Measuring Bias and Uncertainty in Ideal Point Estimates via the Parametric Bootstrap

2004 ◽  
Vol 12 (2) ◽  
pp. 105-127 ◽  
Author(s):  
Jeffrey B. Lewis ◽  
Keith T. Poole
Keyword(s):  
Ideal Point ◽  
Parametric Bootstrap ◽  
Two Dimensions ◽  
Standard Errors ◽  
Legislative Politics ◽  
Political Methodology ◽  
Working Paper ◽  
Political Analysis ◽  
Measuring Bias ◽  
Quantities Of Interest

Over the last 15 years a large amount of scholarship in legislative politics has used NOMINATE or other similar methods to construct measures of legislators' ideological locations. These measures are then used in subsequent analyses. Recent work in political methodology has focused on the pitfalls of using such estimates as variables in subsequent analysis without explicitly accounting for their uncertainty and possible bias (Herron and Shotts 2003, Political Analysis 11:44–64). This presents a problem for those employing NOMINATE scores because estimates of their unconditional sampling uncertainty or bias have until now been unavailable. In this paper, we present a method of forming unconditional standard error estimates and bias estimates for NOMINATE scores using the parametric bootstrap. Standard errors are estimated for the 90th U.S. Senate in two dimensions. Standard errors of first—dimension placements are in the 0.03 to 0.08 range. The results are compared with those obtained using the Markov chain Monte Carlo estimator of Clinton et al. (2002, Stanford University Working Paper). We also show how the bootstrap can be used to construct standard errors and confidence intervals for auxiliary quantities of interest such as ranks and the location of the median senator.

Measuring Bias and Uncertainty in DW-NOMINATE Ideal Point Estimates via the Parametric Bootstrap

2009 ◽  
Vol 17 (3) ◽  
pp. 261-275 ◽  
Author(s):  
Royce Carroll ◽  
Jeffrey B. Lewis ◽  
James Lo ◽  
Keith T. Poole ◽  
Howard Rosenthal
Keyword(s):  
Political Science ◽  
Ideal Point ◽  
Parametric Bootstrap ◽  
Standard Errors ◽  
Point Estimates ◽  
Ideal Points ◽  
Measuring Bias ◽  
The U.S ◽  
Over Time

DW-NOMINATE scores for the U.S. Congress are widely used measures of legislators' ideological locations over time. These scores have been used in a large number of studies in political science and closely related fields. In this paper, we extend the work of Lewis and Poole (2004) on the parametric bootstrap to DW-NOMINATE and obtain standard errors for the legislator ideal points. These standard errors are in the range of 1%–4% of the range of DW-NOMINATE coordinates.

Measuring Bias and Uncertainty in DW-NOMINATE Ideal Point Estimates via the Parametric Bootstrap

2008 ◽  
Author(s):  
Royce Carroll ◽  
Jeffrey B. Lewis ◽  
James Lo ◽  
Keith T. Poole ◽  
Howard Rosenthal
Keyword(s):  
Ideal Point ◽  
Parametric Bootstrap ◽  
Point Estimates ◽  
Measuring Bias

Integrating Voting Theory and Roll Call Analysis: A Framework

2003 ◽  
Vol 11 (4) ◽  
pp. 381-396 ◽  
Author(s):  
Joshua D. Clinton ◽  
Adam Meirowitz
Keyword(s):  
Ideal Point ◽  
Testing Procedure ◽  
Formal Modeling ◽  
Legislative Politics ◽  
Federal Aid ◽  
Voting Theory ◽  
Roll Call ◽  
Legislative Studies ◽  
Point Estimates ◽  
Hypothesis Testing Procedure

Scholars of legislative studies typically use ideal point estimates from scaling procedures to test theories of legislative politics. We contend that theory and methods may be better integrated by directly incorporating maintained and to be tested hypotheses in the statistical model used to estimate legislator preferences. In this view of theory and estimation, formal modeling (1) provides auxiliary assumptions that serve as constraints in the estimation process, and (2) generates testable predictions. The estimation and hypothesis testing procedure uses roll call data to evaluate the validity of theoretically derived to be tested hypotheses in a world where maintained hypotheses are presumed true. We articulate the approach using the language of statistical inference (both frequentist and Bayesian). The approach is demonstrated in analyses of the well-studied Powell amendment to the federal aid-to-education bill in the 84th House and the Compromise of 1790 in the 1st House.

scholarly journals Roll-Call Vote Selection: Implications for the Study of Legislative Politics

2020 ◽  
Vol 114 (3) ◽  
pp. 691-706
Author(s):  
CAITLIN AINSLEY ◽  
CLIFFORD J. CARRUBBA ◽  
BRIAN F. CRISP ◽  
BETUL DEMIRKAYA ◽  
MATTHEW J. GABEL ◽  
...  
Keyword(s):  
Formal Model ◽  
Legislative Behavior ◽  
Legislative Politics ◽  
Roll Call ◽  
Roll Call Vote ◽  
Voting Procedures ◽  
Strategic Incentives ◽  
Theoretical Results ◽  
Representative Samples ◽  
Quantities Of Interest

Roll-call votes provide scholars with the opportunity to measure many quantities of interest. However, the usefulness of the roll-call sample depends on the population it is intended to represent. After laying out why understanding the sample properties of the roll-call record is important, we catalogue voting procedures for 145 legislative chambers, finding that roll calls are typically discretionary. We then consider two arguments for discounting the potential problem: (a) roll calls are ubiquitous, especially where the threshold for invoking them is low or (b) the strategic incentives behind requests are sufficiently benign so as to generate representative samples. We address the first defense with novel empirical evidence regarding roll-call prevalence and the second with an original formal model of the position-taking argument for roll-call vote requests. Both our empirical and theoretical results confirm that inattention to vote method selection should broadly be considered an issue for the study of legislative behavior.

Fast friends or strange bedfellows? Quantitative methods and gender and politics research

2019 ◽  
Vol 2 (2) ◽  
pp. 151-171
Author(s):  
Katelyn E. Stauffer ◽  
Diana Z. O’Brien
Keyword(s):  
Quantitative Methods ◽  
Gender And Politics ◽  
Political Methodology ◽  
Political Analysis ◽  
And Gender

Discussions of method, methodology and epistemology play an important role in the study of gender and politics. Contributing to this conversation, this article documents both gender and politics scholars’ use of quantitative methods and also quantitative methods scholars’ relationship with gender and politics research. Analysing work published in Politics & Gender, the Journal of Women, Politics & Policy and Politics, Groups, and Identities, we show that gender and politics scholars have been more than capable and willing to use quantitative methods. In contrast, our examination of articles published in Political Analysis suggests that the methods community does not typically engage with gender and politics scholarship. This is problematic because the insights provided by gender and politics research could help spur innovations in political methodology. We thus end with a call for greater collaboration between gender and politics scholars and quantitative methodologists.

Valuing Policy Alternatives: A Generalization

2003 ◽  
Vol 11 (4) ◽  
pp. 397-418 ◽  
Author(s):  
Nathan Dietz ◽  
Lawrence S. Rothenberg
Keyword(s):  
Political Science ◽  
Predictive Accuracy ◽  
Ideal Point ◽  
Standard Errors ◽  
Parameter Estimates ◽  
Policy Alternatives ◽  
Point Estimates ◽  
Utility Models ◽  
The Impact ◽  
Consistent Parameter

Those interested in political phenomena such as voting have found random utility models, originally developed for decisions such as transportation choice, especially attractive, as the underlying model can yield a statistical model with a few simple, realistic assumptions. Unfortunately, such models have proven difficult to apply to situations with more than two votes and three alternatives or an unknown cutpoint. Additionally, as we show, standard applications of such models to voting, while producing consistent parameter estimates, yield standard errors that are too small and, due to a failure to employ all relevant theoretical information, biased ideal point estimates. We specify a general model applicable to any number of votes and alternatives, with correct standard errors and unbiased ideal point estimates. We apply this model to a number of cases studied by previous scholars involving legislative voting over the minimum wage: (1) when there are two votes and two known cutpoints (K. Krehbiel and D. Rivers, American Journal of Political Science, 1988, 32, 1151–1174); (2) when there are three votes and three known cutpoints (J. Wilkerson, American Journal of Political Science, 1991, 35, 613–623); and (3) when there are three votes but where one cutpoint is unknown given a lack of knowledge about the impact of a policy (J. Wilkerson, American Journal of Political Science, 1991, 35, 613–623) or the possibility of sophisticated voting (C. Volden, Journal of Politics, 1998, 60, 149–173). We show that in various contexts our analysis improves on existing methods, yielding consistent and efficient ideal point estimates and a better-fitting model with improved predictive accuracy.

scholarly journals Estimating Northern Fur Seal Pup Production: A Case Study of Batch Mark Abundance Estimation

2019 ◽  
Author(s):  
Devin S. Johnson ◽  
Rodney G. Towell ◽  
Jason B. Baker
Keyword(s):  
Mixture Model ◽  
Gaussian Approximation ◽  
Estimation Method ◽  
Parametric Bootstrap ◽  
Approximate Model ◽  
Delta Method ◽  
Standard Errors ◽  
Northern Fur Seal ◽  
Fur Seal ◽  
Analytic Integration

AbstractWe describe a hierarchical N-mixture model for estimating northern fur seal pup production from batch mark-resight data. Our goal was to improve upon a traditional design-based estimation method used for over 50 years. To this end, we propose a hierarchical N-mixture model to account for differences in animal availability for resighting and observer detection probabilities. A Bayesian approach is used for inference with three separate methods proposed for necessary computations. First a straightforward posterior sample is drawn using MCMC. This was considered the gold standard for this analysis. However, we also consider an approximate model-based on Gaussian approximation of the Poisson and binomial distributions used in the exact hierarchical model. By using the Gaussian approximations, analytic integration can be used to marginalize over latent components. Inference can then be made by maximizing the posterior to find the mode. Following this we investigate both delta-method and parametric bootstrap approaches for calculating abundance and the associated standard errors. Each of the three methods produced nearly identical estimates and standard errors, providing support for using Gaussian approximations in other latent abundance models where the abundance is relatively large.

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