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Comparing the measurement of different social networks: Peer nominations, online communication, and proximity data

Published online by Cambridge University Press:  31 January 2020

T. J. van Woudenberg
Affiliation:
Behavioural Science Institute, Radboud University Nijmegen, Nijmegen, The Netherlands
K. E. Bevelander
Affiliation:
Radboud Institute for Health Sciences, Primary and Community Care, Radboud University and Medical Centre Nijmegen, Nijmegen, The Netherlands
W. J. Burk
Affiliation:
Behavioural Science Institute, Radboud University Nijmegen, Nijmegen, The Netherlands
C. R. Smit
Affiliation:
Behavioural Science Institute, Radboud University Nijmegen, Nijmegen, The Netherlands
L. Buijs
Affiliation:
Behavioural Science Institute, Radboud University Nijmegen, Nijmegen, The Netherlands
M. Buijzen
Affiliation:
Behavioural Science Institute, Radboud University Nijmegen, Nijmegen, The Netherlands Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam, Rotterdam, The Netherlands
Corresponding
E-mail address:
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Abstract

Background:

Technological progress has enabled researchers to use new unobtrusive measures of relationships between actors in social network analysis. However, research on how these unobtrusive measures of peer connections relate to traditional sociometric nominations in adolescents is scarce. Therefore, the current study compared traditional peer nominated networks with more unobtrusive measures of peer connections: Communication networks that consist of instant messages in an online social platform and proximity networks based on smartphones’ Bluetooth signals that measure peer proximity. The three social network types were compared in their coverage, stability, overlap, and the extent to which the networks exhibit the often observed sex segregation in adolescent social networks.

Method:

Two samples were derived from the MyMovez project: a longitudinal sample of 444 adolescents who participated in the first three waves of the first year of the project (Y1; 51% male; Mage = 11.29, SDage = 1.26) and a cross-sectional sample of 774 adolescents that participated in fifth wave in the third year (Y3; 48% male; Mage = 10.76, SDage = 1.23). In the project, all participants received a research smartphone and a wrist-worn accelerometer. On the research smartphone, participants received daily questionnaires such as peer nomination questions (i.e., nominated network). In addition, the smartphone automatically scanned for other smartphones via Bluetooth signal every 15 minutes of the day (i.e., proximity network). In the Y3 sample, the research smartphone also had a social platform in which participants could send messages to each other (i.e., communication network).

Results:

The results show that nominated networks provided data for the most participants compared to the other two networks, but in these networks, participants had the lowest number of connections with peers. Nominated networks showed to be more stable over time compared to proximity or communication networks. That is, more connections remained the same in nominated networks than in proximity networks over the three waves of Y1. The overlap between the three networks was rather small, indicating that the networks measured different types of connections. Nominated and communication networks were segregated by sex, whereas this was less the case in proximity networks.

Conclusion:

The communication and proximity networks seem to be promising unobtrusive measures of peer connections and are less of a burden to the participant compared to a nominated network. However, given the structural differences between the networks and the number of connections per wave, the communication and proximity networks should not be used as direct substitutes for sociometric nominations, and researchers should bear in mind what type of connections they wish to assess.

Type
Research Article
Creative Commons
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike licence (http://creativecommons.org/licenses/by-nc-sa/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the same Creative Commons licence is included and the original work is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use.
Copyright
© The Author(s) 2020. Published by Cambridge University Press

References

Adams, J. (2010). Distant friends, close strangers? Inferring friendships from behavior. Proceedings of the National Academy of Sciences, 107(9), E29E30. doi: 10.1073/pnas.0911195107.CrossRefGoogle ScholarPubMed
Aiello, W., Chung, F., & Lu, L. (2000). A random graph model for massive graphs. Oregon, Portland: STOC.CrossRefGoogle Scholar
Antonacci, G., Colladon, A. F., Stefanini, A., & Gloor, P. (2017). It is rotating leaders who build the swarm: Social network determinants of growth for healthcare virtual communities of practice. Journal of Knowledge Management, 21(5), 12181239. doi: 10.1108/JKM-11-2016-0504.Google Scholar
Bates, D. M. (2010). Lme4: Mixed-effects modeling with R (Version 1.1-21). Retrieved from https://cran.r-project.org/web/packages/lme4/index.html.Google Scholar
Bauman, K. E., Faris, R., Ennett, S. T., Hussong, A., & Foshee, V. A. (2007). Adding valued data to social network measures: Does it add to associations with adolescent substance use? Social Networks, 29(1), 110. doi: 10.1016/j.socnet.2005.11.007.CrossRefGoogle Scholar
Berndt, T. J., Hawkins, J. A., & Hoyle, S. G. (1986). Changes in Friendship during a School Year: Effects on Children’s and Changes in friendship during a school year: Effects on children’s and adolescents’ impressions of friendship and sharing with friends. Child Development, 57(5), 12841297. doi: 10.2307/1130451.CrossRefGoogle Scholar
Berndt, T. J., & Hoyle, S. G. (1985). Stability and change in childhood and adolescent friendships. Developmental Psychology, 21(6), 10071015.CrossRefGoogle Scholar
Bevelander, K. E., Smit, C. R., van Woudenberg, T. J., Buijs, L., Burk, W. J., & Buijzen, M. (2018). Youth’s social network structures and peer influences: Study protocol MyMovez project—Phase I. BMC Public Health, 18(1), 504. doi: 10.1186/s12889-018-5353-5.CrossRefGoogle ScholarPubMed
Camarena, P. M., Sarigiani, P. A., & Petersen, A. C. (1990). Gender-specific pathways to intimacy in early adolescence. Journal of Youth and Adolescence, 19(1), 1932. doi: 10.1007/BF01539442.CrossRefGoogle ScholarPubMed
Campbell, R., Starkey, F., Holliday, J., Audrey, S., Bloor, M. J., Parry-Langdon, N., … Moore, L. (2008). An informal school-based peer-led intervention for smoking prevention in adolescence (ASSIST): A cluster randomised trial. Lancet (London, England), 371(9624), 15951602. doi: 10.1016/S0140-6736(08)60692-3.CrossRefGoogle ScholarPubMed
Chan, A., & Poulin, F. (2007). Monthly changes in the composition of friendship networks in early adolescence. Merrill-Palmer Quarterly, 53(4), 578602. doi: 10.1353/mpq.2008.0000.CrossRefGoogle Scholar
Chan, D., & Cheng, G. (2004). A comparison of offline and online friendship qualities at different stages of relationship development. Journal of Social and Personal Relationships, 21(3), 305320. doi: 10.1177/0265407504042834.CrossRefGoogle Scholar
Cho, E., Myers, S. A., & Leskovec, J. (2011). Friendship and mobility: User movement in location-based social networks. 10821090. https://doi.org/10.1145/2020408.2020579.CrossRefGoogle Scholar
Csardi, G., & Nepusz, T. (2005). The igraph software package for complex network research (Version 1.2.4.1). Retrieved from https://cran.r-project.org/web/packages/igraph/.Google Scholar
de la Haye, K., Robins, G., Mohr, P., & Wilson, C. (2011). How physical activity shapes, and is shaped by, adolescent friendships. Social Science & Medicine, 73(5), 719728. doi: 10.1016/j.socscimed.2011.06.023.CrossRefGoogle ScholarPubMed
De Lange, D., Agneessens, F., & Waege, H. (2004). Asking social Network questions: A quality assessment of different measures. Metodoloski Zvezki, 1(2), 351378.Google Scholar
Del Vicario, M., Zollo, F., Caldarelli, G., Scala, A., & Quattrociocchi, W. (2017). Mapping social dynamics on Facebook: The Brexit debate. Social Networks, 50, 616. doi: 10.1016/j.socnet.2017.02.002.CrossRefGoogle Scholar
Dong, W., Olguin-Olguin, D., Waber, B., Kim, T., & Pentland, A. “Sandy.” (2012). Mapping organizational dynamics with body sensor networks. 2012 Ninth International Conference on Wearable and Implantable Body Sensor Networks, 130135. doi: 10.1109/BSN.2012.16.CrossRefGoogle Scholar
Eagle, N., Pentland, A., & Lazer, D. (2009). Inferring friendship network structure by using mobile phone data. Proceedings of the National Academy of Sciences, 106(36), 1527415278. doi: 10.1073/pnas.0900282106.CrossRefGoogle ScholarPubMed
Ebel, H., Mielsch, L.-I., & Bornholdt, S. (2002). Scale-free topology of e-mail networks. Physical Review E, 66(3), 14. doi: 10.1103/PhysRevE.66.035103.CrossRefGoogle ScholarPubMed
Garton, L., Haythornthwaite, C., & Wellman, B. (1997). Studying Online Social Networks. Journal of Computer-Mediated Communication, 3(1), JCMC313. doi: 10.1111/j.1083-6101.1997.tb00062.x.Google Scholar
González-Bailón, S., Wang, N., Rivero, A., Borge-Holthoefer, J., & Moreno, Y. (2014). Assessing the bias in samples of large online networks. Social Networks, 38, 1627. doi: 10.1016/j.socnet.2014.01.004.CrossRefGoogle Scholar
Hamers, L. (1989). Similarity measures in scientometric research: The Jaccard index versus Salton’s cosine formula. Information Processing and Management, 25(3), 315318.CrossRefGoogle Scholar
Hartup, W. W. (1996). The company they keep: Friendships and their developmental significance. Child Development, 67(1), 113. doi: 10.1111/j.1467-8624.1996.tb01714.x.CrossRefGoogle ScholarPubMed
Kim, T., McFee, E., Olguin, D. O., Waber, B., & Pentland, A.Sandy.” (2012). Sociometric badges: Using sensor technology to capture new forms of collaboration. Journal of Organizational Behavior, 33(3), 412427. doi: 10.1002/job.1776.CrossRefGoogle Scholar
Kossinets, G. (2006). Empirical analysis of an evolving social network. Science, 311(5757), 8890. doi: 10.1126/science.1116869.CrossRefGoogle ScholarPubMed
Krause, R. W., Huisman, M., Steglich, C., & Snijders, T. A. (2018). Missing Network Data A Comparison of Different Imputation Methods. 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 159–163. https://doi.org/10.1109/ASONAM.2018.8508716.CrossRefGoogle Scholar
Li, N., & Chen, G. (2009). Analysis of a location-based social network. International Conference on Computational Science and Engineering, 4, 263270. doi: 10.1109/CSE.2009.98.Google Scholar
Marin, A., & Hampton, K. N. (2007). Simplifying the personal network name generator: Alternatives to traditional multiple and single name generators. Field Methods, 19(2), 163193. doi: 10.1177/1525822X06298588.CrossRefGoogle Scholar
Marks, P. E. L., Babcock, B., Cillessen, A. H. N., & Crick, N. R. (2013). The effects of participation rate on the internal reliability of peer nomination measures. Social Development, 22(3609–622. doi: 10.1111/j.1467-9507.2012.00661.x.CrossRefGoogle Scholar
McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27(1), 415444.CrossRefGoogle Scholar
Mercken, L., Snijders, T. A., Steglich, C., & de Vries, H. (2009). Dynamics of adolescent friendship networks and smoking behavior: Social network analyses in six European countries. Social Science & Medicine, 69(10), 15061514.CrossRefGoogle ScholarPubMed
Montanari, A., Nawaz, S., Mascolo, C., & Sailer, K. (2017). A Study of Bluetooth Low Energy performance for human proximity detection in the workplace. 2017 IEEE International Conference on Pervasive Computing and Communications (PerCom), 9099. https://doi.org/10.1109/PERCOM.2017.7917855.CrossRefGoogle Scholar
Olguín-Olguín, D., & Pentland, A. (2010). Sensor-based organisational design and engineering. https://doi.org/10.1504/IJODE.2010.035187.CrossRefGoogle Scholar
Onnela, J.-P., Saramaki, J., Hyvonen, J., Szabo, G., Lazer, D., Kaski, K., … Barabasi, A.-L. (2007). Structure and tie strengths in mobile communication networks. Proceedings of the National Academy of Sciences, 104(18), 73327336. doi: 10.1073/pnas.0610245104.CrossRefGoogle ScholarPubMed
Polastre, J., Szewczyk, R., & Culler, D. (2005). Telos: Enabling Ultra-Low Power Wireless Research.Google Scholar
R Core Team. (2019). R: A language and environment for statistical computing. Retrieved from http://www.R-project.org/.Google Scholar
Salathé, M., & Jones, J. H. (2010). Dynamics and Control of Diseases in Networks with Community Structure. PLOS Computational Biology, 6(4), e1000736. doi: 10.1371/journal.pcbi.1000736.CrossRefGoogle ScholarPubMed
Salathé, M., Kazandjieva, M., Lee, J. W., Levis, P., Feldman, M. W., & Jones, J. H. (2010). A high-resolution human contact network for infectious disease transmission. Proceedings of the National Academy of Sciences, 107(51), 2202022025. doi: 10.1073/pnas.1009094108.CrossRefGoogle ScholarPubMed
Simoski, B., Klein, M., Araujo, E. F. M., van Halteren, A. T., van Woudenberg, T. J., Bevelander, K. E., & Buijzen, M. (2019). Parameter Optimization for Deriving Bluetooth-based Social Network Graphs. Presented at the IEEE Internet of People, Leicester, England.Google Scholar
Subrahmanyam, K., Reich, S. M., Waechter, N., & Espinoza, G. (2008). Online and offline social networks: Use of social networking sites by emerging adults. Journal of Applied Developmental Psychology, 29(6), 420433. doi: 10.1016/j.appdev.2008.07.003.CrossRefGoogle Scholar
Takhteyev, Y., Gruzd, A., & Wellman, B. (2012). Geography of Twitter networks. Social Networks, 34(1), 7381. doi: 10.1016/j.socnet.2011.05.006.CrossRefGoogle Scholar
Valkenburg, P. M., & Peter, J. (2011). Online Communication Among Adolescents: An Integrated Model of Its Attraction, Opportunities, and Risks. Journal of Adolescent Health, 48(2), 121127. doi: 10.1016/j.jadohealth.2010.08.020.CrossRefGoogle ScholarPubMed
Van de Mortel, T. F. (2008). Faking it: Social desirability response bias in self-report research. Australian Journal of Advanced Nursing, The, 25(4), 40.Google Scholar
Wang, C., Lizardo, O., Hachen, D., Strathman, A., Toroczkai, Z., & Chawla, N. V. (2013). A dyadic reciprocity index for repeated interaction networks. Network Science, 1(1), 3148. doi: 10.1017/nws.2012.5.CrossRefGoogle Scholar
Wasserman, S. (1994). Advances in social network analysis: Research in the social and behavioral sciences. Thousand Oaks, CA: SAGE.Google Scholar
Wen, Q., Gloor, P. A., Fronzetti Colladon, A., Tickoo, P., & Joshi, T. (2019). Finding top performers through email patterns analysis. Journal of Information Science, 0165551519849519. doi: 10.1177/0165551519849519.Google Scholar
Wilson, C., Sala, A., Puttaswamy, K. P. N., & Zhao, B. Y. (2012). Beyond social graphs: User interactions in online social networks and their implications. ACM Transactions on the Web, 6(4), 131. doi: 10.1145/2382616.2382620.CrossRefGoogle Scholar

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