Duration data are often subject to various forms of censoring that require adaptations of the likelihood function to properly capture the data generating process, but existing spatial duration models do not yet account for these potential issues. Here, we develop a method to estimate spatial-lag duration models when the outcome suffers from right censoring, the most common form of censoring. We adapt Wei and Tanner's (1991) imputation algorithm for censored (non-spatial) regression data to models of spatially interdependent durations. The algorithm treats the unobserved duration outcomes as censored data and iterates between multiple imputation of the incomplete, that is, right censored, values and estimation of the spatial duration model using these imputed values. We explore the performance of an estimator for log-normal durations in the face of varying degrees of right censoring via Monte Carlo and provide empirical examples of its estimation by analyzing spatial dependence in states' entry dates into World War I.

Politicians and citizens increasingly engage in political conversations on social media outlets such as Twitter. In this article, I show that the structure of the social networks in which they are embedded can be a source of information about their ideological positions. Under the assumption that social networks are homophilic, I develop a Bayesian Spatial Following model that considers ideology as a latent variable, whose value can be inferred by examining which politics actors each user is following. This method allows us to estimate ideology for more actors than any existing alternative, at any point in time and across many polities. I apply this method to estimate ideal points for a large sample of both elite and mass public Twitter users in the United States and five European countries. The estimated positions of legislators and political parties replicate conventional measures of ideology. The method is also able to successfully classify individuals who state their political preferences publicly and a sample of users matched with their party registration records. To illustrate the potential contribution of these estimates, I examine the extent to which online behavior during the 2012 US presidential election campaign is clustered along ideological lines.

Questions about racial or ethnic group identity feature centrally in many social science theories, but detailed data on ethnic composition are often difficult to obtain, out of date, or otherwise unavailable. The proliferation of publicly available geocoded person names provides one potential source of such data'if researchers can effectively link names and group identity. This article examines that linkage and presents a methodology for estimating local ethnic or racial composition using the relationship between group membership and person names. Common approaches for linking names and identity groups perform poorly when estimating group proportions. I have developed a new method for estimating racial or ethnic composition from names which requires no classification of individual names. This method provides more accurate estimates than the standard approach and works in any context where person names contain information about group membership. Illustrations from two very different contexts are provided: the United States and the Republic of Kenya.

Onsets of binary events are often of interest to political scientists, whether they be regime changes, the occurrence of civil war, or the signing of bilateral agreements, to name a few. Often researchers transform the binary event outcome of interest, by setting ongoing years to zero, to create a variable which measures the onset of the event. While this may seem an intuitive way to go about estimating models where onset is the outcome of interest, it results in two problems that can affect substantive inferences. First, it creates two qualitatively different meanings for a unit time period to have a zero, which estimators are unable to “know.” Second, it ignores the possibility that variables may have differing effects upon binary event onsets and durations. This article explores how much this transformation can harm our substantive inferences by analytically demonstrating the resulting bias and the use of Monte Carlo experiments, as well as offering recommendations to avoid these problems. I also conduct a sensitivity analysis on the determinants of civil war onset to examine how substantive inferences are affected by this issue. In doing so, I find that there is considerable difference in the size of estimated coefficients and whether a variable is considered a robust determinant of civil war.

Panel studies typically suffer from attrition. Ignoring the attrition can result in biased inferences if the missing data are systematically related to outcomes of interest. Unfortunately, panel data alone cannot inform the extent of bias due to attrition. Many panel studies also include refreshment samples, which are data collected from a random sample of new individuals during the later waves of the panel. Refreshment samples offer information that can be utilized to correct for biases induced by non-ignorable attrition while reducing reliance on strong assumptions about the attrition process. We present a Bayesian approach to handle attrition in two-wave panels with one refreshment sample and many categorical survey variables. The approach includes (1) an additive non-ignorable selection model for the attrition process; and (2) a Dirichlet process mixture of multinomial distributions for the categorical survey variables. We present Markov chain Monte Carlo algorithms for sampling from the posterior distribution of model parameters and missing data. We apply the model to correct attrition bias in an analysis of data from the 2007–08 Associated Press/Yahoo News election panel study.

In most multidimensional spatial models, the systematic component of agent utility functions is specified as additive separable. We argue that this assumption is too restrictive, at least in the context of spatial voting in mass elections. Here, assuming separability would stipulate that voters do not care about how policy platforms combine positions on multiple policy dimensions. We present a statistical implementation of Davis, Hinich, and Ordeshook's (1970) Weighted Euclidean Distance model that allows for the estimation of the direction and magnitude of non-separability from vote choice data. We demonstrate in a Monte-Carlo experiment that conventional separable model specifications yield biased and/or unreliable estimates of the effect of policy distances on vote choice probabilities in the presence of non-separable preferences. In three empirical applications, we find voter preferences on economic and socio-cultural issues to be non-separable. If non-separability is unaccounted for, researchers run the risk of missing crucial parts of the story. The implications of our findings carry over to other fields of research: checking for non-separability is an essential part of robustness testing in empirical applications of multidimensional spatial models.

Many variables crucial to the social sciences are not directly observed but instead are latent and measured indirectly. When an external variable of interest affects this measurement, estimates of its relationship with the latent variable will then be biased. Such violations of “measurement invariance” may, for example, confound true differences across countries in postmaterialism with measurement differences. To deal with this problem, researchers commonly aim at “partial measurement invariance” that is, to account for those differences that may be present and important. To evaluate this importance directly through sensitivity analysis, the “EPC-interest” was recently introduced for continuous data. However, latent variable models in the social sciences often use categorical data. The current paper therefore extends the EPC-interest to latent variable models for categorical data and demonstrates its use in example analyses of U.S. Senate votes as well as respondent rankings of postmaterialism values in the World Values Study.

Electoral rules have the potential to affect the size and composition of the voting public. Yet scholars disagree over whether requiring voters to register well in advance of Election Day reduces turnout. We present a new approach, using web searches for “voter registration” to measure interest in registering, both before and after registration deadlines for the 2012 U.S. presidential election. Many Americans sought information on “voter registration” even after the deadline in their state had passed. Combining web search data with evidence on the timing of registration for 80 million Americans, we model the relationship between search and registration. Extrapolating this relationship to the post-deadline period, we estimate that an additional 3–4 million Americans would have registered in time to vote, if deadlines had been extended to Election Day. We test our approach by predicting out of sample and with historical data. Web search data provide new opportunities to measure and study information-seeking behavior.

Electoral competitiveness is a key explanatory construct across a broad swath of phenomena, finding application in diverse areas related to political incentives and behavior. Despite its frequent theoretical use, no valid measure of electoral competitiveness exists that applies across different electoral and party systems. We argue that one particular type of electoral competitiveness'electoral risk'can be estimated across institutional contexts and matters most for incumbent behavior. We propose, estimate, and make available a cross-nationally applicable measure for elections in 22 developed democracies between 1960 and 2011. Unlike extant alternatives, our measure captures vote volatility and is constructed at the party (not system) level, exogenous to most policy predictors, and congruent with the perceptions and incentives of policy-makers.

The strategic nature of political interactions has long captured the attention of political scientists. A traditional statistical approach to modeling strategic interactions involves multi-stage estimation, which improves parameter estimates associated with one stage by using the information from other stages. The application of such multi-stage approaches, however, imposes rather strict demands on data availability: data on the dependent variable must be available for each strategic actor at each stage of the interaction. Limited or no data make such approaches difficult or impossible to implement. Political science data, however, especially in the fields of international relations and comparative politics, are not always structured in a manner that is conducive to these approaches. For example, we observe and have plentiful data on the onset of civil wars, but not the preceding stages, in which opposition groups decide to rebel or governments decide to repress them. In this article, I derive an estimator that probabilistically estimates unobserved actor choices related to earlier stages of strategic interactions. I demonstrate the advantages of the estimator over traditional and split-population binary estimators both using Monte Carlo simulations and a substantive example of the strategic rebel–government interaction associated with civil wars.

Dyadic data are common in the social sciences, although inference for such settings involves accounting for a complex clustering structure. Many analyses in the social sciences fail to account for the fact that multiple dyads share a member, and that errors are thus likely correlated across these dyads. We propose a non-parametric, sandwich-type robust variance estimator for linear regression to account for such clustering in dyadic data. We enumerate conditions for estimator consistency. We also extend our results to repeated and weighted observations, including directed dyads and longitudinal data, and provide an implementation for generalized linear models such as logistic regression. We examine empirical performance with simulations and an application to interstate disputes.

High rerunning rates among incumbents and among the two major parties allow studies of U.S. incumbency advantage to bypass the selection problem of who chooses to rerun. In countries where rerunning is not widespread among individuals or parties, estimation using methods developed for the United States may result in a sample selection bias. In countries with party switching, there may be a disconnect between party and individual estimates. This article proposes a definition of incumbency advantage that is valid for countries that present any of these characteristics and that is valid for cross-country comparison: the effect of incumbency for an individual politician on the unconditional probability of winning. I illustrate the issues raised in this article with evidence from Brazilian mayoral elections.

Over the past several years, there has been growing use of the draft lottery instrument to study political attitudes and behaviors (see, e.g., Bergan 2009; Erikson and Stoker 2011; Henderson 2012; Davenport 2015). Draft lotteries, held in the United States from 1969 to 1972, provide a potentially powerful design; in theory, they should provide true randomization for the “treatment” of military service or behavioral reactions to the threat of such service. However, the first draft lottery conducted in 1969 was not conducted in a random manner, giving those citizens born in the fourth quarter of the year disproportionately higher chances of being drafted. In this note, we describe the randomization failure and discuss how this failure could in theory compromise the use of draft lottery numbers as an instrumental variable. We then use American National Election Studies data to provide support for the conclusion that individuals most affected by the randomization failure (those born in the fourth quarter of the year) largely do not look statistically distinct from those born at other times of the year. With some caveats, researchers should be able to treat the 1969 draft numbers as if they were assigned at random. We also discuss broader lessons to draw from this example, both for scholars interested in using the draft lottery as an instrumental variable, and for researchers leveraging other instruments with randomization failures. Specifically, we suggest that scholars should pay particular attention to the sources of randomization failure, sample attrition, treatment and dependent variable selection, and possible failure of the exclusion restriction, and we outline ways in which these problems may apply to the draft lottery instrument and other natural experiments.

Political scientists often turn to natural experiments to draw causal inferences with observational data. Recently, the regression discontinuity design (RD) has become a popular type of natural experiment due to its relatively weak assumptions. We study a special type of regression discontinuity design where the discontinuity in treatment assignment is geographic. In this design, which we call the Geographic Regression Discontinuity (GRD) design, a geographic or administrative boundary splits units into treated and control areas, and analysts make the case that the division into treated and control areas occurs in an as-if random fashion. We show how this design is equivalent to a standard RD with two running variables, but we also clarify several methodological differences that arise in geographical contexts. We also offer a method for estimation of geographically located treatment effects that can also be used to validate the identification assumptions using observable pretreatment characteristics. We illustrate our methodological framework with a re-examination of the effects of political advertisements on voter turnout during a presidential campaign, exploiting the exogenous variation in the volume of presidential ads that is created by media market boundaries.

Recent advances in research tools for the systematic analysis of textual data are enabling exciting new research throughout the social sciences. For comparative politics, scholars who are often interested in non-English and possibly multilingual textual datasets, these advances may be difficult to access. This article discusses practical issues that arise in the processing, management, translation, and analysis of textual data with a particular focus on how procedures differ across languages. These procedures are combined in two applied examples of automated text analysis using the recently introduced Structural Topic Model. We also show how the model can be used to analyze data that have been translated into a single language via machine translation tools. All the methods we describe here are implemented in open-source software packages available from the authors.

Many types of time series data in political science, including polling data and events data, exhibit important features'such as irregular spacing, noninstantaneous observation, overlapping observation times, and sampling or other measurement error'that are ignored in most statistical analyses because of model limitations. Ignoring these properties can lead not only to biased coefficients but also to incorrect inference about the direction of causality. This article develops a continuous-time model to overcome these limitations. This new model treats observations as noisy samples collected over an interval of time and can be viewed as a generalization of the vector autoregressive model. Monte Carlo simulations and two empirical examples demonstrate the importance of modeling these features of the data.

This text reports the results of an evaluation of the performance of multilevel regression modeling and poststratication (MRP) in reconstructing state-level estimates from federal-level data. The evaluation makes use of Eurobarometer data and relies on the fact that Eurobarometer provides representative survey data for each European Union state to further explore the performance of MRP. I repeatedly draw subsets of the entire Eurobarometer sample, then I compute adjusted country means using MRP with census data, and I compare the resulting estimates to the true country means from the full sample. I do that for ten survey items from various Eurobarometer waves. The results show that MRP is generally successful in producing estimates that are highly correlated with the true values (mean of 0.90). But the approach is less capable of reconstructing the relative rankings of the country means and hitting the range of plausible values of the individual state means. I also show that the great part of the adjustment comes from the modeling of the state means and not from poststratification, and that population-weighted samples perform no worse than samples in which countries have equal shares of the pool of respondents.

Courts of last resort in the American states offer researchers considerable leverage to develop and test theories about how institutions influence judicial behavior. One measure critical to this research agenda is the individual judges' preferences, or ideal points, in policy space. Two main strategies for recovering this measure exist in the literature: Brace, Langer, and Hall's (2000, Measuring preferences of state supreme court judges, Journal of Politics 62(2):387–413) Party-Adjusted Judge Ideology and Bonica and Woodruff's (2014, A common-space measure of state supreme court ideology, Journal of Law, Economics, & Organization, doi: 10.1093/jleo/ewu016) judicial CFscores. Here, we introduce a third measurement strategy that combines CFscores with item response (IRT) estimates of judicial voting behavior in all fifty-two state courts of last resort from 1995 to 2010. We show that leveraging two distinct sources of information (votes and CFscores) yields a superior estimation strategy. Specifically, we highlight several key advantages of the combined measure: (1) it is estimated dynamically, allowing for the possibility that judges' ideological leanings change over time and (2) it maps judges into a common space. In a comparison against existing measurement strategies, we find that our measure offers superior performance in predicting judges' votes. We conclude that it is a valuable tool for advancing the study of judicial politics.