2001
DOI: 10.1093/polana/9.3.227
|View full text |Cite
|
Sign up to set email alerts
|

Multidimensional Analysis of Roll Call Data via Bayesian Simulation: Identification, Estimation, Inference, and Model Checking

Abstract: Vote-specific parameters are often by-products of roll call analysis, the primary goal being the measurement of legislators' ideal points. But these vote-specific parameters are more important in higher-dimensional settings: prior restrictions on vote parameters help identify the model, and researchers often have prior beliefs about the nature of the dimensions underlying the proposal space. Bayesian methods provide a straightforward and rigorous way for incorporating these prior beliefs into roll call analysi… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
209
2
5

Year Published

2005
2005
2019
2019

Publication Types

Select...
4
3
1

Relationship

0
8

Authors

Journals

citations
Cited by 230 publications
(216 citation statements)
references
References 19 publications
0
209
2
5
Order By: Relevance
“…For our purposes it is enough to assume that our voters (justices) have latent preferences for each side of each case relative to one another, and that these are correlated across cases. To this end, we do not aim to estimate a small (Jackman 2001), or even a large (Lauderdale and Clark 2014) number of dimensions to summarize behavior:…”
Section: Autoregressive Spatial Preference Estimationmentioning
confidence: 99%
See 1 more Smart Citation
“…For our purposes it is enough to assume that our voters (justices) have latent preferences for each side of each case relative to one another, and that these are correlated across cases. To this end, we do not aim to estimate a small (Jackman 2001), or even a large (Lauderdale and Clark 2014) number of dimensions to summarize behavior:…”
Section: Autoregressive Spatial Preference Estimationmentioning
confidence: 99%
“…Traditional scaling models in political science project a complex high-dimensional space onto a low dimensional space (Poole and Rosenthal 1985;Jackman 2001;Martin and Quinn 2002). As noted earlier, the utility of such scaling is to summarize a great deal of information in a way that captures the systematic patterns underlying the information.…”
Section: Quantities Of Interest and Interpretationmentioning
confidence: 99%
“…Multi-dimensional ideal point models replace scalars u a and x b with K-dimensional vectors u a and x b (Heckman and Jr., 1997;Jackman, 2001;Clinton et al, 2004). Unfortunately, as Lauderdale and Clark (2014) observe, the binary data used for these models are "insufficiently informative to support analyses beyond one or two dimensions", and the additional dimensions are difficult to interpret.…”
Section: Polarization Across Dimensionsmentioning
confidence: 99%
“…A second approach to resolving the aliasing is to choose one of the α j 's, β k 's, or γ k 's and restrict its sign (Jackman 2001 conservative Antonin Scalia. Or, we could constrain Douglas's α j to be less than Scalia's α j .…”
Section: Reflection Invariancementioning
confidence: 99%
“…As we shall discuss, the model with its discrimination parameter allows us to handle and even identify possible miscodings of the directions of the votes (Jackman 2001). with the probability of voting conservatively depending on the "ideal point" α j for each justice, the "position" β k for each case, and a "discrimination parameter" γ k for each case, following the three-parameter logistic model (3):…”
Section: Ideal Point Modeling For Us Supreme Court Justicesmentioning
confidence: 99%