Cluster-robust variance estimation for dyadic data

Peter M. Aronow, Cyrus Samii, Valentina A. Assenova

    Research output: Contribution to journalArticlepeer-review


    Dyadic data are common in the social sciences, although inference for such settings involves accounting for a complex clustering structure. Many analyses in the social sciences fail to account for the fact that multiple dyads share a member, and that errors are thus likely correlated across these dyads. We propose a non-parametric, sandwich-type robust variance estimator for linear regression to account for such clustering in dyadic data. We enumerate conditions for estimator consistency. We also extend our results to repeated and weighted observations, including directed dyads and longitudinal data, and provide an implementation for generalized linear models such as logistic regression. We examine empirical performance with simulations and an application to interstate disputes.

    Original languageEnglish (US)
    Article numbermpv018
    Pages (from-to)564-577
    Number of pages14
    JournalPolitical Analysis
    Issue number4
    StatePublished - Oct 2015

    ASJC Scopus subject areas

    • Sociology and Political Science
    • Political Science and International Relations


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