IDEAS home Printed from https://ideas.repec.org/a/bla/jorssa/v185y2022i4p1855-1881.html
   My bibliography  Save this article

Bayesian analysis of social influence

Author

Listed:
  • Johan Koskinen
  • Galina Daraganova

Abstract

The network influence model is a model for binary outcome variables that accounts for dependencies between outcomes for units that are relationally tied. The basic influence model was previously extended to afford a suite of new dependence assumptions and because of its relation to traditional Markov random field models it is often referred to as the auto logistic actor‐attribute model (ALAAM). We extend on current approaches for fitting ALAAMs by presenting a comprehensive Bayesian inference scheme that supports testing of dependencies across subsets of data and the presence of missing data. We illustrate different aspects of the procedures through three empirical examples: masculinity attitudes in an all‐male Australian school class, educational progression in Swedish schools, and unemployment among adults in a community sample in Australia.

Suggested Citation

  • Johan Koskinen & Galina Daraganova, 2022. "Bayesian analysis of social influence," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1855-1881, October.
    • Handle: RePEc:bla:jorssa:v:185:y:2022:i:4:p:1855-1881
    DOI: 10.1111/rssa.12844
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/rssa.12844
    Download Restriction: no
    File URL: https://libkey.io/10.1111/rssa.12844?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. Charles F. Manski, 1993. "Identification of Endogenous Social Effects: The Reflection Problem," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 60(3), pages 531-542.
    2. William A. Brock & Steven N. Durlauf, 2002. "A Multinomial-Choice Model of Neighborhood Effects," American Economic Review, American Economic Association, vol. 92(2), pages 298-303, May.
    3. Murray Aitkin & Duy Vu & Brian Francis, 2017. "Statistical modelling of a terrorist network," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(3), pages 751-768, June.
    4. Chen, Ming-Hui & Ibrahim, Joseph G. & Kim, Sungduk, 2008. "Properties and Implementation of Jeffreys’s Prior in Binomial Regression Models," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1659-1664.
    5. Bramoullé, Yann & Djebbari, Habiba & Fortin, Bernard, 2009. "Identification of peer effects through social networks," Journal of Econometrics, Elsevier, vol. 150(1), pages 41-55, May.
    6. Letina, Srebrenka, 2016. "Network and actor attribute effects on the performance of researchers in two fields of social science in a small peripheral community," Journal of Informetrics, Elsevier, vol. 10(2), pages 571-595.
    7. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    8. J. Møller & A. N. Pettitt & R. Reeves & K. K. Berthelsen, 2006. "An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants," Biometrika, Biometrika Trust, vol. 93(2), pages 451-458, June.
    9. Garry Robins & Philippa Pattison & Peter Elliott, 2001. "Network models for social influence processes," Psychometrika, Springer;The Psychometric Society, vol. 66(2), pages 161-189, June.
    10. Mark Tranmer & David Steel & William J. Browne, 2014. "Multiple-membership multiple-classification models for social network and group dependences," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 177(2), pages 439-455, February.
    11. Johan Koskinen & Peng Wang & Garry Robins & Philippa Pattison, 2018. "Outliers and Influential Observations in Exponential Random Graph Models," Psychometrika, Springer;The Psychometric Society, vol. 83(4), pages 809-830, December.
    12. Chib S. & Jeliazkov I., 2001. "Marginal Likelihood From the Metropolis-Hastings Output," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 270-281, March.
    13. Bailey K. Fosdick & Peter D. Hoff, 2015. "Testing and Modeling Dependencies Between a Network and Nodal Attributes," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 1047-1056, September.
    14. Hunter, David R. & Goodreau, Steven M. & Handcock, Mark S., 2008. "Goodness of Fit of Social Network Models," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 248-258, March.
    15. Maria Prosperina Vitale & Giovanni C. Porzio & Patrick Doreian, 2016. "Examining the effect of social influence on student performance through network autocorrelation models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(1), pages 115-127, 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. Alex Stivala & Peng Wang & Alessandro Lomi, 2024. "ALAAMEE: Open-source software for fitting autologistic actor attribute models," PLOS Complex Systems, Public Library of Science, vol. 1(4), pages 1-32, December.
    2. Hashimi, Sadaf & Ouellet, Marie, 2025. "Invite only: The prevalence of subgroups within a police department," Journal of Criminal Justice, Elsevier, vol. 96(C).

    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. Jina Park & Ick Hoon Jin & Minjeong Jeon, 2023. "How Social Networks Influence Human Behavior: An Integrated Latent Space Approach for Differential Social Influence," Psychometrika, Springer;The Psychometric Society, vol. 88(4), pages 1529-1555, December.
    2. Steven N. Durlauf & Yannis M. Ioannides, 2010. "Social Interactions," Annual Review of Economics, Annual Reviews, vol. 2(1), pages 451-478, September.
    3. Semih Tumen & Tugba Zeydanli, 2015. "Is Happiness Contagious? Separating Spillover Externalities from the Group-Level Social Context," Journal of Happiness Studies, Springer, vol. 16(3), pages 719-744, June.
    4. William C. Horrace & Hyunseok Jung & Shane Sanders, 2022. "Network Competition and Team Chemistry in the NBA," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 35-49, January.
    5. Chih-Sheng Hsieh & Michael D. Konig & Xiaodong Liu, 2022. "A Structural Model for the Coevolution of Networks and Behavior," The Review of Economics and Statistics, MIT Press, vol. 104(2), pages 355-367, May.
    6. Linardi, Fernando & Diks, Cees & van der Leij, Marco & Lazier, Iuri, 2020. "Dynamic interbank network analysis using latent space models," Journal of Economic Dynamics and Control, Elsevier, vol. 112(C).
    7. Selena Wang & Subhadeep Paul & Paul Boeck, 2023. "Joint Latent Space Model for Social Networks with Multivariate Attributes," Psychometrika, Springer;The Psychometric Society, vol. 88(4), pages 1197-1227, December.
    8. Horrace, William C. & Liu, Xiaodong & Patacchini, Eleonora, 2016. "Endogenous network production functions with selectivity," Journal of Econometrics, Elsevier, vol. 190(2), pages 222-232.
    9. Hsieh, Chih-Sheng & Lin, Xu, 2017. "Gender and racial peer effects with endogenous network formation," Regional Science and Urban Economics, Elsevier, vol. 67(C), pages 135-147.
    10. Jose-Alberto Guerra & Myra Mohnen, 2022. "Multinomial Choice with Social Interactions: Occupations in Victorian London," The Review of Economics and Statistics, MIT Press, vol. 104(4), pages 736-747, October.
    11. Jonathan Pratschke & Giovanni Abbiati, 2023. "“Like with like” or “do like?” Modeling peer effects in the classroom," Social Science Quarterly, Southwestern Social Science Association, vol. 104(3), pages 265-280, May.
    12. Alex Centeno, 2022. "A Structural Model for Detecting Communities in Networks," Papers 2209.08380, arXiv.org, revised Oct 2022.
    13. Carlos Madeira, 2022. "Partial identification of nonlinear peer effects models with missing data," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 158(1), pages 1-18, December.
    14. Liu, Xiaodong & Zhou, Jiannan, 2017. "A social interaction model with ordered choices," Economics Letters, Elsevier, vol. 161(C), pages 86-89.
    15. Morey, Edward R. & Kritzberg, David, 2012. "It's not where you do it, it's who you do it with?," Journal of choice modelling, Elsevier, vol. 5(3), pages 176-191.
    16. Nicola Campigotto & Chiara Rapallini & Aldo Rustichini, 2022. "School friendship networks, homophily and multiculturalism: evidence from European countries," Journal of Population Economics, Springer;European Society for Population Economics, vol. 35(4), pages 1687-1722, October.
    17. Bryan S. Graham & Andrin Pelican, 2023. "Scenario Sampling for Large Supermodular Games," Papers 2307.11857, arXiv.org.
    18. Bhargava, Palaash & Chen, Daniel L. & Sutter, Matthias & Terrier, Camille, 2022. "Homophily and Transmission of Behavioral Traits in Social Networks," IZA Discussion Papers 15840, Institute of Labor Economics (IZA).
    19. Alex Stivala & Peng Wang & Alessandro Lomi, 2024. "ALAAMEE: Open-source software for fitting autologistic actor attribute models," PLOS Complex Systems, Public Library of Science, vol. 1(4), pages 1-32, December.
    20. Humlum, Maria Knoth & Thorsager, Mette, 2021. "The Importance of Peer Quality for Completion of Higher Education," Economics of Education Review, Elsevier, vol. 83(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:bla:jorssa:v:185:y:2022:i:4:p:1855-1881. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    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.