Suchergebnisse

Examines number of concurring and dissenting opinions in Supreme Court cases, to determine if leadership styles influence the tendency of individual justices to express opinions; 1800-1991; US.

Beginning in 1999, Curtis Signorino challenged the use of traditional logits and probits analysis for testing discrete-choice, strategic models. Signorino argues that the complex parametric relationships generated by even the simplest strategic models can lead to wildly inaccurate inferences if one applies these traditional approaches. In their stead, Signorino proposes generating stochastic formal models, from which one can directly derive a maximum likelihood estimator. We propose a simpler, alternative methodology for theoretically and empirically accounting for strategic behavior. In particular, we propose carefully and correctly deriving one's comparative statics from one's formal model, whether it is stochastic or deterministic does not particularly matter, and using standard logit or probit estimation techniques to test the predictions. We demonstrate that this approach performs almost identically to Signorino's more complex suggestion.

After more than a decade of membership stability, the U.S. Supreme Court experienced a pair of vacancies following its 2004–2005 term. In July 2005, Justice Sandra Day O'Connor announced her intention to retire. It is widely believed that her own health, that of her husband, and the favorable political environment influenced her decision to step down in January 2006. In the interim, Chief Justice William Rehnquist, after struggling with ill health for many months, passed away on 3 September 2005, becoming the first sitting justice to die in more than five decades.

Event history models have become a dominant method of analysis in the study of international relations. Conventional event history models, however, retain the assumption that the effects of the covariates remain proportional to each other throughout the duration of the subject's phase. Nonproportional hazard (NPH) models are used, which allow for the effects of covariates to vary over time. These models are then applied to three previously established data sets on the duration of postwar peace, civil wars, and alliances. Results show that NPH analysis is a useful method for testing new hypotheses, as well as removing possible sources of bias from existing analyses.