IDEAS home Printed from https://ideas.repec.org/a/spr/psycho/v86y2021i1d10.1007_s11336-020-09736-z.html
   My bibliography  Save this article

Social Network Mediation Analysis: A Latent Space Approach

Author

Listed:
  • Haiyan Liu

    (University of California, Merced)

  • Ick Hoon Jin

    (Yonsei University)

  • Zhiyong Zhang

    (University of Notre Dame)

  • Ying Yuan

    (The University of Texas MD, Anderson Cancer Center)

Abstract

A social network comprises both actors and the social connections among them. Such connections reflect the dependence among social actors, which is essential for individuals’ mental health and social development. In this article, we propose a mediation model with a social network as a mediator to investigate the potential mediation role of a social network. In the model, the dependence among actors is accounted for by a few mutually orthogonal latent dimensions which form a social space. The individuals’ positions in such a latent social space are directly involved in the mediation process between an independent and dependent variable. After showing that all the latent dimensions are equivalent in terms of their relationship to the social network and the meaning of each dimension is arbitrary, we propose to measure the whole mediation effect of a network. Although individuals’ positions in the latent space are not unique, we rigorously articulate that the proposed network mediation effect is still well defined. We use a Bayesian estimation method to estimate the model and evaluate its performance through an extensive simulation study under representative conditions. The usefulness of the network mediation model is demonstrated through an application to a college friendship network.

Suggested Citation

  • Haiyan Liu & Ick Hoon Jin & Zhiyong Zhang & Ying Yuan, 2021. "Social Network Mediation Analysis: A Latent Space Approach," Psychometrika, Springer;The Psychometric Society, vol. 86(1), pages 272-298, March.
    • Handle: RePEc:spr:psycho:v:86:y:2021:i:1:d:10.1007_s11336-020-09736-z
    DOI: 10.1007/s11336-020-09736-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11336-020-09736-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11336-020-09736-z?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. D. S. Choi & P. J. Wolfe & E. M. Airoldi, 2012. "Stochastic blockmodels with a growing number of classes," Biometrika, Biometrika Trust, vol. 99(2), pages 273-284.
    2. Qingzhao Yu & Kaelen L. Medeiros & Xiaocheng Wu & Roxanne E. Jensen, 2018. "Nonlinear Predictive Models for Multiple Mediation Analysis: With an Application to Explore Ethnic Disparities in Anxiety and Depression Among Cancer Survivors," Psychometrika, Springer;The Psychometric Society, vol. 83(4), pages 991-1006, December.
    3. Hoff P.D. & Raftery A.E. & Handcock M.S., 2002. "Latent Space Approaches to Social Network Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1090-1098, December.
    4. John Geweke, 1991. "Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments," Staff Report 148, Federal Reserve Bank of Minneapolis.
    5. Fuhrer, R. & Stansfeld, S. A., 2002. "How gender affects patterns of social relations and their impact on health: a comparison of one or multiple sources of support from "close persons"," Social Science & Medicine, Elsevier, vol. 54(5), pages 811-825, March.
    6. R. M. Daniel & B. L. De Stavola & S. N. Cousens & S. Vansteelandt, 2015. "Causal mediation analysis with multiple mediators," Biometrics, The International Biometric Society, vol. 71(1), pages 1-14, March.
    7. Lin Su & Wenbin Lu & Rui Song & Danyang Huang, 2020. "Testing and Estimation of Social Network Dependence With Time to Event Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(530), pages 570-582, April.
    8. Tracy M. Sweet, 2019. "Modeling Social Networks as Mediators: A Mixed Membership Stochastic Blockmodel for Mediation," Journal of Educational and Behavioral Statistics, , vol. 44(2), pages 210-240, April.
    9. Mark S. Handcock & Adrian E. Raftery & Jeremy M. Tantrum, 2007. "Model‐based clustering for social networks," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(2), pages 301-354, March.
    10. Imai, Kosuke & Yamamoto, Teppei, 2013. "Identification and Sensitivity Analysis for Multiple Causal Mechanisms: Revisiting Evidence from Framing Experiments," Political Analysis, Cambridge University Press, vol. 21(2), pages 141-171, April.
    11. Sacha Epskamp & Mijke Rhemtulla & Denny Borsboom, 2017. "Generalized Network Psychometrics: Combining Network and Latent Variable Models," Psychometrika, Springer;The Psychometric Society, vol. 82(4), pages 904-927, December.
    12. Daniel K. Sewell & Yuguo Chen, 2015. "Latent Space Models for Dynamic Networks," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1646-1657, December.
    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. Chiara Di Maria & Antonino Abbruzzo & Gianfranco Lovison, 2022. "Networks as mediating variables: a Bayesian latent space approach," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(4), pages 1015-1035, October.

    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. Babkin, Sergii & Stewart, Jonathan R. & Long, Xiaochen & Schweinberger, Michael, 2020. "Large-scale estimation of random graph models with local dependence," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
    2. Samrachana Adhikari & Tracy Sweet & Brian Junker, 2021. "Analysis of longitudinal advice‐seeking networks following implementation of high stakes testing," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1475-1500, October.
    3. Chiara Di Maria & Antonino Abbruzzo & Gianfranco Lovison, 2022. "Networks as mediating variables: a Bayesian latent space approach," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(4), pages 1015-1035, October.
    4. Adrian E. Raftery, 2017. "Comment: Extending the Latent Position Model for Networks," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1531-1534, October.
    5. Sosa, Juan & Betancourt, Brenda, 2022. "A latent space model for multilayer network data," Computational Statistics & Data Analysis, Elsevier, vol. 169(C).
    6. Tracy Sweet & Samrachana Adhikari, 2020. "A Latent Space Network Model for Social Influence," Psychometrika, Springer;The Psychometric Society, vol. 85(2), pages 251-274, June.
    7. Laleh Tafakori & Armin Pourkhanali & Riccardo Rastelli, 2022. "Measuring systemic risk and contagion in the European financial network," Empirical Economics, Springer, vol. 63(1), pages 345-389, July.
    8. 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.
    9. Guang Ouyang & Dipak K. Dey & Panpan Zhang, 2020. "Clique-Based Method for Social Network Clustering," Journal of Classification, Springer;The Classification Society, vol. 37(1), pages 254-274, April.
    10. Xu Qin & Jonah Deutsch & Guanglei Hong, 2021. "Unpacking Complex Mediation Mechanisms And Their Heterogeneity Between Sites In A Job Corps Evaluation," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 40(1), pages 158-190, January.
    11. West, Robert M. & House, Allan O. & Keen, Justin & Ward, Vicky L., 2015. "Using the structure of social networks to map inter-agency relationships in public health services," Social Science & Medicine, Elsevier, vol. 145(C), pages 107-114.
    12. Chen, Mingli & Fernández-Val, Iván & Weidner, Martin, 2021. "Nonlinear factor models for network and panel data," Journal of Econometrics, Elsevier, vol. 220(2), pages 296-324.
    13. Daniel Felix Ahelegbey & Luis Carvalho & Eric D. Kolaczyk, 2020. "A Bayesian Covariance Graph And Latent Position Model For Multivariate Financial Time Series," DEM Working Papers Series 181, University of Pavia, Department of Economics and Management.
    14. 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).
    15. Ick Hoon Jin & Minjeong Jeon, 2019. "A Doubly Latent Space Joint Model for Local Item and Person Dependence in the Analysis of Item Response Data," Psychometrika, Springer;The Psychometric Society, vol. 84(1), pages 236-260, March.
    16. Guanglei Hong & Fan Yang & Xu Qin, 2023. "Posttreatment confounding in causal mediation studies: A cutting‐edge problem and a novel solution via sensitivity analysis," Biometrics, The International Biometric Society, vol. 79(2), pages 1042-1056, June.
    17. Zhao, Yi & Luo, Xi, 2023. "Multilevel mediation analysis with structured unmeasured mediator-outcome confounding," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
    18. Soojin Park & Peter M. Steiner & David Kaplan, 2018. "Identification and Sensitivity Analysis for Average Causal Mediation Effects with Time-Varying Treatments and Mediators: Investigating the Underlying Mechanisms of Kindergarten Retention Policy," Psychometrika, Springer;The Psychometric Society, vol. 83(2), pages 298-320, June.
    19. Chih‐Sheng Hsieh & Hans van Kippersluis, 2018. "Smoking initiation: Peers and personality," Quantitative Economics, Econometric Society, vol. 9(2), pages 825-863, July.
    20. Salter-Townshend, Michael & Murphy, Thomas Brendan, 2013. "Variational Bayesian inference for the Latent Position Cluster Model for network data," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 661-671.

    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:spr:psycho:v:86:y:2021:i:1:d:10.1007_s11336-020-09736-z. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.