Hostname: page-component-78c5997874-ndw9j Total loading time: 0 Render date: 2024-11-17T08:19:33.772Z Has data issue: false hasContentIssue false
  • English
  • Français

Assessing the stability of egocentric networks over time using the digital participant-aided sociogram tool Network Canvas

Published online by Cambridge University Press:  04 November 2019

Bernie Hogan Open the ORCID record for Bernie Hogan [Opens in a new window] ,
Patrick Janulis ,
Gregory Lee Phillips II ,
Joshua Melville ,
Brian Mustanski ,
Noshir Contractor  and
Michelle Birkett Open the ORCID record for Michelle Birkett [Opens in a new window]
Bernie Hogan*
Affiliation:
University of Oxford, Oxford, UK
Patrick Janulis
Affiliation:
Northwestern University, Evanston, IL, USA
Gregory Lee Phillips II
Affiliation:
Northwestern University, Evanston, IL, USA
Joshua Melville
Affiliation:
Northwestern University, Evanston, IL, USA
Brian Mustanski
Affiliation:
Northwestern University, Evanston, IL, USA
Noshir Contractor
Affiliation:
Northwestern University, Evanston, IL, USA
Michelle Birkett
Affiliation:
Northwestern University, Evanston, IL, USA
*
*Corresponding author. Email: bernie.hogan@oii.ox.ac.uk
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

This paper examines the stability of egocentric networks as reported over time using a novel touchscreen-based participant-aided sociogram. Past work has noted the instability of nominated network alters, with a large proportion leaving and reappearing between interview observations. To explain this instability of networks over time, researchers often look to structural embeddedness, namely the notion that alters are connected to other alters within egocentric networks. Recent research has also asked whether the interview situation itself may play a role in conditioning respondents to what might be the appropriate size and shape of a social network, and thereby which alters ought to be nominated or not. We report on change in these networks across three waves and assess whether this change appears to be the result of natural churn in the network or whether changes might be the result of factors in the interview itself, particularly anchoring and motivated underreporting. Our results indicate little change in average network size across waves, particularly for indirect tie nominations. Slight, significant changes were noted between waves one and two particularly among those with the largest networks. Almost no significant differences were observed between waves two and three, either in terms of network size, composition, or density. Data come from three waves of a Chicago-based panel study of young men who have sex with men.

Keywords

Brea L. PerryBernice A. PescosolidoMario L. SmallAnn McCranieegocentric networksparticipant-aided sociogramsreliabilitypanel conditioningymsmpanel studylongitudinal networks
Type
Research Article
Information
Network Science , Volume 8 , Issue 2: Special Issue Ego Networks , June 2020 , pp. 204 - 222
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2019. Published by Cambridge University Press

References

Antonucci, T. (1986). Measuring Social Support Networks: Hierarchical Mapping Technique. Generations, 10, 10–12.Google Scholar
Bellotti, E. (2014). Qualitative networks: mixed methods in sociological research. London, UK: Routledge.Google Scholar
Bernard, H. R., Johnsen, E. C., Killworth, P. D., McCarty, C., Shelley, G. A., & Robinson, S. (1990). Comparing four different methods for measuring personal social networks. Social Networks, 12(3), 179–215.Google Scholar
Bidart, C., & Degenne, A. (2005). Introduction: The dynamics of personal networks. Social Networks, 27(4), 283–287.Google Scholar
Bidart, C., & Lavenu, D. (2005). Evolutions of personal networks and life events. Social Networks, 27(4), 359–376.CrossRefGoogle Scholar
Brewer, D. D. (2000). Forgetting in the recall-based elicitation of personal and social networks. Social Networks, 22(1), 29–43.Google Scholar
Burt, R. (1984). Network Items and the General Social Survey. Social Networks, 6(4), 293–339.Google Scholar
Cicchetti, D. V. (1994). Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology. Psychological Assessment, 6(4), 284–290.CrossRefGoogle Scholar
Crossley, N., Bellotti, E., Edwards, G., Everett, M. G., Koskinen, J., & Tranmer, M. (2015). Statistical analysis of network dynamics. In Social network analysis for ego-nets. London: SAGE Publications Ltd.CrossRefGoogle Scholar
Eagle, D. E., & Proeschold-Bell, R. J. (2015). Methodological considerations in the use of name generators and interpreters. Social Networks, 40, 75–83.CrossRefGoogle Scholar
Eddens, K., & Fagan, J. M. (2018). Comparing nascent approaches for gathering alter-tie data for egocentric studies. Social Networks, 55, 130–141.CrossRefGoogle Scholar
Feng, D. (2018). agRee: Various Methods for Measuring Agreement. Retrieved from https://cran.r-project.org/web/packages/agRee/index.htmlGoogle Scholar
Fischer, C. S. (2009). The 2004 GSS finding of shrunken social networks: An artifact? American Sociological Review, 74(4), 657–669.Google Scholar
Gamper, M., Schönhuth, M., & Kronenwett, M. (2012). Social networking and community behavior modeling: Qualitative and quantitative measures. In Safar, M., & Madhi, K. A. (Eds.), Social networking and community behavior modeling: qualitative and quantitative measures (pp. 193–213). Hershey, PA: IGI Global.CrossRefGoogle Scholar
Hogan, B., Carrasco, J. A., & Wellman, B. (2007). Visualizing personal networks: Working with participant-aided sociograms. Field Methods, 19(2), 116–144.Google Scholar
Hogan, B., Melville, J. R., Phillips, II, G. L., Janulis, P., Contractor, N., Mustanski, B. S., & Birkett, M. (2016). Evaluating the paper-to-screen translation of participant-aided sociograms with high-risk participants. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems - CHI ’16 (pp. 5360–5371). New York, NY: ACM Press.Google Scholar
Jeon, G. Y., Ellison, N. B., Hogan, B., & Greenhow, C. M. (2016). First-generation students and college: the role of facebook networks as information sources. In Proceedings of the 19th ACM conference on computer-supported cooperative work & social computing - CSCW ’16 (pp. 885–897). New York, NY: ACM Press.Google Scholar
Laumann, E. O., Marsden, P. V, & Prensky, D. (1983). The boundary specification problem in network analysis. In Burt, R. S. & Minor, M. J. (Eds.), Applied network analysis (pp. 18–34). Beverly Hills, CA: Sage.Google Scholar
Lee, A., & Archambault, D. (2016). Communities Found by Users – Not Algorithms: Comparing Human and Algorithmically Generated Communities. In Proceedings of the 2016 CHI conference on human factors in computing systems, (pp. 2396–2400). New York, NY: ACM Press.Google Scholar
Lin, L. I.-K. (1989). A concordance correlation coefficient to evaluate reproducibility. Biometrics, 45(1), 255–268.Google ScholarPubMed
Marin, A. (2004). Are respondents more likely to list alters with certain characteristics? Implications for name generator data. Social Networks, 26(4), 289–307.CrossRefGoogle Scholar
McCarty, C., & Govindaramanujam, S. (2005). Modified Elicitation of Personal Networks Using Dynamic Visualization. Connections, 26(2), 9–17.Google Scholar
McCarty, C., Killworth, P. D., & Rennell, J. (2007). Impact of methods for reducing respondent burden on personal network structural measures. Social Networks, 29(2), 300–315.Google Scholar
McConnell, E., Néray, B., Hogan, B., Korpak, A., Clifford, A., & Birkett, M. (2018). “Everybody puts their whole life on Facebook”: Identity management and the online social networks of LGBTQ youth. International Journal of Environmental Research and Public Health, 15(6).Google Scholar
McPherson, J. M., Smith-Lovin, L., & Brashears, M. (2006). Changes in Core Discussion Networks over Two Decades. American Sociological Review, 71(3), 353–375.CrossRefGoogle Scholar
Morgan, D. L., Neal, M. B., & Carder, P. (1997). The stability of core and peripheral networks over time. Social Networks, 19(1), 9–25.Google Scholar
Pahl, R., & Spencer, L. (2004). Personal Communities: Not Simply Families of ‘Fate’ or ‘Choice.’ Current Sociology, 52(2), 199–221.CrossRefGoogle Scholar
Paik, A., & Sanchagrin, K. (2013). Social Isolation in America: An Artifact. American Sociological Review, 78(3), 339–360.Google Scholar
Perry, B. L., & Pescosolido, B. A. (2012). Social Network Dynamics and Biographical Disruption: The Case of “First-Timers” with Mental Illness. American Journal of Sociology, 118(1), 134–175.Google Scholar
Phillips, II, G., Janulis, P., Mustanski, B., & Birkett, M. (2017). Validation of tie corroboration and reported alter characteristics among a sample of young men who have sex with men. Social Networks, 48, 250–255.Google ScholarPubMed
Schroder, K. E., Carey, M. P., & Vanable, P. A. (2003). Methodological challenges in research on sexual risk behavior: II. Accuracy of self-reports. Annals of Behavioral Medicine, 26(2), 104–123.Google ScholarPubMed
Silber, H., Schröder, J., Struminskaya, B., Stocké, V., & Bosnjak, M. (2019). Does panel conditioning affect data quality in ego-centered social network questions? Social Networks, 56, 45–54.Google Scholar
Snijders, T. A. B. (2001). The statistical evaluation of social network dynamics. Sociological Methodology, 31(1), 361–395.Google Scholar
Stark, T. H., & Krosnick, J. A. (2017). GENSI: A new graphical tool to collect ego-centered network data. Social Networks, 48(1), 36–45.CrossRefGoogle Scholar
Suitor, J. J., Wellman, B., & Morgan, D. L. (1997). It’s about time: How, why, and when networks change. Social Networks, 19(1), 1–7.CrossRefGoogle Scholar
Suitor, J., & Keeton, S. (1997). Once a friend, always a friend? Effects of homophily on women’s support networks across a decade. Social Networks, 19(1), 51–62.Google Scholar
Tourangeau, R., Kreuter, F., & Eckman, S. (2012). Motivated Underreporting in Screening Interviews. Public Opinion Quarterly, 76(3), 453–469.Google Scholar
Tubaro, P., Casilli, A. A., & Mounier, L. (2014). Eliciting Personal Network Data in Web Surveys through Participant-generated Sociograms. Field Methods, 26(2), 107–125.Google Scholar
Vehovar, V., Lozar Manfreda, K., Koren, G., & Hlebec, V. (2008). Measuring ego-centered social networks on the web: Questionnaire design issues. Social Networks, 30(3), 213–222.CrossRefGoogle Scholar
Warren, J. R., & Halpern-Manners, A. (2012). Panel Conditioning in Longitudinal Social Science Surveys. Sociological Methods & Research, 41(4), 491–534.Google Scholar
Wellman, B. (1993). An egocentric network tale: comment on Bien et al. (1991). Social Networks, 15(4), 423–436.CrossRefGoogle Scholar
Wright, E. R., & Pescosolido, B. “Sorry I Forgot: The Role of Recall Error in Longitudinal Personal Network Studies”. In Levy, J. L., & Pescosolido, B. (2002). Advances in Medical Sociology: Social Networks and Health, vol. 8 (pp. 113–129) Greenwich, CT: JAI.Google Scholar
Wrzus, C., Hänel, M., Wagner, J., & Neyer, F. J. (2013). Social network changes and life events across the life span: A meta-analysis. Psychological Bulletin, 139(1), 53–80.Google ScholarPubMed