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This article presents an integrated set of Bayesian tools one can use to model heterogeneous event counts. While models for event count cross sections are now widely used, little has been written about how to model counts when contextual factors introduce heterogeneity. The author begins with a discussion of Bayesian cross-sectional count models and discusses an alternative model for counts with overdispersion. To illustrate the Bayesian framework, the author fits the model to the number of women's rights cosponsorships for each member of the 83rd to 102nd House of Representatives. The model is generalized to allow for contextual heterogeneity. The hierarchical model allows one to explicitly model contextual factors and test alternative contextual explanations, even with a small number of contextual units. The author compares the estimates from this model with traditional approaches and discusses software one can use to easily implement these Bayesian models with little start-up cost.

To what extent does the separation of powers affect congressional roll call voting behavior? To answer this question, I offer a strategic model of congressional decision making that asserts members of Congress pursue public policy goals when casting roll call votes. From the equilibrium predictions of a formal model, I generate testable hypotheses by computing the expected net amount of sophisticated (nonsincere) congressional behavior given changes in decision context. I test the predictions of the theoretical model with data from all civil rights roll call votes from the 83d to the 102d Congress. The results demonstrate that both the other legislative chamber and the Supreme Court profoundly constrain House members and senators when casting roll call votes. This is strong evidence of the importance of policy outcomes to members of Congress when voting on the floor.

To what extent does the separation of powers affect congressional roll call voting behavior? To answer this question, I offer a strategic model of congressional decision making that asserts members of Congress pursue public policy goals when casting roll call votes. From the equilibrium predictions of a formal model, I generate testable hypotheses by computing the expected net amount of sophisticated (nonsincere) congressional behavior given changes in decision context. I test the predications of the theoretical model with data from all civil rights roll call votes from the 83d to the 102d Congress. The results demonstrate that both the other legislative chamber & the Supreme Court profoundly constrain House members & senators when casting roll call votes. This is strong evidence of the importance of policy outcomes to members of Congress when voting on the floor. 2 Tables, 7 Figures, 1 Appendix, 49 References. Adapted from the source document.

An Introduction to Empirical Legal Research introduces empirical methodology in a legal context, explaining how empirical analysis can inform legal arguments; how lawyers can set about framing empirical questions, conducting empirical research, analysing data, and presenting or evaluating the results.

A response to Kenneth L. Manning, Bruce A. Carroll, & Robert A. Carp's "Does Age Matter? Judicial Decision Making in Age Discrimination Cases" (2004) argues that decisions made over how to group data are key to the inferences reached. Explored is whether & in what manner their data grouping & model specification influenced their findings. Manning, Carroll, & Carp conclude that age matters in age discrimination suits, as expected, but not in other cases where they do not expect it to have an impact -- race & gender discrimination suits. After detailing Manning, Carroll, & Carp's model specification, a three-cohort model using a proper specification is presented. Respecifying the model in the same way as Manning, Carroll, & Carp, but varying the cutoff points on the age-cohort variable, reveals results far less in accordance with the authors' expectations. Thus, Manning, Carroll, & Carp's inferences are called into question. Reanalysis indicates that only when the age variables are coded exactly per Manning, Carroll, & Carp does the effect of age run in their hypothesized direction. No other grouping choices yield like results. In this light, analyses of the impact of age on judicial decisions in race & gender cases are presented. It is asserted that Manning, Carroll, & Carp's conclusion & inference hinges on their grouping decision. 5 Tables, 2 Figures, 3 References. Adapted from the source document.

Most theoretic models of multiparty electoral competition make the assumption that party leaders are motivated to maximize their vote share or seat share. In plurality-rule systems this is a sensible assumption. However, in proportional representation systems, this assumption is questionable since the ability to make public policy is not strictly increasing in vote shares or seat shares. We present a theoretic model in which party leaders choose electoral declarations with an eye toward the expected policy outcome of the coalition bargaining game induced by the party declarations and the parties' beliefs about citizens' voting behavior. To test this model, we turn to data from the 1989 Dutch parliamentary election. We use Markov chain Monte Carlo methods to estimate the parties' beliefs about mass voting behavior and to average over measurement uncertainty and missing data. Due to the complexity of the parties' objective functions and the uncertainty in objective function estimates, equilibria are found numerically. Unlike previous models of multiparty electoral competition, the equilibrium results are consistent with the empirical declarations of the four major Dutch parties.

At the heart of attitudinal and strategic explanations of judicial behavior is the assumption that justices have policy preferences. In this paper we employ Markov chain Monte Carlo methods to fit a Bayesian measurement model of ideal points for all justices serving on the U.S. Supreme Court from 1953 through 1999. We are particularly interested in determining to what extent ideal points of justices change throughout their tenure on the Court. This is important because judicial politics scholars oftentimes invoke preference measures that are time invariant. To investigate preference change, we posit a dynamic item response model that allows ideal points to change systematically over time. Additionally, we introduce Bayesian methods for fitting multivariate dynamic linear models to political scientists. Our results suggest that many justices do not have temporally constant ideal points. Moreover, our ideal point estimates outperform existing measures and explain judicial behavior quite well across civil rights, civil liberties, economics, and federalism cases.

To investigate the effect of the Supreme Court on public opinion, we offer the conditional response hypothesis based on a theory of Supreme Court legitimacy and a microlevel social-psychological theory of attitude formation. Together these theories predict that the Court may affect public opinion when it initially rules on a salient issue, but that subsequent decisions on the same issue will have little influence on opinion. To test our predictions, we analyze public opinion data before and after the Supreme Court ruled in a highly visible abortion case (Webster v. Reproductive Health Services [1989]) and before and after three key capital punishment rulings (Furman v. Georgia [1972], Gregg v. Georgia [1976], and McCleskey v. Kemp [1987]). The results suggest that our theory is not issue bound but is generally applicable to how the Supreme Court affects public opinion when it rules in highly salient cases.