Counting repressive events is difficult because state leaders have an incentive to conceal actions of their subordinates and destroy evidence of abuse. In this article, we extend existing latent variable modeling techniques in the study of repression to account for the uncertainty inherent in count data generated for this type of difficult-to-observe event. We demonstrate the utility of the model by focusing on a dataset that defines ‘one-sided-killing’ as government-caused deaths of non-combatants. In addition to generating more precise estimates of latent repression levels, the model also estimates the probability that a state engaged in one-sided-killing and the predictive distribution of deaths for each country-year in the dataset. These new event-based, count estimates will be useful for researchers interested in this type of data but skeptical of the comparability of such events across countries and over time. Our modeling framework also provides a principled method for inferring unobserved count variables based on conceptually related categorical information.

No abstract

Researchers face a tradeoff when applying latent variable models to time-series, cross-sectional data. Static models minimize bias but assume data are temporally independent, resulting in a loss of efficiency. Dynamic models explicitly model temporal data structures, but smooth estimates of the latent trait across time, resulting in bias when the latent trait changes rapidly. We address this tradeoff by investigating a new approach for modeling and evaluating latent variable estimates: a robust dynamic model. The robust model is capable of minimizing bias and accommodating volatile changes in the latent trait. Simulations demonstrate that the robust model outperforms other models when the underlying latent trait is subject to rapid change, and is equivalent to the dynamic model in the absence of volatility. We reproduce latent estimates from studies of judicial ideology and democracy. For judicial ideology, the robust model uncovers shocks in judicial voting patterns that were not previously identified in the dynamic model. For democracy, the robust model provides more precise estimates of sudden institutional changes such as the imposition of martial law in the Philippines (1972–1981) and the short-lived Saur Revolution in Afghanistan (1978). Overall, the robust model is a useful alternative to the standard dynamic model for modeling latent traits that change rapidly over time.

The parties as networks approach has become a critical component of understanding American political parties. Research on it has so far mainly focused on variation in the placement of candidates within a network at the national level. This is in part due to a lack of data on state-level party networks. In this article, I fill that gap by developing state party networks for 47 states from 2000 to 2016 using candidate donation data. To do this, I introduce a backboning network analysis method not yet used in political science to infer relationships among donors at the state level. Finally, I validate these state networks and then show how parties have varied across states and over time. The networks developed here will be made publicly available for future research. Being able to quantify variation in party network structure will be important for understanding variation in party–policy linkages at the state level.

Donald Trump’s success in the 2016 presidential primary election prompted scrutiny for the role of news media in elections. Was Trump successful because news media publicized his campaign and crowded out coverage of other candidates? We examine the dynamic relationships between media coverage, public interest, and support for candidates in the time preceding the 2016 Republican presidential primary to determine (1) whether media coverage drives support for candidates at the polls and (2) whether this relationship was different for Trump than for other candidates. We find for all candidates that the quantity of media coverage had significant and long-lasting effects on public interest in that candidate. Most candidates do not perform better in the polls following increases in media coverage. Trump is an exception to this finding, receiving a modest polling bump following an increase in media coverage. These findings suggest that viability cues from news media contributed to Trump’s success and can be influential in setting the stage in primary elections.