Under what conditions are people more likely to support judicial invalidation of legislative acts? We theorize that constitutional recency confers greater democratic legitimacy on constitutional provisions, reducing concerns that judges may use dated language to impose their own will on a living majority. Exploiting differences among US state constitutions, we show in a pre-registered vignette experiment and conjoint analysis that Americans are more supportive of judicial review and original intent interpretation when presented with a younger constitutional provision or constitution. These results imply that Americans might alter their approach to the US Constitution if it were changed as easily and as often as a typical state constitution.

Threat perception provokes a range of behaviour, from cooperation to conflict. Correctly interpreting others’ behaviour, and responding optimally, is thought to be aided by ‘stepping into their shoes’ (i.e. mentalising) to understand the threats they have perceived. But IR scholarship on the effects of attempting this exercise has yielded mixed findings. One missing component in this research is a clear understanding of the link between effort and accuracy. I use a US-based survey experiment (study N = 839; pilot N = 297) and a novel analytic approach to study mentalising accuracy in the domain of threat perception. I find that accurately estimating why someone feels threatened by either climate change or illegal immigration is conditional on sharing a belief in the issue’s overall dangerousness. Similar beliefs about dangerousness are not proxies for shared political identities, and accuracy for those with dissimilar beliefs does not exceed chance. Focusing first on the emotional states of those who felt threatened did not significantly improve accuracy. These findings suggest that: (1) effort does not guarantee accuracy in estimating the threats others see; (2) emotion understanding may not be a solution to threat mis-estimation; and (3) misperception can arise from basic task difficulty, even without information constraints or deception.

This article investigates the effect of priming the existence of corrupt connections to the bureaucracy and of trusted references on the demand for intermediary services. We performed an experimental survey with undergraduate students in Caracas, Venezuela. Participants are presented with a hypothetical situation in which they need to obtain the apostille of their professional degrees in order to migrate and are considering whether to hire an intermediary (“gestor”) or not. The survey randomly reveals the existence of an illicit connection between the gestor and the bureaucracy and whether a trusted individual referred the intermediary. Our findings are not consistent with the “market maker” hypothesis that revealing the existence of illicit connections increases demand. Consistent with the view that trust is a key element in inherently opaque transactions, we find that the demand for intermediaries is price inelastic when gestores are referred by trusted individuals.

The human sciences should seek generalisations wherever possible. For ethical and scientific reasons, it is desirable to sample more broadly than ‘Western, educated, industrialised, rich, and democratic’ (WEIRD) societies. However, restricting the target population is sometimes necessary; for example, young children should not be recruited for studies on elderly care. Under which conditions is unrestricted sampling desirable or undesirable? Here, we use causal diagrams to clarify the structural features of measurement error bias and target population restriction bias (or ‘selection restriction’), focusing on threats to valid causal inference that arise in comparative cultural research. We define any study exhibiting such biases, or confounding biases, as weird (wrongly estimated inferences owing to inappropriate restriction and distortion). We explain why statistical tests such as configural, metric and scalar invariance cannot address the structural biases of weird studies. Overall, we examine how the workflows for causal inference provide the necessary preflight checklists for ambitious, effective and safe comparative cultural research.

Confounding bias arises when a treatment and outcome share a common cause. In randomised controlled experiments (trials), treatment assignment is random, ostensibly eliminating confounding bias. Here, we use causal directed acyclic graphs to unveil eight structural sources of bias that nevertheless persist in these trials. This analysis highlights the crucial role of causal inference methods in the design and analysis of experiments, ensuring the validity of conclusions drawn from experimental data.

What are the consequences of including a “don't know” (DK) response option to attitudinal survey questions? Existing research, based on traditional survey modes, argues that it reduces the effective sample size without improving the quality of responses. We contend that it can have important effects not only on estimates of aggregate public opinion, but also on estimates of opinion differences between subgroups of the population who have different levels of political information. Through a pre-registered online survey experiment conducted in the United States, we find that the DK response option has consequences for opinion estimates in the present day, where most organizations rely on online panels, but mainly for respondents with low levels of political information and on low salience issues. These findings imply that the exclusion of a DK option can matter, with implications for assessments of preference differences and our understanding of their impacts on politics and policy.

Why might female leaders of democratic countries commit more money, equipment, soldiers, and other resources to interstate conflicts than male leaders? We argue that gender bias in the process of democratic election helps explain this behavior. Since running for office is generally more costly for women than for men, only women who place a higher value on winning competitions will choose to run. After election, they also devote more resources to pursuing victory in conflict situations. To provide microfoundational evidence for this claim, we analyze data from an online laboratory game featuring real-time group play in which participants chose to run for election, conducted a simple campaign, and represented their group in a contest game if elected. Women with a higher nonmonetary value to winning were more likely to self-select into candidacy, and when victorious, they spent more resources on intergroup contests than male elected leaders. The data suggest that electoral selection plays an important role in observed differences between male and female leaders in the real world.

Public decision-makers incorporate algorithm decision aids, often developed by private businesses, into the policy process, in part, as a method for justifying difficult decisions. Ethicists have worried that over-trust in algorithm advice and concerns about punishment if departing from an algorithm’s recommendation will result in over-reliance and harm democratic accountability. We test these concerns in a set of two pre-registered survey experiments in the judicial context conducted on three representative U.S. samples. The results show no support for the hypothesized blame dynamics, regardless of whether the judge agrees or disagrees with the algorithm. Algorithms, moreover, do not have a significant impact relative to other sources of advice. Respondents who are generally more trusting of elites assign greater blame to the decision-maker when they disagree with the algorithm, and they assign more blame when they think the decision-maker is abdicating their responsibility by agreeing with an algorithm.

Survey experiments often yield intention-to-treat effects that are either statistically and/or practically “non-significant.” There has been a commendable shift toward publishing such results, either to avoid the “file drawer problem” and/or to encourage studies that conclude in favor of the null hypothesis. But how can researchers more confidently adjudicate between true, versus erroneous, nonsignificant results? Guidance on this critically important question has yet to be synthesized into a single, comprehensive text. The present essay therefore highlights seven “alternative explanations” that can lead to (erroneous) nonsignificant findings. It details how researchers can more rigorously anticipate and investigate these alternative explanations in the design and analysis stages of their studies, and also offers recommendations for subsequent studies. Researchers are thus provided with a set of strategies for better designing their experiments, and more thoroughly investigating their survey-experimental data, before concluding that a given result is indicative of “no significant effect.”

Across the globe, women are underrepresented in elected politics. The study's case countries of Australia (ranked 33), Canada (61) and the United States (66) rank poorly for women's political representation. Drawing on role strain and gender-mainstreaming theories and applying large-scale survey experiments, we examine public opinion on non-quota mechanisms to bolster women's political participation. The experimental design manipulates the politician's gender and level of government (federal/local) before asking about non-quota supports to help the politician. We find public support for policies aimed at lessening work–family role strain is higher for a woman politician; these include a pay raise, childcare subsidies and housework allowances. This support is amplified among women who are presented with a woman politician in our experiment, providing evidence of a gender-affinity effect. The study's findings contribute to scholarship on gender equality and point to gender-mainstreaming mechanisms to help mitigate the gender gap in politics.