IDEAS home Printed from https://ideas.repec.org/p/osf/socarx/2tgm7.html
   My bibliography  Save this paper

Diagnosing Multicollinearity in Exponential Random Graph Models

Author

Listed:
  • Duxbury, Scott W

Abstract

Exponential random graph models (ERGM) have been widely applied in the social sciences in the past ten years. However, diagnostics for ERGM have lagged behind their use. Collinearity-type problems can emerge without detection when fitting ERGM, skewing coefficients, biasing standard errors, and yielding inconsistent model estimates. This article provides a method to detect multicollinearity in ERGM. It outlines the problem and provides a method to calculate the variance inflation factor from ERGM parameters. It then evaluates the method with a Monte Carlo simulation, fitting 216,000 ERGMs and calculating the variance inflation factors for each model. The distribution of variance inflation factors is analyzed using multilevel regression to determine what network characteristics lend themselves to collinearity-type problems. The relationship between variance inflation factors and unstable standard errors (a standard sign of collinearity) is also examined. The method is shown to effectively detect multicollinearity and guidelines for interpretation are discussed.

Suggested Citation

  • Duxbury, Scott W, 2018. "Diagnosing Multicollinearity in Exponential Random Graph Models," SocArXiv 2tgm7, Center for Open Science.
    • Handle: RePEc:osf:socarx:2tgm7
    DOI: 10.31219/osf.io/2tgm7
    as

    Download full text from publisher

    File URL: https://osf.io/download/5aeca003b80ad4001054d39a/
    Download Restriction: no
    File URL: https://libkey.io/10.31219/osf.io/2tgm7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Steven Goodreau & James Kitts & Martina Morris, 2009. "Birds of a feather, or friend of a friend? using exponential random graph models to investigate adolescent social networks," Demography, Springer;Population Association of America (PAA), vol. 46(1), pages 103-125, February.
    2. Pavel N. Krivitsky & Mark S. Handcock, 2014. "A separable model for dynamic networks," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(1), pages 29-46, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Reini Schrama, 2023. "Expert network interaction in the European Medicines Agency," Regulation & Governance, John Wiley & Sons, vol. 17(2), pages 491-511, April.
    2. Duxbury, Scott W, 2019. "Mediation and Moderation in Statistical Network Models," SocArXiv 9bs4u, Center for Open Science.
    3. Long, Emily & Gardani, Maria & McCann, Mark & Sweeting, Helen & Tranmer, Mark & Moore, Laurence, 2020. "Mental health disorders and adolescent peer relationships," Social Science & Medicine, Elsevier, vol. 253(C).
    4. Giroux, Stacey & Kaminski, Patrick & Waldman, Kurt & Blekking, Jordan & Evans, Tom & Caylor, Kelly K., 2023. "Smallholder social networks: Advice seeking and adaptation in rural Kenya," Agricultural Systems, Elsevier, vol. 205(C).
    5. Malte Möck, 2021. "Patterns of Policy Networks at the Local Level in Germany," Review of Policy Research, Policy Studies Organization, vol. 38(4), pages 454-477, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Duxbury, Scott W, 2017. "Diagnosing Multicollinearity in Exponential Random Graph Models," OSF Preprints hz93j, Center for Open Science.
    2. Cornelius Fritz & Michael Lebacher & Göran Kauermann, 2020. "Tempus volat, hora fugit: A survey of tie‐oriented dynamic network models in discrete and continuous time," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 74(3), pages 275-299, August.
    3. Tatiana Didier & Sebastian Herrador & Magali Pinat, 2019. "Network Determinants of Cross-Border Merger and Acquisition Decisions," IMF Working Papers 2019/264, International Monetary Fund.
    4. Lee, Jihui & Li, Gen & Wilson, James D., 2020. "Varying-coefficient models for dynamic networks," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
    5. Ana M. Guerra & Felipe Montes & Andrés F. Useche & Ana María Jaramillo & Silvia A. González & Jose D. Meisel & Catalina Obando & Valentina Cardozo & Ruth F. Hunter & Olga L. Sarmiento, 2020. "Effects of a Physical Activity Program Potentiated with ICTs on the Formation and Dissolution of Friendship Networks of Children in a Middle-Income Country," IJERPH, MDPI, vol. 17(16), pages 1-21, August.
    6. Tyler Prochnow & Meg Patterson & M. Renée Umstattd Meyer & Joseph Lightner & Luis Gomez & Joseph Sharkey, 2022. "Conducting Physical Activity Research on Racially and Ethnically Diverse Adolescents Using Social Network Analysis: Case Studies for Practical Use," IJERPH, MDPI, vol. 19(18), pages 1-16, September.
    7. Gaonkar, Shweta & Mele, Angelo, 2023. "A model of inter-organizational network formation," Journal of Economic Behavior & Organization, Elsevier, vol. 214(C), pages 82-104.
    8. Postigo-Boix, Marcos & Melús-Moreno, José L., 2018. "A social model based on customers’ profiles for analyzing the churning process in the mobile market of data plans," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 571-592.
    9. Bryan S. Graham, 2016. "Homophily and transitivity in dynamic network formation," CeMMAP working papers CWP16/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    10. Tom A. B. Snijders & Christian E. G. Steglich, 2015. "Representing Micro–Macro Linkages by Actor-based Dynamic Network Models," Sociological Methods & Research, , vol. 44(2), pages 222-271, May.
    11. Prochnow, Tyler & Patterson, Megan S. & Hartnell, Logan & West, Geoffrey & Umstattd Meyer, M. Renée, 2021. "Implications of race and ethnicity for child physical activity and social connections at summer care programs," Children and Youth Services Review, Elsevier, vol. 127(C).
    12. Veremyev, Alexander & Boginski, Vladimir & Pasiliao, Eduardo L. & Prokopyev, Oleg A., 2022. "On integer programming models for the maximum 2-club problem and its robust generalizations in sparse graphs," European Journal of Operational Research, Elsevier, vol. 297(1), pages 86-101.
    13. Duxbury, Scott W, 2019. "Mediation and Moderation in Statistical Network Models," SocArXiv 9bs4u, Center for Open Science.
    14. Jiang, Binyan & Li, Jialiang & Yao, Qiwei, 2023. "Autoregressive networks," LSE Research Online Documents on Economics 119983, London School of Economics and Political Science, LSE Library.
    15. Ryohei Hisano & Tsutomu Watanabe & Takayuki Mizuno & Takaaki Ohnishi & Didier Sornette, 2016. "The gradual evolution of buyer-seller networks and their role in aggregate fluctuations," UTokyo Price Project Working Paper Series 068, University of Tokyo, Graduate School of Economics.
    16. Sofia Gil-Clavel & Emilio Zagheni & Valeria Bordone, 2022. "Close Social Networks Among Older Adults: The Online and Offline Perspectives," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 41(3), pages 1111-1135, June.
    17. Elina H. Hwang & Xitong Guo & Yong Tan & Yuanyuan Dang, 2022. "Delivering Healthcare Through Teleconsultations: Implications for Offline Healthcare Disparity," Information Systems Research, INFORMS, vol. 33(2), pages 515-539, June.
    18. Lee, Kevin H. & Xue, Lingzhou & Hunter, David R., 2020. "Model-based clustering of time-evolving networks through temporal exponential-family random graph models," Journal of Multivariate Analysis, Elsevier, vol. 175(C).
    19. Daw, Jonathan & Margolis, Rachel & Verdery, Ashton M., 2015. "Siblings, friends, course-mates, club-mates: How adolescent health behavior homophily varies by race, class, gender, and health status," Social Science & Medicine, Elsevier, vol. 125(C), pages 32-39.
    20. Turnbull, Kathryn & Nemeth, Christopher & Nunes, Matthew & McCormick, Tyler, 2023. "Sequential estimation of temporally evolving latent space network models," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:osf:socarx:2tgm7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: OSF (email available below). General contact details of provider: https://arabixiv.org .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.