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AbstractWe use a novel survey experiment with a broadly representative sample to reveal an important phenomenon in voter integration of campaign communications: preference-mediated partisan motivation. When evaluating the credibility of candidate position changes on minimum wage policy (a readily quantifiable and salient issue domain), partisans do not take a new stance at face value, apply universal skepticism, or simply afford more credibility to co-partisans. Instead, they process a candidate's stance through an interaction between the voter's partisan allegiance and their own policy preference. Partisans update more when a co-partisan moves closer to them than when the candidate shifts away from them. The opposite pattern emerges with the other party's candidates: partisans tend to be more receptive if the candidate moves away from them. This feature of campaign message acceptance has profound implications for political communication and our understanding of partisan cognition.

AbstractMany Americans associate themselves with their political party in a deep, visceral way. Voter identification with a political party has powerful implications for not just how voters behave, but how there are exposed to and receive information about the world. We describe how this tying of one's self-concept to a party, which can be analogous to die-hard sports fandom, plays a central role in political cognition. It leads voters identifying with the two parties to perceive the political (and even seemingly apolitical) world in dramatically different ways. We detail the psychological mechanisms by which this party identity produces these distortions and offer examples of the bias that emerges. We conclude by discussing the implications of these phenomena for perpetuating our current hyper-polarized political discourse.

Published and informal assessments of the prospects for neuroimaging in political science have tended to range from overexuberant to reflexively dismissive. We seek to present a cautious but fair middle ground in considering this new methodology, primarily from an epistemological perspective. Our examination centers on the relationship between two levels of analysis, focusing on the potential for connection between behavior‐based theories of political psychology and cognition and the neural processes and systems involved in generating behaviors and states of mind. We explore the place of each level of analysis on its own, as well as the potential for the fruitful interaction of the two. This analysis brings together opinions and ideas presented by others in various forums and across multiple disciplines, offers a discussion of the the promises and perils of neuroimaging in its application to social science, as well as some practical thoughts regarding its early‐stage incorporation into political psychology. We argue in favor of proceeding with more substantial incorporation of neuroimaging into political psychology's methodological arsenal, but note that this will initially require both (1) greater acceptance of work more focused on presenting empirical results than on providing dispositive evidence in broader theoretical debates and (2) a commitment on the part of those conducting this research to refrain from overstating the definitiveness of its theoretical implications.

The popularity of online surveys has increased the prominence of using sampling weights to enhance claims of representativeness. Yet, much uncertainty remains regarding how these weights should be employed in survey experiment analysis: should they be used? If so, which estimators are preferred? We offer practical advice, rooted in the Neyman–Rubin model, for researchers working with survey experimental data. We examine simple, efficient estimators, and give formulas for their biases and variances. We provide simulations that examine these estimators as well as real examples from experiments administered online through YouGov. We find that for examining the existence of population treatment effects using high-quality, broadly representative samples recruited by top online survey firms, sample quantities, which do not rely on weights, are often sufficient. We found that sample average treatment effect (SATE) estimates did not appear to differ substantially from their weighted counterparts, and they avoided the substantial loss of statistical power that accompanies weighting. When precise estimates of population average treatment effects (PATE) are essential, we analytically show poststratifying on survey weights and/or covariates highly correlated with outcomes to be a conservative choice. While we show substantial gains in simulations, we find limited evidence of them in practice.