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Below results based on the criteria 'roll call analysis'
Total number of records returned: 3

Analyzing the US Senate in 2003: Similarities, Networks, Clusters and Blocs
Jakulin, Aleks

Uploaded 10-27-2004
Keywords roll call analysis
latent variable models
information theory
Abstract To analyze the roll calls in the US Senate in year 2003, we have employed the methods already used throughout the science community for analysis of genes, surveys and text. With information-theoretic measures we assess the association between pairs of senators based on the votes they cast. Furthermore, we can evaluate the influence of a voter by postulating a Shannon information channel between the outcome and a voter. The matrix of associations can be summarized using hierarchical clustering, multi-dimensional scaling and link analysis. With a discrete latent variable model we identify blocs of cohesive voters within the Senate, and contrast it with continuous ideal point methods. Under the bloc-voting model, the Senate can be interpreted as a weighted vote system, and we were able to estimate the empirical voting power of individual blocs through what-if analysis.

Identifying Intra-Party Voting Blocs in the UK House of Commons
Quinn, Kevin
Spirling, Arthur

Uploaded 07-19-2005
Keywords roll-call analysis
UK House of Commons
Bayesian nonparametrics
Dirichlet process mixtures
Abstract Legislative voting records are an important source of information about legislator preferences, intra-party cohesiveness, and the divisiveness of various policy issues. Standard methods of analyzing a legislative voting record tend to have serious drawbacks when applied to legislatures, such as the UK House of Commons, that feature highly disciplined parties, strategic voting, and large amounts of missing data. We present a method (based on a Dirichlet process mixture model) for analyzing such voting records that does not suffer from these same problems. We apply the method to the voting records of Labour and Conservative Party MPs in the 1997-2001 session of the UK House of Commons. Our method has a number of advantages over existing approaches. It is model-based and thus allows one to make probability statements about quantities of interest. It allows one to estimate the number of voting blocs within a party or any other group of MPs. It handles missing data in a principled fashion and does not rely on an ad hoc distance metric between voting profiles. Finally, it can be used as both a predictive model and an exploratory model. We illustrate these points in our analysis of the UK data.

A Hierarchical Bayesian Framework for Item Response Theory Models with Applications in Ideal Point Estimation
Lu, Ying
Wang, Xiaohui

Uploaded 07-15-2006
Keywords item response theory
testlet response theory
random and fixed effect models
vote cast data
roll call analysis
Abstract Ideal point estimation, a variation of item response theory models, has been widely used by political scientists to analyze legislative behaviors. However, many existing ideal point estimation research is based on unrealistic assumptions of independence of different individuals' decisions towards all cases/bills and the independence of one's decisions towards different cases/bills. The violation of such assumptions leads to bias and inefficiency in parameter estimation. More importantly, failing to address these assumptions has hampered the ideal point estimation research from offering intuitive and concise explanations on complex legislative behaviors such as multidimensionality, strategic voting, temporary coalitions. In this paper, we extend one testlet response theory model by Bradlow, Wainer and Wang(1999) to a comprehensive hierarchical Bayesian statistical framework that allows researchers to model inter-individual and intra-individual correlations through random effects and/or fixed effects. Through simulations and an analysis of the US Supreme Court vote cast data, we show that the proposed framework holds good promise for tackling many unsettled issues in ideal point estimations. As a companion to this paper, we also offer an easy-to-use R package with C code that implements the methods discussed herein.

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