IDEAS home Printed from https://ideas.repec.org/p/osf/socarx/9bs4u.html
   My bibliography  Save this paper

Mediation and Moderation in Statistical Network Models

Author

Listed:
  • Duxbury, Scott W

Abstract

Statistical network methods have grown increasingly popular in the social sciences. However, like other nonlinear probability models, statistical network model parameters can only be identified to a scale and cannot be compared between groups or models fit to the same network. This study addresses these issues by developing methods for mediation and moderation analyses in exponential random graph models (ERGM). It first discusses ERGM as an autologistic regression to illustrate that ERGM estimates can be affected by unobserved heterogeneity. Second, it develops methods for mediation analysis for both discrete and continuous mediators. Third, it provides recommendations and methods for interpreting interactions in ERGM. Finally, it considers scenarios where interactions are implicated in mediation analysis. The methodological discussion is accompanied with empirical applications and extensions to other classes of statistical network models are discussed.

Suggested Citation

  • Duxbury, Scott W, 2019. "Mediation and Moderation in Statistical Network Models," SocArXiv 9bs4u, Center for Open Science.
    • Handle: RePEc:osf:socarx:9bs4u
    DOI: 10.31219/osf.io/9bs4u
    as

    Download full text from publisher

    File URL: https://osf.io/download/5d2f471a835aff001953f7b0/
    Download Restriction: no
    File URL: https://libkey.io/10.31219/osf.io/9bs4u?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. Richard Breen & Kristian Bernt Karlson & Anders Holm, 2013. "Total, Direct, and Indirect Effects in Logit and Probit Models," Sociological Methods & Research, , vol. 42(2), pages 164-191, May.
    2. Box-Steffensmeier, Janet M. & Christenson, Dino P. & Morgan, Jason W., 2018. "Modeling Unobserved Heterogeneity in Social Networks with the Frailty Exponential Random Graph Model," Political Analysis, Cambridge University Press, vol. 26(1), pages 3-19, January.
    3. Kuha, Jouni & Mills, Colin, 2018. "On group comparisons with logistic regression models," LSE Research Online Documents on Economics 84163, London School of Economics and Political Science, LSE Library.
    4. Papachristos, Andrew V. & Wildeman, Christopher & Roberto, Elizabeth, 2015. "Tragic, but not random: The social contagion of nonfatal gunshot injuries," Social Science & Medicine, Elsevier, vol. 125(C), pages 139-150.
    5. Gelman, Andrew & Stern, Hal, 2006. "The Difference Between," The American Statistician, American Statistical Association, vol. 60, pages 328-331, November.
    6. Krivitsky, Pavel N., 2017. "Using contrastive divergence to seed Monte Carlo MLE for exponential-family random graph models," Computational Statistics & Data Analysis, Elsevier, vol. 107(C), pages 149-161.
    7. 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.
    8. Duxbury, Scott W, 2018. "Diagnosing Multicollinearity in Exponential Random Graph Models," SocArXiv 2tgm7, Center for Open Science.
    9. Bruce A Desmarais & Skyler J Cranmer, 2012. "Statistical Inference for Valued-Edge Networks: The Generalized Exponential Random Graph Model," PLOS ONE, Public Library of Science, vol. 7(1), pages 1-12, January.
    10. Snijders, Tom A.B. & Lomi, Alessandro, 2019. "Beyond homophily: Incorporating actor variables in statistical network models," Network Science, Cambridge University Press, vol. 7(1), pages 1-19, March.
    11. Leifeld, Philip & Cranmer, Skyler J., 2019. "A theoretical and empirical comparison of the temporal exponential random graph model and the stochastic actor-oriented model," Network Science, Cambridge University Press, vol. 7(1), pages 20-51, March.
    12. Morris, Martina & Handcock, Mark S. & Hunter, David R., 2008. "Specification of Exponential-Family Random Graph Models: Terms and Computational Aspects," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 24(i04).
    13. Handcock, Mark S. & Hunter, David R. & Butts, Carter T. & Goodreau, Steven M. & Morris, Martina, 2008. "statnet: Software Tools for the Representation, Visualization, Analysis and Simulation of Network Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 24(i01).
    14. Stanley Wasserman & Philippa Pattison, 1996. "Logit models and logistic regressions for social networks: I. An introduction to Markov graphs andp," Psychometrika, Springer;The Psychometric Society, vol. 61(3), pages 401-425, September.
    Full references (including those not matched with items on IDEAS)

    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. De Nicola, Giacomo & Fritz, Cornelius & Mehrl, Marius & Kauermann, Göran, 2023. "Dependence matters: Statistical models to identify the drivers of tie formation in economic networks," Journal of Economic Behavior & Organization, Elsevier, vol. 215(C), pages 351-363.
    2. Goodreau, Steven M. & Handcock, Mark S. & Hunter, David R. & Butts, Carter T. & Morris, Martina, 2008. "A statnet Tutorial," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 24(i09).
    3. repec:jss:jstsof:24:i09 is not listed on IDEAS
    4. Angel Ortiz-Pelaez & Getaneh Ashenafi & Francois Roger & Agnes Waret-Szkuta, 2012. "Can Geographical Factors Determine the Choices of Farmers in the Ethiopian Highlands to Trade in Livestock Markets?," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-11, February.
    5. Lee, Jihui & Li, Gen & Wilson, James D., 2020. "Varying-coefficient models for dynamic networks," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
    6. Anna Malinovskaya & Philipp Otto, 2021. "Online network monitoring," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(5), pages 1337-1364, December.
    7. Reini Schrama, 2023. "Expert network interaction in the European Medicines Agency," Regulation & Governance, John Wiley & Sons, vol. 17(2), pages 491-511, April.
    8. Kei, Yik Lun & Chen, Yanzhen & Madrid Padilla, Oscar Hernan, 2023. "A partially separable model for dynamic valued networks," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    9. Vishesh Karwa & Pavel N. Krivitsky & Aleksandra B. Slavković, 2017. "Sharing social network data: differentially private estimation of exponential family random-graph models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(3), pages 481-500, April.
    10. Ashish Arora & Michelle Gittelman & Sarah Kaplan & John Lynch & Will Mitchell & Nicolaj Siggelkow & Ji Youn (Rose) Kim & Michael Howard & Emily Cox Pahnke & Warren Boeker, 2016. "Understanding network formation in strategy research: Exponential random graph models," Strategic Management Journal, Wiley Blackwell, vol. 37(1), pages 22-44, January.
    11. Peter R. Herman, 2022. "Modeling complex network patterns in international trade," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 158(1), pages 127-179, February.
    12. Alex Stivala & Garry Robins & Alessandro Lomi, 2020. "Exponential random graph model parameter estimation for very large directed networks," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-21, January.
    13. Yonghong Ma & Xiaomeng Yang & Sen Qu & Lingkai Kong, 2022. "Research on the formation mechanism of big data technology cooperation networks: empirical evidence from China," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(3), pages 1273-1294, March.
    14. Samrachana Adhikari & Beau Dabbs, 2018. "Social Network Analysis in R: A Software Review," Journal of Educational and Behavioral Statistics, , vol. 43(2), pages 225-253, April.
    15. Krivitsky, Pavel N., 2017. "Using contrastive divergence to seed Monte Carlo MLE for exponential-family random graph models," Computational Statistics & Data Analysis, Elsevier, vol. 107(C), pages 149-161.
    16. 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.
    17. Cody J. Dey & James S. Quinn, 2014. "Individual attributes and self-organizational processes affect dominance network structure in pukeko," Behavioral Ecology, International Society for Behavioral Ecology, vol. 25(6), pages 1402-1408.
    18. 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).
    19. Duncan A. Clark & Mark S. Handcock, 2022. "Comparing the real‐world performance of exponential‐family random graph models and latent order logistic models for social network analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(2), pages 566-587, April.
    20. Pavel N. Krivitsky & Laura M. Koehly & Christopher Steven Marcum, 2020. "Exponential-Family Random Graph Models for Multi-Layer Networks," Psychometrika, Springer;The Psychometric Society, vol. 85(3), pages 630-659, September.
    21. Peng, Tai-Quan, 2015. "Assortative mixing, preferential attachment, and triadic closure: A longitudinal study of tie-generative mechanisms in journal citation networks," Journal of Informetrics, Elsevier, vol. 9(2), pages 250-262.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:9bs4u. 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.