Multidimensional Analysis of Roll Call Data via Bayesian Simulation: Identification, Estimation, Inference, and Model Checking
Open accessAbstract
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 analysis. I demonstrate this by exploiting the close connections among roll call analysis, item-response models, and “full-information” factor analysis. Vote-specific discrimination parameters are equivalent to factor loadings, and as in factor analysis, they (1) enable researchers to discern the substantive content of the recovered dimensions, (2) can be used for assessing dimensionality and model checking, and (3) are an obvious vehicle for introducing and testing researchers' prior beliefs about the dimensions. Bayesian simulation facilitates these uses of discrimination parameters, by simplifying estimation and inference for the massive number of parameters generated by roll call analysis.
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Choosing starting values consistent with the expected direction of the scale (e.g., liberals with -1 and conservatives with +1) is sufficient to ensure global identification in most cases.
“As Jackman (2001) shows, choosing starting values that are consistent with the expected direction of the scale (e.g., liberals with À1 and conservatives with þ1) is sufficient to ensure global identification in most cases.”
From Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data (2015) · cites this paper as Jackman 2001
Section 4 says that starting the Gibbs sampler with Democrats on the left and Republicans on the right is a practical way to select one posterior mode in most unidimensional roll-call settings. It explicitly distinguishes this from global identification: the posterior retains a mirror-image mode, and the priors ensure local identification, not global identification. (LLM, conf=0.90)
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Experimental — accuracy not yet measuredThe unit variance restriction does not solve reflection invariance: the resulting scale can be reversed without changing model predictions.
“However, note that this second approach does not solve the problem of "reflection invariance": the resulting scale can be reversed (flipped left-to-right) without changing the prediction of the models.”
From Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data (2015) · cites this paper as Jackman 2001
A model that locates users' and politicians' positions on two different dimensions would imply additional identifying assumptions.
“In these cases, a model that is able to locate users' and politicians' positions on two different dimensions would be more appropriate, although it would also imply additional identifying assumptions (Jackman 2001).”
From Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data (2015) · cites this paper as Jackman 2001
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Estimating Legislators' Preferred Points
Londregan, J. (1999) · Political Analysis · doi:10.1093/oxfordjournals.pan.a029804 · Cited by 159 · see citing works
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Research works printed but not linked: 10
- Albert, James. 1992. "Bayesian Estimation of Normal Ogive Item Response Curves Using Gibbs Sampling." Journal of Educational Statistics 17:251-269.
- Bollen, Kenneth A. 1989. Structural Equations with Latent Variables. New York: Wiley.
- Enelow, J., and M. Hinich. 1984. The Spatial Theory of Voting: An Introduction. New York: Cambridge University Press.
- Heckman, James J., and James M. Snyder. 1997. "Linear Probabilty Models of the Demand for Attributes with an Empirical Application to Estimating the Preferences of Legislators." RAND Journal of Economics 28:S142- S189.
- Johnson, Valen E., and James H. Albert. 1999. Ordinal Data Modeling. New York: Springer-Verlag. https://doi.org/10.1093/polana/9.3.227 Published online by Cambridge University Press
- Loader, C. 1999. Local Regression and Likelihood. New York: Springer.
- Londregan, John. 2000b. Legislative Institutions and Ideology in Chile's Democratic Transition. New York: Cambridge University Press.
- Poole, Keith T., and Howard Rosenthal. 1991. "Patterns of Congressional Voting." American Journal of Political Science 35:228-278.
- Poole, Keith T., and Howard Rosenthal. 1997. Congress: A Political-Economic History of Roll Call Voting. New York: Oxford University Press.
- Weisberg, Herbert F. 1978. "Evaluating Theories of Congressional Roll-Call Voting." American Journal of Political Science 22:554-577.
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Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data
Barberá, P. (2015) · Political Analysis · doi:10.1093/pan/mpu011 · Cited by 982 · see citing works
Lowe, W. (2008) · Political Analysis · doi:10.1093/pan/mpn004 · Cited by 166 · see citing works
- Authors
- Jackman, S.
- Affiliations
- Stanford University (US)
- Corresponding author
- Jackman, Simon
- ORCID
- Jackman, Simon
- Venue
- Political Analysis
- Publisher
- Cambridge University Press (CUP)
- Published
- 2001-01
- Volume
- 9
- Issue
- 3
- Pages
- 227-241
- ISSN
- 1047-1987 (ISSN-L)
- 1476-4989
- OpenAlex
- W2126841490
- Cited by
- 281
- References
- 1
- Citations by year
- 2026: 6 · 2025: 10 · 2024: 6 · 2023: 4 · 2022: 11 · 2021: 14 · 2020: 6 · 2019: 27 · 2018: 13 · 2017: 13 · 2016: 13 · 2015: 6 · 2014: 14 · 2013: 14 · 2012: 13
- FWCI
- 27.66
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Political Analysis (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Political Science and International Relations › Electoral Systems and Political Participation
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Keywords
- roll call analysis, Bayesian simulation, item response models, factor loadings, dimensionality, model checking, prior beliefs, legislative voting behavior
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