The Swiss StudentLife Study: Investigating the emergence of an undergraduate community through dynamic,multidimensional social network data

The Swiss StudentLife Study (SSL Study) is a longitudinal social network data collection conducted in three undergraduate student cohorts (N₁ = 226, N₂ = 261, N₃ = 660) in 2016−2019. The main goal of the study was to understand the emergence of informal student communities and their effects on different individual outcomes, such as well-being, motivation, and academic success. To this end, multiple dimensions of social ties were assessed, combining computer-based surveys, social sensors, social media data, and field experiments. The dynamics of these social networks were measured on various time scales. In this paper, we present the design and data collection strategy of the SSL Study. We discuss practical challenges and solutions related to the data collection in four areas that were key to the success of our project: study design, research ethics, communication, and population definition.     

Collecting experimental network data from interventions on critical links in workplace networks

This article describes and discusses challenges associated with interventionist network data gathering in organizational settings, with a special focus on dyadic interventions. While pointing out major risks of these approaches, we argue that collecting data in combination with dyadic network alteration methods can enable social network researchers to explore network mechanisms from a new angle and potentially benefit the members of the targeted social networks and the entire collectives, if certain research design and implementation principles are followed. We introduce a facilitated self-disclosure method for strengthening critical dyads in social networks in combination with longitudinal and retrospective network measurement. We assess the participants’ perceptions of the different stages of this process by qualitative interviews. The study illustrates that experimental network data collection includes some extra challenges in addition to the many challenges of observational network data collection but participants also reported practical benefits that would not be gained through observational network surveys alone. The results highlight the importance of early and continuous communication during the data collection process with all research participants, not just the management, and the benefits of sharing more of the preliminary results. The lessons learnt through this study can inform the design of experimental network data collection to prioritize the preferences of the participants and their benefits.     

Beyond interviewer effects in the standardized measurement of ego-centric networks

The effects which interviewers exert on the collection of ego-centric networks have recently come into the focus of methodological considerations. Studies consistently show that the size of networks varies depending on the interviewer. We would like to expand on this research strand by pointing to different aspects which have so far gone unremarked in the discussion. First, size is mainly analysed as a network measure which is influenced during data collection, while other common measures such as network density or composition have not received sufficient consideration. Second, large-scale surveys using face-to-face interviews usually allocate interviewers to a single sampling point. Differences between sampling points (locality effects) are attributed to interviewer effects. Hence, we disentangle the effects of the locality and interviewer. Third, the discussion on interviewer effects often follows an “actor-oriented” consideration of how data collection situations are structured by interviewers. Expanding this approach from a relational perspective, we consider the relationship between the interviewers and respondents and whether this relationship influences the collection of network data. To test our hypotheses about the influence of interviewers, the locality and the interviewer-respondent relationship on different network measures, we use data from the 2010 German General Social Survey (n = 2827 respondents, n = 220 interviewers). The multilevel analyses show that the relationship between the interviewer and the respondent is not very relevant. Furthermore, the analyses show that interviewers have an influence on the network size but not on measures of their composition. However, evidence on the prevalence of locality or interviewer effects is mixed. Finally, homophilous interviewer-respondent relationships have very little effect on network characteristics. We find evidence of training and fatigue effects on network size. However, much of the variation in network size caused by the interviewer still remains unexplained. We draw conclusions on how to organize interview situations in surveys.     

Analytic strategies for longitudinal networks with missing data

Missing data are often problematic when analyzing complete longitudinal social network data. We review approaches for accommodating missing data when analyzing longitudinal network data with stochastic actor-based models. One common practice is to restrict analyses to participants observed at most or all time points, to achieve model convergence. We propose and evaluate an alternative, more inclusive approach to sub-setting and analyzing longitudinal network data, using data from a school friendship network observed at four waves (N = 694). Compared to standard practices, our approach retained more information from partially observed participants, generated a more representative analytic sample, and led to less biased model estimates for this case study. The implications and potential applications for longitudinal network analysis are discussed.     

Measurement error in network data: A re-classification

Research on measurement error in network data has typically focused on missing data. We embed missing data, which we term false negative nodes and edges, in a broader classification of error scenarios. This includes false positive nodes and edges and falsely aggregated and disaggregated nodes. We simulate these six measurement errors using an online social network and a publication citation network, reporting their effects on four node-level measures – degree centrality, clustering coefficient, network constraint, and eigenvector centrality. Our results suggest that in networks with more positively-skewed degree distributions and higher average clustering, these measures tend to be less resistant to most forms of measurement error. In addition, we argue that the sensitivity of a given measure to an error scenario depends on the idiosyncracies of the measure's calculation, thus revising the general claim from past research that the more ‘global’ a measure, the less resistant it is to measurement error. Finally, we anchor our discussion to commonly-used networks in past research that suffer from these different forms of measurement error and make recommendations for correction strategies.     

Does the online collection of ego-centered network data reduce data quality? An experimental comparison

We analyze whether differences in kind and quality of ego-centered network data are related to whether the data are collected online or offline. We report the results of two studies. In the first study respondents could choose between filling out ego-centered data through a web questionnaire and being probed about their network in a personalized interview. The second study used a design in which respondents were allocated at random to either online or offline data collection. Our results show that the data quality suffers from the online data collection and the findings indicate that this is the consequence of the respondents answering “mechanically”. We conclude that network researchers should avoid to simply copy traditional network items into a web questionnaire. More research is needed about how new design elements specific for web questionnaires can motivate respondents to fill out network questions properly.     

Statistical power of the social network autocorrelation model

The network autocorrelation model has become an increasingly popular tool for conducting social network analysis. More and more researchers, however, have documented evidence of a systematic negative bias in the estimation of the network effect (ρ). In this paper, we take a different approach to the problem by investigating conditions under which, despite the underestimation bias, a network effect can still be detected by the network autocorrelation model. Using simulations, we find that moderately-sized network effects (e.g., ρ = .3) are still often detectable in modest-sized networks (i.e., 40 or more nodes). Analyses reveal that statistical power is primarily a nonlinear function of network effect size (ρ) and network size (N), although both of these factors can interact with network density and network structure to impair power under certain rare conditions. We conclude by discussing implications of these findings and guidelines for users of the autocorrelation model.     

Social network structure and the trade-off between social utility and economic performance

We put forward a computational multi-agent model capturing the impact of social network structure on individuals’ social trust, willingness to cooperate, social utility and economic performance. Social network structure is modeled as four distinct social capital dimensions: degree, centrality, bridging and bonding social capital. Model setup draws from socio-economic theory and empirical findings based on our novel survey dataset. Results include aggregate-level comparative statics and individual-level correlations. We find, inter alia, that societies that either are better connected, exhibit a lower frequency of local cliques, or have a smaller share of family-based cliques, record relatively better aggregate economic performance. As long as family ties are sufficiently valuable, there is a trade-off between aggregate social utility and economic performance, and small world networks are then socially optimal. We also find that in dense networks and trustful societies, there is a trade-off between individual social utility and economic performance; otherwise both outcomes are positively correlated in the cross section.     

Social network analysis for assessing college-aged adults' health: A systematic review

Objective: Social network analysis (SNA) is a useful, emerging method for studying health. College students are especially prone to social influence when it comes to health. This review aimed to identify network variables related to college student health and determine how SNA was used in the literature. Participants: A systematic review of relevant literature was conducted in October 2015. Methods: Studies employing egocentric or whole network analysis to study college student health were included. We used Garrard's Matrix Method to extract data from reviewed articles (n = 15). Results: Drinking, smoking, aggression, homesickness, and stress were predicted by network variables in the reviewed literature. Methodological inconsistencies concerning boundary specification, data collection, nomination limits, and statistical analyses were revealed across studies. Conclusions: Results show the consistent relationship between network variables and college health outcomes, justifying further use of SNA to research college health. Suggestions and considerations for future use of SNA are provided.     

Motivation for and use of social networking sites: Comparisons among college students with and without histories of non-suicidal self-injury

Objective: This research examines potential differences in social network use and motivation for social network use by non-suicidal self-injury (NSSI) status. Participants: 367 (73% women; M_age = 20.60) college students were recruited in November–December 2011. Methods: A random sample of 2,500 students was accessed through a university registrar to recruit students interested in an online survey assessing NSSI and various health-related behaviors. Results: Social network use and motivations for social networks did not differ by NSSI status. Conclusions: Results suggest that it is not patterns of use or motivation to use social networks that could lead to concern about online behavior (i.e., behavior increasing risk of future NSSI) among those with NSSI history. Rather, future preventive and intervention efforts should address the NSSI-related content that is available online, since this is unregulated, often explicit, and commonly includes “pro-NSSI” content that may be problematic and increase risk among vulnerable individuals.     

Analysis of social innovation on social networking services

ABSTRACT

Social network services are allowing more social connectivity and making possible sharing information and knowledge of a different nature. This article analyses if social workers related to active social policies from Malaga province (Spain) are using social network services to share information and knowledge and if there is a mirror between online and offline relationships. Since it is an experimental model, through virtual ethnography and through social network analysis methodology, we observed the presence, connectivity and analysed the structure of relationships that keep 235 professionals from 52 organizations in Facebook®. Moreover, the model uses the statistical technique of modularity for detecting online communities, which are compared with distribution of professionals in their organizations. Results show how social network services applied to social intervention are massively and frequently used by professionals. On the other hand, the detected communities reflect existence of analogy between online and offline relationships. The online structure shows a high degree of cohesion and how certain professionals have higher capacity of influence and diffusion depending on position in the online structure. It argues about the opportunity to incorporate social network services towards social intervention as a manner of social innovation to improve cooperation and diffusion of information and knowledge.     

Generalizing from social media data: a formal theory approach

ABSTRACT

Researchers increasingly draw on social media data to answer big questions about social patterns and dynamics. However, as with any data source, social media data present both opportunities and significant challenges. One major critique of social media data is that the data are not generalizable outside of the platforms from which the data originate. Problems of generalizability stem from non-universal participation rates on various platforms, demographically biased samples, as well as limited access to data based on infrastructural constraints and/or user privacy practices. We suggest that instead of empirical generalizability, social media data are theoretically generalizable in the formal theory tradition. Through a case example in which we use YouTube comments to test and extend a key tenet of identity theory, we show how social media data can instantiate theoretical variables and thus generalize to theoretical propositions. Mediated through formal theory, social media data maintain the capacity to address broad social questions while upholding methodological integrity.     

The effect of density on the level of bias in the network autocorrelation model

Researchers interested in the effects of social network ties on behavior are increasingly turning to the network autocorrelation model, which allows for the simultaneous computation of individual-level and network-level effects. Earlier research, however, had pointed to the possibility that the maximum likelihood estimates used to compute the network autocorrelation model yielded negatively biased parameter estimates. In this paper we use simulations to examine whether – and the conditions under which – a negative bias exists. We show that the network parameter estimate ρ is negatively biased under nearly all conditions, and that this bias becomes more severe at higher levels of both ρ and network density. We conclude by discussing the implications of these findings for researchers planning to use the network autocorrelation model.     

Distinguishing among publics,audiences, and stakeholders in the social media era of unanticipated publics

Public relations is failing to distinguish among publics, audiences, and stakeholders in today’s social media era, even though the business and marketing literature has begun discussing “brand publics.” This article clarifies the terms and explains the practical value in the definitions and distinctions. It also introduces a new term, “unanticipated publics” and provides a way to conceptualize those publics’ impact from the social network perspective.     

The social networks of new-generation migrants in China’s urbanized villages: A case study of Guangzhou

The integration of rural migrants into China’s urban society has drawn extensive attention in recent years. There are, however, a growing number of new-generation migrants whose prospects of integration cannot be gleaned from the experience of their predecessors—the old-generation migrants. The reconstruction of migrant network is a lens through which one can examine the extent and pattern of their integration. In this paper, using quantitative data derived from a survey of eight urbanized villages in Guangzhou, we examine the socio-spatial pattern of migrant networks and the role of receiving neighborhoods in social interactions, with a focus on inter-generational differences. The findings show that new-generation migrants are more likely to draw on cross-class, non-kin, and non-territorial networks when seeking social support, but that hometown-based bonds and the urbanite-migrant divisions remain central to their social networks. For the role of receiving neighborhoods, although new-generation migrants have weak neighborly interactions, they construct numerous colleagues and friendship ties that transcend the boundaries of neighborhoods. Moreover, educational attainment, income level, and occupation structure are important determinants of the nature of new-generation migrant networks. The results suggest that generational factors should be highly considered when studying the integration of migrants into Chinese urban society. Accordingly, the integration of migrants into the urban society will be a conflictual and contradictory process.     

An exploratory comparison of name generator content: Data from rural India

Since the 1970s sociologists have explored the best means for measuring social networks, although few name generator analyses have used sociocentric data or data from developing countries, partly because sociocentric studies in developing countries have been scant. Here, we analyze 12 different name generators used in a sociocentric network study conducted in 75 villages in rural Karnataka, India. Having unusual sociocentric data from a non-Western context allowed us to extend previous name generator research through the unique analyses of network structural measures, an extensive consideration of homophily, and investigation of status difference between egos and alters. We found that domestic interaction questions generated networks that were highly clustered and highly centralized. Similarity between respondents and their nominated contacts was strongest for gender, caste, and religion. We also found that domestic interaction name generators yielded the most homogeneous ties, while advice questions yielded the most heterogeneous. Participants were generally more likely to nominate those of higher social status, although certain questions, such as who participants talk to uncovered more egalitarian relationships, while other name generators elicited the names of social contacts distinctly higher or lower in status than the respondent. Some questions also seemed to uncover networks that were specific to the cultural context, suggesting that network researchers should balance local relevance with global generalizability when choosing name generators.     

To tell the truth: Measuring concordance in multiply reported network data

Social network data must accurately reflect actors’ relationships to properly estimate network features. Here, we examine multiple reports of sexual, drug-sharing and social tie data on high-risk networks in Colorado Springs. By comparing multiple reports on the same ties, we can evaluate the reliability of this study's network data. Our findings suggest that these data have a high level of reporting agreement. From these findings, we discuss implications for analysis of these and similar data and provide suggestions for future social network data collection efforts.     

More or less guanxi: Trust is 60% network context, 10% individual difference

The strong ties known in China as guanxi can be distinguished by a high level of trust relatively independent of the surrounding social structure. Using network data from a stratified probability sample of 700 entrepreneurs citing 4664 contacts, we study guanxi relative to other relations to learn how much individual differences such as well-being, business differences, political participation and demographic factors matter for the guanxi distinction. Two findings stand out: First, the connection between trust and social network is robust to most differences between individuals, especially business and political differences. Trust variance is 60% network context, and 10% individual differences. Trust increases within a relationship as network closure increases around the relationship, but some relationships mature into guanxi ties within which trust is high and relatively independent of the surrounding social structure. Second, when individual differences matter, they concern social isolation. Guanxi ties are more distinct in the networks around entrepreneurs with small, marginal families, and around those with small, closed networks. Both categories of entrepreneurs are likely to experience difficulties with respect to resource access and doing business with people beyond their network, which may explain why longstanding guanxi ties linked to important events are particularly distinct for these entrepreneurs.