Using Trellis software to enhance high-quality large-scale network data collection in the field

Trellis is a mobile platform created by the Human Nature Lab at the Yale Institute for Network Science to collect high-quality, location-aware, off-line/online, multi-lingual, multi-relational social network and behavior data in hard-to-reach communities. Respondents use Trellis to identify their social contacts by name and photograph, a procedure especially useful in low-literacy populations or in contexts where names may be similar or confusing. We use social network data collected from 1,969 adult respondents in two villages in Kenya to demonstrate Trellis’ ability to provide unprecedented metadata to monitor and report on the data collection process including artifactual variability based on surveyors, time of day, or location.     

Measuring ego-centered social networks on the web: Questionnaire design issues

Collecting survey data on ego-centered social networks is a difficult task, owing to the complex questionnaire format. Usually, the interviewer handles the dynamics of the question–answer exchange, motivates the respondent and ensures the proper recording of the data. Self-administered modes of data collection, especially web data collection, are more problematic, as the respondents are left alone with a complex and burdensome questionnaire. Therefore, questionnaire layout is crucial for ensuring cooperation and data quality. In this paper we examined three key components of the corresponding web questionnaire: the number of name boxes using a single name generator, question format for assessing alter characteristics (i.e. alter-wise vs. question-wise) and number of name interpreters (i.e. alter characteristics). The number of name boxes was found to be essential for the reported size of social networks and also for some aspects of data quality. Specific data quality effects were also found with respect to variations in question format, where question-wise format performed better than alter-wise. The number of name interpreters had a relatively minor effect. Suggestions for possible standardization of the web interface layout are also given, so that equivalence with other data collection modes can be established.     

Network Canvas: Key decisions in the design of an interviewer-assisted network data collection software suite

Self-reported social network analysis studies are often complex and burdensome, both during the interview process itself, and when conducting data management following the interview. Through funding obtained from the National Institute on Drug Abuse (NIDA/NIH), our team developed the Network Canvas suite of software – a set of complementary tools that are designed to simplify the collection and storage of complex social network data, with an emphasis on usability and accessibility across platforms and devices, and guided by the practical needs of researchers. The suite consists of three applications: Architect: an application for researchers to design and export interview protocols; Interviewer: a touch-optimized application for loading and administering interview protocols to study participants; and Server: an application for researchers to manage the interview deployment process and export their data for analysis. Together, they enable researchers with minimal technological expertise to access a complete research workflow, by building their own network interview protocols, deploying these protocols widely within a variety of contexts, and immediately attaining the resulting data from a secure central location. In this paper, we outline the critical decisions taken in developing this suite of tools for the network research community. We also describe the work which guides our decision-making, including prior experiences and key discovery events. We focus on key design choices, taken for theoretical, philosophical, and pragmatic reasons, and outline their strengths and limitations.     

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.     

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.     

Order of recall and meaning of closeness in collecting affective network data

The paper investigates how study participants handle the so-called “hierarchical mapping technique”, an affective name generator developed by Antonucci (1986), which is accompanied by a diagram enabling respondents to compare alters with regard to different degrees of closeness. By applying the thinking-aloud method, we identified three patterns in the order of recalling alters: closeness as overarching schema (with either role relationships or relationship properties as subordinate schema), roles and foci as overarching schema, and a fraying schema. In addition, we investigated how study participants understand and interpret “closeness”. The meanings of closeness can refer to various relationship properties, cultural framing, and relationship dynamics. Results show that specific meanings of closeness are related to different recall patterns. Furthermore, recall patterns vary according to the socio-economic status of the participants. Finally, implications for the construction of name generators and data collection are discussed.     

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.     

Participant engagement in environmentally focused social network research

Environmentally focused social network analysis (Env. SNA) has increasingly benefited from engagement, which refers to the process of incorporating the individuals, organizations, actors, stakeholders or other study participants into the research process. Research about engagement in the wider field of environmental management shows that successful engagement often requires significant planning and exchange among researchers and stakeholders. While there is no one size fits all approach, several important guiding principles have been established. To date, this engagement literature has not focused specifically on SNA, even though several examples of engaged SNA exist in the literature and point to some specific challenges to engagement when working with relational data. Drawing upon data from a survey of researchers who have incorporated engagement into Env. SNA, we focus specifically on the goals, scope, effectiveness, benefits and challenges of doing engaged Env. SNA research. We additionally highlight four case studies and demonstrate that researchers and participants find engagement to be a valuable experience with benefits in the researchers’ understanding of the context and meaning of their findings. Finally, we provide recommendations to scholars looking to embark on engaged Env. SNA research.     

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.     

Corporate competition: A self-organized network

A substantial number of studies have extended the work on universal properties in physical systems to complex networks in social, biological, and technological systems. In this paper, we present a complex networks perspective on interfirm organizational networks by mapping, analyzing and modeling the spatial structure of a large interfirm competition network across a variety of sectors and industries within the United States. We propose two micro-dynamic models that are able to reproduce empirically observed characteristics of competition networks as a natural outcome of a minimal set of general mechanisms governing the formation of competition networks. Both models, which utilize different approaches yet apply common principles to network formation give comparable results. There is an asymmetry between companies that are considered competitors, and companies that consider others as their competitors. All companies only consider a small number of other companies as competitors; however, there are a few companies that are considered as competitors by many others. Geographically, the density of corporate headquarters strongly correlates with local population density, and the probability two firms are competitors declines with geographic distance. We construct these properties by growing a corporate network with competitive links using random incorporations modulated by population density and geographic distance. Our new analysis, methodology and empirical results are relevant to various phenomena of social and market behavior, and have implications to research fields such as economic geography, economic sociology, and regional economic development.     

Comparing the efficacy of electronic-tablet to paper-based surveys for on-site survey administration

This study compares cost, completion times, and percent completion of electronic tablet (n = 244) to paper-based (n = 398) questionnaires administered to participants of scenic raft trips on the Snake River, Grand Teton National Park. We hypothesized e-tablet questionnaires would (1) cost less (2) be completed faster and (3) be completely filled more frequently than paper-based questionnaires. Our survey resulted in a greater per unit cost of e-tablet compared to paper-based questionnaires ($4.17 and $1.80, respectively). Although e-tablets were completed faster (230 [±66] s) than paper questionnaires (235 [±57] s), the difference was not significant (t₍₆₄₁₎ = 1.97, p = 0.39). E-tablets were filled completely more frequently than paper questionnaires (80% vs. 51%, respectively; χ² = 3.84, p < 0.01). We suggest that for multi-year projects with a sample size target of ≥100, and length ≥ 5 pages, e-tablets are more cost effective and efficient than paper-based surveys.     

Adolescent network positions and memory performance in adulthood: Evidence from sibling fixed effects models with sociometric network data

Although research has explored social factors influencing memory performance during adolescence, the impact of adolescent social network positions remains largely unknown. This study examines whether adolescent network position is associated with memory performance in adulthood, while also considering potential gender differences. The study used a sibling sample from the National Longitudinal Study of Adolescent to Adult Health (N = 2462) and employed sibling fixed effects models to account for unobserved family background factors, such as genetics, parental characteristics, family environment, and childhood neighborhood. Four dimensions of adolescent network position—i.e., popularity, sociality, degree centrality, and closeness centrality—were sociometrically assessed in schools. Memory performance in adulthood was measured using the Rey Auditory Verbal Learning Test. The sibling fixed effects estimates indicate that sociality, degree centrality, and closeness centrality are significantly associated with increased memory performance in adulthood, even after controlling for unobserved family heterogeneity as well as a set of individual-level covariates. In contrast, controlling for unobserved family heterogeneity attenuated the association for popularity, making it statistically insignificant. This study also provides evidence of gender differences in the association between social network position and memory performance. The associations for popularity, sociality, and degree centrality are more pronounced among men than women. This study’s findings highlight the importance of adolescent network positions as social determinants in shaping cognitive outcomes over the life course. Interventions that encourage positive peer interactions and reduce social isolation during adolescence may help improve cognitive health in the population.     

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.     

Using social network analysis to complete literature reviews: a new systematic approach for independent researchers to detect and interpret prominent research programs within large collections of relevant literature

ABSTRACT

Literature reviews are required at early stages of a traditional research progression. Many systematic approaches help researchers identify relevant literature. However, there is far less support for interpreting large collections of references. Understanding the evolution of knowledge within a discipline requires an awareness of the collaborative networks from which significant advances originate. Unfortunately, this relational awareness is only acquired after years of professional experience. Therefore, early-career researchers are at a fundamental disadvantage when conducting literature reviews. Social Network Analysis (SNA) can make the interpretation of large collections of literature more manageable. This paper provides a method for generating a co-authorship network from reference lists. The partition of the co-authorship network into communities provides a reviewer with direction to organise literature into meaningful groups. The interpretation of seemingly disparate and entangled research programs is far more manageable when interpreting smaller collections of highly relevant literature.     

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.     

The analysis of multiple ties in longitudinal egocentric network data: A case study on bidirectional relationships between trust and drug use

We extend multi-level models to examine single egocentric network ties to the joint analysis of paired dynamic ties. Two analytic challenges are addressed. First, inference needs to account for multiple layers of nesting: ties are nested within pairs, pairs are nested within time points, and time points are nested within egos. Second, the focus is on the relationship between two dynamic ties; specification of outcome and predictor may be difficult. Instead, we treat both ties as outcomes. Our approach is used to analyze trust and reported drug use between egos and alters over time in a Bayesian framework.     

Stakeholder influence,government power,and new opportunities for Chinese Internet companies: A multilevel network analysis of cross-sector networks

In recent decades, Chinese Internet companies have experienced exponential growth. As the Internet industry increasingly commends tremendous financial resources, they also face growing stakeholder expectations for corporate social responsibility (CSR) actions. One way through which Chinese Internet companies conduct CSR is by building cross-sectoral collaborations with nonprofit and nongovernmental organizations (NGOs) and governmental agencies. Aiming to understand Internet companies’ strategic relationship building on CSR issues, the researchers drew from stakeholder influence theory and research on a network approach to stakeholder influence, and applied multilevel network analysis to model three networks related to Chinese Internet companies’ CSR collaborations. Specifically, we found that power and urgency are significant predictors of the structure of Internet companies’ cross-sector CSR alliance network. Organizations affiliated or endorsed by the central Chinese government are the most desirable CSR stakeholders. Additionally, the study also revealed that for Internet companies, devoting their attention to Internet-related social issues could increase their desirability as strategic stakeholders from other sectors and among Internet companies.     

Using portable computers in home visits: Effects on programs, data quality, home visitors and caregivers

Collecting and using high-quality data related to participant outcomes are critical for monitoring program quality in home visitation programs for child abuse and neglect prevention, as well as for efforts to improve effectiveness and use limited funds efficiently. An evaluation was conducted to determine the impact of using portable computers to record data from standardized screening tools during home visits. Six home visiting agencies participating in the Early Years Home Visitation Outcomes Project of Wisconsin implemented computerized screenings with wireless uploading of data to a state public health database. Using portable computers saved agencies time and money on all four screening tools tested, with significant cost savings (p < 0.05) on three of the four tools, as compared to paper-and-pencil administration of screenings and manual data entry. The average time used per screening dropped between 9 and 63 min, saving agencies between $2 and $14 per screening administration. Screening completion was also higher when portable computers were used for data capture. There was little effect on home visitors' perceptions of the ease of data collection or their interactions with families being served. The use of portable computers to collect standard screening data holds promise for the field of home visitation.     

Structure in context: A morphological view of whole network performance

Network perspectives in organizational research have focused primarily on how the embeddedness of actors shapes individual, or nodal, outcomes. Against this backdrop, a growing number of researchers have begun to adopt a wider lens on organizational networks, shifting the focus to collective, or whole network, performance. Yet, efforts to understand the relationship between whole network structure and whole network performance have produced conflicting findings, which suggests that a different approach may be needed. Drawing on macrostructural sociology, we propose a "whole network morphology" framework, which argues the whole network structure-performance relationship is contingent on other fundamental—relational and cultural—whole network dimensions. Subsequently, we undertake an application of our framework, through which we demonstrate how a morphological view helps address conflicting findings on the structure-performance relationship. We study 250 whole interorganizational networks known as Accountable Care Organizations (ACOs), which collectively comprise more than 44,000 healthcare organizations and 250,000 physicians. Consistent with previous work, we do not find a clear association between structural connectedness and performance. However, we find that a more disconnected network structure is associated with negative ACO performance when the relational strength of network ties is high. We also find evidence of better ACO performance in the presence of a physician cultural orientation when the whole network is more connected.