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Measurement Error and Attenuation Bias in Exponential Random Graph Models

Author

Listed:
  • Kim Yeaji

    (University of Houston – Political Science, Houston, TX, USA)

  • Antenangeli Leonardo

    (University of Houston – Political Science, Houston, TX, USA)

  • Kirkland Justin

    (University of Houston – Political Science, 3551 Cullen Boulevard Room 447, Houston, TX 77204, USA)

Abstract
Exponential Random Graph Models (ERGMs) are becoming increasingly popular tools for estimating the properties of social networks across the social sciences. While the asymptotic properties of ERGMs are well understood, much less is known about how ERGMs perform in the face of violations of the assumptions that drive those asymptotic properties. Given that empirical social networks rarely meet the strenuous assumptions of the ERGM perfectly, practical researchers are often in the position of knowing their coefficients are imperfect, but not knowing precisely how wrong those coefficients may be. In this research, we examine one violation of the asymptotic assumptions of ERGMs – perfectly measured social networks. Using several Monte Carlo simulations, we demonstrate that even randomly distributed measurement errors in networks under study can cause considerable attenuation in coefficients from ERGMs, and do real harm to subsequent hypothesis tests.

Suggested Citation

  • Kim Yeaji & Antenangeli Leonardo & Kirkland Justin, 2016. "Measurement Error and Attenuation Bias in Exponential Random Graph Models," Statistics, Politics and Policy, De Gruyter, vol. 7(1-2), pages 29-54, December.
    • Handle: RePEc:bpj:statpp:v:7:y:2016:i:1-2:p:29-54:n:1
    DOI: 10.1515/spp-2016-0001
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    References listed on IDEAS

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    1. Box-Steffensmeier, Janet M. & Christenson, Dino P. & Hitt, Matthew P., 2013. "Quality Over Quantity: Amici Influence and Judicial Decision Making," American Political Science Review, Cambridge University Press, vol. 107(3), pages 446-460, August.
    2. Desmarais, Bruce A. & Harden, Jeffrey J. & Boehmke, Frederick J., 2015. "Persistent Policy Pathways: Inferring Diffusion Networks in the American States," American Political Science Review, Cambridge University Press, vol. 109(2), pages 392-406, May.
    3. Cranmer, Skyler J. & Desmarais, Bruce A., 2011. "Inferential Network Analysis with Exponential Random Graph Models," Political Analysis, Cambridge University Press, vol. 19(1), pages 66-86, January.
    4. Hisashi Ohtsuki & Christoph Hauert & Erez Lieberman & Martin A. Nowak, 2006. "A simple rule for the evolution of cooperation on graphs and social networks," Nature, Nature, vol. 441(7092), pages 502-505, May.
    5. Aronow, Peter M. & Samii, Cyrus & Assenova, Valentina A., 2015. "Cluster–Robust Variance Estimation for Dyadic Data," Political Analysis, Cambridge University Press, vol. 23(4), pages 564-577.
    6. Berry, William D. & Baybeck, Brady, 2005. "Using Geographic Information Systems to Study Interstate Competition," American Political Science Review, Cambridge University Press, vol. 99(4), pages 505-519, November.
    7. Eduardo Alemán & Ernesto Calvo, 2013. "Explaining Policy Ties in Presidential Congresses: A Network Analysis of Bill Initiation Data," Political Studies, Political Studies Association, vol. 61(2), pages 356-377, June.
    8. 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.
    9. 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.
    10. Erikson, Robert S. & Pinto, Pablo M. & Rader, Kelly T., 2014. "Dyadic Analysis in International Relations: A Cautionary Tale," Political Analysis, Cambridge University Press, vol. 22(4), pages 457-463.
    11. 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).
    12. Raymond J. Carroll & Kathryn Roeder & Larry Wasserman, 1999. "Flexible Parametric Measurement Error Models," Biometrics, The International Biometric Society, vol. 55(1), pages 44-54, March.
    13. Poast, Paul, 2010. "(Mis)Using Dyadic Data to Analyze Multilateral Events," Political Analysis, Cambridge University Press, vol. 18(4), pages 403-425.
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