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Survey experiments are ubiquitous in social science. A frequent critique is that positive results in these studies stem from experimenter demand effects (EDEs)—bias that occurs when participants infer the purpose of an experiment and respond so as to help confirm a researcher’s hypothesis. We argue that online survey experiments have several features that make them robust to EDEs, and test for their presence in studies that involve over 12,000 participants and replicate five experimental designs touching on all empirical political science subfields. We randomly assign participants information about experimenter intent and show that providing this information does not alter the treatment effects in these experiments. Even financial incentives to respond in line with researcher expectations fail to consistently induce demand effects. Research participants exhibit a limited ability to adjust their behavior to align with researcher expectations, a finding with important implications for the design and interpretation of survey experiments.

Multiplicative interaction models are widely used in social science to examine whether the relationship between an outcome and an independent variable changes with a moderating variable. Current empirical practice tends to overlook two important problems. First, these models assume a linear interaction effect that changes at a constant rate with the moderator. Second, estimates of the conditional effects of the independent variable can be misleading if there is a lack of common support of the moderator. Replicating 46 interaction effects from 22 recent publications in five top political science journals, we find that these core assumptions often fail in practice, suggesting that a large portion of findings across all political science subfields based on interaction models are fragile and model dependent. We propose a checklist of simple diagnostics to assess the validity of these assumptions and offer flexible estimation strategies that allow for nonlinear interaction effects and safeguard against excessive extrapolation. These statistical routines are available in both R and STATA.

The increasingly visible presence of heavily armed police units in American communities has stoked widespread concern over the militarization of local law enforcement. Advocates claim militarized policing protects officers and deters violent crime, while critics allege these tactics are targeted at racial minorities and erode trust in law enforcement. Using a rare geocoded census of SWAT team deployments from Maryland, I show that militarized police units are more often deployed in communities with large shares of African American residents, even after controlling for local crime rates. Further, using nationwide panel data on local police militarization, I demonstrate that militarized policing fails to enhance officer safety or reduce local crime. Finally, using survey experiments—one of which includes a large oversample of African American respondents—I show that seeing militarized police in news reports may diminish police reputation in the mass public. In the case of militarized policing, the results suggest that the often-cited trade-off between public safety and civil liberties is a false choice.

While partisan cues tend to dominate political choice, prior work shows that competing information can rival the effects of partisanship if it relates to salient political issues. But what are the limits of partisan loyalty? How much electoral leeway do co-partisan candidates have to deviate from the party line on important issues? We answer this question using conjoint survey experiments that characterize the role of partisanship relative to issues. We demonstrate a pattern of conditional party loyalty. Partisanship dominates electoral choice when elections center on low-salience issues. But while partisan loyalty is strong, it is finite: the average voter is more likely than not to vote for the co-partisan candidate until that candidate takes dissonant stances on four or more salient issues. These findings illuminate when and why partisanship fails to dominate political choice. They also suggest that, on many issues, public opinion minimally constrains politicians.

Politicians and law enforcement officials have advocated the militarization of local law enforcement on the grounds that it promotes public and officer safety, and some early research seemingly supported those claims. Two new studies reveal limitations in the data used in this prior work. When these issues are addressed, evidence for the benefits of militarization largely vanishes.

Social divisions between American partisans are growing, with Republicans and Democrats exhibiting homophily in a range of seemingly nonpolitical domains. It has been widely claimed that this partisan social divide extends to Americans’ decisions about where to live. In two original survey experiments, we confirm that Democrats are, in fact, more likely than Republicans to prefer living in more Democratic, dense, and racially diverse places. However, improving on previous studies, we test respondents’ stated preferences against their actual moving behavior. While partisans differ in their residential preferences, on average they are not migrating to more politically distinct communities. Using zip-code-level census and partisanship data on the places where respondents live, we provide one explanation for this contradiction: by prioritizing common concerns when deciding where to live, Americans forgo the opportunity to move to more politically compatible communities.

Researchers often lack the necessary data to credibly estimate racial discrimination in policing. In particular, police administrative records lack information on civilians police observe but do not investigate. In this article, we show that if police racially discriminate when choosing whom to investigate, analyses using administrative records to estimate racial discrimination in police behavior are statistically biased, and many quantities of interest are unidentified—even among investigated individuals—absent strong and untestable assumptions. Using principal stratification in a causal mediation framework, we derive the exact form of the statistical bias that results from traditional estimation. We develop a bias-correction procedure and nonparametric sharp bounds for race effects, replicate published findings, and show the traditional estimator can severely underestimate levels of racially biased policing or mask discrimination entirely. We conclude by outlining a general and feasible design for future studies that is robust to this inferential snare.

High-profile incidents of police misconduct have led to widespread calls for law enforcement reform. But prior studies cast doubt on whether police commanders can control officers, and offer few policy remedies because of their focus on potentially immutable officer traits like personality. I advance an alternative, institutional perspective and demonstrate that police officers—sometimes characterized as autonomous—are highly responsive to managerial directives. Using millions of records of police-citizen interactions alongside officer interviews, I evaluate the impact of a change to the protocol for stopping criminal suspects on police performance. An interrupted time series analysis shows the directive produced an immediate increase in the rate of stops producing evidence of the suspected crime. Interviewed officers said the order signaled increased managerial scrutiny, leading them to adopt more conservative tactics. Procedural changes can quickly and dramatically alter officer behavior, suggesting a reform strategy sometimes forestalled by psychological and personality-driven accounts of police reform.

Widespread concern that voter identification laws suppress turnout among racial and ethnic minorities has made empirical evaluations of these laws crucial. But problems with administrative records and survey data impede such evaluations. We replicate and extend Hajnal, Lajevardi, and Nielson’s 2017 article, which concludes that voter ID laws decrease turnout among minorities, using validated turnout data from five national surveys conducted between 2006 and 2014. We show that the results of their article are a product of data inaccuracies, the presented evidence does not support the stated conclusion, and alternative model specifications produce highly variable results. When errors are corrected, one can recover positive, negative, or null estimates of the effect of voter ID laws on turnout, precluding firm conclusions. We highlight more general problems with available data for research on election administration, and we identify more appropriate data sources for research on state voting laws’ effects.