Spatial voting models are widely used in political science to analyze legislators’ preferences and voting behavior. Traditional models assume that legislators’ ideal points are static across different types of votes. This article extends the Bayesian spatial voting model to incorporate hierarchical Bayesian methods, allowing for the identification of covariates that explain differences in legislators’ ideal points across voting domains. We apply this model to procedural and final passage votes in the U.S. House of Representatives from the 93rd through 113th Congresses. Our findings indicate that legislators in the minority party and those representing moderate constituencies are more likely to exhibit different ideal points between procedural and final passage votes. This research advances the methodology of ideal point estimation by simultaneously scaling ideal points and explaining variation in these points, providing a more nuanced understanding of legislative voting behavior.

One of the most robust empirical findings in political science is that in multiparty democracies cabinet ministries are distributed in rough proportion to parties’ legislative seat shares, a pattern known as Gamson’s Law. Yet existing research often overlooks the fact that portfolio and seat shares are compositional—mutually dependent parts of a whole. Standard methods treat them as unconstrained, risking bias, misleading uncertainty estimates, and flawed inference. Unfortunately, the most common strategy for handling compositions—the additive log-ratio (ALR) transformation combined with seemingly unrelated regression (SUR)—fails when the number and identity of compositional elements (like parties) vary across cases. We propose the isometric log-ratio (ILR) transformation—new to political science—as an alternative that both respects compositional geometry and adapts to differing compositional structures. Monte Carlo simulations show that ILR sharply outperforms standard approaches, reducing bias, improving coverage probability, and increasing statistical power. While we apply it to portfolio allocation, ILR provides a general solution for modeling compositional outcomes with other potential uses, including in electoral competition, where ALR+SUR has required strong assumptions or ad hoc adjustments. Using this improved methodology, we find that seat–portfolio proportionality is weaker overall than conventionally reported and varies substantially across governments.

Sentiment analysis and stance detection are key tasks in text analysis, with applications ranging from understanding political opinions to tracking policy positions. Recent advances in large language models (LLMs) offer significant potential to enhance sentiment analysis techniques and to evolve them into the more nuanced task of detecting stances expressed toward specific subjects. In this study, we evaluate lexicon-based models, supervised models, and LLMs for stance detection using two corpuses of social media data—a large corpus of tweets posted by members of the U.S. Congress on Twitter and a smaller sample of tweets from general users—which both focus on opinions concerning presidential candidates during the 2020 election. We consider several fine-tuning strategies to improve performance—including cross-target tuning using an assumption of congressmembers’ stance based on party affiliation—and strategies for fine-tuning LLMs, including few shot and chain-of-thought prompting. Our findings demonstrate that: 1) LLMs can distinguish stance on a specific target even when multiple subjects are mentioned, 2) tuning leads to notable improvements over pretrained models, 3) cross-target tuning can provide a viable alternative to in-target tuning in some settings, and 4) complex prompting strategies lead to improvements over pretrained models but underperform tuning approaches.

Forecasting of armed conflicts is a critical area of research with the potential to save lives and mitigate suffering. While existing forecasting models offer valuable point predictions, they often lack individual-level uncertainty estimates, limiting their usefulness for decision-making. Several approaches exist to estimate uncertainty, such as parametric and Bayesian prediction intervals, bootstrapping, quantile regression, but these methods often rely on restrictive assumptions, struggle to provide well-calibrated intervals across the full range of outcomes, or are computationally intensive. Conformal prediction offers a model-agnostic alternative that guarantees a user-specified level of coverage but typically provides only marginal coverage, potentially resulting in non-uniform coverage across different regions of the outcome space. In this article, we introduce a novel extension called bin-conditional conformal prediction (BCCP), which enhances standard conformal prediction (SCP) by ensuring consistent coverage rates across user-defined subsets (bins) of the outcome variable. We apply BCCP to simulated data as well as the forecasting of fatalities from armed conflicts, and demonstrate that it provides well-calibrated uncertainty estimates across various ranges of the outcome. Compared to SCP, BCCP offers improved local coverage, though this comes at the cost of slightly wider prediction intervals.

This article develops the first dynamic method for systematically estimating the ideologies and other traits of nearly the entire federal judiciary. The Jurist-Derived Judicial Ideology Scores (JuDJIS) method derives from computational text analysis of over 20,000 written evaluations by a representative sample of tens of thousands of jurists as part of an ongoing, systematic survey initiative begun in 1985. The resulting data constitute not only the first such comprehensive federal-court measure that is dynamic, but also the only such measure that is based on judging, and the only such measure that is potentially multi-dimensional. The results of empirical validity tests reflect these advantages. Validation on a set of several-thousand appellate decisions indicates that the ideology estimates predict outcomes significantly more accurately than the existing appellate measures, such as the Judicial Common Space. In addition to informing theoretical debates about the nature of judicial ideology and decision-making, the JuDJIS initiative might lead courts scholars to revisit some of the lower-court research findings of the last two decades, which are generally based on static, non-judicial models. Perhaps most importantly, this method could foster breakthroughs in courts research that, until now, were impossible due to data limitations.

Despite the recent methodological advancements in causal panel data analysis, concerns remain about unobserved unit-specific time-varying confounders that cannot be addressed by unit or time fixed effects or their interactions. We develop a Bayesian sensitivity analysis (BSA) method to address the concern. Our proposed method is built upon a general framework combining Rubin’s Bayesian framework for model-based causal inference (Rubin [1978], The Annals of Statistics 6(1), 34–58) with parametric BSA (McCandless, Gustafson, and Levy [2007], Statistics in Medicine 26(11), 2331–2347). We assess the sensitivity of the causal effect estimate from a linear factor model to the possible existence of unobserved unit-specific time-varying confounding, using the coefficients of the treatment variable and observed confounders in the model for the unobserved confounding as sensitivity parameters. We utilize priors on these coefficients to constrain the hypothetical severity of unobserved confounding. Our proposed approach allows researchers to benchmark the assumed strength of confounding on observed confounders more systematically than conventional frequentist sensitivity analysis techniques. Moreover, to cope with convergence issues typically encountered in nonidentified Bayesian models, we develop an efficient Markov chain Monte Carlo algorithm exploiting transparent parameterization (Gustafson [2005], Statistical Science 20(2), 111–140). We illustrate our proposed method in a Monte Carlo simulation study as well as an empirical example on the effect of war on inheritance tax rates.

Research in political science has begun to explore how to use large language and object detection models to analyze text and visual data. However, few studies have explored how to use these tools for data extraction. Instead, researchers interested in extracting text from poorly formatted sources typically rely on optical character recognition and regular expressions or extract each item by hand. This letter describes a workflow process for structured text extraction using free models and software. I discuss the type of data best suited to this method, its usefulness within political science, and the steps required to convert the text into a usable dataset. Finally, I demonstrate the method by extracting agenda items from city council meeting minutes. I find the method can accurately extract subsections of text from a document and requires only a few hand labeled documents to adequately train.

This article constructs an approach to analyzing longitudinal panel data which combines topological data analysis (TDA) and generative AI applied to graph neural networks (GNNs). TDA is deployed to identify and analyze unobserved topological heterogeneities of a dataset. TDA-extracted information is quantified into a set of measures, called functional principal components. These measures are used to analyze the data in four ways. First, the measures are construed as moderators of the data and their statistical effects are estimated through a Bayesian framework. Second, the measures are used as factors to classify the data into topological classes using generative AI applied to GNNs constructed by transforming the data into graphs. The classification uncovers patterns in the data which are otherwise not accessible through statistical approaches. Third, the measures are used as factors that condition the extraction of latent variables of the data through a deployment of a generative AI model. Fourth, the measures are used as labels for classifying the graphs into classes used to offer a GNN-based effective dimensionality reduction of the original data. The article uses a portion of the militarized international disputes (MIDs) dataset (from 1946 to 2010) as a running example to briefly illustrate its ideas and steps.

When foundation models analyze political content, do they use demographic characteristics as shortcuts for ideological attribution? We conducted detailed experiments with GPT-4o-mini and validated key findings across GPT-4o and LLaVA, using identical, ideologically neutral campaign advertisements with systematically varied candidate demographics. All models consistently attributed more liberal ideologies to women than men. These effects exceeded real-world gender differences from a nationally representative survey. However, racial associations differed by model: strong in GPT-4o-mini (where Black candidates received substantially more liberal attributions), attenuated in GPT-4o, and insignificant in LLaVA. These demographic effects persisted across temperature settings, prompt variations, and even explicit debiasing instructions in GPT-4o-mini. Our findings reveal that visual demographic features can shape AI outputs in ways that vary across models, with implications for applications such as content classification.

People regularly get information about the political world in visual form, such as graphs of past economic growth, nonverbal cues from politicians, or projections of future climate change. Visual characteristics affect people’s preferences, but it is difficult to measure the extent of this effect precisely and concisely in surveys. We present a new adaptive design that measures the impact of visual characteristics on people’s preferences: The plot staircase. We apply it to graphs of time series data, identifying the effect of the slope of a sequence on evaluations of the sequence. The plot staircase replicates the existing finding that people have a strong preference for increasing trends. Using fewer survey questions than past approaches, it measures at the individual level how much overall welfare a survey respondent is willing to sacrifice for an increasing trend. We demonstrate the flexibility of the plot staircase across domains (economic growth, jobs creation, and the COVID-19 vaccine rollout) and across sequence characteristics. Survey measurement is more difficult for concepts that cannot be represented textually or numerically; our method enables researchers to measure preferences for graphical properties not reducible to the individual pieces of information.

Researchers’ racial and gender identities influence their outcomes in academia and the field of political science. This letter interrogates how researcher identity affects the research process: specifically elite interview recruitment. Within an ongoing research project we embed a pre-registered audit experiment randomizing the gender of the researcher conducting outreach to estimate whether there are differences in interviews scheduled holding all other confounders constant. We find that when outreach is conducted by a woman, elites are more likely to schedule an interview. This letter contributes to our understanding of bias and inequality during the research process. In addition, our study offers a new approach to audit experiments that limits deception and wasted time for elites.

Online panels have become an important resource for research in political science, but the compensation offered to panelists incentivizes them to become “survey professionals,” raising concerns about data quality. We provide evidence on survey professionalism exploring three US samples of subjects who donated their browsing data, recruited via Lucid, YouGov, and Facebook (total $n = 3,886$). Survey professionalism is common, but varies across samples: by our most conservative estimate, we find 1.7% of respondents on Facebook, 7.$\color {black}6$% on YouGov, and 34$\color {black}.7$% on Lucid to be professionals (under the assumption that professionals are as likely as non-professionals to donate data after conditioning on observable demographics available from all online survey takers). However, evidence that professionals lower data quality is limited: they do not systematically differ demographically or politically from non-professionals and do not exhibit more response instability. They are, however, somewhat more likely to speed, straightline, and attempt to take questionnaires repeatedly. To address potential selection issues in donating of browsing data, we present sensitivity analyses with lower bounds for survey professionalism. While concerns about professionalism are warranted, we conclude that survey professionals do not, by and large, distort inferences of research based on online panels.

Many networks in political and social research are bipartite, connecting two distinct node types. A common example is cosponsorship networks, where legislators are linked through the bills they support. However, most bipartite network analyses in political science rely on statistical models fitted to a “projected” unipartite network. This approach can lead to aggregation bias and an artificially high degree of clustering, invalidating the study of group roles in network formation. To address these issues, we develop a statistical model of bipartite networks theorized to arise from group interactions, extending the mixed-membership stochastic blockmodel. Our model identifies groups within each node type that exhibit common edge formation patterns and incorporates node and dyad-level covariates as predictors of group membership and observed dyadic relations. We derive an efficient computational algorithm to fit the model and apply it to cosponsorship data from the United States Senate. We show that senators who were perfectly split along party lines remained productive and pass major legislation by forming non-partisan, power-brokering coalitions that found common ground through low-stakes bills. We also find evidence of reciprocity norms and policy expertise impacting cosponsorships. An open-source software package is available for researchers to replicate these insights.

In experimental social science, precise treatment effect estimation is of utmost importance, and researchers can make design choices to increase precision. Specifically, block-randomized and pre-post designs are promoted as effective means to increase precision. However, implementing these designs requires pre-treatment covariates, and collecting this information may decrease sample sizes, which in and of itself harms precision. Therefore, despite the literature’s recommendation to use block-randomized and pre-post designs, it remains unclear when to expect these designs to increase precision in applied settings. We use real-world data to demonstrate a counterintuitive result: precision gains from block-randomized or pre-post designs can withstand significant sample loss that may arise during implementation. Our findings underscore the importance of incorporating researchers’ practical concerns into existing experimental design advice.

In this note, we offer a cautionary tale on the dangers of drawing inferences from low-quality online survey datasets. We reanalyze and replicate a survey experiment studying the effect of acquiescence bias on estimates of conspiratorial beliefs and political misinformation. Correcting a minor data coding error yields a puzzling result: respondents with a postgraduate education appear to be the most prone to acquiescence bias. We conduct two preregistered replication studies to better understand this finding. In our first replication, conducted using the same survey platform as the original study, we find a nearly identical set of results. But in our second replication, conducted with a larger and higher-quality survey panel, this apparent effect disappears. We conclude that the observed relationship was an artifact of inattentive and fraudulent responses in the original survey panel, and that attention checks alone do not fully resolve the problem. This demonstrates how “survey trolls” and inattentive respondents on low-quality survey platforms can generate spurious and theoretically confusing results.

Researchers across disciplines increasingly use Generative Artificial Intelligence (GenAI) to label text and images or as pseudo-respondents in surveys. But of which populations are GenAI models most representative? We use an image classification task—assessing crowd-sourced street view images of urban neighborhoods in an American city—to compare assessments generated by GenAI models with those from a nationally representative survey and a locally representative survey of city residents. While GenAI responses, on average, correlate strongly with the perceptions of a nationally representative survey sample, the models poorly approximate the perceptions of those actually living in the city. Examining perceptions of neighborhood safety, wealth, and disorder reveals a clear bias in GenAI toward national averages over local perspectives. GenAI is also better at recovering relative distributions of ratings, rather than mimicking absolute human assessments. Our results provide evidence that GenAI performs particularly poorly in reflecting the opinions of hard-to-reach populations. Tailoring prompts to encourage alignment with subgroup perceptions generally does not improve accuracy and can lead to greater divergence from actual subgroup views. These results underscore the limitations of using GenAI to study or inform decisions in local communities but also highlight its potential for approximating “average” responses to certain types of questions. Finally, our study emphasizes the importance of carefully considering the identity and representativeness of human raters or labelers—a principle that applies broadly, whether GenAI tools are used or not.