Replication Data for: Taking Dyads Seriously

  • Shahryar Minhas (Contributor)
  • Cassy Dorff (Creator)
  • Max Gallop (Creator)
  • Margaret Foster (Creator)
  • Howard Liu (Creator)
  • Juan Tellez (Creator)
  • Michael Ward (Creator)
  • Shahryar Minhas (Contributor)



International relations scholarship concerns dyads, yet standard modeling approaches fail to adequately capture the data generating process behind dyadic events and processes. As a result, they suffer from biased coefficients and poorly calibrated standard errors. We show how a regression-based approach, the Additive and Multiplicative Effects (AME) model, can be used to account for the inherent dependencies in dyadic data and glean substantive sights in the interrelations between actors. First, we conduct a simulation to highlight how the model captures dependencies and show that accounting for these processes improves our ability to conduct inference on dyadic data. Second, we compare the AME model to approaches used in three prominent studies from recent international relations scholarship. For each study, we find that compared to AME, the modeling approach used performs notably worse at capturing the data generating process. Further, conventional methods misstate the effect of key variables and the uncertainty in these effects. Finally, AME outperforms standard approaches in terms of outof-sample fit. In sum, our work shows the consequences of failing to take the dependencies inherent to dyadic data seriously.

CC0 1.0 Universal (CC0 1.0) Public Domain Dedication

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Date made available11 Sept 2023
PublisherHarvard Dataverse
Date of data production2021

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