We introduce a Bayesian method, LASSOplus, that unifies recent contributions in the sparse modeling literatures, while substantially extending pre-existing estimators in terms of both performance and flexibility. Unlike existing Bayesian variable selection methods, LASSOplus both selects and estimates effects while returning estimated confidence intervals for discovered effects. Furthermore, we show how LASSOplus easily extends to modeling repeated observations and permits a simple Bonferroni correction to control coverage on confidence intervals among discovered effects. We situate LASSOplus in the literature on how to estimate subgroup effects, a topic that often leads to a proliferation of estimation parameters. We also offer a simple preprocessing step that draws on recent theoretical work to estimate higher-order effects that can be interpreted independently of their lower-order terms. A simulation study illustrates the method’s performance relative to several existing variable selection methods. In addition, we apply LASSOplus to an existing study on public support for climate treaties to illustrate the method’s ability to discover substantive and relevant effects. Software implementing the method is publicly available in the R package sparsereg.

I develop a novel method to detect election fraud from irregular patterns in the distribution of vote-shares. I build on a widely discussed observation that in some elections where fraud allegations abound, suspiciously many polling stations return coarse vote-shares (e.g., 0.50, 0.60, 0.75) for the ruling party, which seems highly implausible in large electorates. Using analytical results and simulations, I show that sheer frequency of such coarse vote-shares is entirely plausible due to simple numeric laws and does not by itself constitute evidence of fraud. To avoid false positive errors in fraud detection, I propose a resampled kernel density method (RKD) to measure whether the coarse vote-shares occur too frequently to raise a statistically qualified suspicion of fraud. I illustrate the method on election data from Russia and Canada as well as simulated data. A software package is provided for an easy implementation of the method.

Difference-in-differences (DID) is commonly used for causal inference in time-series cross-sectional data. It requires the assumption that the average outcomes of treated and control units would have followed parallel paths in the absence of treatment. In this paper, we propose a method that not only relaxes this often-violated assumption, but also unifies the synthetic control method (Abadie, Diamond, and Hainmueller 2010) with linear fixed effects models under a simple framework, of which DID is a special case. It imputes counterfactuals for each treated unit using control group information based on a linear interactive fixed effects model that incorporates unit-specific intercepts interacted with time-varying coefficients. This method has several advantages. First, it allows the treatment to be correlated with unobserved unit and time heterogeneities under reasonable modeling assumptions. Second, it generalizes the synthetic control method to the case of multiple treated units and variable treatment periods, and improves efficiency and interpretability. Third, with a built-in cross-validation procedure, it avoids specification searches and thus is easy to implement. An empirical example of Election Day Registration and voter turnout in the United States is provided.

This study analyzes the political agenda of the European Parliament (EP) plenary, how it has evolved over time, and the manner in which Members of the European Parliament (MEPs) have reacted to external and internal stimuli when making plenary speeches. To unveil the plenary agenda and detect latent themes in legislative speeches over time, MEP speech content is analyzed using a new dynamic topic modeling method based on two layers of Non-negative Matrix Factorization (NMF). This method is applied to a new corpus of all English language legislative speeches in the EP plenary from the period 1999 to 2014. Our findings suggest that two-layer NMF is a valuable alternative to existing dynamic topic modeling approaches found in the literature, and can unveil niche topics and associated vocabularies not captured by existing methods. Substantively, our findings suggest that the political agenda of the EP evolves significantly over time and reacts to exogenous events such as EU Treaty referenda and the emergence of the Euro Crisis. MEP contributions to the plenary agenda are also found to be impacted upon by voting behavior and the committee structure of the Parliament.

Scholars of representation are increasingly interested in mass–elite congruence—the degree to which the preferences of elected elites mirror those of voters. Yet existing measures of congruence can be misleading because they ignore information in the data, require arbitrary decisions about quantization, and limit researchers to comparing masses and elites on a single dimension. We introduce a new measure of congruence—borrowed from computer science—that addresses all of these problems: the Earth Mover’s Distance (EMD). We demonstrate its conceptual advantages and apply it to two debates in research on mass–elite congruence: ideological congruence in majoritarian and proportional systems and the determinants of congruence across countries in Latin America. We find that improving measurement using the EMD has important implications for inferences regarding both empirical debates. Even beyond studies of congruence, the EMD is a useful and reliable way for political scientists to compare distributions.

We extend the scaling methodology previously used in Bonica (2014) to jointly scale the American federal judiciary and legal profession in a common space with other political actors. The end result is the first dataset of consistently measured ideological scores across all tiers of the federal judiciary and the legal profession, including 840 federal judges and 380,307 attorneys. To illustrate these measures, we present two examples involving the U.S. Supreme Court. These data open up significant areas of scholarly inquiry.

Researchers studying discrimination and bias frequently conduct experiments that use racially distinctive names to signal race. The ability of these experiments to speak to racial discrimination depends on the excludability assumption that subjects’ responses to these names are driven by their reaction to the individual’s putative race and not some other factor. We use results from an audit study with a large number of aliases and data from detailed public records to empirically test the excludability assumption undergirding the use of racially distinctive names. The detailed public records allow us to measure the signals about socioeconomic status and political resources that each name used in the study possibly could send. We then reanalyze the audit study to see whether these signals predict legislators’ likelihood of responding. We find no evidence that politicians respond to this other information, thus providing empirical support for the excludability assumption.

Validly measuring sensitive issues such as norm violations or stigmatizing traits through self-reports in surveys is often problematic. Special techniques for sensitive questions like the Randomized Response Technique (RRT) and, among its variants, the recent crosswise model should generate more honest answers by providing full response privacy. Different types of validation studies have examined whether these techniques actually improve data validity, with varying results. Yet, most of these studies did not consider the possibility of false positives, i.e., that respondents are misclassified as having a sensitive trait even though they actually do not. Assuming that respondents only falsely deny but never falsely admit possessing a sensitive trait, higher prevalence estimates have typically been interpreted as more valid estimates. If false positives occur, however, conclusions drawn under this assumption might be misleading. We present a comparative validation design that is able to detect false positives without the need for an individual-level validation criterion — which is often unavailable. Results show that the most widely used crosswise-model implementation produced false positives to a nonignorable extent. This defect was not revealed by several previous validation studies that did not consider false positives — apparently a blind spot in past sensitive question research.

Parametric and nonparametric duration models assume proportional hazards: The effect of a covariate on the hazard rate stays constant over time. Researchers have developed techniques to test and correct nonproportional hazards, including interacting the covariates with some function of time. Including this interaction term means that the specification now involves time-varying covariates, and the model specification should reflect this feature. However, in situations with no time-varying covariates initially, researchers often continue to model the duration with only time-invariant covariates. This error results in biased estimates, particularly for the covariates interacted with time. We investigate this issue in over forty political science articles and find that of those studies that begin with time-invariant covariates and correct for nonproportional hazards the majority suffer from incorrect model specification. Proper estimation usually produces substantively or statistically different results.