A common approach when studying the quality of representation involves comparing the latent preferences of voters and legislators, commonly obtained by fitting an item response theory (IRT) model to a common set of stimuli. Despite being exposed to the same stimuli, voters and legislators may not share a common understanding of how these stimuli map onto their latent preferences, leading to differential item functioning (DIF) and incomparability of estimates. We explore the presence of DIF and incomparability of latent preferences obtained through IRT models by reanalyzing an influential survey dataset, where survey respondents expressed their preferences on roll call votes that U.S. legislators had previously voted on. To do so, we propose defining a Dirichlet process prior over item response functions in standard IRT models. In contrast to typical multistep approaches to detecting DIF, our strategy allows researchers to fit a single model, automatically identifying incomparable subgroups with different mappings from latent traits onto observed responses. We find that although there is a group of voters whose estimated positions can be safely compared to those of legislators, a sizeable share of surveyed voters understand stimuli in fundamentally different ways. Ignoring these issues can lead to incorrect conclusions about the quality of representation.

Interest group ideology is theoretically and empirically critical in the study of American politics, yet our measurement of this key concept is lacking both in scope and time. By leveraging network science and ideal point estimation, we provide a novel measure of ideology for amicus curiae briefs and organized interests with accompanying uncertainty estimates. Our Amicus Curiae Network scores cover more than 12,000 unique groups and more than 11,000 briefs across 95 years, providing the largest and longest measure of organized interest ideologies to date. Substantively, the scores reveal that: interests before the Court are ideologically polarized, despite variance in their coalition strategies; interests that donate to campaigns are more conservative and balanced than those that do not; and amicus curiae briefs were more common from liberal organizations until the 1980s, with ideological representation virtually balanced since then.

Separation or “perfect prediction” is a common problem in discrete choice models that, in practice, leads to inflated point estimates and standard errors. Standard statistical packages do not provide clear advice on how to correct these problems. Furthermore, separation can go completely undiagnosed in fitting advanced models that optimize a user-supplied log-likelihood rather than relying on pre-programmed estimation procedures. In this paper, we both describe the problems that separation can cause and address the issue of detecting it in empirical models of strategic interaction. We then consider several solutions based on penalized maximum likelihood estimation. Using Monte Carlo experiments and a replication study, we demonstrate that when separation is detected in the data, the penalized methods we consider are superior to ordinary maximum likelihood estimators.

Conjoint analysis is widely used for estimating the effects of a large number of treatments on multidimensional decision-making. However, it is this substantive advantage that leads to a statistically undesirable property, multiple hypothesis testing. Existing applications of conjoint analysis except for a few do not correct for the number of hypotheses to be tested, and empirical guidance on the choice of multiple testing correction methods has not been provided. This paper first shows that even when none of the treatments has any effect, the standard analysis pipeline produces at least one statistically significant estimate of average marginal component effects in more than 90% of experimental trials. Then, we conduct a simulation study to compare three well-known methods for multiple testing correction, the Bonferroni correction, the Benjamini–Hochberg procedure, and the adaptive shrinkage (Ash). All three methods are more accurate in recovering the truth than the conventional analysis without correction. Moreover, the Ash method outperforms in avoiding false negatives, while reducing false positives similarly to the other methods. Finally, we show how conclusions drawn from empirical analysis may differ with and without correction by reanalyzing applications on public attitudes toward immigration and partner countries of trade agreements.

Contagion across various types of connections is a central process in the study of many political phenomena (e.g., democratization, civil conflict, and voter turnout). Over the last decade, the methodological literature addressing the challenges in causally identifying contagion in networks has exploded. In one of the foundational works in this literature, Shalizi and Thomas (2011, Sociological Methods and Research 40, 211–239.) propose a permutation test for contagion in longitudinal network data that is not confounded by selection (e.g., homophily). We illustrate the properties of this test via simulation. We assess its statistical power under various conditions of the data, including the nature of the contagion, the structure of the network through which contagion occurs, and the number of time periods included in the data. We then apply this test to an example domain that is commonly considered in the context of observational research on contagion—the international spread of democracy. We find evidence of international contagion of democracy. We conclude with a discussion of the practical applicability of the Shalizi and Thomas test to the study of contagion in political networks.

Despite the ongoing success of populist parties in many parts of the world, we lack comprehensive information about parties’ level of populism over time. A recent contribution to Political Analysis by Di Cocco and Monechi (DCM) suggests that this research gap can be closed by predicting parties’ populism scores from their election manifestos using supervised machine learning. In this paper, we provide a detailed discussion of the suggested approach. Building on recent debates about the validation of machine-learning models, we argue that the validity checks provided in DCM’s paper are insufficient. We conduct a series of additional validity checks and empirically demonstrate that the approach is not suitable for deriving populism scores from texts. We conclude that measuring populism over time and between countries remains an immense challenge for empirical research. More generally, our paper illustrates the importance of more comprehensive validations of supervised machine-learning models.

Scholars, pundits, and politicians use opinion surveys to study citizen beliefs about political facts, such as the current unemployment rate, and more conspiratorial beliefs, such as whether Barack Obama was born abroad. Many studies, however, ignore acquiescence-response bias, the tendency for survey respondents to endorse any assertion made in a survey question regardless of content. With new surveys fielding questions asked in recent scholarship, we show that acquiescence bias inflates estimated incidence of conspiratorial beliefs and political misperceptions in the United States and China by up to 50%. Acquiescence bias is disproportionately prevalent among more ideological respondents, inflating correlations between political ideology such as conservatism and endorsement of conspiracies or misperception of facts. We propose and demonstrate two methods to correct for acquiescence bias.

Many large survey courses rely on multiple professors or teaching assistants to judge student responses to open-ended questions. Even following best practices, students with similar levels of conceptual understanding can receive widely varying assessments from different graders. We detail how this can occur and argue that it is an example of differential item functioning (or interpersonal incomparability), where graders interpret the same possible grading range differently. Using both actual assessment data from a large survey course in Comparative Politics and simulation methods, we show that the bias can be corrected by a small number of “bridging” observations across graders. We conclude by offering best practices for fair assessment in large survey courses.

Recent research has shown that interaction effects may often be nonlinear (Hainmueller, Mummolo, and Xu [2019, Political Analysis 27, 163–192]). As standard interaction effect specifications assume a linear interaction effect, that is, the moderator conditions the effect at a constant rate, this can lead to bias. However, allowing nonlinear interaction effects, without accounting for other nonlinearities and nonlinear interaction effects, can also lead to biased estimates. Specifically, researchers can infer nonlinear interaction effects, even though the true interaction effect is linear, when variables used for covariate adjustment that are correlated with the moderator have a nonlinear effect upon the outcome of interest. We illustrate this bias with simulations and show how diagnostic tools recommended in the literature are unable to uncover the issue. We show how using the adaptive Lasso to identify relevant nonlinearities among variables used for covariate adjustment can avoid this issue. Moreover, the use of regularized estimators, which allow for a fuller set of nonlinearities, both independent and interactive, is more generally shown to avoid this bias and more general forms of omitted interaction bias.

Theory has long suggested that swing voting is a response to cross-pressures arising from a mix of individual attributes and contextual factors. Unfortunately, existing regression-based approaches are ill-suited to explore the complex combinations of demographic, policy, and political factors that produce swing voters in American elections. This gap between theory and practice motivates our use of an ensemble of supervised machine learning methods to predict swing voters in the 2012, 2016, and 2020 U.S. presidential elections. The results from the learning ensemble substantiate the existence of swing voters in contemporary American elections. Specifically, we demonstrate that the learning ensemble produces well-calibrated and externally valid predictions of swing voter propensity in later elections and for related behaviors such as split-ticket voting. Although interpreting black-box models is more challenging, they can nonetheless provide meaningful substantive insights meriting further exploration. Here, we use flexible model-agnostic tools to perturb the ensemble and demonstrate that cross-pressures (particularly those involving ideological and policy-related considerations) are essential to accurately predict swing voters.

Researchers of time series cross-sectional data regularly face the change-point problem, which requires them to discern between significant parametric shifts that can be deemed structural changes and minor parametric shifts that must be considered noise. In this paper, we develop a general Bayesian method for change-point detection in high-dimensional data and present its application in the context of the fixed-effect model. Our proposed method, hidden Markov Bayesian bridge model, jointly estimates high-dimensional regime-specific parameters and hidden regime transitions in a unified way. We apply our method to Alvarez, Garrett, and Lange’s (1991, American Political Science Review 85, 539–556) study of the relationship between government partisanship and economic growth and Allee and Scalera’s (2012, International Organization 66, 243–276) study of membership effects in international organizations. In both applications, we found that the proposed method successfully identify substantively meaningful temporal heterogeneity in parameters of regression models.

A potential voter must incur a number of costs in order to successfully cast an in-person ballot, including the costs associated with identifying and traveling to a polling place. In order to investigate how these costs affect voter turnout, we introduce two quasi-experimental designs that can be used to study how the political participation of registered voters is affected by differences in the relative distance that registrants must travel to their assigned Election Day polling place and whether their polling place remains at the same location as in a previous election. Our designs make comparisons of registrants who live on the same residential block, but are assigned to vote at different polling places. We find that living farther from a polling place and being assigned to a new polling place reduce in-person Election Day voting, but that registrants largely offset for this by casting more early in-person and mail ballots.

Political scientists have increasingly deployed conjoint survey experiments to understand multidimensional choices in various settings. In this paper, we show that the average marginal component effect (AMCE) constitutes an aggregation of individual-level preferences that is meaningful both theoretically and empirically. First, extending previous results to allow for arbitrary randomization distributions, we show how the AMCE represents a summary of voters’ multidimensional preferences that combines directionality and intensity according to a probabilistic generalization of the Borda rule. We demonstrate why incorporating both the directionality and intensity of multi-attribute preferences is essential for analyzing real-world elections, in which ceteris paribus comparisons almost never occur. Second, and in further empirical support of this point, we show how this aggregation translates directly into a primary quantity of interest to election scholars: the effect of a change in an attribute on a candidate’s or party’s expected vote share. These properties hold irrespective of the heterogeneity, strength, or interactivity of voters’ preferences and regardless of how votes are aggregated into seats. Finally, we propose, formalize, and evaluate the feasibility of using conjoint data to estimate alternative quantities of interest to electoral studies, including the effect of an attribute on the probability of winning.

Previous research on emotional language relied heavily on off-the-shelf sentiment dictionaries that focus on negative and positive tone. These dictionaries are often tailored to nonpolitical domains and use bag-of-words approaches which come with a series of disadvantages. This paper creates, validates, and compares the performance of (1) a novel emotional dictionary specifically for political text, (2) locally trained word embedding models combined with simple neural network classifiers, and (3) transformer-based models which overcome limitations of the dictionary approach. All tools can measure emotional appeals associated with eight discrete emotions. The different approaches are validated on different sets of crowd-coded sentences. Encouragingly, the results highlight the strengths of novel transformer-based models, which come with easily available pretrained language models. Furthermore, all customized approaches outperform widely used off-the-shelf dictionaries in measuring emotional language in German political discourse.

Keele (2010, Political Analysis 18:189–205) emphasizes that the incumbent test for detecting proportional hazard (PH) violations in Cox duration models can be adversely affected by misspecified covariate functional form(s). In this note, I reevaluate Keele’s evidence by running a full set of Monte Carlo simulations using the original article’s illustrative data-generating processes (DGPs). I make use of the updated PH test calculation available in R’s survival package starting with v3.0-10. Importantly, I find the updated PH test calculation performs better for Keele’s DGPs, suggesting its scope conditions are distinct and worth further investigating. I also uncover some evidence for the traditional calculation suggesting it, too, may have additional scope conditions that could impact practitioners’ interpretation of Keele (2010). On the whole, while we should always be attentive to model misspecification, my results suggest we should also become more attentive to how frequently the PH test’s performance is affected in practice, and that the answer may depend on the calculation’s implementation.

Conventional multidimensional statistical models of roll call votes assume that legislators’ preferences are additively separable over dimensions. In this article, we introduce an item response model of roll call votes that allows for non-separability over latent dimensions. Conceptually, non-separability matters if outcomes over dimensions are related rather than independent in legislators’ decisions. Monte Carlo simulations highlight that separable item response models of roll call votes capture non-separability via correlated ideal points and higher salience of a primary dimension. We apply our model to the U.S. Senate and the European Parliament. In both settings, we find that legislators’ preferences over two basic dimensions are non-separable. These results have general implications for our understanding of legislative decision-making, as well as for empirical descriptions of preferences in legislatures.

Sentiment analysis techniques have a long history in natural language processing and have become a standard tool in the analysis of political texts, promising a conceptually straightforward automated method of extracting meaning from textual data by scoring documents on a scale from positive to negative. However, while these kinds of sentiment scores can capture the overall tone of a document, the underlying concept of interest for political analysis is often actually the document’s stance with respect to a given target—how positively or negatively it frames a specific idea, individual, or group—as this reflects the author’s underlying political attitudes. In this paper, we question the validity of approximating author stance through sentiment scoring in the analysis of political texts, and advocate for greater attention to be paid to the conceptual distinction between a document’s sentiment and its stance. Using examples from open-ended survey responses and from political discussions on social media, we demonstrate that in many political text analysis applications, sentiment and stance do not necessarily align, and therefore sentiment analysis methods fail to reliably capture ground-truth document stance, amplifying noise in the data and leading to faulty conclusions.

A critical barrier to generating cumulative knowledge in political science and related disciplines is the inability of researchers to observe the results from the full set of research designs that scholars have conceptualized, implemented, and analyzed. For a variety of reasons, studies that produce null findings are especially likely to be unobserved, creating biases in publicly accessible research. While several approaches have been suggested to overcome this problem, none have yet proven adequate. We call for the establishment of a new discipline-wide norm in which scholars post short “null results reports” online that summarize their research designs, findings, and interpretations. To address the inevitable incentive problems that earlier proposals for reform were unable to overcome, we argue that decentralized research communities can spur the broader disciplinary norm change that would bring advantage to scientific advance. To facilitate our contribution, we offer a template for these reports that incorporates evaluation of the possible explanations for the null findings, including statistical power, measurement strategy, implementation issues, spillover/contamination, and flaws in theoretical priors. We illustrate the template’s utility with two experimental studies focused on the naturalization of immigrants in the United States and attitudes toward Syrian refugees in Jordan.

It is understood that ensuring equation balance is a necessary condition for a valid model of times series data. Yet, the definition of balance provided so far has been incomplete and there has not been a consistent understanding of exactly why balance is important or how it can be applied. The discussion to date has focused on the estimates produced by the general error correction model (GECM). In this paper, we go beyond the GECM and beyond model estimates. We treat equation balance as a theoretical matter, not merely an empirical one, and describe how to use the concept of balance to test theoretical propositions before longitudinal data have been gathered. We explain how equation balance can be used to check if your theoretical or empirical model is either wrong or incomplete in a way that will prevent a meaningful interpretation of the model. We also raise the issue of “ $I(0)$ balance” and its importance.

While a difference-in-differences (DID) design was originally developed with one pre- and one posttreatment period, data from additional pretreatment periods are often available. How can researchers improve the DID design with such multiple pretreatment periods under what conditions? We first use potential outcomes to clarify three benefits of multiple pretreatment periods: (1) assessing the parallel trends assumption, (2) improving estimation accuracy, and (3) allowing for a more flexible parallel trends assumption. We then propose a new estimator, double DID, which combines all the benefits through the generalized method of moments and contains the two-way fixed effects regression as a special case. We show that the double DID requires a weaker assumption about outcome trends and is more efficient than existing DID estimators. We also generalize the double DID to the staggered adoption design where different units can receive the treatment in different time periods. We illustrate the proposed method with two empirical applications, covering both the basic DID and staggered adoption designs. We offer an open-source R package that implements the proposed methodologies.