Kevin M. Quinn

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Previous methods of analyzing the substance of political attention have had to make several restrictive assumptions or been prohibitively costly when applied to large-scale political texts. Here, we describe a topic model for legislative speech, a statistical learning model that uses word choices to infer topical categories covered in a set of speeches and(More)
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 (MCMC) methods to fit a Bayesian measurement model of ideal points for all justices serving on the U.S. Supreme Court from 1953 to 1999. We are particularly interested in(More)
Entries in the burgeoning ‘‘text-as-data’’ movement are often accompanied by lists or visualizations of how word (or other lexical feature) usage differs across some pair or set of documents. These are intended either to establish some target semantic concept (like the content of partisan frames) to estimate word-specific measures that feed forward into(More)
With too few exceptions, quantitative international relations is the study of annual, quarterly, or sometimes monthly observations of the international system+ Interactions among nations, in contrast, take place on a day-to-day basis: When the Palestinians launch a mortar attack into Israel, the Israeli army does not wait until the end of the calendar year(More)
P olitical scientists and legal academics have long scrutinized the U.S. Supreme Court’s work to understand what motivates the justices. Despite significant differences in methodology, both disciplines seek to explain the Court’s decisions by focusing on examining past cases. This retrospective orientation is surprising. In other areas of government, for(More)
We address a major discrepancy in matching methods for causal inference in observational data. Since these data are typically plentiful, the goal of matching is to reduce bias and only secondarily to keep variance low. However, most matching methods seem designed for the opposite problem, guaranteeing sample size ex ante but limiting bias by controlling for(More)
Randomized natural experiments provide social scientists with rare opportunities to draw credible causal inferences in real-world settings. We capitalize on such a unique experiment to examine how the name order of candidates on ballots affects election outcomes. Since 1975, California has randomized the ballot order for statewide offices with a complex(More)
This Essay reports the results of an interdisciplinary project comparing political science and legal approaches to forecasting Supreme Court decisions. For every argued case during the 2002 Term, we obtained predictions of the outcome prior to oral argument using two methods—one a statistical model that relies on general case characteristics, and the other(More)