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.

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.

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.

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.

Researchers are often interested in whether discrimination on the basis of racial cues persists above and beyond discrimination on the basis of nonracial attributes that decision makers—e.g., employers and legislators—infer from such cues. We show that existing audit experiments may be unable to parse these mechanisms because of an asymmetry in when decision makers are exposed to cues of race and additional signals intended to rule out discrimination due to other attributes. For example, email audit experiments typically cue race via the name in the email address, at which point legislators can choose to open the email, but cue other attributes in the body of the email, which decision makers can be exposed to only after opening the email. We derive the bias resulting from this asymmetry and then propose two distinct solutions for email audit experiments. The first exposes decision makers to all cues before the decision to open. The second crafts the email to ensure no discrimination in opening and then exposes decision makers to all cues in the body of the email after opening. This second solution works without measures of opening, but can be improved when researchers do measure opening, even if with error.

We show that, in some ranked ballot elections, it may be possible to violate the secret vote. There are so many ways to rank even a handful of candidates that many possible rankings might not be cast by any voter. So, a vote buyer could pay someone to rank the candidates a certain way and then use the announced election results to verify that the voter followed through. We examine the feasibility of this attack both theoretically and empirically, focusing on instant runoff voting (IRV). Although many IRV elections have few enough candidates that this scheme is not feasible, we use data from San Francisco and a proposed election rule change in Oakland to show that some important IRV elections can have large numbers of unused rankings. There is no evidence that this vote-buying scheme has ever been used. However, its existence has implications for the administration and security of IRV elections. This scheme is more feasible when more candidates can be ranked in the election and when the election results report all the ways that candidates were ranked.

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.

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.