This article examines the information sharing behavior of U.S. politicians and the mass public by mapping the ideological sharing space of political news on social media. As data, we use the near-universal currency of online information exchange: web links. We introduce a methodological approach and software to unify the measurement of ideology across social media platforms by using sharing data to jointly estimate the ideology of news media organizations, politicians, and the mass public. Empirically, we show that (1) politicians who share ideologically polarized content share, by far, the most political news and commentary and (2) that the less competitive elections are, the more likely politicians are to share polarized information. These results demonstrate that news and commentary shared by politicians come from a highly unrepresentative set of ideologically extreme legislators and that decreases in election pressures (e.g., by gerrymandering) may encourage polarized sharing behavior.

Synthetic controls (SCs) are widely used to estimate the causal effect of a treatment. However, they do not account for the different speeds at which units respond to changes. Reactions may be inelastic or “sticky” and thus slower due to varying regulatory, institutional, or political environments. We show that these different reaction speeds can lead to biased estimates of causal effects. We therefore introduce a dynamic SC approach that accommodates varying speeds in time series, resulting in improved SC estimates. We apply our method to re-estimate the effects of terrorism on income (Abadie and Gardeazabal [2003, American Economic Review 93, 113–132]), tobacco laws on consumption (Abadie, Diamond, and Hainmueller [2010, Journal of the American Statistical Association 105, 493–505]), and German reunification on GDP (Abadie, Diamond, and Hainmueller [2015, American Journal of Political Science 59, 495–510]). We also assess the method’s performance using Monte Carlo simulations. We find that it reduces errors in the estimates of true treatment effects by up to 70% compared to traditional SCs, improving our ability to make robust inferences. An open-source R package, dsc, is made available for easy implementation.

Spatial econometric models allow for interactions among cross-sectional units through spatial weight matrices. This paper parameterizes each spatial weight matrix in the widely used spatial Durbin model with a different instead of one common distance decay parameter, using negative exponential and inverse distance matrices. We propose a joint estimation approach of the decay and response parameters, and we investigate its performance in a Monte Carlo simulation experiment. We also present the results of an empirical application on military expenditures. Indirect effects in particular appear to be sensitive to different parameterizations.

The rise of populism concerns many political scientists and practitioners, yet the detection of its underlying language remains fragmentary. This paper aims to provide a reliable, valid, and scalable approach to measure populist rhetoric. For that purpose, we created an annotated dataset based on parliamentary speeches of the German Bundestag (2013–2021). Following the ideational definition of populism, we label moralizing references to “the virtuous people” or “the corrupt elite” as core dimensions of populist language. To identify, in addition, how the thin ideology of populism is “thickened,” we annotate how populist statements are attached to left-wing or right-wing host ideologies. We then train a transformer-based model (PopBERT) as a multilabel classifier to detect and quantify each dimension. A battery of validation checks reveals that the model has a strong predictive accuracy, provides high qualitative face validity, matches party rankings of expert surveys, and detects out-of-sample text snippets correctly. PopBERT enables dynamic analyses of how German-speaking politicians and parties use populist language as a strategic device. Furthermore, the annotator-level data may also be applied in cross-domain applications or to develop related classifiers.

We develop a new class of spatial voting models for binary preference data that can accommodate both monotonic and non-monotonic response functions, and are more flexible than alternative “unfolding” models previously introduced in the literature. We then use these models to estimate revealed preferences for legislators in the U.S. House of Representatives and justices on the U.S. Supreme Court. The results from these applications indicate that the new models provide superior complexity-adjusted performance to various alternatives and that the additional flexibility leads to preferences’ estimates that more closely match the perceived ideological positions of legislators and justices.

How does candidate order on the ballot affect voting behavior when voters rank candidates? I extend the analysis of ballot order effects to electoral systems with ordinal ballots, where voters rank candidates, including ranked-choice voting (RCV). First, I discuss two types of ballot order effects, including “position effects”—voters vote for specific candidates because of their ballot positions—and “pattern ranking”—voters rank candidates geometrically given their grid-style ballots. Next, I discuss experimental designs for identifying and estimating these effects based on ballot order randomization. Moreover, I illustrate the proposed methods by using survey and natural experiments based on mayoral and congressional RCV elections in 2022. I find that while voters seem less susceptible to specific ballot positions, ballot design can still impact voters’ ranking behavior via pattern ranking. This work has several implications for ballot design, survey research, and ranking data analysis. First, it shows that pattern ranking may affect electoral outcomes in RCV and other systems even when ballot order is fully randomized. Consequently, it may be worth considering an alternative solution to ballot order effects, which does not solely depend on randomization or rotation. Second, similar effects may impact any survey research using ranking questions. Future research must investigate the statistical consequences of pattern ranking for survey research. Finally, ranking data allow researchers to study diverse quantities of interest while targeting many different substantive questions. However, this flexibility also implies that analyzing ranking data can be prone to arbitrary analysis.

Used by politicians, journalists, and citizens, Twitter has been the most important social media platform to investigate political phenomena such as hate speech, polarization, or terrorism for over a decade. A high proportion of Twitter studies of emotionally charged or controversial content limit their ability to replicate findings due to incomplete Twitter-related replication data and the inability to recrawl their datasets entirely. This paper shows that these Twitter studies and their findings are considerably affected by nonrandom tweet mortality and data access restrictions imposed by the platform. While sensitive datasets suffer a notably higher removal rate than nonsensitive datasets, attempting to replicate key findings of Kim’s (2023, Political Science Research and Methods 11, 673–695) influential study on the content of violent tweets leads to significantly different results. The results highlight that access to complete replication data is particularly important in light of dynamically changing social media research conditions. Thus, the study raises concerns and potential solutions about the broader implications of nonrandom tweet mortality for future social media research on Twitter and similar platforms.

Survey experiments on probability samples are a popular method for investigating population-level causal questions due to their strong internal validity. However, lower survey response rates and an increased reliance on online convenience samples raise questions about the generalizability of survey experiments. We examine this concern using data from a collection of 50 survey experiments which represent a wide range of social science studies. Recruitment for these studies employed a unique double sampling strategy that first obtains a sample of “eager” respondents and then employs much more aggressive recruitment methods with the goal of adding “reluctant” respondents to the sample in a second sampling wave. This approach substantially increases the number of reluctant respondents who participate and also allows for straightforward categorization of eager and reluctant survey respondents within each sample. We find no evidence that treatment effects for eager and reluctant respondents differ substantially. Within demographic categories often used for weighting surveys, there is also little evidence of response heterogeneity between eager and reluctant respondents. Our results suggest that social science findings based on survey experiments, even in the modern era of very low response rates, provide reasonable estimates of population average treatment effects among a deeper pool of survey respondents in a wide range of settings.

Large language models (LLMs) offer new research possibilities for social scientists, but their potential as “synthetic data” is still largely unknown. In this paper, we investigate how accurately the popular LLM ChatGPT can recover public opinion, prompting the LLM to adopt different “personas” and then provide feeling thermometer scores for 11 sociopolitical groups. The average scores generated by ChatGPT correspond closely to the averages in our baseline survey, the 2016–2020 American National Election Study (ANES). Nevertheless, sampling by ChatGPT is not reliable for statistical inference: there is less variation in responses than in the real surveys, and regression coefficients often differ significantly from equivalent estimates obtained using ANES data. We also document how the distribution of synthetic responses varies with minor changes in prompt wording, and we show how the same prompt yields significantly different results over a 3-month period. Altogether, our findings raise serious concerns about the quality, reliability, and reproducibility of synthetic survey data generated by LLMs.

When researchers design an experiment, they usually hold potentially relevant features of the experiment constant. We call these details the “topic” of the experiment. For example, researchers studying the impact of party cues on attitudes must inform respondents of the parties’ positions on a particular policy. In doing so, researchers implement just one of many possible designs . Clifford, Leeper, and Rainey (2023. “Generalizing Survey Experiments Using Topic Sampling: An Application to Party Cues.” Forthcoming in Political Behavior. https://doi.org/10.1007/s11109-023-09870-1) argue that researchers should implement many of the possible designs in parallel—what they call “topic sampling”—to generalize to a larger population of topics. We describe two estimators for topic-sampling designs: First, we describe a nonparametric estimator of the typical effect that is unbiased under the assumptions of the design; and second, we describe a hierarchical model that researchers can use to describe the heterogeneity. We suggest describing the heterogeneity across topics in three ways: (1) the standard deviation in treatment effects across topics, (2) the treatment effects for particular topics, and (3) how the treatment effects for particular topics vary with topic-level predictors. We evaluate the performance of the hierarchical model using the Strengthening Democracy Challenge megastudy and show that the hierarchical model works well.

Theoretical expectations regarding communication patterns between legislators and outside agents, such as lobbyists, agency officials, or policy experts, often depend on the relationship between legislators’ and agents’ preferences. However, legislators and nonelected outside agents evaluate the merits of policies using distinct criteria and considerations. We develop a measurement method that flexibly estimates the policy preferences for a class of outside agents—witnesses in committee hearings—separate from that of legislators’ and compute their preference distance across the two dimensions. In our application to Medicare hearings, we find that legislators in the U.S. Congress heavily condition their questioning of witnesses on preference distance, showing that legislators tend to seek policy information from like-minded experts in committee hearings. We do not find this result using a conventional measurement placing both actors on one dimension. The contrast in results lends support for the construct validity of our proposed preference measures.

Instrumental variable (IV) strategies are widely used in political science to establish causal relationships, but the identifying assumptions required by an IV design are demanding, and assessing their validity remains challenging. In this paper, we replicate 67 articles published in three top political science journals from 2010 to 2022 and identify several concerning patterns. First, researchers often overestimate the strength of their instruments due to non-i.i.d. error structures such as clustering. Second, IV estimates are often highly uncertain, and the commonly used t-test for two-stage-least-squares (2SLS) estimates frequently underestimate the uncertainties. Third, in most replicated studies, 2SLS estimates are significantly larger in magnitude than ordinary-least-squares estimates, and their absolute ratio is inversely related to the strength of the instrument in observational studies—a pattern not observed in experimental ones—suggesting potential violations of unconfoundedness or the exclusion restriction in the former. We provide a checklist and software to help researchers avoid these pitfalls and improve their practice.

Copulas are helpful in studying joint distributions of two variables, in particular, whenconfounders are unobserved. However, most conventional copulas cannot model joint distributions where one variable does not increase or decrease in the other in a monotonic manner. For instance, suppose that two variables are linearly positively correlated for one type of unit and negatively for another type of unit. If the type is unobserved, we can observe only a mixture of both types. Seemingly, one variable tends to take either a high or low value (or a middle value) when the other variable is small (large), or vice versa. To address this issue, I consider an overlooked copula with trigonometric functions (Chesneau [2021, Applied Mathematics, 1(1), pp. 3–17]) thatI name the “normal mode copula.” I apply the copula to a dataset about government formation and duration to demonstrate that the normal mode copula has better performance than other conventional copulas.

We present a method for estimating the ideology of political YouTube videos. The subfield of estimating ideology as a latent variable has often focused on traditional actors such as legislators, while more recent work has used social media data to estimate the ideology of ordinary users, political elites, and media sources. We build on this work to estimate the ideology of a political YouTube video. First, we start with a matrix of political Reddit posts linking to YouTube videos and apply correspondence analysis to place those videos in an ideological space. Second, we train a language model with those estimated ideologies as training labels, enabling us to estimate the ideologies of videos not posted on Reddit. These predicted ideologies are then validated against human labels. We demonstrate the utility of this method by applying it to the watch histories of survey respondents to evaluate the prevalence of echo chambers on YouTube in addition to the association between video ideology and viewer engagement. Our approach gives video-level scores based only on supplied text metadata, is scalable, and can be easily adjusted to account for changes in the ideological landscape.

While experiments on elections represent a popular tool in social science, the possibility that experimental interventions could affect who wins office remains a central ethical concern. I formally characterize electoral experimental designs to derive an upper bound on aggregate electoral impact under different assumptions about interference. I then introduce a decision rule based on comparison of this bound to predicted election outcomes to determine whether an experiment should be implemented. Researchers can mitigate the possibility of affecting aggregate outcomes by reducing the saturation of treatment or focusing experiments in districts and electoral systems where treated voters are less likely to be pivotal. These conditions identify novel trade-offs between adhering to ethical commitments and the statistical power and external validity of electoral experiments. More broadly, this paper shows that the formalization of an ethical objective facilitates a closer mapping between ethical considerations and experimental design than is currently practiced.

Valence is a crucial concept in studying spatial voting and party competition. The widely adopted approach is to rely on intercepts of vote choice models and to infer, based on their size and direction, how valence affects party strategies in empirical settings. The approach suffers from fundamental statistical flaws. This contribution provides the statistical fundamentals to advance the empirical modeling of valence. It proposes an appropriate modeling approach to interpret intercepts as valences and alternate specifications to parameterize the effects of valence.

Redistricting reformers have proposed many solutions to the problem of partisan gerrymandering, but they all require either bipartisan consensus or the agreement of both parties on the legitimacy of a neutral third party to resolve disputes. In this paper, we propose a new method for drawing district maps, the Define–Combine Procedure, that substantially reduces partisan gerrymandering without requiring a neutral third party or bipartisan agreement. One party defines a map of $2N$ equal-population contiguous districts. Then the second party combines pairs of contiguous districts to create the final map of N districts. Using real-world geographic and electoral data, we employ simulations and map-drawing algorithms to show that this procedure dramatically reduces the advantage conferred to the party controlling the redistricting process and leads to less-biased maps without requiring cooperation or non-partisan actors.

When using dyadic data (i.e., data indexed by pairs of units), researchers typically assume a linear model, estimate it using Ordinary Least Squares, and conduct inference using “dyadic-robust” variance estimators. The latter assumes that dyads are uncorrelated if they do not share a common unit (e.g., if the same individual is not present in both pairs of data). We show that this assumption does not hold in many empirical applications because indirect links may exist due to network connections, generating correlated outcomes. Hence, “dyadic-robust” estimators can be biased in such situations. We develop a consistent variance estimator for such contexts by leveraging results in network statistics. Our estimator has good finite-sample properties in simulations, while allowing for decay in spillover effects. We illustrate our message with an application to politicians’ voting behavior when they are seating neighbors in the European Parliament.

Political scientists commonly use Grambsch and Therneau’s (1994, Biometrika 81, 515–526) ubiquitous Schoenfeld-based test to diagnose proportional hazard violations in Cox duration models. However, some statistical packages have changed how they implement the test’s calculation. The traditional implementation makes a simplifying assumption about the test’s variance–covariance matrix, while the newer implementation does not. Recent work suggests the test’s performance differs, depending on its implementation. I use Monte Carlo simulations to more thoroughly investigate whether the test’s implementation affects its performance. Surprisingly, I find the newer implementation performs very poorly with correlated covariates, with a false positive rate far above 5%. By contrast, the traditional implementation has no such issues in the same situations. This shocking finding raises new, complex questions for researchers moving forward. It appears to suggest, for now, researchers should favor the traditional implementation in situations where its simplifying assumption is likely met, but researchers must also be mindful that this implementation’s false positive rate can be high in misspecified models.