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Taking dyads seriously

Published online by Cambridge University Press:  15 November 2021

Shahryar Minhas Open the ORCID record for Shahryar Minhas [Opens in a new window] ,
Cassy Dorff ,
Max B. Gallop ,
Margaret Foster ,
Howard Liu ,
Juan Tellez  and
Michael D. Ward
Shahryar Minhas*
Affiliation:
Department of Political Science, Michigan State University, East Lansing, MI, USA
Cassy Dorff
Affiliation:
Department of Political Science, Vanderbilt University, Nashville, TN, USA
Max B. Gallop
Affiliation:
Department of Government and Public Policy, University of Strathclyde, Glasgow, Scotland, UK
Margaret Foster
Affiliation:
Department of Political Science, University of North Carolina, Chapel Hill, NC, USA
Howard Liu
Affiliation:
Department of Government, University of Essex, Colchester, England, UK
Juan Tellez
Affiliation:
Department of Political Science, University of South Carolina, Columbia, SC, USA
Michael D. Ward
Affiliation:
Department of Political Science, Duke University, Durham, NC, USA
*
*Corresponding author. Email: [email protected]
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Abstract

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 insights 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 out-of-sample fit. In sum, our work shows the consequences of failing to take the dependencies inherent to dyadic data seriously. Most importantly, by better modeling the data generating process underlying political phenomena, the AME framework improves scholars’ ability to conduct inferential analyses on dyadic data.

Keywords

Bayesianlatent variable modelsmulti-level and hierarchical models
Type
Original Article
Information
Political Science Research and Methods , Volume 10 , Issue 4 , October 2022 , pp. 703 - 721
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the European Political Science Association

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Footnotes

†

Deceased

**

Present address: Department of Political Science, University of California, Davis, USA

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