Researchers are increasingly reliant on online, opt-in surveys. But prior benchmarking exercises employ national samples, making it unclear whether such surveys can effectively represent Black respondents and other minorities nationwide. This paper presents the results of uncompensated online and in-person surveys administered chiefly in one racially diverse American city—Philadelphia—during its 2023 mayoral primary. The participation rate for online surveys promoted via Facebook and Instagram was .4%, with White residents and those with college degrees more likely to respond. Such biases help explain why neither our surveys nor public polls correctly identified the Democratic primary’s winner, an establishment-backed Black Democrat. Even weighted, geographically stratified online surveys typically underestimate the winner’s support, although an in-person exit poll does not. We identify some similar patterns in Chicago. These results indicate important gaps in the populations represented in contemporary opt-in surveys and suggest that alternative survey modes help reduce them.

Do negative economic shocks heighten public opposition to immigration, and through what mechanisms? Extant research suggests that economic circumstances and levels of labour market competition have little bearing on citizens' immigration attitudes. Yet personal economic shocks have the potential to trigger the threatened, anti-immigration responses – possibly through channels other than labour market competition – that prior cross-sectional research has been unable to detect. To examine these propositions, we used a unique panel study which tracked a large, population-based sample of Americans between 2007 and 2020. We found that adverse economic shocks, especially job losses, spurred opposition to unauthorized immigration. However, such effects are not concentrated among those most likely to face labour market competition from unauthorized immigrants. Instead, they are concentrated among white male Americans. This evidence suggests that the respondents' anti-immigration turn does not stem from economic concerns alone. Instead, personal experiences with the economy are refracted through salient socio-political lenses.

The violent conclusion of Trump's 2017–21 presidency has produced sobering reassessments of American democracy. Elected officials' actions necessarily implicate public opinion, but to what extent did Trump's presidency and its anti-democratic efforts reflect shifts in public opinion in prior years? Were there attitudinal changes that served as early-warning signs? We answer those questions via a fifteen-wave, population-based panel spanning 2007 to 2020. Specifically, we track attitudes on system legitimacy and election fairness, assessments of Trump and other politicians, and open-ended explanations of vote choice and party perceptions. Across measures, there was little movement in public opinion foreshadowing Trump's norm-upending presidency, though levels of out-party animus were consistently high. Recent shifts in public opinion were thus not a primary engine of the Trump presidency's anti-democratic efforts or their violent culmination. Such stability suggests that understanding the precipitating causes of those efforts requires attention to other actors, including activists and elites.

Political scientists have increasingly deployed conjoint survey experiments to understand multidimensional choices in various settings. In this paper, we show that the average marginal component effect (AMCE) constitutes an aggregation of individual-level preferences that is meaningful both theoretically and empirically. First, extending previous results to allow for arbitrary randomization distributions, we show how the AMCE represents a summary of voters’ multidimensional preferences that combines directionality and intensity according to a probabilistic generalization of the Borda rule. We demonstrate why incorporating both the directionality and intensity of multi-attribute preferences is essential for analyzing real-world elections, in which ceteris paribus comparisons almost never occur. Second, and in further empirical support of this point, we show how this aggregation translates directly into a primary quantity of interest to election scholars: the effect of a change in an attribute on a candidate’s or party’s expected vote share. These properties hold irrespective of the heterogeneity, strength, or interactivity of voters’ preferences and regardless of how votes are aggregated into seats. Finally, we propose, formalize, and evaluate the feasibility of using conjoint data to estimate alternative quantities of interest to electoral studies, including the effect of an attribute on the probability of winning.

Many contend that U.S. state parties are increasingly polarized and nationalized, meaning that they have adopted divergent positions matching their national counterparts’ positions. Such trends reflect a transformation of America's historically decentralized party system. Yet, the precise timing of these related trends—as well as the mechanisms underpinning them—remain unclear. We assess these dynamics using a novel data set of 1,783 state party platforms between 1918 and 2017. Applying tools from automated and manual content analysis, we document a dramatic divergence in the topics emphasized by Democrats and Republicans starting in the mid-1990s, just as congressional speech became polarized. During this period, cross-state differences in each party's agenda decreased and regional/sectoral issues became less prominent, suggesting tight connections between polarization, nationalization, and state agendas. We also find that innovative phrases increasingly debut in state (not national) platforms. Overall, the evidence undercuts claims of top-down polarization emanating from national party leaders in Washington, DC. Polarization at the state and federal levels coincided with the development of an integrated network of activists spanning multiple levels of the polity.

Although prior scholarship has made considerable progress in measuring politicians’ positions, it has only rarely considered voters’ or activists’ perceptions of those positions. Here, we present a novel measure of U.S. senators’ perceived ideologies derived from 9,030 pairwise comparisons elicited from party activists in three 2016 YouGov surveys. By focusing on activists, we study a most-likely case for perceiving within-party ideological distinctions. We also gain empirical leverage from Donald Trump’s nomination and heterodox positions on some issues. Our measure of perceived ideology is correlated with nominate but differs in informative ways: senators with very conservative voting records were sometimes perceived as less conservative if they did not support Trump. A confirmatory test shows these trends extended into 2021. Even among activists, perceived ideology appears to be anchored by prominent people as well as policy positions.

Perceived discrimination (PD) is reliably and strongly associated with partisan identity (PID) among US immigrant minorities such as Latinos and Asian Americans. Yet whether PD causes PID remains unclear, since it is possible that partisanship influences perceptions of discrimination or that other factors drive the observed association. Here, we assess the causal influence of group-level PD on PID using five experiments with Latino and Asian American adults. These experiments varied in important ways: they took place inside and outside the lab, occurred prior to and during Donald Trump’s presidential campaign, and tested different manifestations of PD and partisan attitudes (total n = 2,528). These efforts point to a simple but unexpected conclusion: our experiments and operationalizations do not support the claim that group-targeted PD directly causes PID. These results have important implications for understanding partisanship among immigrants and their co-ethnics and the political incorporation of Latinos and Asian Americans.

Recent years have seen a renaissance of conjoint survey designs within social science. To date, however, researchers have lacked guidance on how many attributes they can include within conjoint profiles before survey satisficing leads to unacceptable declines in response quality. This paper addresses that question using pre-registered, two-stage experiments examining choices among hypothetical candidates for US Senate or hotel rooms. In each experiment, we use the first stage to identify attributes which are perceived to be uncorrelated with the attribute of interest, so that their effects are not masked by those of the core attributes. In the second stage, we randomly assign respondents to conjoint designs with varying numbers of those filler attributes. We report the results of these experiments implemented via Amazon's Mechanical Turk and Survey Sampling International. They demonstrate that our core quantities of interest are generally stable, with relatively modest increases in survey satisficing when respondents face large numbers of attributes.

In recent years, political and social scientists have made increasing use of conjoint survey designs to study decision-making. Here, we study a consequential question which researchers confront when implementing conjoint designs: How many choice tasks can respondents perform before survey satisficing degrades response quality? To answer the question, we run a set of experiments where respondents are asked to complete as many as 30 conjoint tasks. Experiments conducted through Amazon’s Mechanical Turk and Survey Sampling International demonstrate the surprising robustness of conjoint designs, as there are detectable but quite limited increases in survey satisficing as the number of tasks increases. Our evidence suggests that in similar study contexts researchers can assign dozens of tasks without substantial declines in response quality.

Retrospective voting is a central explanation for voters’ support of incumbents. Yet, despite the variety of conditions facing American cities, past research has devoted little attention to retrospective voting for mayors. This paper first develops hypotheses about how local retrospective voting might differ from its national analog, due to both differing information sources and the presence of national benchmarks. It then analyzes retrospective voting using the largest data set on big-city mayoral elections between 1990 and 2011 to date. Neither crime rates nor property values consistently influence incumbent mayors’ vote shares, nor do changes in local conditions. However, low city-level unemployment relative to national unemployment correlates with higher incumbent support. The urban voter is a particular type of retrospective voter, one who compares local economic performance to conditions elsewhere. Moreover, these effects appear to be present only in cities that dominate their media markets, suggesting media outlets’ role in facilitating retrospective voting.

Survey experiments are a core tool for causal inference. Yet, the design of classical survey experiments prevents them from identifying which components of a multidimensional treatment are influential. Here, we show how conjoint analysis, an experimental design yet to be widely applied in political science, enables researchers to estimate the causal effects of multiple treatment components and assess several causal hypotheses simultaneously. In conjoint analysis, respondents score a set of alternatives, where each has randomly varied attributes. Here, we undertake a formal identification analysis to integrate conjoint analysis with the potential outcomes framework for causal inference. We propose a new causal estimand and show that it can be nonparametrically identified and easily estimated from conjoint data using a fully randomized design. The analysis enables us to propose diagnostic checks for the identification assumptions. We then demonstrate the value of these techniques through empirical applications to voter decision making and attitudes toward immigrants.

The yields of spring barley during a medium-term (7 years) compost and slurry addition experiment and the soil carbon (C) and nitrogen (N) contents, bacterial community structure, soil microbial biomass and soil respiration rates have been determined to assess the effects of repeated, and in some cases very large, organic amendments on soil and crop parameters. For compost, total additions were equivalent to up to 119 t C/ha and 1·7 t N/ha and for slurry they were 25 t C/ha and 0·35 t N/ha over 7 years, which represented very large additions compared to control soil C and N contents (69 t C/ha and 0·3 t N/ha in the 0–30 cm soil depth). There was an initial positive response to compost and slurry addition on barley yield, but over the experiment the yield differential between the amounts of compost addition declined, indicating that repeated addition of compost at a lower rate over several years had the same cumulative effect as a large single compost application. By the end of the experiment it was clear that the addition of compost and slurry increased soil C and N contents, especially towards the top of the soil profile, as well as soil respiration rates. However, the increases in soil C and N contents were not proportional to the amount of C and N added, suggesting either that: (i) a portion of the added C and N was more vulnerable to loss; (ii) that its addition rendered another C or N pool in the soil more susceptible to loss; or (iii) that the C inputs from additional crop productivity did not increase in line with the organic amendments. Soil microbial biomass was depressed at the highest rate of organic amendment, and whilst this may have been due to genuine toxic or inhibitory effects of large amounts of compost, it could also be due to the inaccuracy of the substrate-induced respiration approach used for determining soil biomass when there is a large supply of organic matter. At the highest compost addition, the bacterial community structure was significantly altered, suggesting that the amendments significantly altered soil community dynamics.

Many developed democracies are experiencing high immigration, and public attitudes likely shape their policy responses. Prior studies of ethnocentrism and stereotyping make divergent predictions about anti-immigration attitudes. Some contend that culturally distinctive immigrants consistently generate increased opposition; others predict that natives’ reactions depend on the particular cultural distinction and associated stereotypes. This article tests these hypotheses using realistic, video-based experiments with representative American samples. The results refute the expectation that more culturally distinctive immigrants necessarily induce anti-immigration views: exposure to Latino immigrants with darker skin tones or who speak Spanish does not increase restrictionist attitudes. Instead, the impact of out-group cues hinges on their content and related norms, as immigrants who speak accented English seem to counteract negative stereotypes related to immigrant assimilation.

Theories of inter-group threat hold that local concentrations of immigrants produce resource competition and anti-immigrant attitudes. Variants of these theories are commonly applied to Britain and the United States. Yet the empirical tests have been inconsistent. This paper analyses geo-coded surveys from both countries to identify when residents’ attitudes are influenced by living near immigrant communities. Pew surveys from the United States and the 2005 British Election Study illustrate how local contextual effects hinge on national politics. Contextual effects appear primarily when immigration is a nationally salient issue, which explains why past research has not always found a threat. Seemingly local disputes have national catalysts. The paper also demonstrates how panel data can reduce selection biases that plague research on local contextual effects.