ICAR Research Data Repository for Knowledge Management
Replication data for: Using Bayesian Aldrich-McKelvey Scaling to Study Citizens' Ideological Preferences and Perceptions
Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)
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| Title |
Replication data for: Using Bayesian Aldrich-McKelvey Scaling to Study Citizens' Ideological Preferences and Perceptions
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| Identifier |
https://doi.org/10.7910/DVN/26638
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| Creator |
Hare, Christopher
Armstrong, David A., II Bakker, Ryan Carroll, Royce Poole, Keith T. |
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| Publisher |
Harvard Dataverse
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| Description |
Aldrich-McKelvey scaling is a powerful method that corrects for differential-item functioning (DIF) in estimating the positions of political stimuli (e.g., parties and candidates) and survey respondents along a latent policy dimension from issue scale data. DIF arises when respondents interpret issue scales (like the standard liberal-conservative scale) differently and distort their placements of the stimuli and themselves. We develop a Bayesian implementation of the classical maximum likelihood Aldrich-McKelvey scaling method that overcomes some important shortcomings in the classical procedure. We then apply this method to study citizens' ideological preferences and perceptions using data from the 2004-2012 American National Election Studies and the 2010 Cooperative Congressional Election Study. Our findings indicate that DIF biases self-placements on the liberal-conservative scale in a way that understates the extent of polarization in the contemporary American electorate and that citizens have remarkably accurate perceptions of the ideological positions of Senators and Senate candidates.
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| Subject |
Social Sciences
Bayesian methods Polarization Ideal point estimation |
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| Contributor |
Christopher Hare
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