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Replication data for: No News is News: Non-Ignorable Non-Response in Roll-Call Data Analysis

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

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Title Replication data for: No News is News: Non-Ignorable Non-Response in Roll-Call Data Analysis
 
Identifier https://doi.org/10.7910/DVN/26457
 
Creator Rosas, Guillermo
Shomer, Yael
Haptonstahl, Stephen
 
Publisher Harvard Dataverse
 
Description Roll-call votes are widely employed to infer the ideological proclivities of legislators. However, many roll-call matrices are characterized by high levels of non-response. Under many circumstances, non-response cannot be assumed to be ignorable. We examine the consequences of violating the ignorability assumption that underlies current methods of roll-call analysis. We present a basic estimation framework to model non-response and vote choice concurrently, build a model that captures the logic of competing principals that underlies accounts of non-response in many legislatures, and illustrate the payoff of addressing non-ignorable non-response through simulations and actual data. We conclude that modeling presumed patterns of non-ignorable non-response can yield important inferential payoffs over current models that assume random missingness, but we also emphasize that the decision to model non-response should be based on theoretical grounds since one cannot rely on measures of goodness of fit for the purpose of model comparison.
 
Subject Social Sciences
Missing data
Voting abstention
Item-response theory
Roll-call data
Bayesian inference
 
Date 2014-06
 
Contributor Guillermo Rosas
 
Type roll-call votes, program source code