Previous research shows that people commonly exaggerate the size of minority populations. Theories of intergroup threat predict that the larger people perceive minority groups to be, the less favorably they feel toward them. We investigate whether correcting Americans’ misperceptions about one such population—immigrants—affects related attitudes. We confirm that non-Hispanic Americans overestimate the percentage of the population that is foreign-born or in the United States without authorization. However, in seven separate survey experiments over 11 years, we find that providing accurate information does little to affect attitudes toward immigration, even though it does reduce the perceived size of the foreign-born population. This is true even when people’s misperceptions are explicitly corrected. These results call into question a potential cognitive mechanism that could underpin intergroup threat theory. Misperceptions about the size of minority groups may be a consequence, rather than a cause, of attitudes toward those groups.

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.

Immigration and demographic change have become highly salient in American politics, partly because of the 2016 campaign of Donald Trump. Previous research indicates that local influxes of immigrants or unfamiliar ethnic groups can generate threatened responses, but has either focused on nonelectoral outcomes or analyzed elections in large geographic units, such as counties. Here, we examine whether demographic changes at low levels of aggregation were associated with vote shifts toward an anti-immigration presidential candidate between 2012 and 2016. To do so, we compile a precinct-level dataset of election results and demographic measures for almost 32,000 precincts in the states of Florida, Georgia, Michigan, Nevada, Ohio, Pennsylvania, and Washington. We employ regression analyses varying model specifications and measures of demographic change. Our estimates uncover little evidence that influxes of Hispanics or noncitizen immigrants benefited Trump relative to past Republicans, instead consistently showing that such changes were associated with shifts to Trump's opponent.

Changes in partisan outcomes between consecutive elections must come from changes in the composition of the electorate or changes in the vote choices of consistent voters. How much composition versus conversion drives electoral change has critical implications for the policy mandates of election victories and campaigning and governing strategies. Here, we analyze electoral change between the 2012 and 2016 U.S. presidential elections using administrative data. We merge precinct-level election returns, the smallest geography at which vote counts are available, with individual-level turnout records from 37 million registered voters in six key states. We find that both factors were substantively meaningful drivers of electoral change, but the balance varied by state. We estimate that pro-Republican Party (GOP) conversion among two-election voters was particularly important in states including Ohio, Michigan, and Pennsylvania where the pro-GOP swings were largest. Our results suggest conversion remains a crucial component of electoral change.

AbstractRecent 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.

Abstract: 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 whichceteris paribuscomparisons 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.