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The Society for Political Methodology is the world's premier academic organization for quantitative political science, addressing the needs of a global membership base united in developing and establishing empirical tools for the study of politics.

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Upcoming Conferences

Dates Conference Location
November 16-21, 2020 IV Latin American PolMeth Online
January 13-15, 2021 Asian PolMeth VIII Online
March 18-19, 2021 I PolMeth Europe Online and Hamburg, Germany
July 14-17, 2021 PolMeth XXXVIII St. Louis


Recent Papers

Ends Against the Middle: Scaling Votes When Ideological Opposites Behave the Same for Antithetical Reasons

Standard methods for measuring ideology from voting records assume that individuals at the ideological ends should never vote together in opposition to moderates. In practice, however, there are many times when individuals from both extremes vote identically but for opposing reasons. Both liberal and conservative justices may dissent from the same Supreme Court decision but provide ideologically contradictory rationales. In legislative settings, ideological opposites may join together to oppose moderate legislation in pursuit of antithetical goals. We introduce a scaling model that accommodates ends against the middle voting and provide a novel estimation approach that improves upon existing routines. We apply this method to voting data from the United States Supreme Court and Congress and show it outperforms standard methods in terms of both congruence with qualitative insights and model fit. We argue our proposed method represents a superior default approach for generating one-dimensional ideological estimates in many important settings.

Considerations for Machine Learning Use in Political Research with Application to Voter Turnout
Moses, Laura, and Janet M. Box-Steffensmeier. Working Paper. “Considerations for Machine Learning Use in Political Research with Application to Voter Turnout”. Abstract

Machine learning is becoming increasingly prevalent in political science research. Improving the accuracy of outcomes, refining measurements of complex processes, addressing non-linearities in data and introducing new kinds of data may be achieved using machine learning. Despite the possible uses of machine learning, a clear understanding of how to use these tools and their pitfalls is still needed. This article provides a foundational guide to machine learning and illustrates how these methods can advance political science research. We address the pitfalls of these methods as well as the specific concerns for using machine learning with social data. Finally, we demonstrate how machine learning can help understand voter turnout through an application of methods with survey data on the 2016 election.

Knowledge Decays: Temporal Validity and Social Science in a Changing World

The "credibility revolution" has forced social scientists to confront the limits of our methods for creating general knowledge. The current approach aims to aggregate valid but local knowledge. At the same time, the increasing centrality of the internet to political and social processes has rendered untenable the implicit ceteris paribus assumptions necessary for aggregating knowledge produced at dierent times. The interaction of these two trends is not yet well understood. I argue that a high rate of change of the objects of our study makes "knowledge decay" a potentially large source of error. "Temporal validity" is a form of external validity in which the target setting is in the future|which, of course, is always the case. A crucial distinction between cross-sectional external validity and temporal validity is that the latter implies a fundamental incompleteness of social science that renders the project of non-parametric knowledge aggregation impossible. I discuss the limitations of extant strategies for knowledge aggregation through the lens of temporal validity, and propose strategies for improving practice.

All (Mayoral) Politics is Local?
Sinclair, Betsy, et al. Working Paper. “All (Mayoral) Politics is Local?”. Abstract

One of the defining characteristics of modern politics in the United States is the increasing nationalization of elite- and voter-level behavior. Relying on measures of electoral vote shares, previous research has found evidence indicating a significant amount of state-level nationalization. Using an alternative source of data -- the political rhetoric used by mayors, state governors, and Members of Congress on Twitter -- we examine and compare the amount of between-office nationalization throughout the federal system. We find that gubernatorial rhetoric closely matches that of Members of Congress but that there are substantial differences in the topics and content of mayoral speech. These results suggest that, on average, American mayors have largely remained focused on their local mandate. More broadly, our findings suggest a limit to which American politics has become nationalized -- in some cases, all politics remains local.

Generative Dynamics of Supreme Court Citations: Analysis with a New Statistical Model
Schmid, Christian S., Ted Hsuan Yun Chen, and Bruce Desmarais. Working Paper. “Generative Dynamics of Supreme Court Citations: Analysis with a New Statistical Model”. Abstract

The significance and influence of US Supreme Court majority opinions derive in large part from opinions’ roles as precedents for future opinions. A growing body of literature seeks to understand what drives the use of opinions as precedents through the study of Supreme Court case citation patterns. We raise two limitations of existing work on Supreme Court citations. First, dyadic citations are typically aggregated to the case level before they are analyzed. Second, citations are treated as if they arise independently. We present a methodology for studying citations between Supreme Court opinions at the dyadic level, as a network, that overcomes these limitations. This methodology—the citation exponential random graph model—enables researchers to account for the effects of case characteristics and complex forms of network dependence in citation formation. We then analyze a network that includes all Supreme Court cases decided between 1950 and 2015. We find evidence for dependence processes, including reciprocity, transitivity, and popularity. The dependence effects are as substantively and statistically significant as the effects of the exogenous covariates we include in the model, indicating that models of Supreme Court citation should incorporate both the effects of case characteristics and the structure of past citations.


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