the activities that correlates positively with GPA in Junco s findings, the effect size is smaller than that of time spent on Facebook. Even if increased time is spent checking up on ever more friends, it is still, overall, negatively predictive of a student s academic achievement and, possibly, academic persistence. Further research into how much time students spend on Schools App relative to their number of friends and the relationship between time spent on Facebook and persistence could help elucidate this theory of ties as time debts, rather than social gains. In addition to possible time costs related to Schools App friendships, there may be content transmitted through the friendship ties that is detrimental to students academic persistence. While socio-academic content flows within Schools App, perhaps the private lines

34 of communication behind friendship ties are further removed from the academic setting. Friendship ties within Schools App are automatically added to a student s general Facebook page. Using Facebook, students can continue to communicate with their online friends outside of Schools App, putting their personal communication outside of the reach of the Schools App data collection. As a result, while some friendship content is observable in the qualitative analysis of the Schools App contents, there is an unknowable portion happening outside of Schools App, unobserved by our research team. If the primary motivations and influences within these friend ties are social rather than academic, friendship ties may carry content that is more in keeping with hedonistic youth culture (Eisenstadt 1956) than socio-academic exchange (Deil-Amen 2012). As Tinto cautioned, students can become socially integrated into a school environment, but if the culture and relationships privilege socializing over studying, the student might not see academic returns to their social integration. A content comparison between the messages and posts that are on the public forum versus those that privately pass between friends may shed light on differences in the content of the public versus private forum on Schools App. The qualitative review of Schools App content cautions us against suggesting that all friendship content is social, however: posts of students seeking study partners were one of the most frequent kinds of posts appearing on the network. Academic rather than social interests behind friendship formation on Schools App, however, still allows for a negative relationship between the number of friendships and persistence. We can interpret the negative relationship between the number of friendships and academic persistence with a different causal direction: perhaps students who start to struggle in their classes go to Schools App to seek out peers that can help them. As a student continues to struggle, she friends more and more peers on Schools App in search of help in the classroom. This mechanism would lead to the same observed

35 negative association between number of Schools App friends and academic persistence, as a student searches for academic help on the Schools App network, but is unable to find any through friendships. A more fine-grained approach to measuring the academic performance of students or combining a co-course-taking network with the online friendship network could help explore this area. An academic interpretation of the mechanism behind friendship formation on Schools App bring us to our second key finding: the presence of peer influence. A student who seeks academic help on Schools App may benefit from doing so, as a student s friends prior credit completion percentage was predictive of the student s own during her first semester of network friendships. Given the prior literature on network autocorrelation (Feld and Grofman 2009) and the empirical work on peer influence in an academic setting (e.g., Hasan and Bagde 2013; McFarland and Pals 2005), it fits our expectations and hypotheses that peer academic influence exists within the Schools App network, perhaps because a student adjusts her own academic behavior to match that of her friends. While prior research has examined this in the context of in-person, offline networks at residential colleges (Hasan and Bagde 2013; Thomas 2000), our study adds evidence of peer influence in the new setting of online networks at a community college. We are cautious not to overstate the implications of the peer influence findings. Heeding the warnings of the online network skeptics (e.g, Fox 1995; Nie 2001; Slouka 1995), we recognize that the evidence for peer influence we identify may not be limited to or because of interactions that students are having online. As Haythornthwaite (2005) found, the stronger the relationship between two people, the greater the number of media that the two will use to communicate. Based on this finding and others (e.g., Ellison et al. 2007), students are unlikely to

36 be only network friends with their Schools App friends. It is likely that they also interact in person or through other media such as phone calls or text messages. As a result, it would be unwise to chalk up our findings of peer influence to exclusively the use of the online Schools App network, since it is likely that students are interacting in other ways, too. Also, our analyses identify correlation rather than causation, which makes a cautious interpretation wise. While the temporal aspect makes it impossible that a student s later academic persistence influences the prior academic persistence of her friends, the students may have been friends offline before becoming Schools App friends, and the relationship may predate the network. In future studies of fully online learning environments where students are meeting for the first time (for example, massively open online courses (MOOCs) or online universities like DeVry University and University of Phoenix), researchers can disentangle real-time, online aspects of peer influence on academic persistence from those related to earlier or offline interaction. Given our findings, online social networks may not be an uncomplicated solution to the institutional challenges of community building and retention that community colleges face. While the evidence for academic peer influence though friendship ties lends weight to the idea that an online social network could help with students persistence and colleges retention problems, the negative relationship between a student s number of friendships and persistence suggests that not all social integration might help students to stay in college. Yet, community colleges interested in trying to boost retention through the use of online social networks can learn from our findings and consider ways in which they can structure an online social network as part of their school so that student engagement and time investment in the network is positively associated with retention. We see evidence of positive academic influence among peers and the

37 potential benefits of online socio-academic exchanges. If these could be separated from the costs to retention associated with increasing online friendships, online networks may be put to good use in alleviating the attrition on community college campuses. Though community colleges educate nearly half of American undergraduate students, they are rarely the subject of academic research. This study helps to address the dearth of research concerning community colleges and, in particular, explores a timely and important issue in the incorporation of online networks into higher education and the relationships these networks have with academic performance and retention. To the scholarly research community, our project suggests future directions in the study of online networks in higher education. Though we find evidence of peer influence in this particular setting, how generalizable may these findings be? Exploring online networks in other institutions of higher education will give us a better sense of their influence in more varied settings and provide important knowledge as distance education increases and higher education become ever more an online venture. More broadly, an understanding of online networks and their relationship with retention at community colleges can contribute to national goals for higher education and human capital development. Increasingly, community colleges are becoming an important player in the politics of higher education. By 2020, President Obama aims to reclaim the position of the world's leading nation in percentage of college graduates. In 2009, he proposed spending an additional $12 billion on community colleges to help generate 5 million more postsecondary graduates (Shear and de Vise 2009). With their weak track record of retention and academic performance, however, community colleges are going to have a hard time living up to the President s expectations. Online social networks, with strategic implementation and an increased emphasis on, perhaps, socio-academic integration, may help to improve student performance in the

38 classroom and could be an inexpensive and feasible way for community colleges to both help their students and improve their educational leverage against the human capital needs of the nation. NOTES 1. For the 2009-2010 academic year, average tuition and required fees for one semester at public, two-year colleges was $2,285, compared to $6,985 for four-year public institutions and $25,552 for four-year, private institutions (U.S. Department of Education 2011). 2. When estimating our models, we tested different cut-off values for dichotomizing our outcome variable. At cut-off points ranging from 50 to 100 percent of units completed, the signs and significance of all but one of the coefficients were consistent across models. For cut-off values ranging from 80 to 100 percent, estimated models had identical signs and significance for all coefficients. Given our interest in retention and persistence, we choose a cut-off value of 100 percent completion for the models presented here. 3. 3,719 students had zero percent credit completion before dichotomizing the outcome variable. 4. The higher mean value of federal funds received for students in the network is, in part, a function of their higher application rate. However, if we reduce the sample to only those students who actually receive funding as a result of their application, the network joiners still receive more than the average funded student in the non-network-joining population: the average network joiner receiving federal funding receives a combined grant and loan amount of $2,765 as compared to $2,431 for students who received funding and are not in the network. 5. Often, in the networks literature, this is called degree. For the sake of clarity, given that seeking a college degree is also a parameter, we call it number of friends.

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Number of Students 46 14000 12000 10000 8000 6000 4000 2000 0 Completed < 100% of Attempted Units Completed 100% of Attempted Units Figure 1. Histogram of Student Credit Completion Percentages in First Semester of Network Usage. Source: 2011-12 administrative data from the Community College.

47 Figure 2. Mean credit completion percentage during the first semester of Schools App network use for bins of students grouped by number of Schools App friends Note: The credit completion values are actual, not predicted. The line is an OLS regression fitted to the graphed points.

48 Figure 3. Predicted interaction effects of joining the Schools App network and distance on likelihood of full credit completion

49 Table 1. Descriptive Statistics of All Students, Non-Joiners of Network, and Network Joiners at CC All Students (Std. Dev) Non-Network (Std. Dev) Network (Std. Dev) T-test Values: Network vs. Non- Network Demographic variables Under 20 0.11 0.11 0.13 0.00 20-29 0.39 0.39 0.36 0.01 Over 30 0.50 0.50 0.51 0.73 Black 0.35 0.35 0.30 0.00 White 0.51 0.51 0.54 0.00 Other race 0.14 0.14 0.16 0.01 Female 0.63 0.63 0.68 0.00 Distance from school a 10.03 9.96 10.50 0.64 (33.87) (28.98) (58.21) Applied for federal funding 0.64 0.62 0.79 0.00 Federal funding received b 1,590 1,500 2,248 0.00 (1938) (1,903) (2,060) Academic variables Prior unit completion 0.76 0.76 0.79 0.00 percentage (0.26) (0.26) (0.24) Degree goal 0.68 0.68 0.72 0.00 GPA 2.68 2.67 2.77 0.00 (0.97) (0.98) (0.95) Network variable Joined 0.12 0.00 1.00 -- N 22,263 19,573 2,690 -- a Measured in miles b A combination of grants and loans; measured in dollars Source: Schools App and 2011-12 administrative data from the Community College.

Table 2. Descriptions of Network Variables M SD Variable Number of friends 3.52 4.64 Closure 0.11 0.20 Interest group memberships 7.72 19.79 50 Note: Means and standard deviations for number of friends and interest group membership are calculated from all students who have joined the network (N=2,690). Closure values are calculated using only students with network friends that also appear in the institutional data (N= 1,845). Source: Schools App data from the Community College, May 2012.

Table 3. Odds Ratios from Logistic Regression Models Predicting 100% Credit Completion for a CC Student During the First Term of Network Exposure Variable Model 1 Model 2 Model 3 Model 4 Marginal Marginal Marginal Marginal Effects Odds Ratio Effects Odds Ratio Effects Odds Ratio Effects Odds Ratio Joined 0.03* 1.13* -0.01 0.95 0.02 1.08 0.01 1.04 (0.01) [1.01, 1.26] (0.01) [0.84,1.06] (0.01) [0.96, 1.21] (0.02) [0.92, 1.17] Num. of Friends -0.01*** 0.95*** -0.004. 0.99. -0.004. 0.98. -0.005* 0.98* (0.002) [0.93, 0.97] (0.002) [0.97,1.00] (0.002) [0.97, 1.00] (0.002) [0.96, 1.00] Log(Interest groups) -0.02** 0.92** -0.02* 0.93* -0.01. 0.95. -0.01 0.96 (0.01) [0.87,0.97] (0.01) [0.87, 0.98] (0.01) [0.89, 1.00] (0.01) [0.90, 1.02] Log(Distance to campus) 0.08*** 1.39*** 0.07*** 1.34*** 0.05*** 1.23*** (0.01) [1.29,1.50] (0.01) [1.24, 1.45] (0.01) [1.13, 1.33] Under 20-0.27*** 0.34*** -0.27*** 0.33*** -0.26*** 0.35*** (0.01) [0.31,0.37] (0.01) [0.30, 0.37] (0.01) [0.32, 0.38] 20-29 -0.17*** 0.50*** -0.17*** 0.50*** -0.12*** 0.60*** (0.01) [0.47,0.53] (0.01) [0.47, 0.53] (0.01) [0.56, 0.64] Black -0.17*** 0.50*** -0.12*** 0.61*** -0.08*** 0.73*** (0.01) [0.47, 0.53] (0.01) [0.57, 0.65] (0.01) [0.68, 0.78] Other race -0.08*** 0.71*** -0.6*** 0.78*** -0.05*** 0.81*** (0.01) [0.65,0.77] (0.01) [0.72, 0.85] (0.01) [0.75, 0.89] Female 0.06*** 1.28*** 0.7*** 1.33*** 0.06*** 1.26*** (0.01) [1.21,1.35] (0.01) [1.25, 1.41] (0.01) [1.19, 1.34] Log(Fed. Funding) -0.15*** 0.54*** -0.17*** 0.49*** (0.01) [0.51, 0.57] (0.01) [0.46, 0.52] Prior Comp. Percentage 0.51*** 8.24*** (0.02) [7.29, 9.32] Degree Goal -0.03*** 0.89*** (0.01) [0.83, 0.95] Intercept 0.08*** 1.41*** -0.01 0.95 1.17*** 127.71*** 1.01*** 65.77*** [1.37, 1.45] (0.03) [0.75, 1.21] (0.06) [79.57, 05.03] (0.06) [40.42, 07.02] Wald Statistic 60.4*** 1,480.5*** 1,945.5*** 2,793.0*** Baseline Probability of 100% 0.59 0.59 0.59 0.59 0.59 0.59 0.59 0.59 Credit Completion Notes : N = 22,263; 95% confidence intervals in square brackets; baseline probability of credit completion is the probability of full credit completion for non-network joiners *** p <= 0.001; ** p <= 0.01; * p <= 0.05;. p <= 0.1

52 Table 4. Odds Ratios from Logistic Regression Models Predicting the Relationship Between Distance and 100% Credit Completion for a CC Student During the First Term of Network Exposure Model 1 Model 2 Model 3 Model 4 Model 5 Marginal Marginal Marginal Marginal Marginal Variable Effects Odds Ratio Effects Odds Ratio Effects Odds Ratio Effects Odds Ratio Effects Odds Ratio Joined 0.24*** 3.08** 0.03* 1.12* 0.03* 1.13* 0.16* 2.06* 0.12. 1.72 (0.06) [1.56, 5.96] (0.01) [1.00, 1.25] (0.01) [1.01, 1.26] (0.07) [1.04, 4.02] (0.07) [0.85, 3.41] Num. of Friends -0.01*** 0.95*** -0.02 0.94-0.01*** 0.95*** -0.004. 0.98. -0.01* 0.98* (0.002) [0.93, 0.97] (0.02) [0.82, 1.08] (0.002) [0.94, 0.97] (0.002) [0.96, 1.00] (0.002) [0.96, 1.00] Log(Interest groups) -0.02** 0.92** -0.02** 0.92** 0.06 1.29-0.014. 0.95. -0.01 0.96 (0.01) [0.87, 0.97] (0.01) [0.87, 0.97] (0.05) [0.86, 1.92] (0.01) [0.89, 1.00] (0.01) [0.90, 1.02] Log(Distance to campus) 0.15*** 1.82*** 0.14*** 1.75*** 0.14*** 1.76*** 0.08*** 1.38*** 0.06*** 1.26*** (0.01) [1.68, 1.98] (0.01) [1.62, 1.89] (0.01) [1.63, 1.90] (0.01) [1.27, 1.50] (0.01) [1.15, 1.37] Log(Dist.) x Joined -0.09** 0.70** -- -- -- -0.06. 0.80. -0.04 0.84 (0.03) [0.56, 0.89] (0.03) [0.63, 1.01] (0.03) [0.66, 1.07] Log(Dist) x Num. Friends 0.001 1.01 -- -- -- (0.01) [0.96, 1.06] Log(Dist) x Log(Grps) -0.03. 0.88. -- -- (0.02) [0.77, 1.02] Under 20-0.27*** 0.33*** -0.26*** 0.35*** (0.01) [0.30, 0.37] (0.01) [0.32, 0.39] 20-29 -0.17*** 0.50*** -0.12*** 0.60*** (0.01) [0.47, 0.53] (0.01) [0.57, 0.64] Black -0.12*** 0.61*** -0.08*** 0.73*** (0.01) [0.57, 0.65] (0.01) [0.68, 0.78] Other race -0.06*** 0.78*** -0.05*** 0.81*** (0.01) [0.72, 0.85] (0.01) [0.74, 0.89] Female 0.07*** 1.33*** 0.06*** 1.26*** (0.01) [1.26, 1.41] (0.01) [1.19, 1.34] Log(Fed. Funding) -0.15*** 0.54*** -0.17*** 0.49*** (0.01) [0.51, 0.57] (0.01) [0.46, 0.52] Prior Comp. Percentage 0.51*** 8.23*** (0.002) [7.29, 9.31] Degree Goal -0.029*** 0.89*** (0.01) [0.83, 0.95] Intercept -0.34*** 0.25*** -0.31*** 0.28*** -0.31*** 0.28*** 1.15*** 117.40*** 1.00*** 61.51*** (0.03) [0.20, 0.32] (0.03) [0.23, 0.35] (0.03) [0.22, 0.35] (0.06) [72.55, 90.01] (0.06) [37.49, 100.95]

53 Wald Statistic 275.3*** 268.1*** 270.7*** 1,947.5*** 2,793.6*** Overall Probability of 0.59 0.59 0.59 0.59 0.59 0.59 0.59 0.59 0.59 0.59 100% Credit Completion Notes : N = 22,263; 95% confidence intervals in square brackets; overall probability of credit completion is the probability of full credit completion for nonnetwork joiners *** p < 0.001; ** p < 0.01; * p < 0.05;. p < 0.1

54 Table 5. Logistic Regression Models Estimating Influence of Peers and Closure on Credit Completion Model 1 Model 2 Model 3 Marginal Effects Odds Ratio Marginal Effects Odds Ratio Marginal Effects Odds Ratio Prior Completion Percentage 0.51*** 7.62*** 0.51*** 7.65*** 0.51*** 7.82*** (0.05) [5.00, 11.75] (0.06) [5.01, 11.82] (0.06) [5.01, 12.35] Mean of Friends Prior Comp. % 0.28*** 3.06*** 0.29*** 3.15*** 0.22*** 2.38** (0.07) [1.73, 5.45] (0.08) [1.70, 5.89] (0.08) [1.32, 4.31] Closure 0.04 1.16 0.11 1.56 -- -- (0.06) [0.72, 1.86] (0.32) [0.12, 18.51] Mean of Friends x Closure -0.09 0.70 -- -- (0.38) [0.04, 14.11] Under 20-0.24*** 0.37*** (0.03) [0.28, 0.50] Age 20-29 -0.12*** 0.62*** (0.03) [0.50, 0.78] Black -0.02 0.93 (0.03) [0.74, 1.17] Other race -0.05 0.83 (0.04) [0.63, 1.11] Female 0.06* 1.25* (0.03) [1.01, 1.54] Log (Fed. Funding) -0.17*** 0.51*** (0.02) [0.42, 0.61] Degree Goal -0.02 0.92 (0.03) [0.73, 1.15] Constant -0.61*** 0.09*** -0.61*** 0.09*** 0.90*** 36.92*** (0.07) [0.05, 0.15] (0.08) [0.05, 0.15] (0.20) [7.50, 183.19] Wald Statistic 108.9*** 107.9*** 183.3*** Baseline Prob. of 100% 0.62 0.62 0.62 0.62 0.62 0.62 Completion Notes: N = 1,845, 95% confidence intervals in square brackets; baseline is calculated for students in the network who have no friends Sig. codes: *** p < 0.001; ** p < 0.01; * p < 0.05;. p < 0.1