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Measurement Error and Attenuation Bias in Exponential Random Graph Models

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  • 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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