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Replication Data for: Taking Dyads Seriously

Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)

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Title Replication Data for: Taking Dyads Seriously
 
Identifier https://doi.org/10.7910/DVN/Z6ASMM
 
Creator Minhas, Shahryar
Dorff, Cassy
Gallop, Max
Foster, Margaret
Liu, Howard
Tellez, Juan
Ward, Michael
 
Publisher Harvard Dataverse
 
Description 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.
 
Subject Social Sciences
latent variable models
multi-level and hierarchical models
bayesian
 
Contributor Minhas, Shahryar