Replication Data for: Hierarchical Bayesian Aldrich-McKelvey Scaling
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'Estimating the ideological positions of political actors is an important step towards answering a number of substantive questions in political science. Survey scales provide useful data for such estimation, but also present a challenge, as respondents tend to interpret the scales differently. The Aldrich-McKelvey model addresses this challenge, but the existing implementations of the model have notable shortcomings. Focusing on the Bayesian version of the model (BAM), the analyses in this article demonstrate that the model is prone to overfitting and yields poor results for a considerable share of respondents. The article addresses these shortcomings by developing a hierarchical Bayesian version of the model (HBAM). The new version treats self-placements as data to be included in the likelihood function, while also modifying the likelih' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Aldrich-McKelvey scaling","Bayesian estimation","hierarchical modeling","ideal point estimation"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Estimating the ideological positions of political actors is an important step towards answering a number of substantive questions in political science. Survey scales provide useful data for such estimation, but also present a challenge, as respondents tend to interpret the scales differently. The Aldrich-McKelvey model addresses this challenge, but the existing implementations of the model have notable shortcomings. Focusing on the Bayesian version of the model (BAM), the analyses in this article demonstrate that the model is prone to overfitting and yields poor results for a considerable share of respondents. The article addresses these shortcomings by developing a hierarchical Bayesian version of the model (HBAM). The new version treats self-placements as data to be included in the likelihood function, while also modifying the likelih' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Estimating the ideological positions of political actors is an important step towards answering a number of substantive questions in political science. Survey scales provide useful data for such estimation, but also present a challenge, as respondents tend to interpret the scales differently. The Aldrich-McKelvey model addresses this challenge, but the existing implementations of the model have notable shortcomings. Focusing on the Bayesian version of the model (BAM), the analyses in this article demonstrate that the model is prone to overfitting and yields poor results for a considerable share of respondents. The article addresses these shortcomings by developing a hierarchical Bayesian version of the model (HBAM). The new version treats self-placements as data to be included in the likelihood function, while also modifying the likelih
Aldrich-McKelvey scaling; Bayesian estimation; hierarchical modeling; ideal point estimation